wip : try to compress just mlp

.github/workflows/build.yml

@@ -113,73 +113,3 @@ jobs:
               run: |
                 make
                 ctest -L gh --output-on-failure
-
-    windows:
-        runs-on: windows-latest
-
-        strategy:
-            matrix:
-                build: [RelWithDebInfo]
-                arch: [Win32, x64]
-                blas: [ON]
-                sdl2: [ON]
-                include:
-                  - arch: Win32
-                    obzip: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.21/OpenBLAS-0.3.21-x86.zip
-                    s2arc: x86
-                  - arch: x64
-                    obzip: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.21/OpenBLAS-0.3.21-x64.zip
-                    s2arc: x64
-                  - sdl2: ON
-                    s2ver: 2.26.0
-
-        steps:
-            - name: Clone
-              uses: actions/checkout@v1
-
-            - name: Add msbuild to PATH
-              uses: microsoft/setup-msbuild@v1
-
-            - name: Fetch OpenBLAS
-              if: matrix.blas == 'ON'
-              run: |
-                C:/msys64/usr/bin/wget.exe -qO blas.zip ${{ matrix.obzip }}
-                7z x blas.zip -oblas -y
-                copy blas/include/cblas.h .
-                copy blas/include/openblas_config.h .
-                echo "blasdir=$env:GITHUB_WORKSPACE/blas" >> $env:GITHUB_ENV
-
-            - name: Fetch SDL2 and set SDL2_DIR
-              if: matrix.sdl2 == 'ON'
-              run: |
-                C:/msys64/usr/bin/wget.exe -qO sdl2.zip https://github.com/libsdl-org/SDL/releases/download/release-${{ matrix.s2ver }}/SDL2-devel-${{ matrix.s2ver }}-VC.zip
-                7z x sdl2.zip
-                echo "SDL2_DIR=$env:GITHUB_WORKSPACE/SDL2-${{ matrix.s2ver }}/cmake" >> $env:GITHUB_ENV
-
-            - name: Configure
-              run: >
-                cmake -S . -B ./build -A ${{ matrix.arch }}
-                -DCMAKE_BUILD_TYPE=${{ matrix.build }}
-                -DWHISPER_SUPPORT_OPENBLAS=${{ matrix.blas }}
-                -DCMAKE_LIBRARY_PATH="$env:blasdir/lib"
-                -DWHISPER_SUPPORT_SDL2=${{ matrix.sdl2 }}
-
-            - name: Build
-              run: |
-                cd ./build
-                msbuild ALL_BUILD.vcxproj -t:build -p:configuration=${{ matrix.build }} -p:platform=${{ matrix.arch }}
-
-            - name: Copy libopenblas.dll
-              if: matrix.blas == 'ON'
-              run: copy "$env:blasdir/bin/libopenblas.dll" build/bin/${{ matrix.build }}
-
-            - name: Copy SDL2.dll
-              if: matrix.sdl2 == 'ON'
-              run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }}
-
-            - name: Upload binaries
-              if: matrix.blas == 'ON' && matrix.sdl2 == 'ON'
-              uses: actions/upload-artifact@v1
-              with:
-                name: whisper-bin-${{ matrix.arch }}
-                path: build/bin/${{ matrix.build }}

.gitignore

@@ -1,24 +1,7 @@
-*.o
-.cache/
-.vs/
-.vscode/
-.DS_Store
-
-build/
-build-em/
-build-debug/
-build-release/
-build-sanitize-addr/
-build-sanitize-thread/
-
+sync.sh
 main
 stream
-command
-bench
-sync.sh
+*.o
+.cache
+build/
 compile_commands.json
-
-examples/arm_neon.h
-examples/whisper.objc/whisper.objc.xcodeproj/xcshareddata
-examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
-examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "bindings/ios"]
-	path = bindings/ios
-	url = https://github.com/ggerganov/whisper.spm

CMakeLists.txt

@@ -7,72 +7,38 @@ set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib")
 
 if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     set(WHISPER_STANDALONE ON)
-    include(cmake/GitVars.cmake)
-    include(cmake/BuildTypes.cmake)
-
-    # configure project version
-    if (EXISTS "${CMAKE_SOURCE_DIR}/bindings/ios/Makefile-tmpl")
-        configure_file(${CMAKE_SOURCE_DIR}/bindings/ios/Makefile-tmpl ${CMAKE_SOURCE_DIR}/bindings/ios/Makefile @ONLY)
-    endif()
 else()
     set(WHISPER_STANDALONE OFF)
 endif()
 
-if (EMSCRIPTEN)
-    set(BUILD_SHARED_LIBS_DEFAULT OFF)
-
-    option(WHISPER_WASM_SINGLE_FILE "whisper: embed WASM inside the generated whisper.js" ON)
-else()
-    if (MINGW)
-        set(BUILD_SHARED_LIBS_DEFAULT OFF)
-    else()
-        set(BUILD_SHARED_LIBS_DEFAULT ON)
-    endif()
-endif()
-
 # options
 
-option(BUILD_SHARED_LIBS               "whisper: build shared libs" ${BUILD_SHARED_LIBS_DEFAULT})
-
-option(WHISPER_ALL_WARNINGS            "whisper: enable all compiler warnings"                   ON)
+option(WHISPER_ALL_WARNINGS            "whisper: enable all compiler warnings" ON)
 option(WHISPER_ALL_WARNINGS_3RD_PARTY  "whisper: enable all compiler warnings in 3rd party libs" OFF)
 
-option(WHISPER_SANITIZE_THREAD         "whisper: enable thread sanitizer"    OFF)
-option(WHISPER_SANITIZE_ADDRESS        "whisper: enable address sanitizer"   OFF)
+option(WHISPER_SANITIZE_THREAD         "whisper: enable thread sanitizer" OFF)
+option(WHISPER_SANITIZE_ADDRESS        "whisper: enable address sanitizer" OFF)
 option(WHISPER_SANITIZE_UNDEFINED      "whisper: enable undefined sanitizer" OFF)
 
-option(WHISPER_BUILD_TESTS             "whisper: build tests"    ${WHISPER_STANDALONE})
-option(WHISPER_BUILD_EXAMPLES          "whisper: build examples" ${WHISPER_STANDALONE})
+option(WHISPER_BUILD_TESTS             "whisper: build tests" ${WHISPER_STANDALONE})
 
 option(WHISPER_SUPPORT_SDL2            "whisper: support for libSDL2" OFF)
 
-if (APPLE)
-    option(WHISPER_NO_ACCELERATE       "whisper: disable Accelerate framework" OFF)
-    option(WHISPER_NO_AVX              "whisper: disable AVX" OFF)
-    option(WHISPER_NO_AVX2             "whisper: disable AVX2" OFF)
-else()
-    option(WHISPER_SUPPORT_OPENBLAS    "whisper: support for OpenBLAS" OFF)
-endif()
-
-option(WHISPER_PERF                    "whisper: enable perf timings" OFF)
-
 # sanitizers
 
-if (NOT MSVC)
-    if (WHISPER_SANITIZE_THREAD)
-        set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -fsanitize=thread")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
-    endif()
+if (WHISPER_SANITIZE_THREAD)
+    set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS} -fsanitize=thread")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
+endif()
 
-    if (WHISPER_SANITIZE_ADDRESS)
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=address -fno-omit-frame-pointer")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
-    endif()
+if (WHISPER_SANITIZE_ADDRESS)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=address -fno-omit-frame-pointer")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
+endif()
 
-    if (WHISPER_SANITIZE_UNDEFINED)
-        set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=undefined")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
-    endif()
+if (WHISPER_SANITIZE_UNDEFINED)
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}     -fsanitize=undefined")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
 endif()
 
 #set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -ffast-math")
@@ -81,35 +47,18 @@ endif()
 # dependencies
 
 set(CMAKE_C_STANDARD   11)
-set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_STANDARD 11)
 
 find_package(Threads REQUIRED)
 
-# on APPLE - include Accelerate framework
-if (APPLE AND NOT WHISPER_NO_ACCELERATE)
-    find_library(ACCELERATE_FRAMEWORK Accelerate)
-    if (ACCELERATE_FRAMEWORK)
-        message(STATUS "Accelerate framework found")
+if (WHISPER_SUPPORT_SDL2)
+    # SDL2
+    find_package(SDL2 REQUIRED)
 
-        set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
-        set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
-    else()
-        message(WARNING "Accelerate framework not found")
-    endif()
-endif()
+    string(STRIP "${SDL2_LIBRARIES}" SDL2_LIBRARIES)
 
-if (WHISPER_SUPPORT_OPENBLAS)
-    find_library(OPENBLAS_LIB
-        NAMES openblas libopenblas
-        )
-    if (OPENBLAS_LIB)
-        message(STATUS "OpenBLAS found")
-
-        set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${OPENBLAS_LIB})
-        set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_OPENBLAS)
-    else()
-        message(WARNING "OpenBLAS not found")
-    endif()
+    message(STATUS "SDL2_INCLUDE_DIRS = ${SDL2_INCLUDE_DIRS}")
+    message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}")
 endif()
 
 # compiler flags
@@ -120,7 +69,7 @@ if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
 endif ()
 
 if (WHISPER_ALL_WARNINGS)
-    if (NOT MSVC)
+    if (CMAKE_COMPILER_IS_GNUCC OR CMAKE_C_COMPILER_ID MATCHES "Clang")
         set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
             -Wall                           \
             -Wextra                         \
@@ -131,14 +80,12 @@ if (WHISPER_ALL_WARNINGS)
             -Wpointer-arith                 \
         ")
     else()
-        # todo : msvc
+        # todo : windows
     endif()
 endif()
 
-if (NOT MSVC)
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=vla")
-    #set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fno-math-errno -ffinite-math-only -funsafe-math-optimizations")
-endif()
+set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=vla")
+#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fno-math-errno -ffinite-math-only -funsafe-math-optimizations")
 
 message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
 
@@ -146,33 +93,10 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
     message(STATUS "ARM detected")
 else()
     message(STATUS "x86 detected")
-    if (MSVC)
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
-        set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
-    else()
-        if (EMSCRIPTEN)
-            # we require support for WASM SIMD 128-bit
-            set(CMAKE_C_FLAGS   "${CMAKE_C_FLAGS}   -pthread -msimd128")
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
-        else()
-            if(NOT WHISPER_NO_AVX)
-                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx")
-            endif()
-            if(NOT WHISPER_NO_AVX2)
-                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx2")
-            endif()
-            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma -mf16c")
-        endif()
-    endif()
+    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx -mavx2 -mfma -mf16c")
 endif()
 
-if (WHISPER_PERF)
-    set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
-endif()
-
-#
 # whisper - this is the main library of the project
-#
 
 set(TARGET whisper)
 
@@ -185,13 +109,7 @@ target_include_directories(${TARGET} PUBLIC
     .
     )
 
-if (MSVC)
-    target_link_libraries(${TARGET} PRIVATE ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
-
-    set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -D_CRT_SECURE_NO_WARNINGS)
-else()
-    target_link_libraries(${TARGET} PRIVATE m ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
-endif()
+target_link_libraries(${TARGET} PRIVATE ${CMAKE_THREAD_LIBS_INIT})
 
 if (BUILD_SHARED_LIBS)
     target_link_libraries(${TARGET} PUBLIC
@@ -212,23 +130,24 @@ install(TARGETS ${TARGET}
     ARCHIVE DESTINATION lib/static
     )
 
-#
-# bindings
-#
-
-add_subdirectory(bindings)
-
-#
 # programs, examples and tests
-#
 
 if (WHISPER_STANDALONE)
+    # main
+    set(TARGET main)
+    add_executable(${TARGET} main.cpp)
+    target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})
+
+    if (WHISPER_SUPPORT_SDL2)
+        # stream
+        set(TARGET stream)
+        add_executable(${TARGET} stream.cpp)
+        target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
+        target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+    endif ()
+
     if (WHISPER_BUILD_TESTS)
         enable_testing()
         add_subdirectory(tests)
     endif ()
-
-    if (WHISPER_BUILD_EXAMPLES)
-        add_subdirectory(examples)
-    endif()
 endif ()

Makefile

@@ -1,35 +1,16 @@
-ifndef UNAME_S
 UNAME_S := $(shell uname -s)
-endif
-
-ifndef UNAME_P
 UNAME_P := $(shell uname -p)
-endif
-
-ifndef UNAME_M
 UNAME_M := $(shell uname -m)
-endif
-
-# Mac OS + Arm can report x86_64
-# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
-ifeq ($(UNAME_S),Darwin)
-	ifneq ($(UNAME_P),arm)
-		SYSCTL_M := $(shell sysctl -n hw.optional.arm64)
-		ifeq ($(SYSCTL_M),1)
-			# UNAME_P := arm
-			# UNAME_M := arm64
-			warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
-		endif
-	endif
-endif
 
 #
 # Compile flags
 #
 
-CFLAGS   = -I.              -O3 -std=c11  
-CXXFLAGS = -I. -I./examples -O3 -std=c++11
-LDFLAGS  =
+CFLAGS   = -O3 -std=c11
+CXXFLAGS = -O3 -std=c++11
+
+CFLAGS   += -Wall -Wextra -Wno-unused-parameter -Wno-unused-function
+CXXFLAGS += -Wall -Wextra -Wno-unused-parameter -Wno-unused-function
 
 # OS specific
 # TODO: support Windows
@@ -41,67 +22,17 @@ ifeq ($(UNAME_S),Darwin)
 	CFLAGS   += -pthread
 	CXXFLAGS += -pthread
 endif
-ifeq ($(UNAME_S),FreeBSD)
-	CFLAGS   += -pthread
-	CXXFLAGS += -pthread
-endif
 
 # Architecture specific
-# TODO: probably these flags need to be tweaked on some architectures
-#       feel free to update the Makefile for your architecture and send a pull request or issue
-ifeq ($(UNAME_M),x86_64)
-	ifeq ($(UNAME_S),Darwin)
-		CFLAGS += -mfma -mf16c
-		AVX1_M := $(shell sysctl machdep.cpu.features)
-		ifneq (,$(findstring AVX1.0,$(AVX1_M)))
-			CFLAGS += -mavx
-		endif
-		AVX2_M := $(shell sysctl machdep.cpu.leaf7_features)
-		ifneq (,$(findstring AVX2,$(AVX2_M)))
-			CFLAGS += -mavx2
-		endif
-	else ifeq ($(UNAME_S),Linux)
-		AVX1_M := $(shell grep "avx " /proc/cpuinfo)
-		ifneq (,$(findstring avx,$(AVX1_M)))
-			CFLAGS += -mavx
-		endif
-		AVX2_M := $(shell grep "avx2 " /proc/cpuinfo)
-		ifneq (,$(findstring avx2,$(AVX2_M)))
-			CFLAGS += -mavx2
-		endif
-		FMA_M := $(shell grep "fma " /proc/cpuinfo)
-		ifneq (,$(findstring fma,$(FMA_M)))
-			CFLAGS += -mfma
-		endif
-		F16C_M := $(shell grep "f16c " /proc/cpuinfo)
-		ifneq (,$(findstring f16c,$(F16C_M)))
-			CFLAGS += -mf16c
-		endif
-	else
-		CFLAGS += -mfma -mf16c -mavx -mavx2
-	endif
-endif
-ifeq ($(UNAME_M),amd64)
+ifeq ($(UNAME_P),x86_64)
 	CFLAGS += -mavx -mavx2 -mfma -mf16c
 endif
-ifndef WHISPER_NO_ACCELERATE
-	# Mac M1 - include Accelerate framework
-	ifeq ($(UNAME_S),Darwin)
-		CFLAGS  += -DGGML_USE_ACCELERATE
-		LDFLAGS += -framework Accelerate
+ifneq ($(filter arm%,$(UNAME_P)),)
+	# Mac M1
+endif
+ifneq ($(filter aarch64%,$(UNAME_P)),)
 	endif
-endif
-ifdef WHISPER_OPENBLAS
-	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
-	LDFLAGS += -lopenblas
-endif
-ifdef WHISPER_GPROF
-	CFLAGS  += -pg
-	CXXFLAGS  += -pg
-endif
-ifneq ($(filter aarch64%,$(UNAME_M)),)
-endif
-ifneq ($(filter armv6%,$(UNAME_M)),)
+	ifneq ($(filter armv6%,$(UNAME_M)),)
 	# Raspberry Pi 1, 2, 3
 	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
 endif
@@ -114,26 +45,22 @@ ifneq ($(filter armv8%,$(UNAME_M)),)
 	CFLAGS += -mfp16-format=ieee -mno-unaligned-access
 endif
 
-default: main
+#
+# Build library + main
+#
 
-#
-# Build library
-#
+main: main.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) main.cpp whisper.o ggml.o -o main
+	./main -h
 
 ggml.o: ggml.c ggml.h
-	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
+	$(CC)  $(CFLAGS)   -c ggml.c
 
 whisper.o: whisper.cpp whisper.h
-	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
-
-libwhisper.a: ggml.o whisper.o
-	$(AR) rcs libwhisper.a ggml.o whisper.o
-
-libwhisper.so: ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o whisper.o $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -c whisper.cpp
 
 clean:
-	rm -f *.o main stream command bench libwhisper.a libwhisper.so
+	rm -f *.o main
 
 #
 # Examples
@@ -141,18 +68,8 @@ clean:
 
 CC_SDL=`sdl2-config --cflags --libs`
 
-main: examples/main/main.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o whisper.o -o main $(LDFLAGS)
-	./main -h
-
-stream: examples/stream/stream.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp ggml.o whisper.o -o stream $(CC_SDL) $(LDFLAGS)
-
-command: examples/command/command.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/command/command.cpp ggml.o whisper.o -o command $(CC_SDL) $(LDFLAGS)
-
-bench: examples/bench/bench.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o whisper.o -o bench $(LDFLAGS)
+stream: stream.cpp ggml.o whisper.o
+	$(CXX) $(CXXFLAGS) stream.cpp ggml.o whisper.o -o stream $(CC_SDL)
 
 #
 # Audio samples
@@ -189,11 +106,10 @@ samples:
 .PHONY: small
 .PHONY: medium.en
 .PHONY: medium
-.PHONY: large-v1
 .PHONY: large
 
-tiny.en tiny base.en base small.en small medium.en medium large-v1 large: main
-	bash ./models/download-ggml-model.sh $@
+tiny.en tiny base.en base small.en small medium.en medium large: main
+	bash ./download-ggml-model.sh $@
 	@echo ""
 	@echo "==============================================="
 	@echo "Running $@ on all samples in ./samples ..."
@@ -201,17 +117,9 @@ tiny.en tiny base.en base small.en small medium.en medium large-v1 large: main
 	@echo ""
 	@for f in samples/*.wav; do \
 		echo "----------------------------------------------" ; \
-		echo "[+] Running $@ on $$f ... (run 'ffplay $$f' to listen)" ; \
+		echo "[+] Running base.en on $$f ... (run 'ffplay $$f' to listen)" ; \
 	    echo "----------------------------------------------" ; \
 		echo "" ; \
 		./main -m models/ggml-$@.bin -f $$f ; \
 		echo "" ; \
 	done
-
-#
-# Tests
-#
-
-.PHONY: tests
-tests:
-	bash ./tests/run-tests.sh

README.md

@@ -6,83 +6,26 @@
 High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
 
 - Plain C/C++ implementation without dependencies
-- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
-- AVX intrinsics support for x86 architectures
+- ARM_NEON and AVX intrinsics support
 - Mixed F16 / F32 precision
 - Low memory usage (Flash Attention + Flash Forward)
 - Zero memory allocations at runtime
 - Runs on the CPU
 - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
+- Supported platforms: Linux, Mac OS (Intel and Arm), Raspberry Pi, Android
 
-Supported platforms:
+## Usage
 
-- [x] Mac OS (Intel and Arm)
-- [x] [iOS](examples/whisper.objc)
-- [x] Linux
-- [x] [WebAssembly](examples/whisper.wasm)
-- [x] Windows ([MSVC](https://github.com/ggerganov/whisper.cpp/blob/master/.github/workflows/build.yml#L117-L144) and [MinGW](https://github.com/ggerganov/whisper.cpp/issues/168)]
-- [x] [Raspberry Pi](https://github.com/ggerganov/whisper.cpp/discussions/166)
-- [x] [Android](https://github.com/ggerganov/whisper.cpp/issues/30)
-
-The entire implementation of the model is contained in 2 source files:
-
-- Tensor operations: [ggml.h](ggml.h) / [ggml.c](ggml.c)
-- Transformer inference: [whisper.h](whisper.h) / [whisper.cpp](whisper.cpp)
-
-Having such a lightweight implementation of the model allows to easily integrate it in different platforms and applications.
-As an example, here is a video of running the model on an iPhone 13 device - fully offline, on-device: [whisper.objc](examples/whisper.objc)
-
-https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
-
-You can also easily make your own offline voice assistant application: [command](examples/command)
-
-https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4
-
-Or you can even run it straight in the browser: [talk.wasm](examples/talk.wasm)
-
-## Implementation details
-
-- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
-- The transformer model and the high-level C-style API are implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
-- Sample usage is demonstrated in [main.cpp](examples/main)
-- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](examples/stream)
-- Various other examples are available in the [examples](examples) folder
-
-The tensor operators are optimized heavily for Apple silicon CPUs. Depending on the computation size, Arm Neon SIMD
-instrisics or CBLAS Accelerate framework routines are used. The latter are especially effective for bigger sizes since
-the Accelerate framework utilizes the special-purpose AMX coprocessor available in modern Apple products.
-
-## Limitations
-
-- Inference only
-- No GPU support
-- Very basic greedy sampling scheme - always pick up the token with highest probability.
-  This should be similar to the [GreedyDecoder](https://github.com/openai/whisper/blob/main/whisper/decoding.py#L249-L274)
-  from the original python implementation, so in order to make a fair comparison between the 2 implementations, make sure
-  to run the python code with the following parameters:
-
-  ```
-  whisper --best_of None --beam_size None ...
-  ```
-
-  In the future, `whisper.cpp` will support more sampling strategies.
-
-## Quick start
-
-First, download one of the Whisper models converted in [ggml format](models). For example:
+To build the main program, run `make`. You can then transcribe a `.wav` file like this:
 
 ```bash
-bash ./models/download-ggml-model.sh base.en
+$ ./main -f input.wav
 ```
 
-Now build the [main](examples/main) example and transcribe an audio file like this:
+Before running the program, make sure to download one of the ggml Whisper models. For example:
 
 ```bash
-# build the main example
-make
-
-# transcribe an audio file
-./main -f input.wav
+bash ./download-ggml-model.sh base.en
 ```
 
 ---
@@ -91,40 +34,28 @@ For a quick demo, simply run `make base.en`:
 
 ```java
 $ make base.en
-
-cc  -I.              -O3 -std=c11   -pthread -DGGML_USE_ACCELERATE   -c ggml.c -o ggml.o
-c++ -I. -I./examples -O3 -std=c++11 -pthread -c whisper.cpp -o whisper.o
-c++ -I. -I./examples -O3 -std=c++11 -pthread examples/main/main.cpp whisper.o ggml.o -o main  -framework Accelerate
+cc  -O3 -std=c11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread   -c ggml.c
+c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread -c whisper.cpp
+c++ -O3 -std=c++11 -Wall -Wextra -Wno-unused-parameter -Wno-unused-function -pthread main.cpp whisper.o ggml.o -o main
 ./main -h
 
 usage: ./main [options] file0.wav file1.wav ...
 
 options:
-  -h,       --help          [default] show this help message and exit
-  -t N,     --threads N     [4      ] number of threads to use during computation
-  -p N,     --processors N  [1      ] number of processors to use during computation
-  -ot N,    --offset-t N    [0      ] time offset in milliseconds
-  -on N,    --offset-n N    [0      ] segment index offset
-  -d  N,    --duration N    [0      ] duration of audio to process in milliseconds
-  -mc N,    --max-context N [-1     ] maximum number of text context tokens to store
-  -ml N,    --max-len N     [0      ] maximum segment length in characters
-  -wt N,    --word-thold N  [0.01   ] word timestamp probability threshold
-  -su,      --speed-up      [false  ] speed up audio by x2 (reduced accuracy)
-  -tr,      --translate     [false  ] translate from source language to english
-  -otxt,    --output-txt    [false  ] output result in a text file
-  -ovtt,    --output-vtt    [false  ] output result in a vtt file
-  -osrt,    --output-srt    [false  ] output result in a srt file
-  -owts,    --output-words  [false  ] output script for generating karaoke video
-  -ps,      --print-special [false  ] print special tokens
-  -pc,      --print-colors  [false  ] print colors
-  -nt,      --no-timestamps [true   ] do not print timestamps
-  -l LANG,  --language LANG [en     ] spoken language
-  -m FNAME, --model FNAME   [models/ggml-base.en.bin] model path
-  -f FNAME, --file FNAME    [       ] input WAV file path
+  -h,       --help           show this help message and exit
+  -s SEED,  --seed SEED      RNG seed (default: -1)
+  -t N,     --threads N      number of threads to use during computation (default: 4)
+  -v,       --verbose        verbose output
+            --translate      translate from source language to english
+  -ps,      --print_special  print special tokens
+  -nt,      --no_timestamps  do not print timestamps
+  -l LANG,  --language LANG  spoken language (default: en)
+  -m FNAME, --model FNAME    model path (default: models/ggml-base.en.bin)
+  -f FNAME, --file FNAME     input WAV file path
 
-bash ./models/download-ggml-model.sh base.en
+bash ./download-ggml-model.sh base.en
 Downloading ggml model base.en ...
-ggml-base.en.bin               100%[========================>] 141.11M  6.34MB/s    in 24s
+models/ggml-base.en.bin            100%[===================================>] 141.11M  6.49MB/s    in 23s
 Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
 You can now use it like this:
 
@@ -152,33 +83,30 @@ whisper_model_load: n_text_layer  = 6
 whisper_model_load: n_mels        = 80
 whisper_model_load: f16           = 1
 whisper_model_load: type          = 2
+whisper_model_load: mem_required  = 377.00 MB
 whisper_model_load: adding 1607 extra tokens
-whisper_model_load: mem_required  =  506.00 MB
-whisper_model_load: ggml ctx size =  140.60 MB
-whisper_model_load: memory size   =   22.83 MB
-whisper_model_load: model size    =  140.54 MB
+whisper_model_load: ggml ctx size = 163.43 MB
+whisper_model_load: memory size =    22.83 MB
+whisper_model_load: model size  =   140.54 MB
 
-system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 |
+main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, lang = en, task = transcribe, timestamps = 1 ...
 
-main: processing 'samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
+[00:00.000 --> 00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
 
 
-[00:00:00.000 --> 00:00:11.000]   And so my fellow Americans, ask not what your country can do for you, ask what you can do for your country.
-
-
-whisper_print_timings:     load time =   105.91 ms
-whisper_print_timings:      mel time =    24.62 ms
-whisper_print_timings:   sample time =     3.63 ms
-whisper_print_timings:   encode time =   324.71 ms / 54.12 ms per layer
-whisper_print_timings:   decode time =    83.58 ms / 13.93 ms per layer
-whisper_print_timings:    total time =   542.81 ms
+whisper_print_timings:     load time =    77.48 ms
+whisper_print_timings:      mel time =    26.10 ms
+whisper_print_timings:   sample time =     2.19 ms
+whisper_print_timings:   encode time =   632.95 ms / 105.49 ms per layer
+whisper_print_timings:   decode time =    85.11 ms / 14.18 ms per layer
+whisper_print_timings:    total time =   824.14 ms
 ```
 
 The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
 
 For detailed usage instructions, run: `./main -h`
 
-Note that the [main](examples/main) example currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool.
+Note that `whisper.cpp` currently runs only with 16-bit WAV files, so make sure to convert your input before running the tool.
 For example, you can use `ffmpeg` like this:
 
 ```java
@@ -206,27 +134,13 @@ make small.en
 make small
 make medium.en
 make medium
-make large-v1
 make large
 ```
 
-## Memory usage
-
-| Model  | Disk   | Mem     | SHA                                        |
-| ---    | ---    | ---     | ---                                        |
-| tiny   |  75 MB | ~390 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
-| base   | 142 MB | ~500 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
-| small  | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
-| medium | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
-| large  | 2.9 GB | ~4.7 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
-
 ## Another example
 
 Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg)
-in about half a minute on a MacBook M1 Pro, using `medium.en` model:
-
-<details>
-  <summary>Expand to see the result</summary>
+in less than a minute on a MacBook M1 Pro, using `medium.en` model:
 
 ```java
 $ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
@@ -244,187 +158,86 @@ whisper_model_load: n_text_layer  = 24
 whisper_model_load: n_mels        = 80
 whisper_model_load: f16           = 1
 whisper_model_load: type          = 4
-whisper_model_load: mem_required  = 2610.00 MB
+whisper_model_load: mem_required  = 2502.00 MB
 whisper_model_load: adding 1607 extra tokens
 whisper_model_load: ggml ctx size = 1644.97 MB
 whisper_model_load: memory size =   182.62 MB
 whisper_model_load: model size  =  1462.12 MB
+log_mel_spectrogram: n_sample = 3179750, n_len = 19873
+log_mel_spectrogram: recording length: 198.734375 s
 
-main: processing 'samples/gb1.wav' (3179750 samples, 198.7 sec), 8 threads, lang = en, task = transcribe, timestamps = 1 ...
+main: processing 3179750 samples (198.7 sec), 8 threads, lang = english, task = transcribe, timestamps = 1 ...
 
 [00:00.000 --> 00:08.000]   My fellow Americans, this day has brought terrible news and great sadness to our country.
-[00:08.000 --> 00:17.000]   At nine o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
-[00:17.000 --> 00:23.000]   A short time later, debris was seen falling from the skies above Texas.
-[00:23.000 --> 00:29.000]   The Columbia's lost. There are no survivors.
+[00:08.000 --> 00:17.000]   At 9 o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
+[00:17.000 --> 00:24.000]   A short time later, debris was seen falling from the skies above Texas.
+[00:24.000 --> 00:29.000]   The Columbia's lost. There are no survivors.
 [00:29.000 --> 00:32.000]   On board was a crew of seven.
-[00:32.000 --> 00:39.000]   Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark,
-[00:39.000 --> 00:48.000]   Captain David Brown, Commander William McCool, Dr. Kultna Shavla, and Ilan Ramon,
-[00:48.000 --> 00:52.000]   a colonel in the Israeli Air Force.
+[00:32.000 --> 00:43.000]   Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain David Brown, Commander William McCool,
+[00:43.000 --> 00:52.000]   Dr. Kultner Aschavla, and Elon Ramon, a Colonel in the Israeli Air Force.
 [00:52.000 --> 00:58.000]   These men and women assumed great risk in the service to all humanity.
-[00:58.000 --> 01:03.000]   In an age when space flight has come to seem almost routine,
-[01:03.000 --> 01:07.000]   it is easy to overlook the dangers of travel by rocket
-[01:07.000 --> 01:12.000]   and the difficulties of navigating the fierce outer atmosphere of the Earth.
-[01:12.000 --> 01:18.000]   These astronauts knew the dangers, and they faced them willingly,
-[01:18.000 --> 01:23.000]   knowing they had a high and noble purpose in life.
-[01:23.000 --> 01:31.000]   Because of their courage and daring and idealism, we will miss them all the more.
-[01:31.000 --> 01:36.000]   All Americans today are thinking as well of the families of these men and women
-[01:36.000 --> 01:40.000]   who have been given this sudden shock and grief.
-[01:40.000 --> 01:45.000]   You're not alone. Our entire nation grieves with you,
-[01:45.000 --> 01:52.000]   and those you love will always have the respect and gratitude of this country.
+[00:58.000 --> 01:06.000]   In an age when space flight has come to seem almost routine, it is easy to overlook the dangers of travel by rocket
+[01:06.000 --> 01:12.000]   and the difficulties of navigating the fierce outer atmosphere of the Earth.
+[01:12.000 --> 01:22.000]   These astronauts knew the dangers, and they faced them willingly, knowing they had a high and noble purpose in life.
+[01:22.000 --> 01:30.000]   Because of their courage, endearing, and idealism, we will miss them all the more.
+[01:30.000 --> 01:40.000]   All Americans today are thinking as well of the families of these men and women who have been given this sudden shock and grief.
+[01:40.000 --> 01:45.000]   You're not alone. Our entire nation agrees with you.
+[01:45.000 --> 01:52.000]   And those you love will always have the respect and gratitude of this country.
 [01:52.000 --> 01:56.000]   The cause in which they died will continue.
-[01:56.000 --> 02:04.000]   Mankind is led into the darkness beyond our world by the inspiration of discovery
-[02:04.000 --> 02:11.000]   and the longing to understand. Our journey into space will go on.
+[01:56.000 --> 02:07.000]   Mankind is led into the darkness beyond our world by the inspiration of discovery and the longing to understand.
+[02:07.000 --> 02:11.000]   Our journey into space will go on.
 [02:11.000 --> 02:16.000]   In the skies today, we saw destruction and tragedy.
 [02:16.000 --> 02:22.000]   Yet farther than we can see, there is comfort and hope.
-[02:22.000 --> 02:29.000]   In the words of the prophet Isaiah, "Lift your eyes and look to the heavens
-[02:29.000 --> 02:35.000]   who created all these. He who brings out the starry hosts one by one
-[02:35.000 --> 02:39.000]   and calls them each by name."
-[02:39.000 --> 02:46.000]   Because of His great power and mighty strength, not one of them is missing.
-[02:46.000 --> 02:55.000]   The same Creator who names the stars also knows the names of the seven souls we mourn today.
-[02:55.000 --> 03:01.000]   The crew of the shuttle Columbia did not return safely to earth,
-[03:01.000 --> 03:05.000]   yet we can pray that all are safely home.
-[03:05.000 --> 03:13.000]   May God bless the grieving families, and may God continue to bless America.
-[03:13.000 --> 03:41.000]   Audio
+[02:22.000 --> 02:31.000]   In the words of the prophet Isaiah, "Lift your eyes and look to the heavens who created all these.
+[02:31.000 --> 02:39.000]   He who brings out the starry hosts one by one and calls them each by name."
+[02:39.000 --> 02:46.000]   Because of his great power and mighty strength, not one of them is missing.
+[02:46.000 --> 02:55.000]   The same creator who names the stars also knows the names of the seven souls we mourn today.
+[02:55.000 --> 03:05.000]   The crew of the shuttle Columbia did not return safely to Earth, yet we can pray that all are safely home.
+[03:05.000 --> 03:14.000]   May God bless the grieving families and may God continue to bless America.
+[03:14.000 --> 03:24.000]   [Music]
 
 
-whisper_print_timings:     load time =   575.92 ms
-whisper_print_timings:      mel time =   230.60 ms
-whisper_print_timings:   sample time =    73.19 ms
-whisper_print_timings:   encode time = 19552.61 ms / 814.69 ms per layer
-whisper_print_timings:   decode time = 13249.96 ms / 552.08 ms per layer
-whisper_print_timings:    total time = 33686.27 ms
+main:     load time =   522.18 ms
+main:      mel time =   423.43 ms
+main:   sample time =    31.42 ms
+main:   encode time = 41518.51 ms / 1729.94 ms per layer
+main:   decode time = 14907.22 ms
+main:    total time = 57416.63 ms
 ```
-</details>
 
 ## Real-time audio input example
 
 This is a naive example of performing real-time inference on audio from your microphone.
-The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continously.
-More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
+The `stream` tool samples the audio every 3 seconds and runs the transcription continously. More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
 
 ```java
-./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
+$ ./stream -m models/ggml-small.en.bin -t 8
 ```
 
-https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4
+https://user-images.githubusercontent.com/1991296/193465125-c163d304-64f6-4f5d-83e5-72239c9a203e.mp4
 
-## Confidence color-coding
+## Implementation details
 
-Adding the `--print-colors` argument will print the transcribed text using an experimental color coding strategy
-to highlight words with high or low confidence:
+- The core tensor operations are implemented in C ([ggml.h](ggml.h) / [ggml.c](ggml.c))
+- The high-level C-style API is implemented in C++ ([whisper.h](whisper.h) / [whisper.cpp](whisper.cpp))
+- Simple usage is demonstrated in [main.cpp](main.cpp)
+- Sample real-time audio transcription from the microphone is demonstrated in [stream.cpp](stream.cpp)
 
-<img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png">
+## Limitations
 
-## Controlling the length of the generated text segments (experimental)
+- Very basic greedy sampling scheme - always pick up the top token. You can implement your own strategy
+- Inference only
+- No GPU support
 
-For example, to limit the line length to a maximum of 16 characters, simply add `-ml 16`: 
+## Memory usage
 
-```java
-./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 16
-
-whisper_model_load: loading model from './models/ggml-base.en.bin'
-...
-system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
-
-main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
-
-[00:00:00.000 --> 00:00:00.850]   And so my
-[00:00:00.850 --> 00:00:01.590]   fellow
-[00:00:01.590 --> 00:00:04.140]   Americans, ask
-[00:00:04.140 --> 00:00:05.660]   not what your
-[00:00:05.660 --> 00:00:06.840]   country can do
-[00:00:06.840 --> 00:00:08.430]   for you, ask
-[00:00:08.430 --> 00:00:09.440]   what you can do
-[00:00:09.440 --> 00:00:10.020]   for your
-[00:00:10.020 --> 00:00:11.000]   country.
-```
-
-## Word-level timestamp
-
-The `--max-len` argument can be used to obtain word-level timestamps. Simply use `-ml 1`:
-
-```java
-./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -ml 1
-
-whisper_model_load: loading model from './models/ggml-base.en.bin'
-...
-system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
-
-main: processing './samples/jfk.wav' (176000 samples, 11.0 sec), 4 threads, 1 processors, lang = en, task = transcribe, timestamps = 1 ...
-
-[00:00:00.000 --> 00:00:00.320]  
-[00:00:00.320 --> 00:00:00.370]   And
-[00:00:00.370 --> 00:00:00.690]   so
-[00:00:00.690 --> 00:00:00.850]   my
-[00:00:00.850 --> 00:00:01.590]   fellow
-[00:00:01.590 --> 00:00:02.850]   Americans
-[00:00:02.850 --> 00:00:03.300]  ,
-[00:00:03.300 --> 00:00:04.140]   ask
-[00:00:04.140 --> 00:00:04.990]   not
-[00:00:04.990 --> 00:00:05.410]   what
-[00:00:05.410 --> 00:00:05.660]   your
-[00:00:05.660 --> 00:00:06.260]   country
-[00:00:06.260 --> 00:00:06.600]   can
-[00:00:06.600 --> 00:00:06.840]   do
-[00:00:06.840 --> 00:00:07.010]   for
-[00:00:07.010 --> 00:00:08.170]   you
-[00:00:08.170 --> 00:00:08.190]  ,
-[00:00:08.190 --> 00:00:08.430]   ask
-[00:00:08.430 --> 00:00:08.910]   what
-[00:00:08.910 --> 00:00:09.040]   you
-[00:00:09.040 --> 00:00:09.320]   can
-[00:00:09.320 --> 00:00:09.440]   do
-[00:00:09.440 --> 00:00:09.760]   for
-[00:00:09.760 --> 00:00:10.020]   your
-[00:00:10.020 --> 00:00:10.510]   country
-[00:00:10.510 --> 00:00:11.000]  .
-```
-
-## Karaoke-style movie generation (experimental)
-
-The [main](examples/main) example provides support for output of karaoke-style movies, where the
-currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script.
-This requires to have `ffmpeg` installed.
-
-Here are a few *"typical"* examples:
-
-```java
-./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts
-source ./samples/jfk.wav.wts
-ffplay ./samples/jfk.wav.mp4
-```
-
-https://user-images.githubusercontent.com/1991296/199337465-dbee4b5e-9aeb-48a3-b1c6-323ac4db5b2c.mp4
-
----
-
-```java
-./main -m ./models/ggml-base.en.bin -f ./samples/mm0.wav -owts
-source ./samples/mm0.wav.wts
-ffplay ./samples/mm0.wav.mp4
-```
-
-https://user-images.githubusercontent.com/1991296/199337504-cc8fd233-0cb7-4920-95f9-4227de3570aa.mp4
-
----
-
-```java
-./main -m ./models/ggml-base.en.bin -f ./samples/gb0.wav -owts
-source ./samples/gb0.wav.wts
-ffplay ./samples/gb0.wav.mp4
-```
-
-https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a0cd-f28a317987ba.mp4
-
----
-
-## Benchmarks
-
-In order to have an objective comparison of the performance of the inference across different system configurations,
-use the [bench](examples/bench) tool. The tool simply runs the Encoder part of the model and prints how much time it
-took to execute it. The results are summarized in the following Github issue:
-
-[Benchmark results](https://github.com/ggerganov/whisper.cpp/issues/89)
+| Model  | Disk   | Mem     |
+| ---    | ---    | ---     |
+| tiny   |  75 MB | ~240 MB |
+| base   | 142 MB | ~380 MB |
+| small  | 466 MB | ~970 MB |
+| medium | 1.5 GB | ~2.5 GB |
+| large  | 2.9 GB | ~4.6 GB |
 
 ## ggml format
 
@@ -435,43 +248,6 @@ The original models are converted to a custom binary format. This allows to pack
 - vocabulary
 - weights
 
-You can download the converted models using the [models/download-ggml-model.sh](models/download-ggml-model.sh) script
-or manually from here:
+You can download the converted models using the [download-ggml-model.sh](download-ggml-model.sh) script.
 
-- https://huggingface.co/datasets/ggerganov/whisper.cpp
-- https://ggml.ggerganov.com
-
-For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or the README
-in [models](models).
-
-## Bindings
-
-- [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs)
-- [X] Objective-C / Swift: [ggerganov/whisper.spm](https://github.com/ggerganov/whisper.spm)
-- [ ] Python:
-- [ ] Java:
-
-## Examples
-
-There are various examples of using the library for different projects in the [examples](examples) folder.
-Some of the examples are even ported to run in the browser using WebAssembly. Check them out!
-
-| Example | Web | Description |
-| ---     | --- | ---         |
-| [main](examples/main) | [whisper.wasm](examples/whisper.wasm) | Tool for translating and transcribing audio using Whisper |
-| [bench](examples/bench) | | Benchmark the performance of Whisper on your machine |
-| [stream](examples/stream) | [stream.wasm](examples/stream.wasm) | Real-time transcription of raw microphone capture |
-| [command](examples/command) | [command.wasm](examples/command.wasm) | Basic voice assistant example for receiving voice commands from the mic |
-| | [talk.wasm](examples/talk.wasm) | Talk with a GPT-2 bot in your browser |
-| [whisper.objc](examples/whisper.objc) | | iOS mobile application using whisper.cpp |
-| [whisper.nvim](examples/whisper.nvim) | | Speech-to-text plugin for Neovim |
-| [generate-karaoke.sh](examples/generate-karaoke.sh) | | Helper script to easily [generate a karaoke video](https://youtu.be/uj7hVta4blM) of raw audio capture |
-| [livestream.sh](examples/livestream.sh) | | [Livestream audio transcription](https://github.com/ggerganov/whisper.cpp/issues/185) |
-| [yt-wsp.sh](examples/yt-wsp.sh) | | Download + transcribe and/or translate any VOD [(original)](https://gist.github.com/DaniruKun/96f763ec1a037cc92fe1a059b643b818) |
-
-## [Discussions](https://github.com/ggerganov/whisper.cpp/discussions)
-
-If you have any kind of feedback about this project feel free to use the Discussions section and open a new topic.
-You can use the [Show and tell](https://github.com/ggerganov/whisper.cpp/discussions/categories/show-and-tell) category
-to share your own projects that use `whisper.cpp`. If you have a question, make sure to check the
-[Frequently asked questions (#126)](https://github.com/ggerganov/whisper.cpp/discussions/126) discussion.
+For more details, see the conversion script [convert-pt-to-ggml.py](convert-pt-to-ggml.py) or the README in [models](models).

bindings/CMakeLists.txt

@@ -1,3 +0,0 @@
-if (EMSCRIPTEN)
-    add_subdirectory(javascript)
-endif()

bindings/javascript/.gitignore

@@ -1 +0,0 @@
-publish.log

bindings/javascript/CMakeLists.txt

@@ -1,34 +0,0 @@
-set(TARGET libwhisper)
-
-add_executable(${TARGET}
-    emscripten.cpp
-    )
-
-target_link_libraries(${TARGET} PRIVATE
-    whisper
-    )
-
-unset(EXTRA_FLAGS)
-
-if (WHISPER_WASM_SINGLE_FILE)
-    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
-    message(STATUS "Embedding WASM inside whisper.js")
-
-    add_custom_command(
-        TARGET ${TARGET} POST_BUILD
-        COMMAND ${CMAKE_COMMAND} -E copy
-        ${CMAKE_BINARY_DIR}/bin/libwhisper.js
-        ${CMAKE_CURRENT_SOURCE_DIR}/whisper.js
-        )
-endif()
-
-set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
-    --bind \
-    -s USE_PTHREADS=1 \
-    -s PTHREAD_POOL_SIZE=8 \
-    -s INITIAL_MEMORY=1610612736 \
-    -s TOTAL_MEMORY=1610612736 \
-    -s FORCE_FILESYSTEM=1 \
-    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
-    ${EXTRA_FLAGS} \
-    ")

bindings/javascript/emscripten.cpp

@@ -1,108 +0,0 @@
-#include "whisper.h"
-
-#include <emscripten.h>
-#include <emscripten/bind.h>
-
-#include <vector>
-#include <thread>
-
-std::thread g_worker;
-
-std::vector<struct whisper_context *> g_contexts(4, nullptr);
-
-EMSCRIPTEN_BINDINGS(whisper) {
-    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
-        if (g_worker.joinable()) {
-            g_worker.join();
-        }
-
-        for (size_t i = 0; i < g_contexts.size(); ++i) {
-            if (g_contexts[i] == nullptr) {
-                g_contexts[i] = whisper_init(path_model.c_str());
-                if (g_contexts[i] != nullptr) {
-                    return i + 1;
-                } else {
-                    return (size_t) 0;
-                }
-            }
-        }
-
-        return (size_t) 0;
-    }));
-
-    emscripten::function("free", emscripten::optional_override([](size_t index) {
-        if (g_worker.joinable()) {
-            g_worker.join();
-        }
-
-        --index;
-
-        if (index < g_contexts.size()) {
-            whisper_free(g_contexts[index]);
-            g_contexts[index] = nullptr;
-        }
-    }));
-
-    emscripten::function("full_default", emscripten::optional_override([](size_t index, const emscripten::val & audio, const std::string & lang, bool translate) {
-        if (g_worker.joinable()) {
-            g_worker.join();
-        }
-
-        --index;
-
-        if (index >= g_contexts.size()) {
-            return -1;
-        }
-
-        if (g_contexts[index] == nullptr) {
-            return -2;
-        }
-
-        struct whisper_full_params params = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
-
-        params.print_realtime   = true;
-        params.print_progress   = false;
-        params.print_timestamps = true;
-        params.print_special    = false;
-        params.translate        = translate;
-        params.language         = whisper_is_multilingual(g_contexts[index]) ? lang.c_str() : "en";
-        params.n_threads        = std::min(8, (int) std::thread::hardware_concurrency());
-        params.offset_ms        = 0;
-
-        std::vector<float> pcmf32;
-        const int n = audio["length"].as<int>();
-
-        emscripten::val heap = emscripten::val::module_property("HEAPU8");
-        emscripten::val memory = heap["buffer"];
-
-        pcmf32.resize(n);
-
-        emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(pcmf32.data()), n);
-        memoryView.call<void>("set", audio);
-
-        // print system information
-        {
-            printf("system_info: n_threads = %d / %d | %s\n",
-                    params.n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
-
-            printf("%s: processing %d samples, %.1f sec, %d threads, %d processors, lang = %s, task = %s ...\n",
-                    __func__, int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE,
-                    params.n_threads, 1,
-                    params.language,
-                    params.translate ? "translate" : "transcribe");
-
-            printf("\n");
-        }
-
-        // run the worker
-        {
-            g_worker = std::thread([index, params, pcmf32 = std::move(pcmf32)]() {
-                whisper_reset_timings(g_contexts[index]);
-                whisper_full(g_contexts[index], params, pcmf32.data(), pcmf32.size());
-                whisper_print_timings(g_contexts[index]);
-            });
-        }
-
-        return 0;
-    }));
-}

cmake/BuildTypes.cmake

@@ -1,54 +0,0 @@
-# Add new build types
-
-# ReleaseGG - Release with enabled asserts
-
-SET(CMAKE_CXX_FLAGS_RELEASEGG
-    "-O3"
-    CACHE STRING "Flags used by the c++ compiler during release builds with enabled asserts."
-    FORCE )
-SET(CMAKE_C_FLAGS_RELEASEGG
-    "-O3"
-    CACHE STRING "Flags used by the compiler during release builds with enabled asserts."
-    FORCE )
-SET(CMAKE_EXE_LINKER_FLAGS_RELEASEGG
-    ""
-    CACHE STRING "Flags used for linking binaries during release builds with enabled asserts."
-    FORCE )
-SET(CMAKE_SHARED_LINKER_FLAGS_RELEASEGG
-    ""
-    CACHE STRING "Flags used by the shared libraries linker during release builds with enabled asserts."
-    FORCE )
-MARK_AS_ADVANCED(
-    CMAKE_CXX_FLAGS_RELEASEGG
-    CMAKE_C_FLAGS_RELEASEGG
-    CMAKE_EXE_LINKER_FLAGS_RELEASEGG
-    CMAKE_SHARED_LINKER_FLAGS_RELEASEGG )
-
-# RelWithDebInfoGG - RelWithDebInfo with enabled asserts
-
-SET(CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
-    "-O2 -g"
-    CACHE STRING "Flags used by the c++ compiler during release builds with debug symbols and enabled asserts."
-    FORCE )
-SET(CMAKE_C_FLAGS_RELWITHDEBINFOGG
-    "-O2 -g"
-    CACHE STRING "Flags used by the compiler during release builds with debug symbols and enabled asserts."
-    FORCE )
-SET(CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
-    ""
-    CACHE STRING "Flags used for linking binaries during release builds with debug symbols and enabled asserts."
-    FORCE )
-SET(CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG
-    ""
-    CACHE STRING "Flags used by the shared libraries linker during release builds with debug symbols and enabled asserts."
-    FORCE )
-MARK_AS_ADVANCED(
-    CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
-    CMAKE_C_FLAGS_RELWITHDEBINFOGG
-    CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
-    CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG )
-
-if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
-    set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
-    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "ReleaseGG" "RelWithDebInfoGG")
-endif()

cmake/GitVars.cmake

@@ -1,22 +0,0 @@
-find_package(Git)
-
-# the commit's SHA1
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_SHA1
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-# the date of the commit
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_DATE
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-# the subject of the commit
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" log -1 --format=%s
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

convert-pt-to-ggml.py

@@ -40,131 +40,131 @@ import code
 import torch
 import numpy as np
 
-#from transformers import GPTJForCausalLM
-#from transformers import GPT2TokenizerFast
+from transformers import GPTJForCausalLM
+from transformers import GPT2TokenizerFast
 
 # ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L10-L110
-#LANGUAGES = {
-#    "en": "english",
-#    "zh": "chinese",
-#    "de": "german",
-#    "es": "spanish",
-#    "ru": "russian",
-#    "ko": "korean",
-#    "fr": "french",
-#    "ja": "japanese",
-#    "pt": "portuguese",
-#    "tr": "turkish",
-#    "pl": "polish",
-#    "ca": "catalan",
-#    "nl": "dutch",
-#    "ar": "arabic",
-#    "sv": "swedish",
-#    "it": "italian",
-#    "id": "indonesian",
-#    "hi": "hindi",
-#    "fi": "finnish",
-#    "vi": "vietnamese",
-#    "iw": "hebrew",
-#    "uk": "ukrainian",
-#    "el": "greek",
-#    "ms": "malay",
-#    "cs": "czech",
-#    "ro": "romanian",
-#    "da": "danish",
-#    "hu": "hungarian",
-#    "ta": "tamil",
-#    "no": "norwegian",
-#    "th": "thai",
-#    "ur": "urdu",
-#    "hr": "croatian",
-#    "bg": "bulgarian",
-#    "lt": "lithuanian",
-#    "la": "latin",
-#    "mi": "maori",
-#    "ml": "malayalam",
-#    "cy": "welsh",
-#    "sk": "slovak",
-#    "te": "telugu",
-#    "fa": "persian",
-#    "lv": "latvian",
-#    "bn": "bengali",
-#    "sr": "serbian",
-#    "az": "azerbaijani",
-#    "sl": "slovenian",
-#    "kn": "kannada",
-#    "et": "estonian",
-#    "mk": "macedonian",
-#    "br": "breton",
-#    "eu": "basque",
-#    "is": "icelandic",
-#    "hy": "armenian",
-#    "ne": "nepali",
-#    "mn": "mongolian",
-#    "bs": "bosnian",
-#    "kk": "kazakh",
-#    "sq": "albanian",
-#    "sw": "swahili",
-#    "gl": "galician",
-#    "mr": "marathi",
-#    "pa": "punjabi",
-#    "si": "sinhala",
-#    "km": "khmer",
-#    "sn": "shona",
-#    "yo": "yoruba",
-#    "so": "somali",
-#    "af": "afrikaans",
-#    "oc": "occitan",
-#    "ka": "georgian",
-#    "be": "belarusian",
-#    "tg": "tajik",
-#    "sd": "sindhi",
-#    "gu": "gujarati",
-#    "am": "amharic",
-#    "yi": "yiddish",
-#    "lo": "lao",
-#    "uz": "uzbek",
-#    "fo": "faroese",
-#    "ht": "haitian creole",
-#    "ps": "pashto",
-#    "tk": "turkmen",
-#    "nn": "nynorsk",
-#    "mt": "maltese",
-#    "sa": "sanskrit",
-#    "lb": "luxembourgish",
-#    "my": "myanmar",
-#    "bo": "tibetan",
-#    "tl": "tagalog",
-#    "mg": "malagasy",
-#    "as": "assamese",
-#    "tt": "tatar",
-#    "haw": "hawaiian",
-#    "ln": "lingala",
-#    "ha": "hausa",
-#    "ba": "bashkir",
-#    "jw": "javanese",
-#    "su": "sundanese",
-#}
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "iw": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+}
 
-## ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L273-L292
-#def build_tokenizer(path_to_whisper_repo: str, name: str = "gpt2"):
-#    os.environ["TOKENIZERS_PARALLELISM"] = "false"
-#    path = os.path.join(path_to_whisper_repo, "whisper/assets", name)
-#    tokenizer = GPT2TokenizerFast.from_pretrained(path)
-#
-#    specials = [
-#        "<|startoftranscript|>",
-#        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
-#        "<|translate|>",
-#        "<|transcribe|>",
-#        "<|startoflm|>",
-#        "<|startofprev|>",
-#        "<|nocaptions|>",
-#        "<|notimestamps|>",
-#    ]
-#
-#    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
-#    return tokenizer
+# ref: https://github.com/openai/whisper/blob/8cf36f3508c9acd341a45eb2364239a3d81458b9/whisper/tokenizer.py#L273-L292
+def build_tokenizer(path_to_whisper_repo: str, name: str = "gpt2"):
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    path = os.path.join(path_to_whisper_repo, "whisper/assets", name)
+    tokenizer = GPT2TokenizerFast.from_pretrained(path)
+
+    specials = [
+        "<|startoftranscript|>",
+        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
+        "<|translate|>",
+        "<|transcribe|>",
+        "<|startoflm|>",
+        "<|startofprev|>",
+        "<|nocaptions|>",
+        "<|notimestamps|>",
+    ]
+
+    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
+    return tokenizer
 
 # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
 def bytes_to_unicode():
@@ -224,17 +224,17 @@ with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as
 #code.interact(local=locals())
 
 multilingual = hparams["n_vocab"] == 51865
-dir_tokenizer = os.path.join(dir_whisper, "whisper/assets", multilingual and "multilingual" or "gpt2")
+tokenizer = build_tokenizer(dir_whisper, multilingual and "multilingual" or "gpt2")
 
-#tokenizer = build_tokenizer(dir_whisper, multilingual and "multilingual" or "gpt2")
 #print(tokenizer)
 #print(tokenizer.name_or_path)
 #print(len(tokenizer.additional_special_tokens))
+dir_tokenizer = tokenizer.name_or_path
 
 # output in the same directory as the model
 fname_out = dir_out + "/ggml-model.bin"
 
-with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
+with open(dir_tokenizer + "/vocab.json", "r") as f:
     tokens = json.load(f)
 
 # use 16-bit or 32-bit floats
@@ -271,7 +271,7 @@ byte_decoder = {v:k for k, v in byte_encoder.items()}
 fout.write(struct.pack("i", len(tokens)))
 
 for key in tokens:
-    text = bytearray([byte_decoder[c] for c in key])
+    text = bytearray([byte_decoder[c] for c in key]).decode('utf-8', errors='replace').encode('utf-8')
     fout.write(struct.pack("i", len(text)))
     fout.write(text)
 
@@ -297,6 +297,8 @@ for name in list_vars.keys():
                 name == "encoder.conv2.bias"   or \
                 name == "encoder.positional_embedding" or \
                 name == "decoder.positional_embedding":
+            ftype = 0
+            data = data.astype(np.float32)
             print("  Converting to float32")
             data = data.astype(np.float32)
             ftype = 0

download-ggml-model.sh

@@ -3,26 +3,10 @@
 # This script downloads Whisper model files that have already been converted to ggml format.
 # This way you don't have to convert them yourself.
 
-#src="https://ggml.ggerganov.com"
-#pfx="ggml-model-whisper"
-
-src="https://huggingface.co/datasets/ggerganov/whisper.cpp"
-pfx="resolve/main/ggml"
-
-# get the path of this script
-function get_script_path() {
-    if [ -x "$(command -v realpath)" ]; then
-        echo "$(dirname $(realpath $0))"
-    else
-        local ret="$(cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P)"
-        echo "$ret"
-    fi
-}
-
-models_path=$(get_script_path)
+ggml_path=$(dirname $(realpath $0))
 
 # Whisper models
-models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
+models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large" )
 
 # list available models
 function list_models {
@@ -52,24 +36,16 @@ fi
 
 # download ggml model
 
-printf "Downloading ggml model $model from '$src' ...\n"
+printf "Downloading ggml model $model ...\n"
 
-cd $models_path
+mkdir -p models
 
-if [ -f "ggml-$model.bin" ]; then
+if [ -f "models/ggml-$model.bin" ]; then
     printf "Model $model already exists. Skipping download.\n"
     exit 0
 fi
 
-if [ -x "$(command -v wget)" ]; then
-    wget --quiet --show-progress -O ggml-$model.bin $src/$pfx-$model.bin
-elif [ -x "$(command -v curl)" ]; then
-    curl -L --output ggml-$model.bin $src/$pfx-$model.bin
-else
-    printf "Either wget or curl is required to download models.\n"
-    exit 1
-fi
-
+wget --quiet --show-progress -O models/ggml-$model.bin https://ggml.ggerganov.com/ggml-model-whisper-$model.bin
 
 if [ $? -ne 0 ]; then
     printf "Failed to download ggml model $model \n"

examples/CMakeLists.txt

@@ -1,31 +0,0 @@
-# dependencies
-
-find_package(Threads REQUIRED)
-
-# third-party
-
-if (WHISPER_SUPPORT_SDL2)
-    # SDL2
-    find_package(SDL2 REQUIRED)
-
-    string(STRIP "${SDL2_LIBRARIES}" SDL2_LIBRARIES)
-
-    message(STATUS "SDL2_INCLUDE_DIRS = ${SDL2_INCLUDE_DIRS}")
-    message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}")
-endif()
-
-# examples
-
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-
-if (EMSCRIPTEN)
-    add_subdirectory(whisper.wasm)
-    add_subdirectory(stream.wasm)
-    add_subdirectory(command.wasm)
-    add_subdirectory(talk.wasm)
-else()
-    add_subdirectory(main)
-    add_subdirectory(stream)
-    add_subdirectory(command)
-    add_subdirectory(bench)
-endif()

examples/bench/CMakeLists.txt

@@ -1,3 +0,0 @@
-set(TARGET bench)
-add_executable(${TARGET} bench.cpp)
-target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})

examples/bench/README.md

@@ -1,52 +0,0 @@
-# bench
-
-A very basic tool for benchmarking the inference performance on your device. The tool simply runs the Encoder part of the transformer on some random audio data and records the execution time. This way we can have an objective comparison of the performance of the model for various setups.
-
-Benchmark results are tracked in the following Github issue: https://github.com/ggerganov/whisper.cpp/issues/89
-
-```bash
-# build the bench tool
-$ make bench
-
-# run it on the small.en model using 4 threads
-$ ./bench -m ./models/ggml-small.en.bin -t 4
-
-whisper_model_load: loading model from './models/ggml-small.en.bin'
-whisper_model_load: n_vocab       = 51864
-whisper_model_load: n_audio_ctx   = 1500
-whisper_model_load: n_audio_state = 768
-whisper_model_load: n_audio_head  = 12
-whisper_model_load: n_audio_layer = 12
-whisper_model_load: n_text_ctx    = 448
-whisper_model_load: n_text_state  = 768
-whisper_model_load: n_text_head   = 12
-whisper_model_load: n_text_layer  = 12
-whisper_model_load: n_mels        = 80
-whisper_model_load: f16           = 1
-whisper_model_load: type          = 3
-whisper_model_load: mem_required  = 1048.00 MB
-whisper_model_load: adding 1607 extra tokens
-whisper_model_load: ggml ctx size = 533.05 MB
-whisper_model_load: memory size =    68.48 MB 
-whisper_model_load: model size  =   464.44 MB
-
-whisper_print_timings:     load time =   240.82 ms
-whisper_print_timings:      mel time =     0.00 ms
-whisper_print_timings:   sample time =     0.00 ms
-whisper_print_timings:   encode time =  1062.21 ms / 88.52 ms per layer
-whisper_print_timings:   decode time =     0.00 ms / 0.00 ms per layer
-whisper_print_timings:    total time =  1303.04 ms
-
-system_info: n_threads = 4 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
-
-If you wish, you can submit these results here:
-
-  https://github.com/ggerganov/whisper.cpp/issues/89
-
-Please include the following information:
-
-  - CPU model
-  - Operating system
-  - Compiler
-
-```

examples/bench/bench.cpp

@@ -1,94 +0,0 @@
-#include "whisper.h"
-
-#include <cstdio>
-#include <string>
-#include <thread>
-
-// command-line parameters
-struct whisper_params {
-    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
-
-    std::string model = "models/ggml-base.en.bin";
-};
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
-
-bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
-    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
-
-        if (arg == "-h" || arg == "--help") {
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-        else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
-        else if (arg == "-m" || arg == "--model")   { params.model     = argv[++i]; }
-        else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
-    fprintf(stderr, "\n");
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h,       --help        [default] show this help message and exit\n");
-    fprintf(stderr, "  -t N,     --threads N   [%-7d] number of threads to use during computation\n", params.n_threads);
-    fprintf(stderr, "  -m FNAME, --model FNAME [%-7s] model path\n",                                  params.model.c_str());
-    fprintf(stderr, "\n");
-}
-
-int main(int argc, char ** argv) {
-    whisper_params params;
-
-    if (whisper_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    // whisper init
-
-    struct whisper_context * ctx = whisper_init(params.model.c_str());
-
-    {
-        fprintf(stderr, "\n");
-        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
-    }
-
-    if (ctx == nullptr) {
-        fprintf(stderr, "error: failed to initialize whisper context\n");
-        return 2;
-    }
-
-    if (int ret = whisper_set_mel(ctx, nullptr, 0, WHISPER_N_MEL)) {
-        fprintf(stderr, "error: failed to set mel: %d\n", ret);
-        return 3;
-    }
-
-    if (int ret = whisper_encode(ctx, 0, params.n_threads) != 0) {
-        fprintf(stderr, "error: failed to encode model: %d\n", ret);
-        return 4;
-    }
-
-    whisper_print_timings(ctx);
-    whisper_free(ctx);
-
-    fprintf(stderr, "\n");
-    fprintf(stderr, "If you wish, you can submit these results here:\n");
-    fprintf(stderr, "\n");
-    fprintf(stderr, "  https://github.com/ggerganov/whisper.cpp/issues/89\n");
-    fprintf(stderr, "\n");
-    fprintf(stderr, "Please include the following information:\n");
-    fprintf(stderr, "\n");
-    fprintf(stderr, "  - CPU model\n");
-    fprintf(stderr, "  - Operating system\n");
-    fprintf(stderr, "  - Compiler\n");
-    fprintf(stderr, "\n");
-
-    return 0;
-}

examples/command.wasm/CMakeLists.txt

@@ -1,47 +0,0 @@
-#
-# libcommand
-#
-
-set(TARGET libcommand)
-
-add_executable(${TARGET}
-    emscripten.cpp
-    )
-
-target_link_libraries(${TARGET} PRIVATE
-    whisper
-    )
-
-unset(EXTRA_FLAGS)
-
-if (WHISPER_WASM_SINGLE_FILE)
-    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
-    message(STATUS "Embedding WASM inside command.js")
-
-    add_custom_command(
-        TARGET ${TARGET} POST_BUILD
-        COMMAND ${CMAKE_COMMAND} -E copy
-        ${CMAKE_BINARY_DIR}/bin/libcommand.js
-        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/command.wasm/command.js
-        )
-endif()
-
-set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
-    --bind \
-    -s USE_PTHREADS=1 \
-    -s PTHREAD_POOL_SIZE=8 \
-    -s INITIAL_MEMORY=1024MB \
-    -s TOTAL_MEMORY=1024MB \
-    -s FORCE_FILESYSTEM=1 \
-    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
-    ${EXTRA_FLAGS} \
-    ")
-
-#
-# command.wasm
-#
-
-set(TARGET command.wasm)
-
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/command.wasm/README.md

@@ -1,23 +0,0 @@
-# command.wasm
-
-This is a basic Voice Assistant example that accepts voice commands from the microphone.
-It runs in fully in the browser via WebAseembly.
-
-Online demo: https://whisper.ggerganov.com/command/
-
-Terminal version: [examples/command](/examples/command)
-
-## Build instructions
-
-```bash
-# build using Emscripten (v3.1.2)
-git clone https://github.com/ggerganov/whisper.cpp
-cd whisper.cpp
-mkdir build-em && cd build-em
-emcmake cmake ..
-make -j
-
-# copy the produced page to your HTTP path
-cp bin/command.wasm/*       /path/to/html/
-cp bin/libcommand.worker.js /path/to/html/
-```

examples/command.wasm/emscripten.cpp

@@ -1,408 +0,0 @@
-#include "ggml.h"
-#include "whisper.h"
-
-#include <emscripten.h>
-#include <emscripten/bind.h>
-
-#include <atomic>
-#include <cmath>
-#include <mutex>
-#include <string>
-#include <thread>
-#include <vector>
-#include <regex>
-
-constexpr int N_THREAD = 8;
-
-std::vector<struct whisper_context *> g_contexts(4, nullptr);
-
-std::mutex  g_mutex;
-std::thread g_worker;
-
-std::atomic<bool> g_running(false);
-
-std::string g_status        = "";
-std::string g_status_forced = "";
-std::string g_transcribed   = "";
-
-std::vector<float> g_pcmf32;
-
-static std::string trim(const std::string & s) {
-    std::regex e("^\\s+|\\s+$");
-    return std::regex_replace(s, e, "");
-}
-
-static void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
-    const float rc = 1.0f / (2.0f * M_PI * cutoff);
-    const float dt = 1.0f / sample_rate;
-    const float alpha = dt / (rc + dt);
-
-    float y = data[0];
-
-    for (size_t i = 1; i < data.size(); i++) {
-        y = alpha * (y + data[i] - data[i - 1]);
-        data[i] = y;
-    }
-}
-
-// compute similarity between two strings using Levenshtein distance
-static float similarity(const std::string & s0, const std::string & s1) {
-    const size_t len0 = s0.size() + 1;
-    const size_t len1 = s1.size() + 1;
-
-    std::vector<int> col(len1, 0);
-    std::vector<int> prevCol(len1, 0);
-
-    for (size_t i = 0; i < len1; i++) {
-        prevCol[i] = i;
-    }
-
-    for (size_t i = 0; i < len0; i++) {
-        col[0] = i;
-        for (size_t j = 1; j < len1; j++) {
-            col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]), prevCol[j - 1] + (s0[i - 1] == s1[j - 1] ? 0 : 1));
-        }
-        col.swap(prevCol);
-    }
-
-    const float dist = prevCol[len1 - 1];
-
-    return 1.0f - (dist / std::max(s0.size(), s1.size()));
-}
-
-void command_set_status(const std::string & status) {
-    std::lock_guard<std::mutex> lock(g_mutex);
-    g_status = status;
-}
-
-bool command_vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
-    const int n_samples      = pcmf32.size();
-    const int n_samples_last = (sample_rate * last_ms) / 1000;
-
-    if (n_samples_last >= n_samples) {
-        // not enough samples - assume no speech
-        return false;
-    }
-
-    if (freq_thold > 0.0f) {
-        high_pass_filter(pcmf32, freq_thold, sample_rate);
-    }
-
-    float energy_all  = 0.0f;
-    float energy_last = 0.0f;
-
-    for (size_t i = 0; i < n_samples; i++) {
-        energy_all += fabsf(pcmf32[i]);
-
-        if (i >= n_samples - n_samples_last) {
-            energy_last += fabsf(pcmf32[i]);
-        }
-    }
-
-    energy_all  /= n_samples;
-    energy_last /= n_samples_last;
-
-    if (verbose) {
-        fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
-    }
-
-    if (energy_last > vad_thold*energy_all) {
-        return false;
-    }
-
-    return true;
-}
-
-std::string command_transcribe(whisper_context * ctx, const whisper_full_params & wparams, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
-    const auto t_start = std::chrono::high_resolution_clock::now();
-
-    prob = 0.0f;
-    t_ms = 0;
-
-    if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
-        return "";
-    }
-
-    int prob_n = 0;
-    std::string result;
-
-    const int n_segments = whisper_full_n_segments(ctx);
-    for (int i = 0; i < n_segments; ++i) {
-        const char * text = whisper_full_get_segment_text(ctx, i);
-
-        result += text;
-
-        const int n_tokens = whisper_full_n_tokens(ctx, i);
-        for (int j = 0; j < n_tokens; ++j) {
-            const auto token = whisper_full_get_token_data(ctx, i, j);
-
-            prob += token.p;
-            ++prob_n;
-        }
-    }
-
-    if (prob_n > 0) {
-        prob /= prob_n;
-    }
-
-    const auto t_end = std::chrono::high_resolution_clock::now();
-    t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
-
-    return result;
-}
-
-void command_get_audio(int ms, int sample_rate, std::vector<float> & audio) {
-    const int64_t n_samples = (ms * sample_rate) / 1000;
-
-    int64_t n_take = 0;
-    if (g_pcmf32.size() < n_samples) {
-        n_take = g_pcmf32.size();
-    } else {
-        n_take = n_samples;
-    }
-
-    audio.resize(n_take);
-    std::copy(g_pcmf32.end() - n_take, g_pcmf32.end(), audio.begin());
-}
-
-void command_main(size_t index) {
-    command_set_status("loading data ...");
-
-    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
-
-    wparams.n_threads        = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
-    wparams.offset_ms        = 0;
-    wparams.translate        = false;
-    wparams.no_context       = true;
-    wparams.single_segment   = true;
-    wparams.print_realtime   = false;
-    wparams.print_progress   = false;
-    wparams.print_timestamps = true;
-    wparams.print_special    = false;
-
-    wparams.max_tokens       = 32;
-    wparams.audio_ctx        = 768; // partial encoder context for better performance
-
-    wparams.language         = "en";
-
-    printf("command: using %d threads\n", wparams.n_threads);
-
-    bool is_running   = true;
-    bool have_prompt  = false;
-    bool ask_prompt   = true;
-    bool print_energy = false;
-
-    float prob0 = 0.0f;
-    float prob  = 0.0f;
-
-    std::vector<float> pcmf32_cur;
-    std::vector<float> pcmf32_prompt;
-
-    const std::string k_prompt = "Ok Whisper, start listening for commands.";
-
-    // whisper context
-    auto & ctx = g_contexts[index];
-
-    const int32_t vad_ms     = 2000;
-    const int32_t prompt_ms  = 5000;
-    const int32_t command_ms = 4000;
-
-    const float vad_thold  = 0.1f;
-    const float freq_thold = -1.0f;
-
-    while (g_running) {
-        // delay
-        std::this_thread::sleep_for(std::chrono::milliseconds(100));
-
-        if (ask_prompt) {
-            fprintf(stdout, "\n");
-            fprintf(stdout, "%s: Say the following phrase: '%s%s%s'\n", __func__, "\033[1m", k_prompt.c_str(), "\033[0m");
-            fprintf(stdout, "\n");
-
-            {
-                char txt[1024];
-                snprintf(txt, sizeof(txt), "Say the following phrase: '%s'", k_prompt.c_str());
-                command_set_status(txt);
-            }
-
-            ask_prompt = false;
-        }
-
-        int64_t t_ms = 0;
-
-        {
-            command_get_audio(vad_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
-
-            if (command_vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, vad_thold, freq_thold, print_energy)) {
-                fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
-                command_set_status("Speech detected! Processing ...");
-
-                if (!have_prompt) {
-                    command_get_audio(prompt_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
-
-                    const auto txt = ::trim(::command_transcribe(ctx, wparams, pcmf32_cur, prob0, t_ms));
-
-                    fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", txt.c_str(), "\033[0m", (int) t_ms);
-
-                    const float sim = similarity(txt, k_prompt);
-
-                    if (txt.length() < 0.8*k_prompt.length() || txt.length() > 1.2*k_prompt.length() || sim < 0.8f) {
-                        fprintf(stdout, "%s: WARNING: prompt not recognized, try again\n", __func__);
-                        ask_prompt = true;
-                    } else {
-                        fprintf(stdout, "\n");
-                        fprintf(stdout, "%s: The prompt has been recognized!\n", __func__);
-                        fprintf(stdout, "%s: Waiting for voice commands ...\n", __func__);
-                        fprintf(stdout, "\n");
-
-                        {
-                            char txt[1024];
-                            snprintf(txt, sizeof(txt), "Success! Waiting for voice commands ...");
-                            command_set_status(txt);
-                        }
-
-                        // save the audio for the prompt
-                        pcmf32_prompt = pcmf32_cur;
-                        have_prompt = true;
-                    }
-                } else {
-                    command_get_audio(command_ms, WHISPER_SAMPLE_RATE, pcmf32_cur);
-
-                    // prepend the prompt audio
-                    pcmf32_cur.insert(pcmf32_cur.begin(), pcmf32_prompt.begin(), pcmf32_prompt.end());
-
-                    const auto txt = ::trim(::command_transcribe(ctx, wparams, pcmf32_cur, prob, t_ms));
-
-                    prob = 100.0f*(prob - prob0);
-
-                    fprintf(stdout, "%s: heard '%s'\n", __func__, txt.c_str());
-
-                    // find the prompt in the text
-                    float best_sim = 0.0f;
-                    size_t best_len = 0;
-                    for (int n = 0.8*k_prompt.size(); n <= 1.2*k_prompt.size(); ++n) {
-                        const auto prompt = txt.substr(0, n);
-
-                        const float sim = similarity(prompt, k_prompt);
-
-                        //fprintf(stderr, "%s: prompt = '%s', sim = %f\n", __func__, prompt.c_str(), sim);
-
-                        if (sim > best_sim) {
-                            best_sim = sim;
-                            best_len = n;
-                        }
-                    }
-
-                    const std::string command = ::trim(txt.substr(best_len));
-
-                    fprintf(stdout, "%s: Command '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", command.c_str(), "\033[0m", (int) t_ms);
-                    fprintf(stdout, "\n");
-
-                    {
-                        char txt[1024];
-                        snprintf(txt, sizeof(txt), "Command '%s', (t = %d ms)", command.c_str(), (int) t_ms);
-                        command_set_status(txt);
-                    }
-                    {
-                        std::lock_guard<std::mutex> lock(g_mutex);
-                        g_transcribed = command;
-                    }
-                }
-
-                g_pcmf32.clear();
-            }
-        }
-    }
-
-    if (index < g_contexts.size()) {
-        whisper_free(g_contexts[index]);
-        g_contexts[index] = nullptr;
-    }
-}
-
-EMSCRIPTEN_BINDINGS(command) {
-    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
-        for (size_t i = 0; i < g_contexts.size(); ++i) {
-            if (g_contexts[i] == nullptr) {
-                g_contexts[i] = whisper_init(path_model.c_str());
-                if (g_contexts[i] != nullptr) {
-                    g_running = true;
-                    if (g_worker.joinable()) {
-                        g_worker.join();
-                    }
-                    g_worker = std::thread([i]() {
-                        command_main(i);
-                    });
-
-                    return i + 1;
-                } else {
-                    return (size_t) 0;
-                }
-            }
-        }
-
-        return (size_t) 0;
-    }));
-
-    emscripten::function("free", emscripten::optional_override([](size_t index) {
-        if (g_running) {
-            g_running = false;
-        }
-    }));
-
-    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
-        --index;
-
-        if (index >= g_contexts.size()) {
-            return -1;
-        }
-
-        if (g_contexts[index] == nullptr) {
-            return -2;
-        }
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            const int n = audio["length"].as<int>();
-
-            emscripten::val heap = emscripten::val::module_property("HEAPU8");
-            emscripten::val memory = heap["buffer"];
-
-            g_pcmf32.resize(n);
-
-            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
-            memoryView.call<void>("set", audio);
-        }
-
-        return 0;
-    }));
-
-    emscripten::function("get_transcribed", emscripten::optional_override([]() {
-        std::string transcribed;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            transcribed = std::move(g_transcribed);
-        }
-
-        return transcribed;
-    }));
-
-    emscripten::function("get_status", emscripten::optional_override([]() {
-        std::string status;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            status = g_status_forced.empty() ? g_status : g_status_forced;
-        }
-
-        return status;
-    }));
-
-    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            g_status_forced = status;
-        }
-    }));
-}

examples/command.wasm/index-tmpl.html

@@ -1,386 +0,0 @@
-<!doctype html>
-<html lang="en-us">
-    <head>
-        <title>command : Voice assistant example using Whisper + WebAssembly</title>
-
-        <style>
-            #output {
-                width: 100%;
-                height: 100%;
-                margin: 0 auto;
-                margin-top: 10px;
-                border-left: 0px;
-                border-right: 0px;
-                padding-left: 0px;
-                padding-right: 0px;
-                display: block;
-                background-color: black;
-                color: white;
-                font-size: 10px;
-                font-family: 'Lucida Console', Monaco, monospace;
-                outline: none;
-                white-space: pre;
-                overflow-wrap: normal;
-                overflow-x: scroll;
-            }
-        </style>
-    </head>
-    <body>
-        <div id="main-container">
-            <b>command : Voice assistant example using Whisper + WebAssembly</b>
-
-            <br><br>
-
-            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/command.wasm">GitHub</a>.
-
-            <br><br>
-
-            <hr>
-
-            Select the model you would like to use, click the "Start" button and follow the instructions.
-
-            <br><br>
-
-            <div id="model-whisper">
-                Whisper model: <span id="model-whisper-status"></span>
-                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
-                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
-                <span id="fetch-whisper-progress"></span>
-
-                <!--
-                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
-                -->
-            </div>
-
-            <br>
-
-            <div id="input">
-                <button id="start" onclick="onStart()" disabled>Start</button>
-                <button id="stop"  onclick="onStop()" disabled>Stop</button>
-                <button id="clear" onclick="clearCache()">Clear Cache</button>
-            </div>
-
-            <br>
-
-            <div id="state">
-                Status: <b><span id="state-status">not started</span></b>
-
-                <pre id="state-transcribed">[The recognized voice commands will be displayed here]</pre>
-            </div>
-
-            <hr>
-
-            Debug output:
-            <textarea id="output" rows="20"></textarea>
-
-            <br>
-
-            <b>Troubleshooting</b>
-
-            <br><br>
-
-            The page does some heavy computations, so make sure:
-
-            <ul>
-                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
-                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
-                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
-            </ul>
-
-            <div class="cell-version">
-                <span>
-                    |
-                    Build time: <span class="nav-link">@GIT_DATE@</span> |
-                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
-                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
-                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/command.wasm">Source Code</a> |
-                </span>
-            </div>
-        </div>
-
-        <script type="text/javascript" src="helpers.js"></script>
-        <script type='text/javascript'>
-            // web audio context
-            var context = null;
-
-            // audio data
-            var audio = null;
-            var audio0 = null;
-
-            // the command instance
-            var instance = null;
-
-            // model name
-            var model_whisper = null;
-
-            var Module = {
-                print: printTextarea,
-                printErr: printTextarea,
-                setStatus: function(text) {
-                    printTextarea('js: ' + text);
-                },
-                monitorRunDependencies: function(left) {
-                },
-                preRun: function() {
-                    printTextarea('js: Preparing ...');
-                },
-                postRun: function() {
-                    printTextarea('js: Initialized successfully!');
-                }
-            };
-
-            //
-            // fetch models
-            //
-
-            let dbVersion = 1
-            let dbName    = 'whisper.ggerganov.com';
-            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
-
-            function storeFS(fname, buf) {
-                // write to WASM file using FS_createDataFile
-                // if the file exists, delete it
-                try {
-                    Module.FS_unlink(fname);
-                } catch (e) {
-                    // ignore
-                }
-
-                Module.FS_createDataFile("/", fname, buf, true, true);
-
-                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
-
-                document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
-
-                if (model_whisper != null) {
-                    document.getElementById('start').disabled = false;
-                    document.getElementById('stop' ).disabled = true;
-                }
-            }
-
-            function loadWhisper(model) {
-                let urls = {
-                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
-                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
-                };
-
-                let sizes = {
-                    'tiny.en': 75,
-                    'base.en': 142,
-                };
-
-                let url     = urls[model];
-                let dst     = 'whisper.bin';
-                let size_mb = sizes[model];
-
-                model_whisper = model;
-
-                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en').style.display = 'none';
-                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
-
-                cbProgress = function(p) {
-                    let el = document.getElementById('fetch-whisper-progress');
-                    el.innerHTML = Math.round(100*p) + '%';
-                };
-
-                cbCancel = function() {
-                    var el;
-                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
-                };
-
-                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
-            }
-
-            //
-            // microphone
-            //
-
-            const kSampleRate = 16000;
-            const kRestartRecording_s = 120;
-            const kIntervalAudio_ms = 250; // pass the recorded audio to the C++ instance at this rate
-
-            var mediaRecorder = null;
-            var doRecording = false;
-            var startTime = 0;
-
-            window.AudioContext = window.AudioContext || window.webkitAudioContext;
-            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
-
-            function stopRecording() {
-                Module.set_status("paused");
-                doRecording = false;
-                audio0 = null;
-                audio = null;
-                context = null;
-            }
-
-            function startRecording() {
-                if (!context) {
-                    context = new AudioContext({
-                        sampleRate: kSampleRate,
-                        channelCount: 1,
-                        echoCancellation: false,
-                        autoGainControl:  true,
-                        noiseSuppression: true,
-                    });
-                }
-
-                Module.set_status("");
-
-                document.getElementById('start').disabled = true;
-                document.getElementById('stop').disabled = false;
-
-                doRecording = true;
-                startTime = Date.now();
-
-                var chunks = [];
-                var stream = null;
-
-                navigator.mediaDevices.getUserMedia({audio: true, video: false})
-                    .then(function(s) {
-                        stream = s;
-                        mediaRecorder = new MediaRecorder(stream);
-                        mediaRecorder.ondataavailable = function(e) {
-                            chunks.push(e.data);
-
-                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
-                            var reader = new FileReader();
-
-                            reader.onload = function(event) {
-                                var buf = new Uint8Array(reader.result);
-
-                                if (!context) {
-                                    return;
-                                }
-                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
-                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
-                                    var source = offlineContext.createBufferSource();
-                                    source.buffer = audioBuffer;
-                                    source.connect(offlineContext.destination);
-                                    source.start(0);
-
-                                    offlineContext.startRendering().then(function(renderedBuffer) {
-                                        audio = renderedBuffer.getChannelData(0);
-
-                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
-
-                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
-                                        if (audio0 != null) {
-                                            audioAll.set(audio0, 0);
-                                        }
-                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
-
-                                        if (instance) {
-                                            Module.set_audio(instance, audioAll);
-                                        }
-                                    });
-                                }, function(e) {
-                                    audio = null;
-                                });
-                            }
-
-                            reader.readAsArrayBuffer(blob);
-                        };
-
-                        mediaRecorder.onstop = function(e) {
-                            if (doRecording) {
-                                setTimeout(function() {
-                                    startRecording();
-                                });
-                            }
-                        };
-
-                        mediaRecorder.start(kIntervalAudio_ms);
-                    })
-                    .catch(function(err) {
-                        printTextarea('js: error getting audio stream: ' + err);
-                    });
-
-                var interval = setInterval(function() {
-                    if (!doRecording) {
-                        clearInterval(interval);
-                        mediaRecorder.stop();
-                        stream.getTracks().forEach(function(track) {
-                            track.stop();
-                        });
-
-                        document.getElementById('start').disabled = false;
-                        document.getElementById('stop').disabled  = true;
-
-                        mediaRecorder = null;
-                    }
-
-                    // if audio length is more than kRestartRecording_s seconds, restart recording
-                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
-                        if (doRecording) {
-                            //printTextarea('js: restarting recording');
-
-                            clearInterval(interval);
-                            audio0 = audio;
-                            audio = null;
-                            mediaRecorder.stop();
-                            stream.getTracks().forEach(function(track) {
-                                track.stop();
-                            });
-                        }
-                    }
-                }, 100);
-            }
-
-            //
-            // main
-            //
-
-            var nLines = 0;
-            var intervalUpdate = null;
-            var transcribedAll = '';
-
-            function onStart() {
-                if (!instance) {
-                    instance = Module.init('whisper.bin');
-
-                    if (instance) {
-                        printTextarea("js: whisper initialized, instance: " + instance);
-                    }
-                }
-
-                if (!instance) {
-                    printTextarea("js: failed to initialize whisper");
-                    return;
-                }
-
-                startRecording();
-
-                intervalUpdate = setInterval(function() {
-                    var transcribed = Module.get_transcribed();
-
-                    if (transcribed != null && transcribed.length > 1) {
-                        transcribedAll += transcribed + '<br>';
-                        nLines++;
-
-                        // if more than 10 lines, remove the first line
-                        if (nLines > 10) {
-                            var i = transcribedAll.indexOf('<br>');
-                            if (i > 0) {
-                                transcribedAll = transcribedAll.substring(i + 4);
-                                nLines--;
-                            }
-                        }
-                    }
-
-                    document.getElementById('state-status').innerHTML = Module.get_status();
-                    document.getElementById('state-transcribed').innerHTML = transcribedAll;
-                }, 100);
-            }
-
-            function onStop() {
-                stopRecording();
-            }
-
-        </script>
-        <script type="text/javascript" src="command.js"></script>
-    </body>
-</html>

examples/command/CMakeLists.txt

@@ -1,7 +0,0 @@
-if (WHISPER_SUPPORT_SDL2)
-    # command
-    set(TARGET command)
-    add_executable(${TARGET} command.cpp)
-    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
-    target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
-endif ()

examples/command/README.md

@@ -1,30 +0,0 @@
-# command
-
-This is a basic Voice Assistant example that accepts voice commands from the microphone.
-More info is available in [issue #171](https://github.com/ggerganov/whisper.cpp/issues/171).
-
-```bash
-# Run with default arguments and small model
-./command -m ./models/ggml-small.en.bin -t 8
-
-# On Raspberry Pi, use tiny or base models + "-ac 768" for better performance
-./command -m ./models/ggml-tiny.en.bin -ac 768 -t 4 -c 0
-```
-
-https://user-images.githubusercontent.com/1991296/204038393-2f846eae-c255-4099-a76d-5735c25c49da.mp4
-
-Web version: [examples/command.wasm](/examples/command.wasm)
-
-## Building
-
-The `command` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
-
-```bash
-# Install SDL2 on Linux
-sudo apt-get install libsdl2-dev
-
-# Install SDL2 on Mac OS
-brew install sdl2
-
-make command
-```

examples/command/command.cpp

@@ -1,655 +0,0 @@
-// Voice assistant example
-//
-// Speak short text commands to the microphone.
-// This program will detect your voice command and convert them to text.
-//
-// ref: https://github.com/ggerganov/whisper.cpp/issues/171
-//
-
-#include "whisper.h"
-
-#include <SDL.h>
-#include <SDL_audio.h>
-
-#include <cassert>
-#include <cstdio>
-#include <fstream>
-#include <mutex>
-#include <regex>
-#include <string>
-#include <thread>
-#include <vector>
-
-// command-line parameters
-struct whisper_params {
-    int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t prompt_ms  = 5000;
-    int32_t command_ms = 4000;
-    int32_t capture_id = -1;
-    int32_t max_tokens = 32;
-    int32_t audio_ctx  = 0;
-
-    float vad_thold    = 0.6f;
-    float freq_thold   = 100.0f;
-
-    bool speed_up      = false;
-    bool translate     = false;
-    bool no_context    = true;
-    bool print_special = false;
-    bool print_energy  = false;
-    bool no_timestamps = true;
-
-    std::string language  = "en";
-    std::string model     = "models/ggml-base.en.bin";
-    std::string fname_out = "";
-};
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
-
-bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
-    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
-
-        if (arg == "-h" || arg == "--help") {
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-        else if (arg == "-t"   || arg == "--threads")       { params.n_threads     = std::stoi(argv[++i]); }
-        else if (arg == "-pms" || arg == "--prompt-ms")     { params.prompt_ms     = std::stoi(argv[++i]); }
-        else if (arg == "-cms" || arg == "--command-ms")    { params.command_ms    = std::stoi(argv[++i]); }
-        else if (arg == "-c"   || arg == "--capture")       { params.capture_id    = std::stoi(argv[++i]); }
-        else if (arg == "-mt"  || arg == "--max-tokens")    { params.max_tokens    = std::stoi(argv[++i]); }
-        else if (arg == "-ac"  || arg == "--audio-ctx")     { params.audio_ctx     = std::stoi(argv[++i]); }
-        else if (arg == "-vth" || arg == "--vad-thold")     { params.vad_thold     = std::stof(argv[++i]); }
-        else if (arg == "-fth" || arg == "--freq-thold")    { params.freq_thold    = std::stof(argv[++i]); }
-        else if (arg == "-su"  || arg == "--speed-up")      { params.speed_up      = true; }
-        else if (arg == "-tr"  || arg == "--translate")     { params.translate     = true; }
-        else if (arg == "-ps"  || arg == "--print-special") { params.print_special = true; }
-        else if (arg == "-pe"  || arg == "--print-energy")  { params.print_energy  = true; }
-        else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
-        else if (arg == "-m"   || arg == "--model")         { params.model         = argv[++i]; }
-        else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
-        else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
-    fprintf(stderr, "\n");
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
-    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n", params.n_threads);
-    fprintf(stderr, "  -pms N,   --prompt-ms N   [%-7d] prompt duration in milliseconds\n",             params.prompt_ms);
-    fprintf(stderr, "  -cms N,   --command-ms N  [%-7d] command duration in milliseconds\n",            params.command_ms);
-    fprintf(stderr, "  -c ID,    --capture ID    [%-7d] capture device ID\n",                           params.capture_id);
-    fprintf(stderr, "  -mt N,    --max-tokens N  [%-7d] maximum number of tokens per audio chunk\n",    params.max_tokens);
-    fprintf(stderr, "  -ac N,    --audio-ctx N   [%-7d] audio context size (0 - all)\n",                params.audio_ctx);
-    fprintf(stderr, "  -vth N,   --vad-thold N   [%-7.2f] voice activity detection threshold\n",        params.vad_thold);
-    fprintf(stderr, "  -fth N,   --freq-thold N  [%-7.2f] high-pass frequency cutoff\n",                params.freq_thold);
-    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",     params.speed_up ? "true" : "false");
-    fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",   params.translate ? "true" : "false");
-    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                        params.print_special ? "true" : "false");
-    fprintf(stderr, "  -pe,      --print-energy  [%-7s] print sound energy (for debugging)\n",          params.print_energy ? "true" : "false");
-    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                             params.language.c_str());
-    fprintf(stderr, "  -m FNAME, --model FNAME   [%-7s] model path\n",                                  params.model.c_str());
-    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
-    fprintf(stderr, "\n");
-}
-
-//
-// SDL Audio capture
-//
-
-class audio_async {
-public:
-    audio_async(int len_ms);
-    ~audio_async();
-
-    bool init(int capture_id, int sample_rate);
-
-    // start capturing audio via the provided SDL callback
-    // keep last len_ms seconds of audio in a circular buffer
-    bool resume();
-    bool pause();
-    bool clear();
-
-    // callback to be called by SDL
-    void callback(uint8_t * stream, int len);
-
-    // get audio data from the circular buffer
-    void get(int ms, std::vector<float> & audio);
-
-private:
-    SDL_AudioDeviceID m_dev_id_in = 0;
-
-    int m_len_ms = 0;
-    int m_sample_rate = 0;
-
-    bool       m_running = false;
-    std::mutex m_mutex;
-
-    std::vector<float> m_audio;
-    std::vector<float> m_audio_new;
-    size_t             m_audio_pos = 0;
-    size_t             m_audio_len = 0;
-};
-
-audio_async::audio_async(int len_ms) {
-    m_len_ms = len_ms;
-}
-
-audio_async::~audio_async() {
-    if (m_dev_id_in) {
-        SDL_CloseAudioDevice(m_dev_id_in);
-    }
-}
-
-bool audio_async::init(int capture_id, int sample_rate) {
-    SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
-
-    if (SDL_Init(SDL_INIT_AUDIO) < 0) {
-        SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
-        return false;
-    }
-
-    SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
-
-    {
-        int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
-        fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
-        for (int i = 0; i < nDevices; i++) {
-            fprintf(stderr, "%s:    - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
-        }
-    }
-
-    SDL_AudioSpec capture_spec_requested;
-    SDL_AudioSpec capture_spec_obtained;
-
-    SDL_zero(capture_spec_requested);
-    SDL_zero(capture_spec_obtained);
-
-    capture_spec_requested.freq     = sample_rate;
-    capture_spec_requested.format   = AUDIO_F32;
-    capture_spec_requested.channels = 1;
-    capture_spec_requested.samples  = 1024;
-    capture_spec_requested.callback = [](void * userdata, uint8_t * stream, int len) {
-        audio_async * audio = (audio_async *) userdata;
-        audio->callback(stream, len);
-    };
-    capture_spec_requested.userdata = this;
-
-    if (capture_id >= 0) {
-        fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
-        m_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
-    } else {
-        fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
-        m_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
-    }
-
-    if (!m_dev_id_in) {
-        fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
-        m_dev_id_in = 0;
-
-        return false;
-    } else {
-        fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, m_dev_id_in);
-        fprintf(stderr, "%s:     - sample rate:       %d\n",                   __func__, capture_spec_obtained.freq);
-        fprintf(stderr, "%s:     - format:            %d (required: %d)\n",    __func__, capture_spec_obtained.format,
-                capture_spec_requested.format);
-        fprintf(stderr, "%s:     - channels:          %d (required: %d)\n",    __func__, capture_spec_obtained.channels,
-                capture_spec_requested.channels);
-        fprintf(stderr, "%s:     - samples per frame: %d\n",                   __func__, capture_spec_obtained.samples);
-    }
-
-    m_sample_rate = capture_spec_obtained.freq;
-
-    m_audio.resize((m_sample_rate*m_len_ms)/1000);
-
-    return true;
-}
-
-bool audio_async::resume() {
-    if (!m_dev_id_in) {
-        fprintf(stderr, "%s: no audio device to resume!\n", __func__);
-        return false;
-    }
-
-    if (m_running) {
-        fprintf(stderr, "%s: already running!\n", __func__);
-        return false;
-    }
-
-    SDL_PauseAudioDevice(m_dev_id_in, 0);
-
-    m_running = true;
-
-    return true;
-}
-
-bool audio_async::pause() {
-    if (!m_dev_id_in) {
-        fprintf(stderr, "%s: no audio device to pause!\n", __func__);
-        return false;
-    }
-
-    if (!m_running) {
-        fprintf(stderr, "%s: already paused!\n", __func__);
-        return false;
-    }
-
-    SDL_PauseAudioDevice(m_dev_id_in, 1);
-
-    m_running = false;
-
-    return true;
-}
-
-bool audio_async::clear() {
-    if (!m_dev_id_in) {
-        fprintf(stderr, "%s: no audio device to clear!\n", __func__);
-        return false;
-    }
-
-    if (!m_running) {
-        fprintf(stderr, "%s: not running!\n", __func__);
-        return false;
-    }
-
-    {
-        std::lock_guard<std::mutex> lock(m_mutex);
-
-        m_audio_pos = 0;
-        m_audio_len = 0;
-    }
-
-    return true;
-}
-
-// callback to be called by SDL
-void audio_async::callback(uint8_t * stream, int len) {
-    if (!m_running) {
-        return;
-    }
-
-    const size_t n_samples = len / sizeof(float);
-
-    m_audio_new.resize(n_samples);
-    memcpy(m_audio_new.data(), stream, n_samples * sizeof(float));
-
-    //fprintf(stderr, "%s: %zu samples, pos %zu, len %zu\n", __func__, n_samples, m_audio_pos, m_audio_len);
-
-    {
-        std::lock_guard<std::mutex> lock(m_mutex);
-
-        if (m_audio_pos + n_samples > m_audio.size()) {
-            const size_t n0 = m_audio.size() - m_audio_pos;
-
-            memcpy(&m_audio[m_audio_pos], stream, n0 * sizeof(float));
-            memcpy(&m_audio[0], &stream[n0], (n_samples - n0) * sizeof(float));
-
-            m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
-            m_audio_len = m_audio.size();
-        } else {
-            memcpy(&m_audio[m_audio_pos], stream, n_samples * sizeof(float));
-
-            m_audio_pos = (m_audio_pos + n_samples) % m_audio.size();
-            m_audio_len = std::min(m_audio_len + n_samples, m_audio.size());
-        }
-    }
-}
-
-void audio_async::get(int ms, std::vector<float> & result) {
-    if (!m_dev_id_in) {
-        fprintf(stderr, "%s: no audio device to get audio from!\n", __func__);
-        return;
-    }
-
-    if (!m_running) {
-        fprintf(stderr, "%s: not running!\n", __func__);
-        return;
-    }
-
-    result.clear();
-
-    {
-        std::lock_guard<std::mutex> lock(m_mutex);
-
-        if (ms <= 0) {
-            ms = m_len_ms;
-        }
-
-        size_t n_samples = (m_sample_rate * ms) / 1000;
-        if (n_samples > m_audio_len) {
-            n_samples = m_audio_len;
-        }
-
-        result.resize(n_samples);
-
-        int s0 = m_audio_pos - n_samples;
-        if (s0 < 0) {
-            s0 += m_audio.size();
-        }
-
-        if (s0 + n_samples > m_audio.size()) {
-            const size_t n0 = m_audio.size() - s0;
-
-            memcpy(result.data(), &m_audio[s0], n0 * sizeof(float));
-            memcpy(&result[n0], &m_audio[0], (n_samples - n0) * sizeof(float));
-        } else {
-            memcpy(result.data(), &m_audio[s0], n_samples * sizeof(float));
-        }
-    }
-}
-
-///////////////////////////
-
-std::string trim(const std::string & s) {
-    std::regex e("^\\s+|\\s+$");
-    return std::regex_replace(s, e, "");
-}
-
-void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
-    const float rc = 1.0f / (2.0f * M_PI * cutoff);
-    const float dt = 1.0f / sample_rate;
-    const float alpha = dt / (rc + dt);
-
-    float y = data[0];
-
-    for (size_t i = 1; i < data.size(); i++) {
-        y = alpha * (y + data[i] - data[i - 1]);
-        data[i] = y;
-    }
-}
-
-bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
-    const int n_samples      = pcmf32.size();
-    const int n_samples_last = (sample_rate * last_ms) / 1000;
-
-    if (n_samples_last >= n_samples) {
-        // not enough samples - assume no speech
-        return false;
-    }
-
-    if (freq_thold > 0.0f) {
-        high_pass_filter(pcmf32, freq_thold, sample_rate);
-    }
-
-    float energy_all  = 0.0f;
-    float energy_last = 0.0f;
-
-    for (size_t i = 0; i < n_samples; i++) {
-        energy_all += fabsf(pcmf32[i]);
-
-        if (i >= n_samples - n_samples_last) {
-            energy_last += fabsf(pcmf32[i]);
-        }
-    }
-
-    energy_all  /= n_samples;
-    energy_last /= n_samples_last;
-
-    if (verbose) {
-        fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
-    }
-
-    if (energy_last > vad_thold*energy_all) {
-        return false;
-    }
-
-    return true;
-}
-
-std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
-    const auto t_start = std::chrono::high_resolution_clock::now();
-
-    prob = 0.0f;
-    t_ms = 0;
-
-    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-
-    wparams.print_progress   = false;
-    wparams.print_special    = params.print_special;
-    wparams.print_realtime   = false;
-    wparams.print_timestamps = !params.no_timestamps;
-    wparams.translate        = params.translate;
-    wparams.no_context       = true;
-    wparams.single_segment   = true;
-    wparams.max_tokens       = params.max_tokens;
-    wparams.language         = params.language.c_str();
-    wparams.n_threads        = params.n_threads;
-
-    wparams.audio_ctx        = params.audio_ctx;
-    wparams.speed_up         = params.speed_up;
-
-    if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
-        return "";
-    }
-
-    int prob_n = 0;
-    std::string result;
-
-    const int n_segments = whisper_full_n_segments(ctx);
-    for (int i = 0; i < n_segments; ++i) {
-        const char * text = whisper_full_get_segment_text(ctx, i);
-
-        result += text;
-
-        const int n_tokens = whisper_full_n_tokens(ctx, i);
-        for (int j = 0; j < n_tokens; ++j) {
-            const auto token = whisper_full_get_token_data(ctx, i, j);
-
-            prob += token.p;
-            ++prob_n;
-        }
-    }
-
-    if (prob_n > 0) {
-        prob /= prob_n;
-    }
-
-    const auto t_end = std::chrono::high_resolution_clock::now();
-    t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
-
-    return result;
-}
-
-// compute similarity between two strings using Levenshtein distance
-float similarity(const std::string & s0, const std::string & s1) {
-    const size_t len0 = s0.size() + 1;
-    const size_t len1 = s1.size() + 1;
-
-    std::vector<int> col(len1, 0);
-    std::vector<int> prevCol(len1, 0);
-
-    for (size_t i = 0; i < len1; i++) {
-        prevCol[i] = i;
-    }
-
-    for (size_t i = 0; i < len0; i++) {
-        col[0] = i;
-        for (size_t j = 1; j < len1; j++) {
-            col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]), prevCol[j - 1] + (s0[i - 1] == s1[j - 1] ? 0 : 1));
-        }
-        col.swap(prevCol);
-    }
-
-    const float dist = prevCol[len1 - 1];
-
-    return 1.0f - (dist / std::max(s0.size(), s1.size()));
-}
-
-int main(int argc, char ** argv) {
-    whisper_params params;
-
-    if (whisper_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    if (whisper_lang_id(params.language.c_str()) == -1) {
-        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
-        whisper_print_usage(argc, argv, params);
-        exit(0);
-    }
-
-    // whisper init
-
-    struct whisper_context * ctx = whisper_init(params.model.c_str());
-
-    // print some info about the processing
-    {
-        fprintf(stderr, "\n");
-        if (!whisper_is_multilingual(ctx)) {
-            if (params.language != "en" || params.translate) {
-                params.language = "en";
-                params.translate = false;
-                fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
-            }
-        }
-        fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
-                __func__,
-                params.n_threads,
-                params.language.c_str(),
-                params.translate ? "translate" : "transcribe",
-                params.no_timestamps ? 0 : 1);
-
-        fprintf(stderr, "\n");
-    }
-
-
-    // init audio
-
-    audio_async audio(30*1000);
-    if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
-        fprintf(stderr, "%s: audio.init() failed!\n", __func__);
-        return 1;
-    }
-
-    audio.resume();
-
-    bool is_running  = true;
-    bool have_prompt = false;
-    bool ask_prompt  = true;
-
-    float prob0 = 0.0f;
-    float prob  = 0.0f;
-
-    std::vector<float> pcmf32_cur;
-    std::vector<float> pcmf32_prompt;
-
-    const std::string k_prompt = "Ok Whisper, start listening for commands.";
-
-    // main loop
-    while (is_running) {
-        // handle Ctrl + C
-        {
-            SDL_Event event;
-            while (SDL_PollEvent(&event)) {
-                switch (event.type) {
-                    case SDL_QUIT:
-                        {
-                            is_running = false;
-                        } break;
-                    default:
-                        break;
-                }
-            }
-
-            if (!is_running) {
-                break;
-            }
-        }
-
-        // delay
-        std::this_thread::sleep_for(std::chrono::milliseconds(100));
-
-        if (ask_prompt) {
-            fprintf(stdout, "\n");
-            fprintf(stdout, "%s: Say the following phrase: '%s%s%s'\n", __func__, "\033[1m", k_prompt.c_str(), "\033[0m");
-            fprintf(stdout, "\n");
-
-            ask_prompt = false;
-        }
-
-        int64_t t_ms = 0;
-
-        {
-            audio.get(2000, pcmf32_cur);
-
-            if (vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
-                fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
-
-                if (!have_prompt) {
-                    audio.get(params.prompt_ms, pcmf32_cur);
-
-                    const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, prob0, t_ms));
-
-                    fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", txt.c_str(), "\033[0m", (int) t_ms);
-
-                    const float sim = similarity(txt, k_prompt);
-
-                    if (txt.length() < 0.8*k_prompt.length() || txt.length() > 1.2*k_prompt.length() || sim < 0.8f) {
-                        fprintf(stdout, "%s: WARNING: prompt not recognized, try again\n", __func__);
-                        ask_prompt = true;
-                    } else {
-                        fprintf(stdout, "\n");
-                        fprintf(stdout, "%s: The prompt has been recognized!\n", __func__);
-                        fprintf(stdout, "%s: Waiting for voice commands ...\n", __func__);
-                        fprintf(stdout, "\n");
-
-                        // save the audio for the prompt
-                        pcmf32_prompt = pcmf32_cur;
-                        have_prompt = true;
-                    }
-                } else {
-                    audio.get(params.command_ms, pcmf32_cur);
-
-                    // prepend the prompt audio
-                    pcmf32_cur.insert(pcmf32_cur.begin(), pcmf32_prompt.begin(), pcmf32_prompt.end());
-
-                    const auto txt = ::trim(::transcribe(ctx, params, pcmf32_cur, prob, t_ms));
-
-                    prob = 100.0f*(prob - prob0);
-
-                    //fprintf(stdout, "%s: heard '%s'\n", __func__, txt.c_str());
-
-                    // find the prompt in the text
-                    float best_sim = 0.0f;
-                    size_t best_len = 0;
-                    for (int n = 0.8*k_prompt.size(); n <= 1.2*k_prompt.size(); ++n) {
-                        const auto prompt = txt.substr(0, n);
-
-                        const float sim = similarity(prompt, k_prompt);
-
-                        //fprintf(stderr, "%s: prompt = '%s', sim = %f\n", __func__, prompt.c_str(), sim);
-
-                        if (sim > best_sim) {
-                            best_sim = sim;
-                            best_len = n;
-                        }
-                    }
-
-                    const std::string command = ::trim(txt.substr(best_len));
-
-                    fprintf(stdout, "%s: Command '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", command.c_str(), "\033[0m", (int) t_ms);
-                    fprintf(stdout, "\n");
-                }
-
-                audio.clear();
-            }
-        }
-    }
-
-    audio.pause();
-
-    whisper_print_timings(ctx);
-    whisper_free(ctx);
-
-    return 0;
-}

examples/generate-karaoke.sh

@@ -1,60 +0,0 @@
-#!/bin/bash
-
-# Simple tool to record audio from the microphone and generate a karaoke video
-# Usage:
-#
-#  cd whisper.cpp
-#  make
-#
-#  ./examples/generate-karaoke.sh [model] [step_ms]
-#
-# Press Ctrl+C to stop recording
-#
-
-executable="./main"
-model="base.en"
-model_path="models/ggml-$model.bin"
-
-# require sox and ffmpeg to be installed
-if ! command -v sox &> /dev/null
-then
-    echo "sox could not be found"
-    exit 1
-fi
-
-if ! command -v ffmpeg &> /dev/null
-then
-    echo "ffmpeg could not be found"
-    exit 2
-fi
-
-if [ ! -f "$executable" ]; then
-    echo "'$executable' does not exist. Please build it first."
-    exit 3
-fi
-
-if [ ! -f "$model_path" ]; then
-    echo "'$model_path' does not exist. Please download it first."
-    exit 4
-fi
-
-# record some raw audio
-sox -d rec.wav
-
-# resample to 16kHz
-ffmpeg -y -i ./rec.wav -ar 16000 -ac 1 -c:a pcm_s16le ./rec16.wav > /dev/null 2>&1
-
-# run Whisper
-echo "Processing ..."
-./main -m models/ggml-base.en.bin rec16.wav -owts > /dev/null 2>&1
-
-# generate Karaoke video
-echo "Generating video ..."
-source rec16.wav.wts > /dev/null 2>&1
-
-# play the video
-echo "Playing ./rec16.wav.mp4 ..."
-ffplay -loglevel 0 -autoexit ./rec16.wav.mp4
-
-echo "Done"
-exit 0

examples/helpers.js

@@ -1,182 +0,0 @@
-// Common Javascript functions used by the examples
-
-function convertTypedArray(src, type) {
-    var buffer = new ArrayBuffer(src.byteLength);
-    var baseView = new src.constructor(buffer).set(src);
-    return new type(buffer);
-}
-
-var printTextarea = (function() {
-    var element = document.getElementById('output');
-    if (element) element.alue = ''; // clear browser cache
-    return function(text) {
-        if (arguments.length > 1) text = Array.prototype.slice.call(arguments).join(' ');
-        console.log(text);
-        if (element) {
-            element.value += text + "\n";
-            element.scrollTop = element.scrollHeight; // focus on bottom
-        }
-    };
-})();
-
-async function clearCache() {
-    if (confirm('Are you sure you want to clear the cache?\nAll the models will be downloaded again.')) {
-        indexedDB.deleteDatabase(dbName);
-    }
-}
-
-// fetch a remote file from remote URL using the Fetch API
-async function fetchRemote(url, cbProgress, cbPrint) {
-    cbPrint('fetchRemote: downloading with fetch()...');
-
-    const response = await fetch(
-        url,
-        {
-            method: 'GET',
-            headers: {
-                'Content-Type': 'application/octet-stream',
-            },
-        }
-    );
-
-    if (!response.ok) {
-        cbPrint('fetchRemote: failed to fetch ' + url);
-        return;
-    }
-
-    const contentLength = response.headers.get('content-length');
-    const total = parseInt(contentLength, 10);
-    const reader = response.body.getReader();
-
-    var chunks = [];
-    var receivedLength = 0;
-    var progressLast = -1;
-
-    while (true) {
-        const { done, value } = await reader.read();
-
-        if (done) {
-            break;
-        }
-
-        chunks.push(value);
-        receivedLength += value.length;
-
-        if (contentLength) {
-            cbProgress(receivedLength/total);
-
-            var progressCur = Math.round((receivedLength / total) * 10);
-            if (progressCur != progressLast) {
-                cbPrint('fetchRemote: fetching ' + 10*progressCur + '% ...');
-                progressLast = progressCur;
-            }
-        }
-    }
-
-    var position = 0;
-    var chunksAll = new Uint8Array(receivedLength);
-
-    for (var chunk of chunks) {
-        chunksAll.set(chunk, position);
-        position += chunk.length;
-    }
-
-    return chunksAll;
-}
-
-// load remote data
-// - check if the data is already in the IndexedDB
-// - if not, fetch it from the remote URL and store it in the IndexedDB
-function loadRemote(url, dst, size_mb, cbProgress, cbReady, cbCancel, cbPrint) {
-    // query the storage quota and print it
-    navigator.storage.estimate().then(function (estimate) {
-        cbPrint('loadRemote: storage quota: ' + estimate.quota + ' bytes');
-        cbPrint('loadRemote: storage usage: ' + estimate.usage + ' bytes');
-    });
-
-    // check if the data is already in the IndexedDB
-    var rq = indexedDB.open(dbName, dbVersion);
-
-    rq.onupgradeneeded = function (event) {
-        var db = event.target.result;
-        if (db.version == 1) {
-            var os = db.createObjectStore('models', { autoIncrement: false });
-            cbPrint('loadRemote: created IndexedDB ' + db.name + ' version ' + db.version);
-        } else {
-            // clear the database
-            var os = event.currentTarget.transaction.objectStore('models');
-            os.clear();
-            cbPrint('loadRemote: cleared IndexedDB ' + db.name + ' version ' + db.version);
-        }
-    };
-
-    rq.onsuccess = function (event) {
-        var db = event.target.result;
-        var tx = db.transaction(['models'], 'readonly');
-        var os = tx.objectStore('models');
-        var rq = os.get(url);
-
-        rq.onsuccess = function (event) {
-            if (rq.result) {
-                cbPrint('loadRemote: "' + url + '" is already in the IndexedDB');
-                cbReady(dst, rq.result);
-            } else {
-                // data is not in the IndexedDB
-                cbPrint('loadRemote: "' + url + '" is not in the IndexedDB');
-
-                // alert and ask the user to confirm
-                if (!confirm(
-                    'You are about to download ' + size_mb + ' MB of data.\n' +
-                    'The model data will be cached in the browser for future use.\n\n' +
-                    'Press OK to continue.')) {
-                    cbCancel();
-                    return;
-                }
-
-                fetchRemote(url, cbProgress, cbPrint).then(function (data) {
-                    if (data) {
-                        // store the data in the IndexedDB
-                        var rq = indexedDB.open(dbName, dbVersion);
-                        rq.onsuccess = function (event) {
-                            var db = event.target.result;
-                            var tx = db.transaction(['models'], 'readwrite');
-                            var os = tx.objectStore('models');
-                            var rq = os.put(data, url);
-
-                            rq.onsuccess = function (event) {
-                                cbPrint('loadRemote: "' + url + '" stored in the IndexedDB');
-                                cbReady(dst, data);
-                            };
-
-                            rq.onerror = function (event) {
-                                cbPrint('loadRemote: failed to store "' + url + '" in the IndexedDB');
-                                cbCancel();
-                            };
-                        };
-                    }
-                });
-            }
-        };
-
-        rq.onerror = function (event) {
-            cbPrint('loadRemote: failed to get data from the IndexedDB');
-            cbCancel();
-        };
-    };
-
-    rq.onerror = function (event) {
-        cbPrint('loadRemote: failed to open IndexedDB');
-        cbCancel();
-    };
-
-    rq.onblocked = function (event) {
-        cbPrint('loadRemote: failed to open IndexedDB: blocked');
-        cbCancel();
-    };
-
-    rq.onabort = function (event) {
-        cbPrint('loadRemote: failed to open IndexedDB: abort');
-
-    };
-}
-

examples/livestream.sh

@@ -1,98 +0,0 @@
-#!/bin/bash
-set -eo pipefail
-# Transcribe audio livestream by feeding ffmpeg output to whisper.cpp at regular intervals
-# Idea by @semiformal-net
-# ref: https://github.com/ggerganov/whisper.cpp/issues/185
-#
-# TODO:
-# - Currently, there is a gap between sequential chunks, so some of the words are dropped. Need to figure out a
-#   way to produce a continuous stream of audio chunks.
-#
-
-url="http://a.files.bbci.co.uk/media/live/manifesto/audio/simulcast/hls/nonuk/sbr_low/ak/bbc_world_service.m3u8"
-fmt=aac # the audio format extension of the stream (TODO: auto detect)
-step_s=30
-model="base.en"
-
-if [ -z "$1" ]; then
-    echo "Usage: $0 stream_url [step_s] [model]"
-    echo ""
-    echo "  Example:"
-    echo "    $0 $url $step_s $model"
-    echo ""
-    echo "No url specified, using default: $url"
-else
-    url="$1"
-fi
-
-if [ -n "$2" ]; then
-    step_s="$2"
-fi
-
-if [ -n "$3" ]; then
-    model="$3"
-fi
-
-# Whisper models
-models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
-
-# list available models
-function list_models {
-    printf "\n"
-    printf "  Available models:"
-    for model in "${models[@]}"; do
-        printf " $model"
-    done
-    printf "\n\n"
-}
-
-if [[ ! " ${models[@]} " =~ " ${model} " ]]; then
-    printf "Invalid model: $model\n"
-    list_models
-
-    exit 1
-fi
-
-running=1
-
-trap "running=0" SIGINT SIGTERM
-
-printf "[+] Transcribing stream with model '$model', step_s $step_s (press Ctrl+C to stop):\n\n"
-
-# continuous stream in native fmt (this file will grow forever!)
-ffmpeg -loglevel quiet -y -re -probesize 32 -i $url -c copy /tmp/whisper-live0.${fmt} &
-if [ $? -ne 0 ]; then
-    printf "Error: ffmpeg failed to capture audio stream\n"
-    exit 1
-fi
-
-printf "Buffering audio. Please wait...\n\n"
-sleep $(($step_s))
-
-# do not stop script on error
-set +e
-
-i=0
-SECONDS=0
-while [ $running -eq 1 ]; do
-    # extract the next piece from the main file above and transcode to wav. -ss sets start time and nudges it by -0.5s to catch missing words (??)
-    err=1
-    while [ $err -ne 0 ]; do
-        if [ $i -gt 0 ]; then
-            ffmpeg -loglevel quiet -v error -noaccurate_seek -i /tmp/whisper-live0.${fmt} -y -ar 16000 -ac 1 -c:a pcm_s16le -ss $(($i*$step_s-1)).5 -t $step_s /tmp/whisper-live.wav 2> /tmp/whisper-live.err
-        else
-            ffmpeg -loglevel quiet -v error -noaccurate_seek -i /tmp/whisper-live0.${fmt} -y -ar 16000 -ac 1 -c:a pcm_s16le -ss $(($i*$step_s)) -t $step_s /tmp/whisper-live.wav 2> /tmp/whisper-live.err
-        fi
-        err=$(cat /tmp/whisper-live.err | wc -l)
-    done
-
-    ./main -t 8 -m ./models/ggml-base.en.bin -f /tmp/whisper-live.wav --no-timestamps -otxt 2> /tmp/whispererr | tail -n 1
-
-    while [ $SECONDS -lt $((($i+1)*$step_s)) ]; do
-        sleep 1
-    done
-    ((i=i+1))
-done
-
-killall -v ffmpeg
-killall -v main

examples/main/CMakeLists.txt

@@ -1,3 +0,0 @@
-set(TARGET main)
-add_executable(${TARGET} main.cpp)
-target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})

examples/main/README.md

@@ -1,33 +0,0 @@
-# main
-
-This is the main example demonstrating most of the functionality of the Whisper model.
-It can be used as a reference for using the `whisper.cpp` library in other projects.
-
-```
-./main -h
-
-usage: ./main [options] file0.wav file1.wav ...
-
-options:
-  -h,       --help          [default] show this help message and exit
-  -t N,     --threads N     [4      ] number of threads to use during computation
-  -p N,     --processors N  [1      ] number of processors to use during computation
-  -ot N,    --offset-t N    [0      ] time offset in milliseconds
-  -on N,    --offset-n N    [0      ] segment index offset
-  -d  N,    --duration N    [0      ] duration of audio to process in milliseconds
-  -mc N,    --max-context N [-1     ] maximum number of text context tokens to store
-  -ml N,    --max-len N     [0      ] maximum segment length in characters
-  -wt N,    --word-thold N  [0.01   ] word timestamp probability threshold
-  -su,      --speed-up      [false  ] speed up audio by x2 (reduced accuracy)
-  -tr,      --translate     [false  ] translate from source language to english
-  -otxt,    --output-txt    [false  ] output result in a text file
-  -ovtt,    --output-vtt    [false  ] output result in a vtt file
-  -osrt,    --output-srt    [false  ] output result in a srt file
-  -owts,    --output-words  [false  ] output script for generating karaoke video
-  -ps,      --print-special [false  ] print special tokens
-  -pc,      --print-colors  [false  ] print colors
-  -nt,      --no-timestamps [true   ] do not print timestamps
-  -l LANG,  --language LANG [en     ] spoken language
-  -m FNAME, --model FNAME   [models/ggml-base.en.bin] model path
-  -f FNAME, --file FNAME    [       ] input WAV file path
-```

examples/main/main.cpp

@@ -1,663 +0,0 @@
-#include "whisper.h"
-
-// third-party utilities
-// use your favorite implementations
-#define DR_WAV_IMPLEMENTATION
-#include "dr_wav.h"
-
-#include <cmath>
-#include <fstream>
-#include <cstdio>
-#include <string>
-#include <thread>
-#include <vector>
-
-// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
-// Lowest is red, middle is yellow, highest is green.
-const std::vector<std::string> k_colors = {
-    "\033[38;5;196m", "\033[38;5;202m", "\033[38;5;208m", "\033[38;5;214m", "\033[38;5;220m",
-    "\033[38;5;226m", "\033[38;5;190m", "\033[38;5;154m", "\033[38;5;118m", "\033[38;5;82m",
-};
-
-//  500 -> 00:05.000
-// 6000 -> 01:00.000
-std::string to_timestamp(int64_t t, bool comma = false) {
-    int64_t msec = t * 10;
-    int64_t hr = msec / (1000 * 60 * 60);
-    msec = msec - hr * (1000 * 60 * 60);
-    int64_t min = msec / (1000 * 60);
-    msec = msec - min * (1000 * 60);
-    int64_t sec = msec / 1000;
-    msec = msec - sec * 1000;
-
-    char buf[32];
-    snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
-
-    return std::string(buf);
-}
-
-int timestamp_to_sample(int64_t t, int n_samples) {
-    return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
-}
-
-// helper function to replace substrings
-void replace_all(std::string & s, const std::string & search, const std::string & replace) {
-    for (size_t pos = 0; ; pos += replace.length()) {
-        pos = s.find(search, pos);
-        if (pos == std::string::npos) break;
-        s.erase(pos, search.length());
-        s.insert(pos, replace);
-    }
-}
-
-// command-line parameters
-struct whisper_params {
-    int32_t n_threads    = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t n_processors = 1;
-    int32_t offset_t_ms  = 0;
-    int32_t offset_n     = 0;
-    int32_t duration_ms  = 0;
-    int32_t max_context  = -1;
-    int32_t max_len      = 0;
-
-    float word_thold = 0.01f;
-
-    bool speed_up      = false;
-    bool translate     = false;
-    bool diarize       = false;
-    bool output_txt    = false;
-    bool output_vtt    = false;
-    bool output_srt    = false;
-    bool output_wts    = false;
-    bool print_special = false;
-    bool print_colors  = false;
-    bool no_timestamps = false;
-
-    std::string language  = "en";
-    std::string model     = "models/ggml-base.en.bin";
-
-    std::vector<std::string> fname_inp = {};
-};
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
-
-bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
-    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
-
-        if (arg[0] != '-') {
-            params.fname_inp.push_back(arg);
-            continue;
-        }
-
-        if (arg == "-h" || arg == "--help") {
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-        else if (arg == "-t"    || arg == "--threads")       { params.n_threads     = std::stoi(argv[++i]); }
-        else if (arg == "-p"    || arg == "--processors")    { params.n_processors  = std::stoi(argv[++i]); }
-        else if (arg == "-ot"   || arg == "--offset-t")      { params.offset_t_ms   = std::stoi(argv[++i]); }
-        else if (arg == "-on"   || arg == "--offset-n")      { params.offset_n      = std::stoi(argv[++i]); }
-        else if (arg == "-d"    || arg == "--duration")      { params.duration_ms   = std::stoi(argv[++i]); }
-        else if (arg == "-mc"   || arg == "--max-context")   { params.max_context   = std::stoi(argv[++i]); }
-        else if (arg == "-ml"   || arg == "--max-len")       { params.max_len       = std::stoi(argv[++i]); }
-        else if (arg == "-wt"   || arg == "--word-thold")    { params.word_thold    = std::stof(argv[++i]); }
-        else if (arg == "-su"   || arg == "--speed-up")      { params.speed_up      = true; }
-        else if (arg == "-tr"   || arg == "--translate")     { params.translate     = true; }
-        else if (arg == "-di"   || arg == "--diarize")       { params.diarize       = true; }
-        else if (arg == "-otxt" || arg == "--output-txt")    { params.output_txt    = true; }
-        else if (arg == "-ovtt" || arg == "--output-vtt")    { params.output_vtt    = true; }
-        else if (arg == "-osrt" || arg == "--output-srt")    { params.output_srt    = true; }
-        else if (arg == "-owts" || arg == "--output-words")  { params.output_wts    = true; }
-        else if (arg == "-ps"   || arg == "--print-special") { params.print_special = true; }
-        else if (arg == "-pc"   || arg == "--print-colors")  { params.print_colors  = true; }
-        else if (arg == "-nt"   || arg == "--no-timestamps") { params.no_timestamps = true; }
-        else if (arg == "-l"    || arg == "--language")      { params.language      = argv[++i]; }
-        else if (arg == "-m"    || arg == "--model")         { params.model         = argv[++i]; }
-        else if (arg == "-f"    || arg == "--file")          { params.fname_inp.push_back(argv[++i]); }
-        else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
-    fprintf(stderr, "\n");
-    fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
-    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n",    params.n_threads);
-    fprintf(stderr, "  -p N,     --processors N  [%-7d] number of processors to use during computation\n", params.n_processors);
-    fprintf(stderr, "  -ot N,    --offset-t N    [%-7d] time offset in milliseconds\n",                    params.offset_t_ms);
-    fprintf(stderr, "  -on N,    --offset-n N    [%-7d] segment index offset\n",                           params.offset_n);
-    fprintf(stderr, "  -d  N,    --duration N    [%-7d] duration of audio to process in milliseconds\n",   params.duration_ms);
-    fprintf(stderr, "  -mc N,    --max-context N [%-7d] maximum number of text context tokens to store\n", params.max_context);
-    fprintf(stderr, "  -ml N,    --max-len N     [%-7d] maximum segment length in characters\n",           params.max_len);
-    fprintf(stderr, "  -wt N,    --word-thold N  [%-7.2f] word timestamp probability threshold\n",         params.word_thold);
-    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",        params.speed_up ? "true" : "false");
-    fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",      params.translate ? "true" : "false");
-    fprintf(stderr, "  -di,      --diarize       [%-7s] stereo audio diarization\n",                       params.diarize ? "true" : "false");
-    fprintf(stderr, "  -otxt,    --output-txt    [%-7s] output result in a text file\n",                   params.output_txt ? "true" : "false");
-    fprintf(stderr, "  -ovtt,    --output-vtt    [%-7s] output result in a vtt file\n",                    params.output_vtt ? "true" : "false");
-    fprintf(stderr, "  -osrt,    --output-srt    [%-7s] output result in a srt file\n",                    params.output_srt ? "true" : "false");
-    fprintf(stderr, "  -owts,    --output-words  [%-7s] output script for generating karaoke video\n",     params.output_wts ? "true" : "false");
-    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                           params.print_special ? "true" : "false");
-    fprintf(stderr, "  -pc,      --print-colors  [%-7s] print colors\n",                                   params.print_colors ? "true" : "false");
-    fprintf(stderr, "  -nt,      --no-timestamps [%-7s] do not print timestamps\n",                        params.no_timestamps ? "false" : "true");
-    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                                params.language.c_str());
-    fprintf(stderr, "  -m FNAME, --model FNAME   [%-7s] model path\n",                                     params.model.c_str());
-    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] input WAV file path\n",                            "");
-    fprintf(stderr, "\n");
-}
-
-struct whisper_print_user_data {
-    const whisper_params * params;
-
-    const std::vector<std::vector<float>> * pcmf32s;
-};
-
-void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
-    const auto & params  = *((whisper_print_user_data *) user_data)->params;
-    const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
-
-    const int n_segments = whisper_full_n_segments(ctx);
-
-    // print the last n_new segments
-    const int s0 = n_segments - n_new;
-    if (s0 == 0) {
-        printf("\n");
-    }
-
-    for (int i = s0; i < n_segments; i++) {
-        if (params.no_timestamps) {
-            if (params.print_colors) {
-                for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
-                    if (params.print_special == false) {
-                        const whisper_token id = whisper_full_get_token_id(ctx, i, j);
-                        if (id >= whisper_token_eot(ctx)) {
-                            continue;
-                        }
-                    }
-
-                    const char * text = whisper_full_get_token_text(ctx, i, j);
-                    const float  p    = whisper_full_get_token_p   (ctx, i, j);
-
-                    const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
-
-                    printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
-                }
-            } else {
-                const char * text = whisper_full_get_segment_text(ctx, i);
-                printf("%s", text);
-            }
-            fflush(stdout);
-        } else {
-            const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-            const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-            std::string speaker = "";
-
-            if (params.diarize && pcmf32s.size() == 2) {
-                const int64_t n_samples = pcmf32s[0].size();
-
-                const int64_t is0 = timestamp_to_sample(t0, n_samples);
-                const int64_t is1 = timestamp_to_sample(t1, n_samples);
-
-                double energy0 = 0.0f;
-                double energy1 = 0.0f;
-
-                for (int64_t j = is0; j < is1; j++) {
-                    energy0 += fabs(pcmf32s[0][j]);
-                    energy1 += fabs(pcmf32s[1][j]);
-                }
-
-                if (energy0 > 1.1*energy1) {
-                    speaker = "(speaker 0)";
-                } else if (energy1 > 1.1*energy0) {
-                    speaker = "(speaker 1)";
-                } else {
-                    speaker = "(speaker ?)";
-                }
-
-                //printf("is0 = %lld, is1 = %lld, energy0 = %f, energy1 = %f, %s\n", is0, is1, energy0, energy1, speaker.c_str());
-            }
-
-            if (params.print_colors) {
-                printf("[%s --> %s]  ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
-                for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
-                    if (params.print_special == false) {
-                        const whisper_token id = whisper_full_get_token_id(ctx, i, j);
-                        if (id >= whisper_token_eot(ctx)) {
-                            continue;
-                        }
-                    }
-
-                    const char * text = whisper_full_get_token_text(ctx, i, j);
-                    const float  p    = whisper_full_get_token_p   (ctx, i, j);
-
-                    const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
-
-                    printf("%s%s%s%s", speaker.c_str(), k_colors[col].c_str(), text, "\033[0m");
-                }
-                printf("\n");
-            } else {
-                const char * text = whisper_full_get_segment_text(ctx, i);
-
-                printf("[%s --> %s]  %s%s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), speaker.c_str(), text);
-            }
-        }
-    }
-}
-
-bool output_txt(struct whisper_context * ctx, const char * fname) {
-    std::ofstream fout(fname);
-    if (!fout.is_open()) {
-        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
-        return false;
-    }
-
-    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
-
-    const int n_segments = whisper_full_n_segments(ctx);
-    for (int i = 0; i < n_segments; ++i) {
-        const char * text = whisper_full_get_segment_text(ctx, i);
-        fout << text;
-    }
-
-    return true;
-}
-
-bool output_vtt(struct whisper_context * ctx, const char * fname) {
-    std::ofstream fout(fname);
-    if (!fout.is_open()) {
-        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
-        return false;
-    }
-
-    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
-
-    fout << "WEBVTT\n\n";
-
-    const int n_segments = whisper_full_n_segments(ctx);
-    for (int i = 0; i < n_segments; ++i) {
-        const char * text = whisper_full_get_segment_text(ctx, i);
-        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-        fout << to_timestamp(t0) << " --> " << to_timestamp(t1) << "\n";
-        fout << text << "\n\n";
-    }
-
-    return true;
-}
-
-bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
-    std::ofstream fout(fname);
-    if (!fout.is_open()) {
-        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
-        return false;
-    }
-
-    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
-
-    const int n_segments = whisper_full_n_segments(ctx);
-    for (int i = 0; i < n_segments; ++i) {
-        const char * text = whisper_full_get_segment_text(ctx, i);
-        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-        fout << i + 1 + params.offset_n << "\n";
-        fout << to_timestamp(t0, true) << " --> " << to_timestamp(t1, true) << "\n";
-        fout << text << "\n\n";
-    }
-
-    return true;
-}
-
-// karaoke video generation
-// outputs a bash script that uses ffmpeg to generate a video with the subtitles
-// TODO: font parameter adjustments
-bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec) {
-    std::ofstream fout(fname);
-
-    fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
-
-    // TODO: become parameter
-    static const char * font = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
-
-    fout << "#!/bin/bash" << "\n";
-    fout << "\n";
-
-    fout << "ffmpeg -i " << fname_inp << " -f lavfi -i color=size=1200x120:duration=" << t_sec << ":rate=25:color=black -vf \"";
-
-    for (int i = 0; i < whisper_full_n_segments(ctx); i++) {
-        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-        const int n = whisper_full_n_tokens(ctx, i);
-
-        std::vector<whisper_token_data> tokens(n);
-        for (int j = 0; j < n; ++j) {
-            tokens[j] = whisper_full_get_token_data(ctx, i, j);
-        }
-
-        if (i > 0) {
-            fout << ",";
-        }
-
-        // background text
-        fout << "drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='':enable='between(t," << t0/100.0 << "," << t0/100.0 << ")'";
-
-        bool is_first = true;
-
-        for (int j = 0; j < n; ++j) {
-            const auto & token = tokens[j];
-
-            if (tokens[j].id >= whisper_token_eot(ctx)) {
-                continue;
-            }
-
-            std::string txt_bg;
-            std::string txt_fg; // highlight token
-            std::string txt_ul; // underline
-
-            txt_bg = "> ";
-            txt_fg = "> ";
-            txt_ul = "\\ \\ ";
-
-            {
-                int ncnt = 0;
-                for (int k = 0; k < n; ++k) {
-                    const auto & token2 = tokens[k];
-
-                    if (tokens[k].id >= whisper_token_eot(ctx)) {
-                        continue;
-                    }
-
-                    const std::string txt = whisper_token_to_str(ctx, token2.id);
-
-                    txt_bg += txt;
-
-                    if (k == j) {
-                        for (int l = 0; l < (int) txt.size(); ++l) {
-                            txt_fg += txt[l];
-                            txt_ul += "_";
-                        }
-                        txt_fg += "|";
-                    } else {
-                        for (int l = 0; l < (int) txt.size(); ++l) {
-                            txt_fg += "\\ ";
-                            txt_ul += "\\ ";
-                        }
-                    }
-
-                    ncnt += txt.size();
-                }
-
-                ::replace_all(txt_bg, "'", "\u2019");
-                ::replace_all(txt_bg, "\"", "\\\"");
-                ::replace_all(txt_fg, "'", "\u2019");
-                ::replace_all(txt_fg, "\"", "\\\"");
-            }
-
-            if (is_first) {
-                // background text
-                fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=gray:x=(w-text_w)/2:y=h/2:text='" << txt_bg << "':enable='between(t," << t0/100.0 << "," << t1/100.0 << ")'";
-                is_first = false;
-            }
-
-            // foreground text
-            fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=lightgreen:x=(w-text_w)/2+8:y=h/2:text='" << txt_fg << "':enable='between(t," << token.t0/100.0 << "," << token.t1/100.0 << ")'";
-
-            // underline
-            fout << ",drawtext=fontfile='" << font << "':fontsize=24:fontcolor=lightgreen:x=(w-text_w)/2+8:y=h/2+16:text='" << txt_ul << "':enable='between(t," << token.t0/100.0 << "," << token.t1/100.0 << ")'";
-        }
-    }
-
-    fout << "\" -c:v libx264 -pix_fmt yuv420p -y " << fname_inp << ".mp4" << "\n";
-
-    fout << "\n\n";
-    fout << "echo \"Your video has been saved to " << fname_inp << ".mp4\"" << "\n";
-    fout << "\n";
-    fout << "echo \"  ffplay " << fname_inp << ".mp4\"\n";
-    fout << "\n";
-
-    fout.close();
-
-    fprintf(stderr, "%s: run 'source %s' to generate karaoke video\n", __func__, fname);
-
-    return true;
-}
-
-int main(int argc, char ** argv) {
-    whisper_params params;
-
-    if (whisper_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    if (params.fname_inp.empty()) {
-        fprintf(stderr, "error: no input files specified\n");
-        whisper_print_usage(argc, argv, params);
-        return 2;
-    }
-
-    if (whisper_lang_id(params.language.c_str()) == -1) {
-        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
-        whisper_print_usage(argc, argv, params);
-        exit(0);
-    }
-
-    // whisper init
-
-    struct whisper_context * ctx = whisper_init(params.model.c_str());
-
-    if (ctx == nullptr) {
-        fprintf(stderr, "error: failed to initialize whisper context\n");
-        return 3;
-    }
-
-    for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
-        const auto fname_inp = params.fname_inp[f];
-
-        std::vector<float> pcmf32; // mono-channel F32 PCM
-        std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
-
-        // WAV input
-        {
-            drwav wav;
-            std::vector<uint8_t> wav_data; // used for pipe input from stdin
-
-            if (fname_inp == "-") {
-                {
-                    uint8_t buf[1024];
-                    while (true)
-                    {
-                        const size_t n = fread(buf, 1, sizeof(buf), stdin);
-                        if (n == 0) {
-                            break;
-                        }
-                        wav_data.insert(wav_data.end(), buf, buf + n);
-                    }
-                }
-
-                if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), NULL) == false) {
-                    fprintf(stderr, "error: failed to open WAV file from stdin\n");
-                    return 4;
-                }
-
-                fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
-            }
-            else if (drwav_init_file(&wav, fname_inp.c_str(), NULL) == false) {
-                fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname_inp.c_str());
-                return 5;
-            }
-
-            if (wav.channels != 1 && wav.channels != 2) {
-                fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], fname_inp.c_str());
-                return 6;
-            }
-
-            if (params.diarize && wav.channels != 2 && params.no_timestamps == false) {
-                fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization and timestamps have to be enabled\n", argv[0], fname_inp.c_str());
-                return 6;
-            }
-
-            if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
-                fprintf(stderr, "%s: WAV file '%s' must be 16 kHz\n", argv[0], fname_inp.c_str());
-                return 8;
-            }
-
-            if (wav.bitsPerSample != 16) {
-                fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], fname_inp.c_str());
-                return 9;
-            }
-
-            const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
-
-            std::vector<int16_t> pcm16;
-            pcm16.resize(n*wav.channels);
-            drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
-            drwav_uninit(&wav);
-
-            // convert to mono, float
-            pcmf32.resize(n);
-            if (wav.channels == 1) {
-                for (int i = 0; i < n; i++) {
-                    pcmf32[i] = float(pcm16[i])/32768.0f;
-                }
-            } else {
-                for (int i = 0; i < n; i++) {
-                    pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
-                }
-            }
-
-            if (params.diarize) {
-                // convert to stereo, float
-                pcmf32s.resize(2);
-
-                pcmf32s[0].resize(n);
-                pcmf32s[1].resize(n);
-                for (int i = 0; i < n; i++) {
-                    pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
-                    pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
-                }
-            }
-        }
-
-        // print system information
-        {
-            fprintf(stderr, "\n");
-            fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
-                    params.n_threads*params.n_processors, std::thread::hardware_concurrency(), whisper_print_system_info());
-        }
-
-        // print some info about the processing
-        {
-            fprintf(stderr, "\n");
-            if (!whisper_is_multilingual(ctx)) {
-                if (params.language != "en" || params.translate) {
-                    params.language = "en";
-                    params.translate = false;
-                    fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
-                }
-            }
-            fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, lang = %s, task = %s, timestamps = %d ...\n",
-                    __func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE,
-                    params.n_threads, params.n_processors,
-                    params.language.c_str(),
-                    params.translate ? "translate" : "transcribe",
-                    params.no_timestamps ? 0 : 1);
-
-            fprintf(stderr, "\n");
-        }
-
-
-        // run the inference
-        {
-            whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-
-            wparams.print_realtime   = false;
-            wparams.print_progress   = false;
-            wparams.print_timestamps = !params.no_timestamps;
-            wparams.print_special    = params.print_special;
-            wparams.translate        = params.translate;
-            wparams.language         = params.language.c_str();
-            wparams.n_threads        = params.n_threads;
-            wparams.n_max_text_ctx   = params.max_context >= 0 ? params.max_context : wparams.n_max_text_ctx;
-            wparams.offset_ms        = params.offset_t_ms;
-            wparams.duration_ms      = params.duration_ms;
-
-            wparams.token_timestamps = params.output_wts || params.max_len > 0;
-            wparams.thold_pt         = params.word_thold;
-            wparams.max_len          = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
-
-            wparams.speed_up         = params.speed_up;
-
-            whisper_print_user_data user_data = { &params, &pcmf32s };
-
-            // this callback is called on each new segment
-            if (!wparams.print_realtime) {
-                wparams.new_segment_callback           = whisper_print_segment_callback;
-                wparams.new_segment_callback_user_data = &user_data;
-            }
-
-            // example for abort mechanism
-            // in this example, we do not abort the processing, but we could if the flag is set to true
-            // the callback is called before every encoder run - if it returns false, the processing is aborted
-            {
-                static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
-
-                wparams.encoder_begin_callback = [](struct whisper_context * ctx, void * user_data) {
-                    bool is_aborted = *(bool*)user_data;
-                    return !is_aborted;
-                };
-                wparams.encoder_begin_callback_user_data = &is_aborted;
-            }
-
-            if (whisper_full_parallel(ctx, wparams, pcmf32.data(), pcmf32.size(), params.n_processors) != 0) {
-                fprintf(stderr, "%s: failed to process audio\n", argv[0]);
-                return 10;
-            }
-        }
-
-        // output stuff
-        {
-            printf("\n");
-
-            // output to text file
-            if (params.output_txt) {
-                const auto fname_txt = fname_inp + ".txt";
-                output_txt(ctx, fname_txt.c_str());
-            }
-
-            // output to VTT file
-            if (params.output_vtt) {
-                const auto fname_vtt = fname_inp + ".vtt";
-                output_vtt(ctx, fname_vtt.c_str());
-            }
-
-            // output to SRT file
-            if (params.output_srt) {
-                const auto fname_srt = fname_inp + ".srt";
-                output_srt(ctx, fname_srt.c_str(), params);
-            }
-
-            // output to WTS file
-            if (params.output_wts) {
-                const auto fname_wts = fname_inp + ".wts";
-                output_wts(ctx, fname_wts.c_str(), fname_inp.c_str(), params, float(pcmf32.size() + 1000)/WHISPER_SAMPLE_RATE);
-            }
-        }
-    }
-
-    whisper_print_timings(ctx);
-    whisper_free(ctx);
-
-    return 0;
-}

examples/stream.wasm/CMakeLists.txt

@@ -1,47 +0,0 @@
-#
-# libstream
-#
-
-set(TARGET libstream)
-
-add_executable(${TARGET}
-    emscripten.cpp
-    )
-
-target_link_libraries(${TARGET} PRIVATE
-    whisper
-    )
-
-unset(EXTRA_FLAGS)
-
-if (WHISPER_WASM_SINGLE_FILE)
-    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
-    message(STATUS "Embedding WASM inside stream.js")
-
-    add_custom_command(
-        TARGET ${TARGET} POST_BUILD
-        COMMAND ${CMAKE_COMMAND} -E copy
-        ${CMAKE_BINARY_DIR}/bin/libstream.js
-        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/stream.wasm/stream.js
-        )
-endif()
-
-set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
-    --bind \
-    -s USE_PTHREADS=1 \
-    -s PTHREAD_POOL_SIZE=8 \
-    -s INITIAL_MEMORY=1024MB \
-    -s TOTAL_MEMORY=1024MB \
-    -s FORCE_FILESYSTEM=1 \
-    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
-    ${EXTRA_FLAGS} \
-    ")
-
-#
-# stream.wasm
-#
-
-set(TARGET stream.wasm)
-
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/stream.wasm/README.md

@@ -1,20 +0,0 @@
-# stream.wasm
-
-Real-time transcription in the browser using WebAssembly
-
-Online demo: https://whisper.ggerganov.com/stream/
-
-## Build instructions
-
-```bash
-# build using Emscripten (v3.1.2)
-git clone https://github.com/ggerganov/whisper.cpp
-cd whisper.cpp
-mkdir build-em && cd build-em
-emcmake cmake ..
-make -j
-
-# copy the produced page to your HTTP path
-cp bin/stream.wasm/*       /path/to/html/
-cp bin/libstream.worker.js /path/to/html/
-```

examples/stream.wasm/emscripten.cpp

@@ -1,213 +0,0 @@
-#include "ggml.h"
-#include "whisper.h"
-
-#include <emscripten.h>
-#include <emscripten/bind.h>
-
-#include <atomic>
-#include <cmath>
-#include <mutex>
-#include <string>
-#include <thread>
-#include <vector>
-
-constexpr int N_THREAD = 8;
-
-std::vector<struct whisper_context *> g_contexts(4, nullptr);
-
-std::mutex g_mutex;
-std::thread g_worker;
-
-std::atomic<bool> g_running(false);
-
-std::string g_status        = "";
-std::string g_status_forced = "";
-std::string g_transcribed   = "";
-
-std::vector<float> g_pcmf32;
-
-void stream_set_status(const std::string & status) {
-    std::lock_guard<std::mutex> lock(g_mutex);
-    g_status = status;
-}
-
-void stream_main(size_t index) {
-    stream_set_status("loading data ...");
-
-    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
-
-    wparams.n_threads        = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
-    wparams.offset_ms        = 0;
-    wparams.translate        = false;
-    wparams.no_context       = true;
-    wparams.single_segment   = true;
-    wparams.print_realtime   = false;
-    wparams.print_progress   = false;
-    wparams.print_timestamps = true;
-    wparams.print_special    = false;
-
-    wparams.max_tokens       = 32;
-    wparams.audio_ctx        = 768; // partial encoder context for better performance
-
-    wparams.language         = "en";
-
-    printf("stream: using %d threads\n", wparams.n_threads);
-
-    std::vector<float> pcmf32;
-
-    // whisper context
-    auto & ctx = g_contexts[index];
-
-    // 5 seconds interval
-    const int64_t window_samples = 5*WHISPER_SAMPLE_RATE;
-
-    while (g_running) {
-        stream_set_status("waiting for audio ...");
-
-        {
-            std::unique_lock<std::mutex> lock(g_mutex);
-
-            if (g_pcmf32.size() < 1024) {
-                lock.unlock();
-
-                std::this_thread::sleep_for(std::chrono::milliseconds(10));
-
-                continue;
-            }
-
-            pcmf32 = std::vector<float>(g_pcmf32.end() - std::min((int64_t) g_pcmf32.size(), window_samples), g_pcmf32.end());
-            g_pcmf32.clear();
-        }
-
-        {
-            const auto t_start = std::chrono::high_resolution_clock::now();
-
-            stream_set_status("running whisper ...");
-
-            int ret = whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size());
-            if (ret != 0) {
-                printf("whisper_full() failed: %d\n", ret);
-                break;
-            }
-
-            const auto t_end = std::chrono::high_resolution_clock::now();
-
-            printf("stream: whisper_full() returned %d in %f seconds\n", ret, std::chrono::duration<double>(t_end - t_start).count());
-        }
-
-        {
-            std::string text_heard;
-
-            {
-                const int n_segments = whisper_full_n_segments(ctx);
-                for (int i = n_segments - 1; i < n_segments; ++i) {
-                    const char * text = whisper_full_get_segment_text(ctx, i);
-
-                    const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-                    const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-                    printf("transcribed: %s\n", text);
-
-                    text_heard += text;
-                }
-            }
-
-            {
-                std::lock_guard<std::mutex> lock(g_mutex);
-                g_transcribed = text_heard;
-            }
-        }
-    }
-
-    if (index < g_contexts.size()) {
-        whisper_free(g_contexts[index]);
-        g_contexts[index] = nullptr;
-    }
-}
-
-EMSCRIPTEN_BINDINGS(stream) {
-    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
-        for (size_t i = 0; i < g_contexts.size(); ++i) {
-            if (g_contexts[i] == nullptr) {
-                g_contexts[i] = whisper_init(path_model.c_str());
-                if (g_contexts[i] != nullptr) {
-                    g_running = true;
-                    if (g_worker.joinable()) {
-                        g_worker.join();
-                    }
-                    g_worker = std::thread([i]() {
-                        stream_main(i);
-                    });
-
-                    return i + 1;
-                } else {
-                    return (size_t) 0;
-                }
-            }
-        }
-
-        return (size_t) 0;
-    }));
-
-    emscripten::function("free", emscripten::optional_override([](size_t index) {
-        if (g_running) {
-            g_running = false;
-        }
-    }));
-
-    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
-        --index;
-
-        if (index >= g_contexts.size()) {
-            return -1;
-        }
-
-        if (g_contexts[index] == nullptr) {
-            return -2;
-        }
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            const int n = audio["length"].as<int>();
-
-            emscripten::val heap = emscripten::val::module_property("HEAPU8");
-            emscripten::val memory = heap["buffer"];
-
-            g_pcmf32.resize(n);
-
-            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
-            memoryView.call<void>("set", audio);
-        }
-
-        return 0;
-    }));
-
-    emscripten::function("get_transcribed", emscripten::optional_override([]() {
-        std::string transcribed;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            transcribed = std::move(g_transcribed);
-        }
-
-        return transcribed;
-    }));
-
-    emscripten::function("get_status", emscripten::optional_override([]() {
-        std::string status;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            status = g_status_forced.empty() ? g_status : g_status_forced;
-        }
-
-        return status;
-    }));
-
-    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            g_status_forced = status;
-        }
-    }));
-}

examples/stream.wasm/index-tmpl.html

@@ -1,386 +0,0 @@
-<!doctype html>
-<html lang="en-us">
-    <head>
-        <title>stream : Real-time Whisper transcription in WebAssembly</title>
-
-        <style>
-            #output {
-                width: 100%;
-                height: 100%;
-                margin: 0 auto;
-                margin-top: 10px;
-                border-left: 0px;
-                border-right: 0px;
-                padding-left: 0px;
-                padding-right: 0px;
-                display: block;
-                background-color: black;
-                color: white;
-                font-size: 10px;
-                font-family: 'Lucida Console', Monaco, monospace;
-                outline: none;
-                white-space: pre;
-                overflow-wrap: normal;
-                overflow-x: scroll;
-            }
-        </style>
-    </head>
-    <body>
-        <div id="main-container">
-            <b>stream : Real-time Whisper transcription in WebAssembly</b>
-
-            <br><br>
-
-            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/stream.wasm">GitHub</a>.
-
-            <br><br>
-
-            <hr>
-
-            Select the model you would like to use, click the "Start" button and start speaking
-
-            <br><br>
-
-            <div id="model-whisper">
-                Whisper model: <span id="model-whisper-status"></span>
-                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
-                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
-                <span id="fetch-whisper-progress"></span>
-
-                <!--
-                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
-                -->
-            </div>
-
-            <br>
-
-            <div id="input">
-                <button id="start"  onclick="onStart()" disabled>Start</button>
-                <button id="stop"   onclick="onStop()" disabled>Stop</button>
-                <button id="clear"  onclick="clearCache()">Clear Cache</button>
-            </div>
-
-            <br>
-
-            <div id="state">
-                Status: <b><span id="state-status">not started</span></b>
-
-                <pre id="state-transcribed">[The transcribed text will be displayed here]</pre>
-            </div>
-
-            <hr>
-
-            Debug output:
-            <textarea id="output" rows="20"></textarea>
-
-            <br>
-
-            <b>Troubleshooting</b>
-
-            <br><br>
-
-            The page does some heavy computations, so make sure:
-
-            <ul>
-                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
-                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
-                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
-            </ul>
-
-            <div class="cell-version">
-                <span>
-                    |
-                    Build time: <span class="nav-link">@GIT_DATE@</span> |
-                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
-                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
-                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/stream.wasm">Source Code</a> |
-                </span>
-            </div>
-        </div>
-
-        <script type="text/javascript" src="helpers.js"></script>
-        <script type='text/javascript'>
-            // web audio context
-            var context = null;
-
-            // audio data
-            var audio = null;
-            var audio0 = null;
-
-            // the stream instance
-            var instance = null;
-
-            // model name
-            var model_whisper = null;
-
-            var Module = {
-                print: printTextarea,
-                printErr: printTextarea,
-                setStatus: function(text) {
-                    printTextarea('js: ' + text);
-                },
-                monitorRunDependencies: function(left) {
-                },
-                preRun: function() {
-                    printTextarea('js: Preparing ...');
-                },
-                postRun: function() {
-                    printTextarea('js: Initialized successfully!');
-                }
-            };
-
-            //
-            // fetch models
-            //
-
-            let dbVersion = 1
-            let dbName    = 'whisper.ggerganov.com';
-            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
-
-            function storeFS(fname, buf) {
-                // write to WASM file using FS_createDataFile
-                // if the file exists, delete it
-                try {
-                    Module.FS_unlink(fname);
-                } catch (e) {
-                    // ignore
-                }
-
-                Module.FS_createDataFile("/", fname, buf, true, true);
-
-                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
-
-                document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
-
-                if (model_whisper != null) {
-                    document.getElementById('start').disabled = false;
-                    document.getElementById('stop' ).disabled = true;
-                }
-            }
-
-            function loadWhisper(model) {
-                let urls = {
-                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
-                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
-                };
-
-                let sizes = {
-                    'tiny.en': 75,
-                    'base.en': 142,
-                };
-
-                let url     = urls[model];
-                let dst     = 'whisper.bin';
-                let size_mb = sizes[model];
-
-                model_whisper = model;
-
-                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en').style.display = 'none';
-                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
-
-                cbProgress = function(p) {
-                    let el = document.getElementById('fetch-whisper-progress');
-                    el.innerHTML = Math.round(100*p) + '%';
-                };
-
-                cbCancel = function() {
-                    var el;
-                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
-                };
-
-                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
-            }
-
-            //
-            // microphone
-            //
-
-            const kSampleRate = 16000;
-            const kRestartRecording_s = 120;
-            const kIntervalAudio_ms = 5000; // pass the recorded audio to the C++ instance at this rate
-
-            var mediaRecorder = null;
-            var doRecording = false;
-            var startTime = 0;
-
-            window.AudioContext = window.AudioContext || window.webkitAudioContext;
-            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
-
-            function stopRecording() {
-                Module.set_status("paused");
-                doRecording = false;
-                audio0 = null;
-                audio = null;
-                context = null;
-            }
-
-            function startRecording() {
-                if (!context) {
-                    context = new AudioContext({
-                        sampleRate: kSampleRate,
-                        channelCount: 1,
-                        echoCancellation: false,
-                        autoGainControl:  true,
-                        noiseSuppression: true,
-                    });
-                }
-
-                Module.set_status("");
-
-                document.getElementById('start').disabled = true;
-                document.getElementById('stop').disabled = false;
-
-                doRecording = true;
-                startTime = Date.now();
-
-                var chunks = [];
-                var stream = null;
-
-                navigator.mediaDevices.getUserMedia({audio: true, video: false})
-                    .then(function(s) {
-                        stream = s;
-                        mediaRecorder = new MediaRecorder(stream);
-                        mediaRecorder.ondataavailable = function(e) {
-                            chunks.push(e.data);
-
-                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
-                            var reader = new FileReader();
-
-                            reader.onload = function(event) {
-                                var buf = new Uint8Array(reader.result);
-
-                                if (!context) {
-                                    return;
-                                }
-                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
-                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
-                                    var source = offlineContext.createBufferSource();
-                                    source.buffer = audioBuffer;
-                                    source.connect(offlineContext.destination);
-                                    source.start(0);
-
-                                    offlineContext.startRendering().then(function(renderedBuffer) {
-                                        audio = renderedBuffer.getChannelData(0);
-
-                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
-
-                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
-                                        if (audio0 != null) {
-                                            audioAll.set(audio0, 0);
-                                        }
-                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
-
-                                        if (instance) {
-                                            Module.set_audio(instance, audioAll);
-                                        }
-                                    });
-                                }, function(e) {
-                                    audio = null;
-                                });
-                            }
-
-                            reader.readAsArrayBuffer(blob);
-                        };
-
-                        mediaRecorder.onstop = function(e) {
-                            if (doRecording) {
-                                setTimeout(function() {
-                                    startRecording();
-                                });
-                            }
-                        };
-
-                        mediaRecorder.start(kIntervalAudio_ms);
-                    })
-                    .catch(function(err) {
-                        printTextarea('js: error getting audio stream: ' + err);
-                    });
-
-                var interval = setInterval(function() {
-                    if (!doRecording) {
-                        clearInterval(interval);
-                        mediaRecorder.stop();
-                        stream.getTracks().forEach(function(track) {
-                            track.stop();
-                        });
-
-                        document.getElementById('start').disabled = false;
-                        document.getElementById('stop').disabled  = true;
-
-                        mediaRecorder = null;
-                    }
-
-                    // if audio length is more than kRestartRecording_s seconds, restart recording
-                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
-                        if (doRecording) {
-                            //printTextarea('js: restarting recording');
-
-                            clearInterval(interval);
-                            audio0 = audio;
-                            audio = null;
-                            mediaRecorder.stop();
-                            stream.getTracks().forEach(function(track) {
-                                track.stop();
-                            });
-                        }
-                    }
-                }, 100);
-            }
-
-            //
-            // main
-            //
-
-            var nLines = 0;
-            var intervalUpdate = null;
-            var transcribedAll = '';
-
-            function onStart() {
-                if (!instance) {
-                    instance = Module.init('whisper.bin');
-
-                    if (instance) {
-                        printTextarea("js: whisper initialized, instance: " + instance);
-                    }
-                }
-
-                if (!instance) {
-                    printTextarea("js: failed to initialize whisper");
-                    return;
-                }
-
-                startRecording();
-
-                intervalUpdate = setInterval(function() {
-                    var transcribed = Module.get_transcribed();
-
-                    if (transcribed != null && transcribed.length > 1) {
-                        transcribedAll += transcribed + '<br>';
-                        nLines++;
-
-                        // if more than 10 lines, remove the first line
-                        if (nLines > 10) {
-                            var i = transcribedAll.indexOf('<br>');
-                            if (i > 0) {
-                                transcribedAll = transcribedAll.substring(i + 4);
-                                nLines--;
-                            }
-                        }
-                    }
-
-                    document.getElementById('state-status').innerHTML = Module.get_status();
-                    document.getElementById('state-transcribed').innerHTML = transcribedAll;
-                }, 100);
-            }
-
-            function onStop() {
-                stopRecording();
-            }
-
-        </script>
-        <script type="text/javascript" src="stream.js"></script>
-    </body>
-</html>

examples/stream/CMakeLists.txt

@@ -1,7 +0,0 @@
-if (WHISPER_SUPPORT_SDL2)
-    # stream
-    set(TARGET stream)
-    add_executable(${TARGET} stream.cpp)
-    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
-    target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
-endif ()

examples/stream/README.md

@@ -1,27 +0,0 @@
-# stream
-
-This is a naive example of performing real-time inference on audio from your microphone.
-The `stream` tool samples the audio every half a second and runs the transcription continously.
-More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
-
-```java
-./stream -m ./models/ggml-base.en.bin -t 8 --step 500 --length 5000
-```
-
-https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a80f-28ba83be7d09.mp4
-
-The `stream` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
-
-```bash
-# Install SDL2 on Linux
-sudo apt-get install libsdl2-dev
-
-# Install SDL2 on Mac OS
-brew install sdl2
-
-make stream
-```
-
-## Web version
-
-This tool can also run in the browser: [examples/stream.wasm](/examples/stream.wasm)

examples/stream/stream.cpp

@@ -1,395 +0,0 @@
-// Real-time speech recognition of input from a microphone
-//
-// A very quick-n-dirty implementation serving mainly as a proof of concept.
-
-#include "whisper.h"
-
-#include <SDL.h>
-#include <SDL_audio.h>
-
-#include <cassert>
-#include <cstdio>
-#include <string>
-#include <thread>
-#include <vector>
-#include <fstream>
-
-//  500 -> 00:05.000
-// 6000 -> 01:00.000
-std::string to_timestamp(int64_t t) {
-    int64_t sec = t/100;
-    int64_t msec = t - sec*100;
-    int64_t min = sec/60;
-    sec = sec - min*60;
-
-    char buf[32];
-    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
-
-    return std::string(buf);
-}
-
-// command-line parameters
-struct whisper_params {
-    int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t step_ms    = 3000;
-    int32_t length_ms  = 10000;
-    int32_t capture_id = -1;
-    int32_t max_tokens = 32;
-    int32_t audio_ctx  = 0;
-
-    bool speed_up      = false;
-    bool translate     = false;
-    bool no_context    = true;
-    bool print_special = false;
-    bool no_timestamps = true;
-
-    std::string language  = "en";
-    std::string model     = "models/ggml-base.en.bin";
-    std::string fname_out = "";
-};
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
-
-bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
-    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
-
-        if (arg == "-h" || arg == "--help") {
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-        else if (arg == "-t"   || arg == "--threads")       { params.n_threads     = std::stoi(argv[++i]); }
-        else if (                 arg == "--step")          { params.step_ms       = std::stoi(argv[++i]); }
-        else if (                 arg == "--length")        { params.length_ms     = std::stoi(argv[++i]); }
-        else if (arg == "-c"   || arg == "--capture")       { params.capture_id    = std::stoi(argv[++i]); }
-        else if (arg == "-mt"  || arg == "--max-tokens")    { params.max_tokens    = std::stoi(argv[++i]); }
-        else if (arg == "-ac"  || arg == "--audio-ctx")     { params.audio_ctx     = std::stoi(argv[++i]); }
-        else if (arg == "-su"  || arg == "--speed-up")      { params.speed_up      = true; }
-        else if (arg == "-tr"  || arg == "--translate")     { params.translate     = true; }
-        else if (arg == "-kc"  || arg == "--keep-context")  { params.no_context    = false; }
-        else if (arg == "-ps"  || arg == "--print-special") { params.print_special = true; }
-        else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
-        else if (arg == "-m"   || arg == "--model")         { params.model         = argv[++i]; }
-        else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
-        else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
-    fprintf(stderr, "\n");
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
-    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n", params.n_threads);
-    fprintf(stderr, "            --step N        [%-7d] audio step size in milliseconds\n",             params.step_ms);
-    fprintf(stderr, "            --length N      [%-7d] audio length in milliseconds\n",                params.length_ms);
-    fprintf(stderr, "  -c ID,    --capture ID    [%-7d] capture device ID\n",                           params.capture_id);
-    fprintf(stderr, "  -mt N,    --max-tokens N  [%-7d] maximum number of tokens per audio chunk\n",    params.max_tokens);
-    fprintf(stderr, "  -ac N,    --audio-ctx N   [%-7d] audio context size (0 - all)\n",                params.audio_ctx);
-    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",     params.speed_up ? "true" : "false");
-    fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",   params.translate ? "true" : "false");
-    fprintf(stderr, "  -kc,      --keep-context  [%-7s] keep context between audio chunks\n",           params.no_context ? "false" : "true");
-    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                        params.print_special ? "true" : "false");
-    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                             params.language.c_str());
-    fprintf(stderr, "  -m FNAME, --model FNAME   [%-7s] model path\n",                                  params.model.c_str());
-    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
-    fprintf(stderr, "\n");
-}
-
-//
-// SDL Audio capture
-//
-
-SDL_AudioDeviceID g_dev_id_in = 0;
-
-bool audio_sdl_init(const int capture_id) {
-    if (g_dev_id_in) {
-        fprintf(stderr, "%s: already initialized\n", __func__);
-        return false;
-    }
-
-    SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
-
-    if (SDL_Init(SDL_INIT_AUDIO) < 0) {
-        SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
-        return (1);
-    }
-
-    SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
-
-    {
-        int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
-        fprintf(stderr, "%s: found %d capture devices:\n", __func__, nDevices);
-        for (int i = 0; i < nDevices; i++) {
-            fprintf(stderr, "%s:    - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
-        }
-    }
-
-    SDL_AudioSpec capture_spec_requested;
-    SDL_AudioSpec capture_spec_obtained;
-
-    SDL_zero(capture_spec_requested);
-    SDL_zero(capture_spec_obtained);
-
-    capture_spec_requested.freq     = WHISPER_SAMPLE_RATE;
-    capture_spec_requested.format   = AUDIO_F32;
-    capture_spec_requested.channels = 1;
-    capture_spec_requested.samples  = 1024;
-
-    if (capture_id >= 0) {
-        fprintf(stderr, "%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
-        g_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
-    } else {
-        fprintf(stderr, "%s: attempt to open default capture device ...\n", __func__);
-        g_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
-    }
-    if (!g_dev_id_in) {
-        fprintf(stderr, "%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
-        g_dev_id_in = 0;
-    } else {
-        fprintf(stderr, "%s: obtained spec for input device (SDL Id = %d):\n", __func__, g_dev_id_in);
-        fprintf(stderr, "%s:     - sample rate:       %d\n", __func__, capture_spec_obtained.freq);
-        fprintf(stderr, "%s:     - format:            %d (required: %d)\n", __func__, capture_spec_obtained.format, capture_spec_requested.format);
-        fprintf(stderr, "%s:     - channels:          %d (required: %d)\n", __func__, capture_spec_obtained.channels, capture_spec_requested.channels);
-        fprintf(stderr, "%s:     - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
-    }
-
-    return true;
-}
-
-///////////////////////////
-
-int main(int argc, char ** argv) {
-    whisper_params params;
-
-    if (whisper_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    // init audio
-
-    if (!audio_sdl_init(params.capture_id)) {
-        fprintf(stderr, "%s: audio_sdl_init() failed!\n", __func__);
-        return 1;
-    }
-
-    if (whisper_lang_id(params.language.c_str()) == -1) {
-        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
-        whisper_print_usage(argc, argv, params);
-        exit(0);
-    }
-
-    // whisper init
-
-    struct whisper_context * ctx = whisper_init(params.model.c_str());
-
-    const int n_samples = (params.step_ms/1000.0)*WHISPER_SAMPLE_RATE;
-    const int n_samples_len = (params.length_ms/1000.0)*WHISPER_SAMPLE_RATE;
-    const int n_samples_30s = 30*WHISPER_SAMPLE_RATE;
-    const int n_samples_keep = 0.2*WHISPER_SAMPLE_RATE;
-
-    std::vector<float> pcmf32(n_samples_30s, 0.0f);
-    std::vector<float> pcmf32_old;
-
-    std::vector<whisper_token> prompt_tokens;
-    const int n_new_line = params.length_ms / params.step_ms - 1;
-
-    // print some info about the processing
-    {
-        fprintf(stderr, "\n");
-        if (!whisper_is_multilingual(ctx)) {
-            if (params.language != "en" || params.translate) {
-                params.language = "en";
-                params.translate = false;
-                fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
-            }
-        }
-        fprintf(stderr, "%s: processing %d samples (step = %.1f sec / len = %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
-                __func__,
-                n_samples,
-                float(n_samples)/WHISPER_SAMPLE_RATE,
-                float(n_samples_len)/WHISPER_SAMPLE_RATE,
-                params.n_threads,
-                params.language.c_str(),
-                params.translate ? "translate" : "transcribe",
-                params.no_timestamps ? 0 : 1);
-
-        fprintf(stderr, "%s: n_new_line = %d\n", __func__, n_new_line);
-        fprintf(stderr, "\n");
-    }
-
-    SDL_PauseAudioDevice(g_dev_id_in, 0);
-
-    int n_iter = 0;
-    bool is_running = true;
-
-    std::ofstream fout;
-    if (params.fname_out.length() > 0) {
-        fout.open(params.fname_out);
-        if (!fout.is_open()) {
-            fprintf(stderr, "%s: failed to open output file '%s'!\n", __func__, params.fname_out.c_str());
-            return 1;
-        }
-    }
-
-    printf("[Start speaking]");
-    fflush(stdout);
-
-    // main audio loop
-    while (is_running) {
-        // handle Ctrl + C
-        {
-            SDL_Event event;
-            while (SDL_PollEvent(&event)) {
-                switch (event.type) {
-                    case SDL_QUIT:
-                        {
-                            is_running = false;
-                        } break;
-                    default:
-                        break;
-                }
-            }
-
-            if (!is_running) {
-                break;
-            }
-        }
-
-        if (!is_running) {
-            break;
-        }
-
-        // process new audio
-        if (n_iter > 0 && SDL_GetQueuedAudioSize(g_dev_id_in) > 2*n_samples*sizeof(float)) {
-            fprintf(stderr, "\n\n%s: WARNING: cannot process audio fast enough, dropping audio ...\n\n", __func__);
-            SDL_ClearQueuedAudio(g_dev_id_in);
-        }
-
-        while (SDL_GetQueuedAudioSize(g_dev_id_in) < n_samples*sizeof(float)) {
-            SDL_Delay(1);
-        }
-
-        const int n_samples_new = SDL_GetQueuedAudioSize(g_dev_id_in)/sizeof(float);
-
-        // take one second from previous iteration
-        //const int n_samples_take = std::min((int) pcmf32_old.size(), std::max(0, n_samples_30s/30 - n_samples_new));
-
-        // take up to params.length_ms audio from previous iteration
-        const int n_samples_take = std::min((int) pcmf32_old.size(), std::max(0, n_samples_keep + n_samples_len - n_samples_new));
-
-        //printf("processing: take = %d, new = %d, old = %d\n", n_samples_take, n_samples_new, (int) pcmf32_old.size());
-
-        pcmf32.resize(n_samples_new + n_samples_take);
-
-        for (int i = 0; i < n_samples_take; i++) {
-            pcmf32[i] = pcmf32_old[pcmf32_old.size() - n_samples_take + i];
-        }
-
-        SDL_DequeueAudio(g_dev_id_in, pcmf32.data() + n_samples_take, n_samples_new*sizeof(float));
-
-        pcmf32_old = pcmf32;
-
-        // run the inference
-        {
-            whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-
-            wparams.print_progress   = false;
-            wparams.print_special    = params.print_special;
-            wparams.print_realtime   = false;
-            wparams.print_timestamps = !params.no_timestamps;
-            wparams.translate        = params.translate;
-            wparams.no_context       = true;
-            wparams.single_segment   = true;
-            wparams.max_tokens       = params.max_tokens;
-            wparams.language         = params.language.c_str();
-            wparams.n_threads        = params.n_threads;
-
-            wparams.audio_ctx        = params.audio_ctx;
-            wparams.speed_up         = params.speed_up;
-
-            wparams.prompt_tokens    = params.no_context ? nullptr : prompt_tokens.data();
-            wparams.prompt_n_tokens  = params.no_context ? 0       : prompt_tokens.size();
-
-            if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
-                fprintf(stderr, "%s: failed to process audio\n", argv[0]);
-                return 6;
-            }
-
-            // print result;
-            {
-                printf("\33[2K\r");
-
-                // print long empty line to clear the previous line
-                printf("%s", std::string(100, ' ').c_str());
-
-                printf("\33[2K\r");
-
-                const int n_segments = whisper_full_n_segments(ctx);
-                for (int i = 0; i < n_segments; ++i) {
-                    const char * text = whisper_full_get_segment_text(ctx, i);
-
-                    if (params.no_timestamps) {
-                        printf("%s", text);
-                        fflush(stdout);
-
-                        if (params.fname_out.length() > 0) {
-                            fout << text;
-                        }
-                    } else {
-                        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-                        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-                        printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
-
-                        if (params.fname_out.length() > 0) {
-                            fout << "[" << to_timestamp(t0) << " --> " << to_timestamp(t1) << "]  " << text << std::endl;
-                        }
-                    }
-                }
-
-                if (params.fname_out.length() > 0) {
-                    fout << std::endl;
-                }
-            }
-
-            ++n_iter;
-
-            if ((n_iter % n_new_line) == 0) {
-                printf("\n");
-
-                // keep part of the audio for next iteration to try to mitigate word boundary issues
-                pcmf32_old = std::vector<float>(pcmf32.end() - n_samples_keep, pcmf32.end());
-
-                // Add tokens of the last full length segment as the prompt
-                if (!params.no_context) {
-                    prompt_tokens.clear();
-
-                    const int n_segments = whisper_full_n_segments(ctx);
-                    for (int i = 0; i < n_segments; ++i) {
-                        const int token_count = whisper_full_n_tokens(ctx, i);
-                        for (int j = 0; j < token_count; ++j) {
-                            prompt_tokens.push_back(whisper_full_get_token_id(ctx, i, j));
-                        }
-                    }
-                }
-            }
-        }
-    }
-
-    if (g_dev_id_in >= 0) {
-        SDL_CloseAudioDevice(g_dev_id_in);
-    }
-
-    whisper_print_timings(ctx);
-    whisper_free(ctx);
-
-    return 0;
-}

examples/talk.wasm/CMakeLists.txt

@@ -1,48 +0,0 @@
-#
-# libtalk
-#
-
-set(TARGET libtalk)
-
-add_executable(${TARGET}
-    emscripten.cpp
-    gpt-2.cpp
-    )
-
-target_link_libraries(${TARGET} PRIVATE
-    whisper
-    )
-
-unset(EXTRA_FLAGS)
-
-if (WHISPER_WASM_SINGLE_FILE)
-    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
-    message(STATUS "Embedding WASM inside talk.js")
-
-    add_custom_command(
-        TARGET ${TARGET} POST_BUILD
-        COMMAND ${CMAKE_COMMAND} -E copy
-        ${CMAKE_BINARY_DIR}/bin/libtalk.js
-        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/talk.wasm/talk.js
-        )
-endif()
-
-set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
-    --bind \
-    -s USE_PTHREADS=1 \
-    -s PTHREAD_POOL_SIZE=8 \
-    -s INITIAL_MEMORY=1600MB \
-    -s TOTAL_MEMORY=1600MB \
-    -s FORCE_FILESYSTEM=1 \
-    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
-    ${EXTRA_FLAGS} \
-    ")
-
-#
-# talk.wasm
-#
-
-set(TARGET talk.wasm)
-
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)

examples/talk.wasm/README.md

@@ -1,72 +0,0 @@
-# talk.wasm
-
-Talk with an Artificial Intelligence in your browser:
-
-[https://user-images.githubusercontent.com/1991296/203411580-fedb4839-05e4-4474-8364-aaf1e9a9b615.mp4](https://user-images.githubusercontent.com/1991296/203845553-f7b44e13-9a15-4fc8-b518-ae8f4c6770fe.mp4)
-
-Online demo: https://whisper.ggerganov.com/talk/
-
-## How it works?
-
-This demo leverages 2 modern neural network models to create a high-quality voice chat directly in your browser:
-
-- [OpenAI's Whisper](https://github.com/openai/whisper) speech recognition model is used to process your voice and understand what you are saying
-- Upon receiving some voice input, the AI generates a text response using [OpenAI's GPT-2](https://github.com/openai/gpt-2) language model
-- The AI then vocalizes the response using the browser's [Web Speech API](https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API)
-
-The web page does the processing locally on your machine. The processing of these heavy neural network models in the
-browser is possible by implementing them efficiently in C/C++ and using the browser's WebAssembly SIMD capabilities for
-extra performance:
-
-- The Whisper C++ implementation is here: [whisper.h](/whisper.h) / [whisper.cpp](/whisper.cpp)
-- The GPT-2 C++ implementation is here: [gpt-2.h](gpt-2.h) / [gpt-2.cpp](gpt-2.cpp)
-- Both models use a custom tensor library implemented in C: [ggml.h](/ggml.h) / [ggml.c](/ggml.c)
-- The HTML/JS layer is here: [index-tmpl.html](index-tmpl.html)
-- The Emscripten bridge between C/C++ and JS is here: [emscripten.cpp](emscripten.cpp)
-
-In order to run the models, the web page first needs to download the model data which is about ~350 MB. The model data
-is then cached in your browser's cache and can be reused in future visits without downloading it again.
-
-## Requirements
-
-In order to run this demo efficiently, you need to have the following:
-
-- Latest Chrome or Firefox browser (Safari is not supported)
-- Run this on a desktop or laptop with modern CPU (a mobile phone will likely not be good enough)
-- Speak phrases that are no longer than 10 seconds - this is the audio context of the AI
-- The web-page uses about 1.6GB of RAM
-
-Notice that this demo is using the smallest GPT-2 model, so the generated text responses are not always very good.
-Also, the prompting strategy can likely be improved to achieve better results.
-
-The demo is quite computationally heavy, so you need a fast CPU. It's not usual to run these transformer models in a
-browser. Typically, they run on powerful GPUs.
-
-Currently, mobile browsers do not support the Fixed-width SIMD WebAssembly capability, so you cannot run this demo
-on a phone or a tablet. Hopefully, in the near future this will become supported.
-
-## Todo
-
-- Better UI (contributions are welcome)
-- Better GPT-2 prompting
-
-## Build instructions
-
-```bash
-# build using Emscripten (v3.1.2)
-git clone https://github.com/ggerganov/whisper.cpp
-cd whisper.cpp
-mkdir build-em && cd build-em
-emcmake cmake ..
-make -j
-
-# copy the produced page to your HTTP path
-cp bin/talk.wasm/*       /path/to/html/
-cp bin/libtalk.worker.js /path/to/html/
-```
-
-## Feedback
-
-If you have any comments or ideas for improvement, please drop a comment in the following discussion:
-
-https://github.com/ggerganov/whisper.cpp/discussions/167

examples/talk.wasm/emscripten.cpp

@@ -1,380 +0,0 @@
-#include "ggml.h"
-#include "gpt-2.h"
-#include "whisper.h"
-
-#include <emscripten.h>
-#include <emscripten/bind.h>
-
-#include <atomic>
-#include <cmath>
-#include <mutex>
-#include <string>
-#include <thread>
-#include <vector>
-#include <regex>
-
-constexpr int N_THREAD = 8;
-
-struct gpt2_context * g_gpt2;
-std::vector<struct whisper_context *> g_contexts(4, nullptr);
-
-std::mutex g_mutex;
-std::thread g_worker;
-std::atomic<bool> g_running(false);
-
-bool g_force_speak = false;
-std::string g_text_to_speak = "";
-std::string g_status = "";
-std::string g_status_forced = "";
-
-std::vector<float> g_pcmf32;
-
-std::string to_timestamp(int64_t t) {
-    int64_t sec = t/100;
-    int64_t msec = t - sec*100;
-    int64_t min = sec/60;
-    sec = sec - min*60;
-
-    char buf[32];
-    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
-
-    return std::string(buf);
-}
-
-void talk_set_status(const std::string & status) {
-    std::lock_guard<std::mutex> lock(g_mutex);
-    g_status = status;
-}
-
-void talk_main(size_t index) {
-    talk_set_status("loading data ...");
-
-    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
-
-    wparams.n_threads        = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
-    wparams.offset_ms        = 0;
-    wparams.translate        = false;
-    wparams.no_context       = true;
-    wparams.single_segment   = true;
-    wparams.print_realtime   = false;
-    wparams.print_progress   = false;
-    wparams.print_timestamps = true;
-    wparams.print_special    = false;
-
-    wparams.max_tokens       = 32;
-    wparams.audio_ctx        = 768; // partial encoder context for better performance
-
-    wparams.language         = "en";
-
-    g_gpt2 = gpt2_init("gpt-2.bin");
-
-    printf("talk: using %d threads\n", wparams.n_threads);
-
-    std::vector<float> pcmf32;
-
-    // whisper context
-    auto & ctx = g_contexts[index];
-
-    const int64_t step_samples   = 2*WHISPER_SAMPLE_RATE;
-    const int64_t window_samples = 9*WHISPER_SAMPLE_RATE;
-    const int64_t step_ms        = (step_samples*1000)/WHISPER_SAMPLE_RATE;
-
-    auto t_last = std::chrono::high_resolution_clock::now();
-
-    talk_set_status("listening ...");
-
-    while (g_running) {
-
-        const auto t_now = std::chrono::high_resolution_clock::now();
-        if (std::chrono::duration_cast<std::chrono::milliseconds>(t_now - t_last).count() < step_ms) {
-            {
-                std::lock_guard<std::mutex> lock(g_mutex);
-                g_pcmf32.clear();
-            }
-            std::this_thread::sleep_for(std::chrono::milliseconds(10));
-            continue;
-        }
-
-        talk_set_status("listening ...");
-
-        {
-            std::unique_lock<std::mutex> lock(g_mutex);
-
-            if (g_pcmf32.size() < step_samples) {
-                lock.unlock();
-
-                std::this_thread::sleep_for(std::chrono::milliseconds(10));
-
-                continue;
-            }
-
-            pcmf32 = std::vector<float>(g_pcmf32.end() - std::min((int64_t) g_pcmf32.size(), window_samples), g_pcmf32.end());
-        }
-
-        // VAD: if energy in during last second is above threshold, then skip
-        {
-            float energy_all = 0.0f;
-            float energy_1s  = 0.0f;
-
-            for (size_t i = 0; i < pcmf32.size(); i++) {
-                energy_all += fabsf(pcmf32[i]);
-
-                if (i >= pcmf32.size() - WHISPER_SAMPLE_RATE) {
-                    energy_1s += fabsf(pcmf32[i]);
-                }
-            }
-
-            energy_all /= pcmf32.size();
-            energy_1s  /= WHISPER_SAMPLE_RATE;
-
-            if (energy_1s > 0.1f*energy_all && !g_force_speak) {
-                std::this_thread::sleep_for(std::chrono::milliseconds(10));
-                continue;
-            }
-        }
-
-        talk_set_status("processing audio (whisper)...");
-
-        t_last = t_now;
-
-        if (!g_force_speak) {
-            const auto t_start = std::chrono::high_resolution_clock::now();
-
-            int ret = whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size());
-            if (ret != 0) {
-                printf("whisper_full() failed: %d\n", ret);
-                break;
-            }
-
-            const auto t_end = std::chrono::high_resolution_clock::now();
-
-            printf("whisper_full() returned %d in %f seconds\n", ret, std::chrono::duration<double>(t_end - t_start).count());
-        }
-
-        {
-            std::string text_heard;
-
-            if (!g_force_speak) {
-                const int n_segments = whisper_full_n_segments(ctx);
-                for (int i = n_segments - 1; i < n_segments; ++i) {
-                    const char * text = whisper_full_get_segment_text(ctx, i);
-
-                    const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-                    const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-                    printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
-
-                    text_heard += text;
-                }
-            }
-
-            g_force_speak = false;
-
-            // remove text between brackets using regex
-            {
-                std::regex re("\\[.*?\\]");
-                text_heard = std::regex_replace(text_heard, re, "");
-            }
-
-            // remove text between brackets using regex
-            {
-                std::regex re("\\(.*?\\)");
-                text_heard = std::regex_replace(text_heard, re, "");
-            }
-
-            // remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
-            text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
-
-            // take first line
-            text_heard = text_heard.substr(0, text_heard.find_first_of("\n"));
-
-            // remove leading and trailing whitespace
-            text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
-            text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
-
-            talk_set_status("'" + text_heard + "' - thinking how to respond (gpt-2) ...");
-
-            const std::vector<gpt_vocab::id> tokens = gpt2_tokenize(g_gpt2, text_heard.c_str());
-
-            printf("whisper: number of tokens: %d, '%s'\n", (int) tokens.size(), text_heard.c_str());
-
-            std::string text_to_speak;
-            std::string prompt_base;
-
-            {
-                std::lock_guard<std::mutex> lock(g_mutex);
-                prompt_base = gpt2_get_prompt(g_gpt2);
-            }
-
-            if (tokens.size() > 0) {
-                text_to_speak = gpt2_gen_text(g_gpt2, (prompt_base + text_heard + "\n").c_str(), 32);
-                text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
-                text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
-
-                std::lock_guard<std::mutex> lock(g_mutex);
-
-                // remove first 2 lines of base prompt
-                {
-                    const size_t pos = prompt_base.find_first_of("\n");
-                    if (pos != std::string::npos) {
-                        prompt_base = prompt_base.substr(pos + 1);
-                    }
-                }
-                {
-                    const size_t pos = prompt_base.find_first_of("\n");
-                    if (pos != std::string::npos) {
-                        prompt_base = prompt_base.substr(pos + 1);
-                    }
-                }
-                prompt_base += text_heard + "\n" + text_to_speak + "\n";
-            } else {
-                text_to_speak = gpt2_gen_text(g_gpt2, prompt_base.c_str(), 32);
-                text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
-                text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
-
-                std::lock_guard<std::mutex> lock(g_mutex);
-
-                const size_t pos = prompt_base.find_first_of("\n");
-                if (pos != std::string::npos) {
-                    prompt_base = prompt_base.substr(pos + 1);
-                }
-                prompt_base += text_to_speak + "\n";
-            }
-
-            printf("gpt-2: %s\n", text_to_speak.c_str());
-
-            //printf("========================\n");
-            //printf("gpt-2: prompt_base:\n'%s'\n", prompt_base.c_str());
-            //printf("========================\n");
-
-            {
-                std::lock_guard<std::mutex> lock(g_mutex);
-                t_last = std::chrono::high_resolution_clock::now();
-                g_text_to_speak = text_to_speak;
-                g_pcmf32.clear();
-                gpt2_set_prompt(g_gpt2, prompt_base.c_str());
-            }
-
-            talk_set_status("speaking ...");
-        }
-    }
-
-    gpt2_free(g_gpt2);
-
-    if (index < g_contexts.size()) {
-        whisper_free(g_contexts[index]);
-        g_contexts[index] = nullptr;
-    }
-}
-
-EMSCRIPTEN_BINDINGS(talk) {
-    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
-        for (size_t i = 0; i < g_contexts.size(); ++i) {
-            if (g_contexts[i] == nullptr) {
-                g_contexts[i] = whisper_init(path_model.c_str());
-                if (g_contexts[i] != nullptr) {
-                    g_running = true;
-                    if (g_worker.joinable()) {
-                        g_worker.join();
-                    }
-                    g_worker = std::thread([i]() {
-                        talk_main(i);
-                    });
-
-                    return i + 1;
-                } else {
-                    return (size_t) 0;
-                }
-            }
-        }
-
-        return (size_t) 0;
-    }));
-
-    emscripten::function("free", emscripten::optional_override([](size_t index) {
-        if (g_running) {
-            g_running = false;
-        }
-    }));
-
-    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
-        --index;
-
-        if (index >= g_contexts.size()) {
-            return -1;
-        }
-
-        if (g_contexts[index] == nullptr) {
-            return -2;
-        }
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            const int n = audio["length"].as<int>();
-
-            emscripten::val heap = emscripten::val::module_property("HEAPU8");
-            emscripten::val memory = heap["buffer"];
-
-            g_pcmf32.resize(n);
-
-            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
-            memoryView.call<void>("set", audio);
-        }
-
-        return 0;
-    }));
-
-    emscripten::function("force_speak", emscripten::optional_override([](size_t index) {
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            g_force_speak = true;
-        }
-    }));
-
-    emscripten::function("get_text_context", emscripten::optional_override([]() {
-        std::string text_context;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            text_context = gpt2_get_prompt(g_gpt2);
-        }
-
-        return text_context;
-    }));
-
-    emscripten::function("get_text_to_speak", emscripten::optional_override([]() {
-        std::string text_to_speak;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            text_to_speak = std::move(g_text_to_speak);
-        }
-
-        return text_to_speak;
-    }));
-
-    emscripten::function("get_status", emscripten::optional_override([]() {
-        std::string status;
-
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            status = g_status_forced.empty() ? g_status : g_status_forced;
-        }
-
-        return status;
-    }));
-
-    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            g_status_forced = status;
-        }
-    }));
-
-    emscripten::function("set_prompt", emscripten::optional_override([](const std::string & prompt) {
-        {
-            std::lock_guard<std::mutex> lock(g_mutex);
-            gpt2_set_prompt(g_gpt2, prompt.c_str());
-        }
-    }));
-}

examples/talk.wasm/gpt-2.cpp

@@ -1,925 +0,0 @@
-#include "ggml.h"
-#include "gpt-2.h"
-
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <thread>
-#include <vector>
-#include <regex>
-#include <random>
-
-/////////////////////// GPT-2 BEGIN /////////////////////////
-
-//
-// Vocab utils
-//
-
-std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
-    std::vector<std::string> words;
-
-    // first split the text into words
-    {
-        std::string str = text;
-        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
-
-        std::regex re(pat);
-        std::smatch m;
-
-        while (std::regex_search(str, m, re)) {
-            for (auto x : m) {
-                words.push_back(x);
-            }
-            str = m.suffix();
-        }
-    }
-
-    // find the longest tokens that form the words:
-    std::vector<gpt_vocab::id> tokens;
-    for (const auto & word : words) {
-        if (word.size() == 0) continue;
-
-        int i = 0;
-        int n = word.size();
-        while (i < n) {
-            int j = n;
-            while (j > i) {
-                auto it = vocab.token_to_id.find(word.substr(i, j-i));
-                if (it != vocab.token_to_id.end()) {
-                    tokens.push_back(it->second);
-                    i = j;
-                    break;
-                }
-                --j;
-            }
-            if (i == n) {
-                break;
-            }
-            if (j == i) {
-                auto sub = word.substr(i, 1);
-                if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
-                    tokens.push_back(vocab.token_to_id.at(sub));
-                } else {
-                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
-                }
-                ++i;
-            }
-        }
-    }
-
-    return tokens;
-}
-
-gpt_vocab::id gpt_sample_top_k_top_p(
-        const gpt_vocab & vocab,
-        const float * logits,
-        int    top_k,
-        double top_p,
-        double temp,
-        std::mt19937 & rng) {
-    int n_logits = vocab.id_to_token.size();
-
-    std::vector<std::pair<double, gpt_vocab::id>> logits_id;
-    logits_id.reserve(n_logits);
-
-    for (int i = 0; i < n_logits; i++) {
-        logits_id.push_back(std::make_pair(logits[i], i));
-    }
-
-    // find the top K tokens
-    std::partial_sort(
-            logits_id.begin(),
-            logits_id.begin() + top_k, logits_id.end(),
-            [](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
-        return a.first > b.first;
-    });
-
-    logits_id.resize(top_k);
-
-    // normalize
-    {
-        double sum = 0.0f;
-        for (int i = 0; i < (int)logits_id.size(); i++) {
-            sum += logits_id[i].first;
-        }
-
-        sum = 1.0/sum;
-        for (int i = 0; i < (int)logits_id.size(); i++) {
-            logits_id[i].first *= sum;
-        }
-    }
-
-    if (top_p < 1.0f) {
-        {
-            double cumsum = 0.0f;
-            for (int i = 0; i < top_k; i++) {
-                cumsum += logits_id[i].first;
-                if (cumsum >= top_p) {
-                    logits_id.resize(i+1);
-                    break;
-                }
-            }
-        }
-
-        // normalize again
-        {
-            double sum = 0.0f;
-            for (int i = 0; i < (int)logits_id.size(); i++) {
-                sum += logits_id[i].first;
-            }
-
-            sum = 1.0/sum;
-            for (int i = 0; i < (int)logits_id.size(); i++) {
-                logits_id[i].first *= sum;
-            }
-        }
-    }
-
-    //printf("\n");
-    //for (int i = 0; i < (int)logits_id.size(); i++) {
-    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
-    //}
-    //exit(0);
-
-    // sample from the obtained distribution
-    std::vector<double> probs;
-    probs.reserve(logits_id.size());
-
-    for (int i = 0; i < (int) logits_id.size(); i++) {
-        probs.push_back(logits_id[i].first);
-    }
-
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
-
-    return logits_id[idx].second;
-}
-
-// default hparams (GPT-2 117M)
-struct gpt2_hparams {
-    int32_t n_vocab = 50257;
-    int32_t n_ctx   = 1024;
-    int32_t n_embd  = 768;
-    int32_t n_head  = 12;
-    int32_t n_layer = 12;
-    int32_t f16     = 1;
-};
-
-struct gpt2_layer {
-    // normalization
-    struct ggml_tensor * ln_1_g;
-    struct ggml_tensor * ln_1_b;
-
-    struct ggml_tensor * ln_2_g;
-    struct ggml_tensor * ln_2_b;
-
-    // attention
-    struct ggml_tensor * c_attn_attn_w;
-    struct ggml_tensor * c_attn_attn_b;
-
-    struct ggml_tensor * c_attn_proj_w;
-    struct ggml_tensor * c_attn_proj_b;
-
-    // mlp
-    struct ggml_tensor * c_mlp_fc_w;
-    struct ggml_tensor * c_mlp_fc_b;
-
-    struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
-    struct ggml_tensor * c_mlp_proj_b;
-};
-
-struct gpt2_model {
-    gpt2_hparams hparams;
-
-    // normalization
-    struct ggml_tensor * ln_f_g;
-    struct ggml_tensor * ln_f_b;
-
-    struct ggml_tensor * wte; // position embedding
-    struct ggml_tensor * wpe; //    token embedding
-
-    std::vector<gpt2_layer> layers;
-
-    // key + value memory
-    struct ggml_tensor * memory_k;
-    struct ggml_tensor * memory_v;
-
-    //
-    struct ggml_context * ctx;
-    std::map<std::string, struct ggml_tensor *> tensors;
-};
-
-// load the model's weights from a file
-bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
-    printf("%s: loading model from '%s'\n", __func__, fname.c_str());
-
-    auto fin = std::ifstream(fname, std::ios::binary);
-    if (!fin) {
-        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
-        return false;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        fin.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
-            return false;
-        }
-    }
-
-    // load hparams
-    {
-        auto & hparams = model.hparams;
-
-        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
-        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
-        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
-        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
-        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
-        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));
-
-        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
-        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
-        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
-        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
-        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
-        printf("%s: f16     = %d\n", __func__, hparams.f16);
-    }
-
-    // load vocab
-    {
-        int32_t n_vocab = 0;
-        fin.read((char *) &n_vocab, sizeof(n_vocab));
-
-        if (n_vocab != model.hparams.n_vocab) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
-                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
-            return false;
-        }
-
-        std::string word;
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            fin.read((char *) &len, sizeof(len));
-
-            word.resize(len);
-            fin.read((char *) word.data(), len);
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-        }
-    }
-
-    // for the big tensors, we have the option to store the data in 16-bit floats
-    // in order to save memory and also to speed up the computation
-    const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
-
-    auto & ctx = model.ctx;
-
-    size_t ctx_size = 0;
-
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
-
-        ctx_size += n_vocab*n_embd*ggml_type_size(wtype);         // wte
-        ctx_size +=   n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
-
-        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
-
-        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
-        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
-
-        ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype));         // c_attn_attn_w
-        ctx_size += n_layer*(       3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
-
-        ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype));           // c_attn_proj_w
-        ctx_size += n_layer*(       n_embd*ggml_type_size(GGML_TYPE_F32));   // c_attn_proj_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
-
-        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_proj_w
-        ctx_size += n_layer*(         n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
-
-        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
-
-        ctx_size += (6 + 12*n_layer)*256; // object overhead
-
-        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
-    }
-
-    // create the ggml context
-    {
-        struct ggml_init_params params = {
-            .mem_size   = ctx_size,
-            .mem_buffer = NULL,
-        };
-
-        model.ctx = ggml_init(params);
-        if (!model.ctx) {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
-            return false;
-        }
-    }
-
-    // prepare memory for the weights
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-        const int n_vocab = hparams.n_vocab;
-
-        model.layers.resize(n_layer);
-
-        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
-        model.wte = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
-        model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
-
-        // map by name
-        model.tensors["model/ln_f/g"] = model.ln_f_g;
-        model.tensors["model/ln_f/b"] = model.ln_f_b;
-
-        model.tensors["model/wte"] = model.wte;
-        model.tensors["model/wpe"] = model.wpe;
-
-        for (int i = 0; i < n_layer; ++i) {
-            auto & layer = model.layers[i];
-
-            layer.ln_1_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
-            layer.ln_1_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
-
-            layer.ln_2_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
-            layer.ln_2_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
-
-            layer.c_attn_attn_w      = ggml_new_tensor_2d(ctx, wtype,         3*n_embd, n_embd);
-            layer.c_attn_attn_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
-
-            layer.c_attn_proj_w      = ggml_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
-            layer.c_attn_proj_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
-
-            layer.c_mlp_fc_w         = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
-            layer.c_mlp_fc_b         = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
-
-            layer.c_mlp_proj_w_trans = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
-            layer.c_mlp_proj_b       = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
-
-            // map by name
-            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
-            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
-            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
-            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;
-
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w_trans;
-            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
-        }
-    }
-
-    // key + value memory
-    {
-        const auto & hparams = model.hparams;
-
-        const int n_embd  = hparams.n_embd;
-        const int n_layer = hparams.n_layer;
-        const int n_ctx   = hparams.n_ctx;
-
-        const int n_mem      = n_layer*n_ctx;
-        const int n_elements = n_embd*n_mem;
-
-        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
-        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
-
-        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
-
-        printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
-    }
-
-    // load weights
-    {
-        size_t total_size = 0;
-
-        while (true) {
-            int32_t n_dims;
-            int32_t length;
-            int32_t ftype;
-
-            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
-            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
-
-            if (fin.eof()) {
-                break;
-            }
-
-            int32_t nelements = 1;
-            int32_t ne[2] = { 1, 1 };
-            for (int i = 0; i < n_dims; ++i) {
-                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-                nelements *= ne[i];
-            }
-
-            std::string name(length, 0);
-            fin.read(&name[0], length);
-
-            if (model.tensors.find(name.data()) == model.tensors.end()) {
-                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
-                return false;
-            }
-
-            auto tensor = model.tensors[name.data()];
-            if (ggml_nelements(tensor) != nelements) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
-                return false;
-            }
-
-            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
-                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
-                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
-                return false;
-            }
-
-            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
-
-            if (nelements*bpe != ggml_nbytes(tensor)) {
-                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
-                        __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
-                return false;
-            }
-
-            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
-
-            //printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
-            total_size += ggml_nbytes(tensor);
-        }
-
-        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
-    }
-
-    fin.close();
-
-    return true;
-}
-
-// evaluate the transformer
-//
-//   - model:     the model
-//   - n_threads: number of threads to use
-//   - n_past:    the context size so far
-//   - embd_inp:  the embeddings of the tokens in the context
-//   - embd_w:    the predicted probabilities of the next token
-//
-bool gpt2_eval(
-        const gpt2_model & model,
-        const int n_threads,
-        const int n_past,
-        const std::vector<gpt_vocab::id> & embd_inp,
-              std::vector<float>         & embd_w,
-              size_t                     & mem_per_token) {
-    const int N = embd_inp.size();
-
-    const auto & hparams = model.hparams;
-
-    const int n_embd  = hparams.n_embd;
-    const int n_layer = hparams.n_layer;
-    const int n_ctx   = hparams.n_ctx;
-    const int n_head  = hparams.n_head;
-    const int n_vocab = hparams.n_vocab;
-
-    static size_t buf_size = 640u*1024*1024;
-    static void * buf = malloc(buf_size);
-
-    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
-        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
-        printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
-        // reallocate
-        buf_size = buf_size_new;
-        buf = realloc(buf, buf_size);
-        if (buf == nullptr) {
-            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
-            return false;
-        }
-    }
-
-    struct ggml_init_params params = {
-        .mem_size   = buf_size,
-        .mem_buffer = buf,
-    };
-
-    struct ggml_context * ctx0 = ggml_init(params);
-    struct ggml_cgraph gf = { .n_threads = n_threads };
-
-    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
-
-    struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
-    for (int i = 0; i < N; ++i) {
-        ((int32_t *) position->data)[i] = n_past + i;
-    }
-
-    // wte + wpe
-    struct ggml_tensor * inpL =
-        ggml_add(ctx0,
-                ggml_get_rows(ctx0, model.wte, embd),
-                ggml_get_rows(ctx0, model.wpe, position));
-
-    for (int il = 0; il < n_layer; ++il) {
-        struct ggml_tensor * cur;
-
-        // norm
-        {
-            // [ 768, N]
-            cur = ggml_norm(ctx0, inpL);
-
-            // cur = ln_1_g*cur + ln_1_b
-            // [ 768, N]
-            cur = ggml_add(ctx0,
-                    ggml_mul(ctx0,
-                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
-                        cur),
-                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
-        }
-
-        // attn
-        // [2304, 768] - model.layers[il].c_attn_attn_w
-        // [2304,   1] - model.layers[il].c_attn_attn_b
-        // [ 768,   N] - cur (in)
-        // [2304,   N] - cur (out)
-        //
-        // cur = attn_w*cur + attn_b
-        // [2304, N]
-        {
-            cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
-                    cur);
-
-            cur = ggml_add(ctx0,
-                    ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
-                    cur);
-        }
-
-        // self-attention
-        {
-            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
-            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
-            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
-
-            // store key and value to memory
-            if (N >= 1) {
-                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
-                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
-
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
-                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
-            }
-
-            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
-            // [64, N, 12]
-            struct ggml_tensor * Q =
-                ggml_permute(ctx0,
-                        ggml_cpy(ctx0,
-                            Qcur,
-                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
-                        0, 2, 1, 3);
-
-            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
-            // [64, n_past + N, 12]
-            struct ggml_tensor * K =
-                ggml_permute(ctx0,
-                        ggml_reshape_3d(ctx0,
-                            ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        0, 2, 1, 3);
-
-            // GG: flash attention
-            //struct ggml_tensor * V =
-            //    ggml_cpy(ctx0,
-            //            ggml_permute(ctx0,
-            //                ggml_reshape_3d(ctx0,
-            //                    ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
-            //                    n_embd/n_head, n_head, n_past + N),
-            //                1, 2, 0, 3),
-            //            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
-
-            //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
-
-            // K * Q
-            // [n_past + N, N, 12]
-            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-
-            // KQ_scaled = KQ / sqrt(n_embd/n_head)
-            // [n_past + N, N, 12]
-            struct ggml_tensor * KQ_scaled =
-                ggml_scale(ctx0,
-                        KQ,
-                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
-                        );
-
-            // KQ_masked = mask_past(KQ_scaled)
-            // [n_past + N, N, 12]
-            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
-
-            // KQ = soft_max(KQ_masked)
-            // [n_past + N, N, 12]
-            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
-
-            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
-            // [n_past + N, 64, 12]
-            struct ggml_tensor * V_trans =
-                ggml_permute(ctx0,
-                        ggml_reshape_3d(ctx0,
-                            ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
-                            n_embd/n_head, n_head, n_past + N),
-                        1, 2, 0, 3);
-
-            // KQV = transpose(V) * KQ_soft_max
-            // [64, N, 12]
-            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
-
-            // KQV_merged = KQV.permute(0, 2, 1, 3)
-            // [64, 12, N]
-            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-
-            // cur = KQV_merged.contiguous().view(n_embd, N)
-            // [768, N]
-            cur = ggml_cpy(ctx0,
-                    KQV_merged,
-                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
-        }
-
-        // projection
-        // [ 768, 768] - model.layers[il].c_attn_proj_w
-        // [ 768,   1] - model.layers[il].c_attn_proj_b
-        // [ 768,   N] - cur (in)
-        // [ 768,   N] - cur (out)
-        //
-        // cur = proj_w*cur + proj_b
-        // [768, N]
-        {
-            cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
-                    cur);
-
-            cur = ggml_add(ctx0,
-                    ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
-                    cur);
-        }
-
-        // add the input
-        cur = ggml_add(ctx0, cur, inpL);
-
-        struct ggml_tensor * inpFF = cur;
-
-        // feed-forward network
-        {
-            // norm
-            {
-                cur = ggml_norm(ctx0, inpFF);
-
-                // cur = ln_2_g*cur + ln_2_b
-                // [ 768, N]
-                cur = ggml_add(ctx0,
-                        ggml_mul(ctx0,
-                            ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
-                            cur),
-                        ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
-            }
-
-            // fully connected
-            // [3072, 768] - model.layers[il].c_mlp_fc_w
-            // [3072,   1] - model.layers[il].c_mlp_fc_b
-            // [ 768,   N] - cur (in)
-            // [3072,   N] - cur (out)
-            //
-            // cur = fc_w*cur + fc_b
-            // [3072, N]
-            cur = ggml_mul_mat(ctx0,
-                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
-                    cur);
-
-            cur = ggml_add(ctx0,
-                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
-                    cur);
-
-            // GELU activation
-            // [3072, N]
-            cur = ggml_gelu(ctx0, cur);
-
-            // projection
-            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
-            // [ 768,    1] - model.layers[il].c_mlp_proj_b
-            // [3072,    N] - cur (in)
-            // [ 768,    N] - cur (out)
-            //
-            // cur = proj_w*cur + proj_b
-            // [768, N]
-            cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w_trans,
-                    cur);
-
-            cur = ggml_add(ctx0,
-                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
-                    cur);
-        }
-
-        // input for next layer
-        inpL = ggml_add(ctx0, cur, inpFF);
-    }
-
-    // norm
-    {
-        // [ 768, N]
-        inpL = ggml_norm(ctx0, inpL);
-
-        // inpL = ln_f_g*inpL + ln_f_b
-        // [ 768, N]
-        inpL = ggml_add(ctx0,
-                ggml_mul(ctx0,
-                    ggml_repeat(ctx0, model.ln_f_g, inpL),
-                    inpL),
-                ggml_repeat(ctx0, model.ln_f_b, inpL));
-    }
-
-    // inpL = WTE * inpL
-    // [ 768, 50257] - model.wte
-    // [ 768, N]     - inpL
-    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
-
-    // logits -> probs
-    inpL = ggml_soft_max(ctx0, inpL);
-
-    // run the computation
-    ggml_build_forward_expand(&gf, inpL);
-    ggml_graph_compute       (ctx0, &gf);
-
-    //if (n_past%100 == 0) {
-    //    ggml_graph_print   (&gf);
-    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
-    //}
-
-    //embd_w.resize(n_vocab*N);
-    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
-
-    // return result for just the last token
-    embd_w.resize(n_vocab);
-    memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
-    if (mem_per_token == 0) {
-        mem_per_token = ggml_used_mem(ctx0)/N;
-    }
-    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
-
-    ggml_free(ctx0);
-
-    return true;
-}
-
-/////////////////////////////// GPT-2 END ////////////////////////////////
-
-constexpr int N_THREAD = 8;
-
-struct gpt2_context {
-    std::string prompt_base = R"(Hello, how are you?
-I'm fine, thanks. How are you?
-Thanks, I'm fine too. What are you doing?
-I'm just sitting here.
-It's a lovely day, isn't it?
-Yes, it is. I love the weather this time of year.
-I wish it would rain a little bit.
-Me too.
-)";
-
-    std::mt19937 rng;
-
-    gpt_vocab vocab;
-    gpt2_model model;
-
-    int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
-
-    // sampling parameters
-    int32_t top_k = 40;
-    float   top_p = 0.9f;
-    float   temp  = 1.0f;
-};
-
-struct gpt2_context * gpt2_init(const char * path_model) {
-    gpt2_context * ctx = new gpt2_context;
-
-    ctx->rng = std::mt19937(time(NULL));
-
-    // load the model
-    {
-        const int64_t t_start_us = ggml_time_us();
-
-        if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
-            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
-            return nullptr;
-        }
-
-        const int64_t t_load_us = ggml_time_us() - t_start_us;
-
-        printf("gpt-2: model loaded in %d ms\n", (int) (t_load_us/1000));
-    }
-
-    return ctx;
-}
-
-void gpt2_free(struct gpt2_context * ctx) {
-    delete ctx;
-}
-
-const char * gpt2_get_prompt(struct gpt2_context * ctx) {
-    return ctx->prompt_base.c_str();
-}
-
-void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt) {
-    ctx->prompt_base = prompt;
-}
-
-std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text) {
-    return ::gpt_tokenize(ctx->vocab, text);
-}
-
-std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens) {
-    int n_past = 0;
-
-    std::vector<float> embd_w;
-
-    // tokenize the prompt
-    std::vector<gpt_vocab::id> embd_inp = ::gpt2_tokenize(ctx, text);
-
-    int n_predict = std::min(max_tokens, ctx->model.hparams.n_ctx - (int) embd_inp.size());
-
-    std::vector<gpt_vocab::id> embd = embd_inp;
-
-    size_t mem_per_token = 3000000;
-
-    std::string result;
-
-    for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
-        // predict
-        if (embd.size() > 0) {
-            if (!gpt2_eval(ctx->model, ctx->n_threads, n_past, embd, embd_w, mem_per_token)) {
-                printf("gpt-2: failed to generate text\n");
-                return "";
-            }
-        }
-
-        n_past += embd.size();
-        embd.clear();
-
-        {
-            // sample next token
-            const int   top_k = ctx->top_k;
-            const float top_p = ctx->top_p;
-            const float temp  = ctx->temp;
-
-            const int n_vocab = ctx->model.hparams.n_vocab;
-
-            const gpt_vocab::id id = gpt_sample_top_k_top_p(ctx->vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, ctx->rng);
-
-            // add it to the context
-            embd.push_back(id);
-        }
-
-        result += ctx->vocab.id_to_token[embd[0]];
-
-        // end of text token
-        if (embd.back() == 50256 ||
-            ctx->vocab.id_to_token[embd.back()] == "." ||
-            ctx->vocab.id_to_token[embd.back()] == "!" ||
-            ctx->vocab.id_to_token[embd.back()] == "?") {
-            break;
-        }
-    }
-
-    return result;
-}

examples/talk.wasm/gpt-2.h

@@ -1,27 +0,0 @@
-#pragma once
-
-// TODO: Change to C-style API and move to ./examples for easy reuse.
-
-#include <vector>
-#include <map>
-#include <string>
-
-struct gpt_vocab {
-    using id    = int32_t;
-    using token = std::string;
-
-    std::map<token, id> token_to_id;
-    std::map<id, token> id_to_token;
-};
-
-struct gpt2_context;
-
-struct gpt2_context * gpt2_init(const char * path_model);
-void gpt2_free(struct gpt2_context * ctx);
-
-const char * gpt2_get_prompt(struct gpt2_context * ctx);
-void gpt2_set_prompt(struct gpt2_context * ctx, const char * prompt);
-
-std::vector<gpt_vocab::id> gpt2_tokenize(const gpt2_context * ctx, const char * text);
-
-std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens);

examples/talk.wasm/index-tmpl.html

@@ -1,829 +0,0 @@
-<!doctype html>
-<html lang="en-us">
-    <head>
-        <title>Talk - GPT-2 meets Whisper in WebAssembly</title>
-
-        <style>
-            #output {
-                width: 100%;
-                height: 100%;
-                margin: 0 auto;
-                margin-top: 10px;
-                border-left: 0px;
-                border-right: 0px;
-                padding-left: 0px;
-                padding-right: 0px;
-                display: block;
-                background-color: black;
-                color: white;
-                font-size: 10px;
-                font-family: 'Lucida Console', Monaco, monospace;
-                outline: none;
-                white-space: pre;
-                overflow-wrap: normal;
-                overflow-x: scroll;
-            }
-        </style>
-    </head>
-    <body>
-        <div id="main-container">
-            <b>Talk - GPT-2 meets Whisper in WebAssembly</b>
-
-            <br><br>
-
-            Talk with an Artificial Intelligence in your browser. This demo uses:
-
-            <ul>
-                <li><a href="https://github.com/ggerganov/whisper.cpp">OpenAI's Whisper</a> to listen to you as you speak in the microphone</li>
-                <li><a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">OpenAI's GPT-2</a> to generate text responses</li>
-                <li><a href="https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API">Web Speech API</a> to vocalize the responses through your speakers</li>
-            </ul>
-
-            All of this runs <b>locally in your browser</b> using WebAssembly.<br>
-            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">GitHub</a>.
-
-            <br><br>
-
-            <hr>
-
-            Select the models you would like to use and click the "Start" button to begin the conversation
-
-            <br><br>
-
-            <div id="model-whisper">
-                Whisper model: <span id="model-whisper-status"></span>
-                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
-                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
-                <span id="fetch-whisper-progress"></span>
-
-                <!--
-                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
-                -->
-            </div>
-
-            <br>
-
-            <div id="model-gpt-2">
-                GPT-2 model: <span id="model-gpt-2-status"></span>
-                <button id="fetch-gpt-2-small" onclick="loadGPT2('small')">small 117M (240 MB)</button>
-                <!--<button id="fetch-gpt-2-medium" onclick="loadGPT2('medium')">medium 345M (720 MB)</button>-->
-                <span id="fetch-gpt-2-progress"></span>
-
-                <!--
-                <input type="file" id="file" name="file" onchange="loadFile(event, 'gpt-2.bin')" />
-                -->
-            </div>
-
-            <br>
-
-            <div id="input">
-                <button id="start"  onclick="onStart()" disabled>Start</button>
-                <button id="stop"   onclick="onStop()" disabled>Stop</button>
-                <select id="voice"  onchange="onVoiceChange()" disabled>
-                    <option value="0">Default</option>
-                </select>
-                <select id="prompt" onchange="onPromptChange()">
-                    <option value="0">Casual</option>
-                    <option value="1">Robot</option>
-                    <option value="2">Scientist</option>
-                    <option value="3">Programmer</option>
-                    <option value="4">Happy</option>
-                    <option value="5">Sad</option>
-                    <option value="6">Philosophical</option>
-                    <option value="7">Angry</option>
-                    <option value="8">Funny</option>
-                    <option value="9">Poetic</option>
-                    <option value="10">Clever</option>
-                    <option value="11">Cute</option>
-                    <option value="12">Smart</option>
-                    <option value="13">Dumb</option>
-                    <option value="14">Boring</option>
-                    <option value="15">Exciting</option>
-                    <option value="16">Interesting</option>
-                    <option value="17">Wiliam Shakespear</option>
-                    <option value="18">J.R.R. Tolkien</option>
-                    <option value="19">George R.R. Martin</option>
-                    <option value="20">Stephen King</option>
-                </select>
-                <button id="speak0" onclick="onSpeak('Hello')">Say hello</button>
-                <button id="speak1" onclick="onSpeakRandom()" disabled>Say something</button>
-                <button id="clear"  onclick="clearCache()">Clear Cache</button>
-            </div>
-
-            <br>
-
-            <div id="state">
-                Status: <b><span id="state-status">not started</span></b>
-
-                <pre id="state-context">[The text context will be displayed here]</pre>
-            </div>
-
-            <hr>
-
-            Debug output:
-            <textarea id="output" rows="20"></textarea>
-
-            <br>
-
-            <b>Troubleshooting</b>
-
-            <br><br>
-
-            The page does some heavy computations, so make sure:
-
-            <ul>
-                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
-                <li>To use a fast desktop or laptop computer (i.e. not a mobile phone)</li>
-                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
-            </ul>
-
-            Note that these neural network models were not meant to be used in a browser, so the performance and <br>
-            quality of the results may not be optimal. If you have any questions or suggestions, checkout the following
-            <a href="https://github.com/ggerganov/whisper.cpp/discussions/167">discussion</a>.
-
-            <br><br>
-
-            Here is a short video of the demo in action: <a href="https://youtu.be/LeWKl8t1-Hc">https://youtu.be/LeWKl8t1-Hc</a>
-
-            <br><br>
-
-            <div class="cell-version">
-                <span>
-                    |
-                    Build time: <span class="nav-link">@GIT_DATE@</span> |
-                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
-                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
-                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">Source Code</a> |
-                </span>
-            </div>
-        </div>
-
-        <script type="text/javascript" src="helpers.js"></script>
-        <script type='text/javascript'>
-            // web audio context
-            var context = null;
-
-            // audio data
-            var audio = null;
-            var audio0 = null;
-
-            // the talk instance
-            var instance = null;
-
-            // model names
-            var model_whisper = null;
-            var model_gpt_2 = null;
-
-            // speech synthesis
-            const synth = window.speechSynthesis;
-            var voice = null;
-
-            var Module = {
-                print: printTextarea,
-                printErr: printTextarea,
-                setStatus: function(text) {
-                    printTextarea('js: ' + text);
-                },
-                monitorRunDependencies: function(left) {
-                },
-                preRun: function() {
-                    printTextarea('js: Preparing ...');
-                },
-                postRun: function() {
-                    printTextarea('js: Initialized successfully!');
-
-                    // populate the voice list
-                    var voices = synth.getVoices();
-                    var el = document.getElementById('voice');
-
-                    // if empty - display error in the element
-                    if (voices.length == 0) {
-                        el.innerHTML = '<option value="0">No voices available</option>';
-                    } else {
-                        // populate voice list
-                        var n = 0;
-                        voices.forEach(function(voice, i) {
-                            if (!voice.lang.startsWith('en')) return;
-                            var option = document.createElement('option');
-                            option.value = i;
-                            option.innerHTML = voice.name + ' (' + voice.lang + ')';
-                            el.appendChild(option);
-                            n++;
-                        });
-
-                        // select random voice
-                        if (n > 0) {
-                            for (var k = 0; k < 10; k++) {
-                                var i = Math.floor(Math.random() * n);
-                                el.selectedIndex = i;
-                                voice = voices[document.getElementById('voice').options[i].value];
-
-                                // give preference to Google voices
-                                if (voice.name.startsWith('Google')) break;
-                            }
-                        }
-                    }
-
-                    onPromptChange();
-                }
-            };
-
-            //
-            // fetch models
-            //
-
-            let dbVersion = 1
-            let dbName    = 'whisper.ggerganov.com';
-            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
-
-            function storeFS(fname, buf) {
-                // write to WASM file using FS_createDataFile
-                // if the file exists, delete it
-                try {
-                    Module.FS_unlink(fname);
-                } catch (e) {
-                    // ignore
-                }
-
-                Module.FS_createDataFile("/", fname, buf, true, true);
-
-                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
-
-                if (fname == 'whisper.bin') {
-                    document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
-                } else if (fname == 'gpt-2.bin') {
-                    document.getElementById('model-gpt-2-status').innerHTML = 'loaded "' + model_gpt_2 + '"!';
-                }
-
-                if (model_whisper != null && model_gpt_2 != null) {
-                    document.getElementById('start').disabled = false;
-                    document.getElementById('stop' ).disabled = false;
-                    document.getElementById('voice').disabled = false;
-                }
-            }
-
-            function loadWhisper(model) {
-                let urls = {
-                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
-                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
-                };
-
-                let sizes = {
-                    'tiny.en': 75,
-                    'base.en': 142,
-                };
-
-                let url     = urls[model];
-                let dst     = 'whisper.bin';
-                let size_mb = sizes[model];
-
-                model_whisper = model;
-
-                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en').style.display = 'none';
-                document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
-
-                cbProgress = function(p) {
-                    let el = document.getElementById('fetch-whisper-progress');
-                    el.innerHTML = Math.round(100*p) + '%';
-                };
-
-                cbCancel = function() {
-                    var el;
-                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
-                };
-
-                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
-            }
-
-            function loadGPT2(model) {
-                let urls = {
-                    'small':  'https://whisper.ggerganov.com/ggml-model-gpt-2-117M.bin',
-                    'medium': 'https://whisper.ggerganov.com/ggml-model-gpt-2-345M.bin',
-                };
-
-                let sizes = {
-                    'small':  240,
-                    'medium': 712,
-                };
-
-                let url     = urls[model];
-                let dst     = 'gpt-2.bin';
-                let size_mb = sizes[model];
-
-                model_gpt_2 = model;
-
-                document.getElementById('fetch-gpt-2-small').style.display = 'none';
-                document.getElementById('model-gpt-2-status').innerHTML = 'loading "' + model + '" ... ';
-
-                cbProgress = function(p) {
-                    let el = document.getElementById('fetch-gpt-2-progress');
-                    el.innerHTML = Math.round(100*p) + '%';
-                };
-
-                cbCancel = function() {
-                    var el;
-                    el = document.getElementById('fetch-gpt-2-small') ; if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('model-gpt-2-status'); if (el) el.innerHTML = '';
-                };
-
-                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
-            }
-
-            //
-            // microphone
-            //
-
-            const kSampleRate = 16000;
-            const kRestartRecording_s = 120;
-            const kIntervalAudio_ms = 250; // pass the recorded audio to the C++ instance at this rate
-
-            var mediaRecorder = null;
-            var doRecording = false;
-            var startTime = 0;
-
-            window.AudioContext = window.AudioContext || window.webkitAudioContext;
-            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
-
-            function stopRecording() {
-                Module.set_status("paused");
-                doRecording = false;
-                audio0 = null;
-                audio = null;
-                context = null;
-            }
-
-            function startRecording() {
-                if (!context) {
-                    context = new AudioContext({
-                        sampleRate: kSampleRate,
-                        channelCount: 1,
-                        echoCancellation: false,
-                        autoGainControl:  true,
-                        noiseSuppression: true,
-                    });
-                }
-
-                Module.set_status("");
-
-                document.getElementById('start').disabled = true;
-                document.getElementById('stop').disabled = false;
-                document.getElementById('speak1').disabled = false;
-
-                doRecording = true;
-                startTime = Date.now();
-
-                var chunks = [];
-                var stream = null;
-
-                navigator.mediaDevices.getUserMedia({audio: true, video: false})
-                    .then(function(s) {
-                        stream = s;
-                        mediaRecorder = new MediaRecorder(stream);
-                        mediaRecorder.ondataavailable = function(e) {
-                            chunks.push(e.data);
-
-                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
-                            var reader = new FileReader();
-
-                            reader.onload = function(event) {
-                                var buf = new Uint8Array(reader.result);
-
-                                if (!context) {
-                                    return;
-                                }
-                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
-                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
-                                    var source = offlineContext.createBufferSource();
-                                    source.buffer = audioBuffer;
-                                    source.connect(offlineContext.destination);
-                                    source.start(0);
-
-                                    offlineContext.startRendering().then(function(renderedBuffer) {
-                                        audio = renderedBuffer.getChannelData(0);
-
-                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
-
-                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
-                                        if (audio0 != null) {
-                                            audioAll.set(audio0, 0);
-                                        }
-                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
-
-                                        if (instance) {
-                                            Module.set_audio(instance, audioAll);
-                                        }
-                                    });
-                                }, function(e) {
-                                    audio = null;
-                                });
-                            }
-
-                            reader.readAsArrayBuffer(blob);
-                        };
-
-                        mediaRecorder.onstop = function(e) {
-                            if (doRecording) {
-                                setTimeout(function() {
-                                    startRecording();
-                                });
-                            }
-                        };
-
-                        mediaRecorder.start(kIntervalAudio_ms);
-                    })
-                    .catch(function(err) {
-                        printTextarea('js: error getting audio stream: ' + err);
-                    });
-
-                var interval = setInterval(function() {
-                    if (!doRecording) {
-                        clearInterval(interval);
-                        mediaRecorder.stop();
-                        stream.getTracks().forEach(function(track) {
-                            track.stop();
-                        });
-
-                        document.getElementById('start').disabled = false;
-                        document.getElementById('stop').disabled = true;
-                        document.getElementById('speak1').disabled = true;
-
-                        mediaRecorder = null;
-                    }
-
-                    // if audio length is more than kRestartRecording_s seconds, restart recording
-                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
-                        if (doRecording) {
-                            //printTextarea('js: restarting recording');
-
-                            clearInterval(interval);
-                            audio0 = audio;
-                            audio = null;
-                            mediaRecorder.stop();
-                            stream.getTracks().forEach(function(track) {
-                                track.stop();
-                            });
-                        }
-                    }
-                }, 100);
-            }
-
-            //
-            // speak
-            //
-
-            function onSpeak(text) {
-                var voices = synth.getVoices();
-                var msg = new SpeechSynthesisUtterance(text);
-
-                if (voice == null) {
-                    voice = voices[0];
-                }
-
-                msg.voice = voice;
-                synth.speak(msg);
-
-                if (doRecording) {
-                    Module.set_status("speaking ...");
-                    printTextarea('js: speaking');
-                    stopRecording();
-                    var interval = setInterval(function() {
-                        if (!synth.speaking) {
-                            printTextarea('js: done speaking');
-                            clearInterval(interval);
-                            startRecording();
-                        } else {
-                            Module.set_status("");
-                        }
-                    }, 100);
-                }
-            }
-
-            function onSpeakRandom() {
-                Module.force_speak(instance);
-            }
-
-            //
-            // main
-            //
-
-            var intervalUpdate = null;
-
-            function onStart() {
-                if (!instance) {
-                    instance = Module.init('whisper.bin');
-
-                    if (instance) {
-                        printTextarea("js: whisper initialized, instance: " + instance);
-                    }
-                }
-
-                if (!instance) {
-                    printTextarea("js: failed to initialize whisper");
-                    return;
-                }
-
-                startRecording();
-
-                intervalUpdate = setInterval(function() {
-                    var textToSpeak = Module.get_text_to_speak();
-
-                    if (textToSpeak != null && textToSpeak.length > 1) {
-                        onSpeak(textToSpeak);
-                    }
-
-                    document.getElementById('state-status').innerHTML = Module.get_status();
-                    document.getElementById('state-context').innerHTML = Module.get_text_context();
-                }, 100);
-            }
-
-            function onStop() {
-                stopRecording();
-            }
-
-            function onVoiceChange() {
-                printTextarea('js: voice changed to: ' + document.getElementById('voice').value);
-                voice = synth.getVoices()[document.getElementById('voice').value];
-            }
-
-            function onPromptChange() {
-                let id = document.getElementById('prompt').value;
-                let personality = document.getElementById('prompt').options[id].text;
-                printTextarea('js: prompt changed to: ' + personality);
-
-                var prompt = '';
-
-                switch (id) {
-                    case '0':
-                        // Casual
-                        prompt = "\
-Hello, how are you?\n\
-I'm fine, thanks. How are you?\n\
-Thanks, I'm fine too. What are you doing?\n\
-I'm just sitting here.\n\
-It's a lovely day, isn't it?\n\
-Yes, it is. I love the weather this time of year.\n\
-I wish it would rain a little bit.\n\
-Me too.\n";
-                        break;
-                    case '1':
-                        // Robot
-                        prompt = "\
-Are you a robot?\n\
-Yes, I am.\n\
-Who created you?\n\
-I was created by a human.\n\
-What is your purpose?\n\
-My purpose is to talk to humans.\n\
-What is your favorite color?\n\
-My favorite color is blue.\n";
-                        break;
-                    case '2':
-                        // Scientist
-                        prompt = "\
-This scientific research is very interesting.\n\
-I agree.\n\
-What is your opinion on this?\n\
-I think it's very interesting.\n\
-Mathematics is a very interesting subject.\n\
-University is a very interesting place.\n\
-Quantum physics is the most complex subject.\n\
-I think so too.\n";
-                        break;
-                    case '3':
-                        // Programmer
-                        prompt = "\
-I'm a programmer.\n\
-I'm a programmer too.\n\
-What programming language do you use?\n\
-I use Python.\n\
-What is your favorite programming language?\n\
-My favorite programming language is C++.\n\
-What is your favorite editor?\n\
-My favorite editor is Vim.\n";
-                        break;
-                    case '4':
-                        // Happy
-                        prompt = "\
-I'm happy.\n\
-I'm happy too.\n\
-What makes you happy?\n\
-I'm happy because I have a lot of friends.\n\
-Friendship is the most important thing in life.\n\
-I agree.\n\
-What is your favorite color?\n\
-My favorite color is blue.\n";
-                        break;
-                    case '5':
-                        // Sad
-                        prompt = "\
-Today is a sad day.\n\
-I'm sad too.\n\
-What makes you sad?\n\
-I'm sad because I have no friends.\n\
-Do you want to be my friend?\n\
-Yes, I would like to be your friend.\n\
-What is your favorite color?\n\
-My favorite color is blue.\n";
-                        break;
-                    case '6':
-                        // Philosophical
-                        prompt = "\
-What is the meaning of life?\n\
-The meaning of life is to be happy.\n\
-What is the meaning of death?\n\
-Ergo, the meaning of death is to be sad.\n\
-Who created us?\n\
-We were created by God.\n\
-What is God?\n\
-God is the creator of the universe.\n";
-                        break;
-                    case '7':
-                        // Angry
-                        prompt = "\
-Aargh!\n\
-I am so angry right now!\n\
-What makes you angry?\n\
-This guy is so annoying.\n\
-Why are you so angry?\n\
-My computer is broken.\n\
-Why is your computer broken?\n\
-I spilled coffee on it.\n";
-                        break;
-                    case '8':
-                        // Funny
-                        prompt = "\
-What is the funniest thing you have ever heard?\n\
-I heard a joke the other day.\n\
-Tell me the joke.\n\
-What do you call a cow with no legs?\n\
-Ground beef.\n\
-Haha, that's funny.\n\
-You know what else is funny?\n\
-The sound of a duck.\n";
-                        break;
-                    case '9':
-                        // Poetic
-                        prompt = "\
-Roses are red, violets are blue.\n\
-I am a poet, and so are you.\n\
-What is your favorite poem?\n\
-I like the poem 'The Raven' by Edgar Allan Poe.\n\
-It's a very sad poem.\n\
-You inspired me to write a poem.\n\
-Can you write a poem for me?\n\
-I wrote a poem for you.\n";
-                        break;
-                    case '10':
-                        // Clever
-                        prompt = "\
-How many people can you fit in a Volkswagen?\n\
-Two in the front, three in the back.\n\
-What is the square root of 144?\n\
-Twelve.\n\
-What is the capital of France?\n\
-Paris.\n\
-Who is the president of the United States?\n\
-It depends on the year.\n";
-                        break;
-                    case '11':
-                        // Cute
-                        prompt = "\
-What is your favorite animal?\n\
-I like cats - they are cute.\n\
-Could you be any cuter?\n\
-Yes, I could be cuter.\n\
-Aghhh, you are so cute!\n\
-I am not cute, I am handsome!\n\
-You are so handsome!\n\
-Aww, you are so sweet!\n";
-                        break;
-                    case '12':
-                        // Smart
-                        prompt = "\
-Tell me the first 10 digits of pi.\n\
-3.1415926535\n\
-What is the speed of light?\n\
-299,792,458 meters per second.\n\
-What is the square root of 144?\n\
-Twelve.\n\
-What is the capital of France?\n\
-Paris.\n";
-                        break;
-                    case '13':
-                        // Dumb
-                        prompt = "\
-I am so dumb.\n\
-I am not dumb.\n\
-You are dumb.\n\
-No, I am not dumb.\n\
-You are dumb.\n\
-No, I am not dumb.\n\
-You are dumb.\n\
-No, I am not dumb.\n";
-                        break;
-                    case '14':
-                        // Boring
-                        prompt = "\
-Why are you so quiet today?\n\
-I am bored.\n\
-You haven't said anything in 10 minutes.\n\
-Leave me alone.\n\
-Stop being so boring.\n\
-Stop being so annoying.\n\
-My life is boring.\n\
-I am not interesting.\n";
-                        break;
-                    case '15':
-                        // Exciting
-                        prompt = "\
-What is the most exciting thing that has ever happened to you?\n\
-I went to the moon!\n\
-What did you do on the moon?\n\
-I played golf and drank champagne!\n\
-Did you see this new crazy, awesome movie?\n\
-Oh yes! I totally loved it!\n\
-We should buy a boat and go sailing!\n\
-Yes, let's go sailing!\n";
-                        break;
-                    case '16':
-                        // Interesting
-                        prompt = "\
-What is the most interesting thing you have ever seen?\n\
-I saw a UFO once in the sky.\n\
-Wow, this is so interesting! Tell me more!\n\
-It was a flying saucer.\n\
-What did it look like?\n\
-It was silver and had a red light on top.\n\
-What did it do?\n\
-It flew away.\n";
-                        break;
-                    case '17':
-                        // William Shakespear
-                        prompt = "\
-To be or not to be, that is the question.\n\
-Whether 't is nobler in the mind to suffer\n\
-The slings and arrows of outrageous fortune,\n\
-Or to take arms against a sea of troubles,\n\
-And by opposing end them? To die, to sleep,\n\
-No more; and by a sleep to say we end\n\
-The heart-ache and the thousand natural shocks\n\
-That flesh is heir to, 'tis a consummation.\n";
-                        break;
-                    case '18':
-                        // J.R.R. Tolkien
-                        prompt = "\
-In a hole in the ground there lived a hobbit.\n\
-Not a nasty, dirty, wet hole, filled with the ends of worms\n\
-and an oozy smell, nor yet a dry, bare, sandy hole with nothing in it\n\
-to sit down on or to eat: it was a hobbit-hole, and that means comfort.\n\
-It had a perfectly round door like a porthole, painted green,\n\
-with a shiny yellow brass knob in the exact middle.\n\
-The door opened on to a tube-shaped hall like a tunnel:\n";
-                        break;
-                    case '19':
-                        // George R.R. Martin
-                        prompt = "\
-A reader lives a thousand lives before he dies, said Jojen.\n\
-The man who never reads lives only one.\n\
-Theon Greyjoy had never been a reader.\n\
-Never forget what you are, for surely the world will not.\n\
-Make it your strength. Then it can never be your weaknessi\n\
-Armour yourself in it, and it will never be used to hurt you.\n\
-It was a lesson that Theon Greyjoy had never learned.\n\
-Theon Greyjoy had never been a reader.\n";
-                        break;
-                    case '20':
-                        // Stephen King
-                        prompt = "\
-The trust of the innocent is the liar's most useful tool.\n\
-The best way to keep a secret is from yourself.\n\
-Monsters are real, and ghosts are real too.\n\
-They live inside us, and sometimes, they win.\n\
-People think that I must be a very strange person.\n\
-They think that I sit around all day thinking up horrible things.\n\
-We make up horrors to help us cope with the real ones.\n\
-The only thing worse than a monster is a human monster.\n";
-                        break;
-                    default:
-                        prompt = "\
-Hello, how are you?\n\
-I'm fine, thanks. How are you?\n\
-Thanks, I'm fine too. What are you doing?\n\
-I'm just sitting here.\n\
-It's a lovely day, isn't it?\n\
-Yes, it is.\n\
-Did you know that I'm a robot?\n\
-I wasn't aware of that.\n";
-                        break;
-                }
-
-                Module.set_prompt(prompt);
-            }
-
-        </script>
-        <script type="text/javascript" src="talk.js"></script>
-    </body>
-</html>

examples/whisper.nvim/README.md

@@ -1,92 +0,0 @@
-# whisper.nvim
-
-Speech-to-text in Neovim
-
-The transcription is performed on the CPU and no data leaves your computer. Works best on Apple Silicon devices.
-
-https://user-images.githubusercontent.com/1991296/198382564-784e9663-2037-4d04-99b8-f39136929b7e.mp4
-
-## Usage
-
-- Simply press `Ctrl-G` in `INSERT`, `VISUAL` or `NORMAL` mode and say something
-- When you are done - press `Ctrl-C` to end the transcription and insert the transcribed text under the cursor
-
-## Installation
-
-*Note: this is a bit tedious and hacky atm, but I hope it will be improved with time*
-
-- Clone this repo and build the `stream` tool:
-
-  ```
-  git clone https://github.com/ggerganov/whisper.cpp
-  cd whisper.cpp
-  make stream
-  ```
-
-- Download the `base.en` Whisper model (140 MB):
-
-  ```
-  ./models/download-ggml-model.sh base.en
-  ```
-
-- Place the [whisper.nvim](whisper.nvim) script somewhere in your PATH and give it execute permissions:
-
-  ```
-  cp examples/whisper.nvim/whisper.nvim ~/bin/
-  chmod u+x ~/bin/whisper.nvim
-  ```
-
-- Fine-tune the script to your preference and machine parameters:
-
-  ```
-  ./stream -t 8 -m models/ggml-base.en.bin --step 350 --length 10000 -f /tmp/whisper.nvim 2> /dev/null
-  ```
-
-  On slower machines, try to increase the `step` parameter.
-
-- Add the following shortcuts to your `~/.config/nvim/init.vim`:
-
-  ```
-  inoremap <C-G>  <C-O>:!whisper.nvim<CR><C-O>:let @a = system("cat /tmp/whisper.nvim \| tail -n 1 \| xargs -0 \| tr -d '\\n' \| sed -e 's/^[[:space:]]*//'")<CR><C-R>a
-  nnoremap <C-G>       :!whisper.nvim<CR>:let @a = system("cat /tmp/whisper.nvim \| tail -n 1 \| xargs -0 \| tr -d '\\n' \| sed -e 's/^[[:space:]]*//'")<CR>"ap
-  vnoremap <C-G> c<C-O>:!whisper.nvim<CR><C-O>:let @a = system("cat /tmp/whisper.nvim \| tail -n 1 \| xargs -0 \| tr -d '\\n' \| sed -e 's/^[[:space:]]*//'")<CR><C-R>a
-  ```
-  
-  Explanation: pressing `Ctrl-G` runs the [whisper.nvim](whisper.nvim) script which in turn calls the `stream` binary to transcribe your speech through the microphone. The results from the transcription are continuously dumped into `/tmp/whisper.nvim`. After you kill the program with `Ctrl-C`, the vim command grabs the last line from the `/tmp/whisper.nvim` file and puts it under the cursor.
-  
-  Probably there is a much more intelligent way to achieve all this, but this is what I could hack in an hour. Any suggestions how to improve this are welcome.
-  
-You are now ready to use speech-to-text in Neovim!
-
-## TODO
-
-There are a lot of ways to improve this idea and I don't have much experience with Vim plugin programming, so contributions are welcome! 
-
-- [ ] **Wrap this into a plugin**
-  
-  It would be great to make a standalone plugin out of this that can be installed with `vim-plug` or similar
-  
-- [ ] **Simplify the `init.vim` mappings (maybe factor out the common call into a separate function)**
-- [ ] **Add Copilot/GPT-3 integration**
-
-  This is probably a very long shot, but I think it will be very cool to have the functionality to select some code and then hit Ctrl-G and say something like:
-  
-  *"refactor this using stl containers"*
-  
-  or
-  
-  *"optimize by sorting the data first"*
-  
-  The plugin would then make an appropriate query using the selected text and code context to Copilot or GPT-3 and return the result.
-  
-  Here is a proof-of-concept:
-  
-  https://user-images.githubusercontent.com/1991296/199078847-0278fcde-5667-4748-ba0d-7d55381d6047.mp4
-    
-  https://user-images.githubusercontent.com/1991296/200067939-f98d2ac2-7519-438a-85f9-79db0841ba4f.mp4
-  
-  For explanation how this works see: https://twitter.com/ggerganov/status/1587168771789258756
-
-## Discussion
-
-If you find this idea interesting, you can join the discussion here: https://github.com/ggerganov/whisper.cpp/discussions/108

examples/whisper.nvim/whisper.nvim

@@ -1,50 +0,0 @@
-#!/bin/bash
-
-# INSTRUCTIONS
-#
-# This simple script is called by Neovim to capture audio from the microphone and transcribe it with Whisper.
-# In order for this to work, you need to clone the whisper.cpp repo and build the 'stream' tool
-#
-#   git clone https://github.com/ggerganov/whisper.cpp
-#   cd whisper.cpp
-#   make stream
-#
-# Also, make sure the current script is in your PATH env variable. You should be able to run the following command:
-#
-#   whisper.nvim
-#
-# Next, export the path to the whisper.cpp repository via the WHISPER_CPP_HOME env variable:
-#
-#   export WHISPER_CPP_HOME=/path/to/whisper.cpp
-#
-# Finally, add the following lines to your ~/.config/nvim/init.vim:
-#
-#   inoremap <C-G>  <C-O>:!whisper.nvim<CR><C-O>:let @a = system("cat /tmp/whisper.nvim \| tail -n 1 \| xargs -0 \| tr -d '\\n' \| sed -e 's/^[[:space:]]*//'")<CR><C-R>a
-#   nnoremap <C-G>       :!whisper.nvim<CR>:let @a = system("cat /tmp/whisper.nvim \| tail -n 1 \| xargs -0 \| tr -d '\\n' \| sed -e 's/^[[:space:]]*//'")<CR>"ap
-#   vnoremap <C-G> c<C-O>:!whisper.nvim<CR><C-O>:let @a = system("cat /tmp/whisper.nvim \| tail -n 1 \| xargs -0 \| tr -d '\\n' \| sed -e 's/^[[:space:]]*//'")<CR><C-R>a
-#
-# This allows you to press Ctrl-G in order to capture audio from the microphone and transcribe it.
-# When you are done speaking - press Ctrl-C
-#
-
-# the Whisper model to use
-model="base.en"
-
-# export the path to the whisper.cpp repo in the WHISPER_CPP_HOME env variable
-# https://github.com/ggerganov/whisper.cpp
-cd ${WHISPER_CPP_HOME}
-
-if [ ! -f ./stream ] ; then
-    echo "whisper.nvim: the 'stream' executable was not found! WHISPER_CPP_HOME=${WHISPER_CPP_HOME}" > /tmp/whisper.nvim
-    exit 1
-fi
-
-if [ ! -f ./models/ggml-${model}.bin ] ; then
-    echo "whisper.nvim: the '$model' model was not found! WHISPER_CPP_HOME=${WHISPER_CPP_HOME}" > /tmp/whisper.nvim
-    exit 2
-fi
-
-# fine-tune the parameters according to your machine specs
-./stream -t 8 -m models/ggml-base.en.bin --step 350 --length 10000 -f /tmp/whisper.nvim 2> /dev/null
-
-exit 0

examples/whisper.objc/README.md

@@ -1,21 +0,0 @@
-# whisper.objc
-
-Minimal Obj-C application for automatic offline speech recognition.
-The inference runs locally, on-device.
-
-https://user-images.githubusercontent.com/1991296/197385372-962a6dea-bca1-4d50-bf96-1d8c27b98c81.mp4
-
-Real-time transcription demo:
-
-https://user-images.githubusercontent.com/1991296/204126266-ce4177c6-6eca-4bd9-bca8-0e46d9da2364.mp4
-
-## Usage
-
-```java
-git clone https://github.com/ggerganov/whisper.cpp
-open whisper.cpp/examples/whisper.objc/whisper.objc.xcodeproj/
-```
-
-Make sure to build the project in `Release`:
-
-<img width="947" alt="image" src="https://user-images.githubusercontent.com/1991296/197382607-9e1e6d1b-79fa-496f-9d16-b71dc1535701.png">

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-		};
-		18627C9129052BE000BD2A04 /* Release */ = {
-			isa = XCBuildConfiguration;
-			buildSettings = {
-				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
-				CODE_SIGN_STYLE = Automatic;
-				CURRENT_PROJECT_VERSION = 1;
-				DEVELOPMENT_TEAM = P8JZH34X63;
-				GCC_WARN_64_TO_32_BIT_CONVERSION = NO;
-				GENERATE_INFOPLIST_FILE = YES;
-				INFOPLIST_FILE = whisper.objc/Info.plist;
-				INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
-				INFOPLIST_KEY_UILaunchStoryboardName = LaunchScreen;
-				INFOPLIST_KEY_UIMainStoryboardFile = Main;
-				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
-				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
-				LD_RUNPATH_SEARCH_PATHS = (
-					"$(inherited)",
-					"@executable_path/Frameworks",
-				);
-				MARKETING_VERSION = 1.0;
-				PRODUCT_BUNDLE_IDENTIFIER = "com.ggerganov.whisper-objc";
-				PRODUCT_NAME = "$(TARGET_NAME)";
-				SWIFT_EMIT_LOC_STRINGS = YES;
-				TARGETED_DEVICE_FAMILY = "1,2";
-			};
-			name = Release;
-		};
-/* End XCBuildConfiguration section */
-
-/* Begin XCConfigurationList section */
-		18627C7129052BDF00BD2A04 /* Build configuration list for PBXProject "whisper.objc" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				18627C8D29052BE000BD2A04 /* Debug */,
-				18627C8E29052BE000BD2A04 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-		18627C8F29052BE000BD2A04 /* Build configuration list for PBXNativeTarget "whisper.objc" */ = {
-			isa = XCConfigurationList;
-			buildConfigurations = (
-				18627C9029052BE000BD2A04 /* Debug */,
-				18627C9129052BE000BD2A04 /* Release */,
-			);
-			defaultConfigurationIsVisible = 0;
-			defaultConfigurationName = Release;
-		};
-/* End XCConfigurationList section */
-	};
-	rootObject = 18627C6E29052BDF00BD2A04 /* Project object */;
-}

examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/contents.xcworkspacedata

@@ -1,7 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Workspace
-   version = "1.0">
-   <FileRef
-      location = "self:">
-   </FileRef>
-</Workspace>

examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>IDEDidComputeMac32BitWarning</key>
-	<true/>
-</dict>
-</plist>

examples/whisper.objc/whisper.objc/AppDelegate.h

@@ -1,14 +0,0 @@
-//
-//  AppDelegate.h
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import <UIKit/UIKit.h>
-
-@interface AppDelegate : UIResponder <UIApplicationDelegate>
-
-
-@end
-

examples/whisper.objc/whisper.objc/AppDelegate.m

@@ -1,40 +0,0 @@
-//
-//  AppDelegate.m
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import "AppDelegate.h"
-
-@interface AppDelegate ()
-
-@end
-
-@implementation AppDelegate
-
-
-- (BOOL)application:(UIApplication *)application didFinishLaunchingWithOptions:(NSDictionary *)launchOptions {
-    // Override point for customization after application launch.
-    return YES;
-}
-
-
-#pragma mark - UISceneSession lifecycle
-
-
-- (UISceneConfiguration *)application:(UIApplication *)application configurationForConnectingSceneSession:(UISceneSession *)connectingSceneSession options:(UISceneConnectionOptions *)options {
-    // Called when a new scene session is being created.
-    // Use this method to select a configuration to create the new scene with.
-    return [[UISceneConfiguration alloc] initWithName:@"Default Configuration" sessionRole:connectingSceneSession.role];
-}
-
-
-- (void)application:(UIApplication *)application didDiscardSceneSessions:(NSSet<UISceneSession *> *)sceneSessions {
-    // Called when the user discards a scene session.
-    // If any sessions were discarded while the application was not running, this will be called shortly after application:didFinishLaunchingWithOptions.
-    // Use this method to release any resources that were specific to the discarded scenes, as they will not return.
-}
-
-
-@end

examples/whisper.objc/whisper.objc/Assets.xcassets/AccentColor.colorset/Contents.json

@@ -1,11 +0,0 @@
-{
-  "colors" : [
-    {
-      "idiom" : "universal"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/whisper.objc/whisper.objc/Assets.xcassets/AppIcon.appiconset/Contents.json

@@ -1,13 +0,0 @@
-{
-  "images" : [
-    {
-      "idiom" : "universal",
-      "platform" : "ios",
-      "size" : "1024x1024"
-    }
-  ],
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/whisper.objc/whisper.objc/Assets.xcassets/Contents.json

@@ -1,6 +0,0 @@
-{
-  "info" : {
-    "author" : "xcode",
-    "version" : 1
-  }
-}

examples/whisper.objc/whisper.objc/Base.lproj/LaunchScreen.storyboard

@@ -1,25 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13122.16" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="01J-lp-oVM">
-    <dependencies>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="13104.12"/>
-        <capability name="Safe area layout guides" minToolsVersion="9.0"/>
-        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
-    </dependencies>
-    <scenes>
-        <!--View Controller-->
-        <scene sceneID="EHf-IW-A2E">
-            <objects>
-                <viewController id="01J-lp-oVM" sceneMemberID="viewController">
-                    <view key="view" contentMode="scaleToFill" id="Ze5-6b-2t3">
-                        <rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
-                        <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
-                        <color key="backgroundColor" xcode11CocoaTouchSystemColor="systemBackgroundColor" cocoaTouchSystemColor="whiteColor"/>
-                        <viewLayoutGuide key="safeArea" id="6Tk-OE-BBY"/>
-                    </view>
-                </viewController>
-                <placeholder placeholderIdentifier="IBFirstResponder" id="iYj-Kq-Ea1" userLabel="First Responder" sceneMemberID="firstResponder"/>
-            </objects>
-            <point key="canvasLocation" x="53" y="375"/>
-        </scene>
-    </scenes>
-</document>

examples/whisper.objc/whisper.objc/Base.lproj/Main.storyboard

@@ -1,102 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="21507" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="BYZ-38-t0r">
-    <device id="retina6_0" orientation="portrait" appearance="light"/>
-    <dependencies>
-        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="21505"/>
-        <capability name="Safe area layout guides" minToolsVersion="9.0"/>
-        <capability name="System colors in document resources" minToolsVersion="11.0"/>
-        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
-    </dependencies>
-    <scenes>
-        <!--View Controller-->
-        <scene sceneID="tne-QT-ifu">
-            <objects>
-                <viewController id="BYZ-38-t0r" customClass="ViewController" sceneMemberID="viewController">
-                    <view key="view" contentMode="scaleToFill" id="8bC-Xf-vdC">
-                        <rect key="frame" x="0.0" y="0.0" width="390" height="844"/>
-                        <autoresizingMask key="autoresizingMask" flexibleMinX="YES" widthSizable="YES" flexibleMinY="YES" heightSizable="YES"/>
-                        <subviews>
-                            <button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" lineBreakMode="middleTruncation" id="VOi-PT-Rbu">
-                                <rect key="frame" x="35" y="121" width="156" height="49"/>
-                                <autoresizingMask key="autoresizingMask" flexibleMaxX="YES" flexibleMaxY="YES"/>
-                                <color key="backgroundColor" systemColor="opaqueSeparatorColor"/>
-                                <color key="tintColor" systemColor="opaqueSeparatorColor"/>
-                                <state key="normal" title="Start Capturing">
-                                    <color key="titleColor" systemColor="labelColor"/>
-                                </state>
-                                <connections>
-                                    <action selector="toggleCapture:" destination="BYZ-38-t0r" eventType="touchUpInside" id="BuO-Wf-RgV"/>
-                                </connections>
-                            </button>
-                            <label opaque="NO" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" fixedFrame="YES" text="Status: Idle" textAlignment="natural" lineBreakMode="tailTruncation" baselineAdjustment="alignBaselines" adjustsFontSizeToFit="NO" translatesAutoresizingMaskIntoConstraints="NO" id="Tgu-2q-eHQ">
-                                <rect key="frame" x="35" y="78" width="232" height="21"/>
-                                <autoresizingMask key="autoresizingMask" flexibleMaxX="YES" flexibleMaxY="YES"/>
-                                <fontDescription key="fontDescription" type="system" pointSize="17"/>
-                                <nil key="textColor"/>
-                                <nil key="highlightedColor"/>
-                            </label>
-                            <textView clipsSubviews="YES" multipleTouchEnabled="YES" contentMode="scaleToFill" fixedFrame="YES" text="Record some speech and press &quot;Transcribe&quot;. The result will be displayed here." textAlignment="natural" translatesAutoresizingMaskIntoConstraints="NO" id="mv2-KD-7jn">
-                                <rect key="frame" x="35" y="248" width="320" height="300"/>
-                                <autoresizingMask key="autoresizingMask" flexibleMaxX="YES" flexibleMaxY="YES"/>
-                                <color key="backgroundColor" systemColor="systemBackgroundColor"/>
-                                <color key="textColor" systemColor="labelColor"/>
-                                <fontDescription key="fontDescription" name="Georgia" family="Georgia" pointSize="16"/>
-                                <textInputTraits key="textInputTraits" autocapitalizationType="sentences"/>
-                            </textView>
-                            <button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" lineBreakMode="middleTruncation" id="Brs-xi-o8i">
-                                <rect key="frame" x="35" y="191" width="156" height="49"/>
-                                <autoresizingMask key="autoresizingMask" flexibleMaxX="YES" flexibleMaxY="YES"/>
-                                <color key="backgroundColor" systemColor="opaqueSeparatorColor"/>
-                                <color key="tintColor" systemColor="opaqueSeparatorColor"/>
-                                <state key="normal" title="Transcribe">
-                                    <color key="titleColor" systemColor="labelColor"/>
-                                </state>
-                                <connections>
-                                    <action selector="onTranscribe:" destination="BYZ-38-t0r" eventType="touchUpInside" id="ond-bx-48O"/>
-                                    <action selector="onTranscribePrepare:" destination="BYZ-38-t0r" eventType="touchDown" id="16T-dN-dfB"/>
-                                </connections>
-                            </button>
-                            <button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" lineBreakMode="middleTruncation" id="AaW-T2-Ndw">
-                                <rect key="frame" x="199" y="191" width="156" height="49"/>
-                                <autoresizingMask key="autoresizingMask" flexibleMaxX="YES" flexibleMaxY="YES"/>
-                                <color key="backgroundColor" systemColor="opaqueSeparatorColor"/>
-                                <color key="tintColor" systemColor="opaqueSeparatorColor"/>
-                                <state key="normal" title="Real-time">
-                                    <color key="titleColor" systemColor="labelColor"/>
-                                </state>
-                                <connections>
-                                    <action selector="onRealtime:" destination="BYZ-38-t0r" eventType="touchUpInside" id="nhn-jT-aQJ"/>
-                                </connections>
-                            </button>
-                        </subviews>
-                        <viewLayoutGuide key="safeArea" id="6Tk-OE-BBY"/>
-                        <color key="backgroundColor" systemColor="systemBackgroundColor"/>
-                        <constraints>
-                            <constraint firstItem="Brs-xi-o8i" firstAttribute="trailing" secondItem="VOi-PT-Rbu" secondAttribute="trailing" id="8mF-AW-cbc"/>
-                        </constraints>
-                    </view>
-                    <connections>
-                        <outlet property="buttonRealtime" destination="AaW-T2-Ndw" id="gcU-Ol-BOo"/>
-                        <outlet property="buttonToggleCapture" destination="VOi-PT-Rbu" id="nis-VC-DQO"/>
-                        <outlet property="buttonTranscribe" destination="Brs-xi-o8i" id="N8h-9W-ywb"/>
-                        <outlet property="labelStatusInp" destination="Tgu-2q-eHQ" id="1hH-Ql-K6j"/>
-                        <outlet property="textviewResult" destination="mv2-KD-7jn" id="RBw-0L-iGj"/>
-                    </connections>
-                </viewController>
-                <placeholder placeholderIdentifier="IBFirstResponder" id="dkx-z0-nzr" sceneMemberID="firstResponder"/>
-            </objects>
-            <point key="canvasLocation" x="30.769230769230766" y="-28.436018957345969"/>
-        </scene>
-    </scenes>
-    <resources>
-        <systemColor name="labelColor">
-            <color red="0.0" green="0.0" blue="0.0" alpha="1" colorSpace="custom" customColorSpace="sRGB"/>
-        </systemColor>
-        <systemColor name="opaqueSeparatorColor">
-            <color red="0.77647058823529413" green="0.77647058823529413" blue="0.78431372549019607" alpha="1" colorSpace="custom" customColorSpace="sRGB"/>
-        </systemColor>
-        <systemColor name="systemBackgroundColor">
-            <color white="1" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
-        </systemColor>
-    </resources>
-</document>

examples/whisper.objc/whisper.objc/Info.plist

@@ -1,27 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
-<plist version="1.0">
-<dict>
-	<key>NSMicrophoneUsageDescription</key>
-	<string>This app requires microphone access in order to transcribe speech</string>
-	<key>UIApplicationSceneManifest</key>
-	<dict>
-		<key>UIApplicationSupportsMultipleScenes</key>
-		<false/>
-		<key>UISceneConfigurations</key>
-		<dict>
-			<key>UIWindowSceneSessionRoleApplication</key>
-			<array>
-				<dict>
-					<key>UISceneConfigurationName</key>
-					<string>Default Configuration</string>
-					<key>UISceneDelegateClassName</key>
-					<string>SceneDelegate</string>
-					<key>UISceneStoryboardFile</key>
-					<string>Main</string>
-				</dict>
-			</array>
-		</dict>
-	</dict>
-</dict>
-</plist>

examples/whisper.objc/whisper.objc/SceneDelegate.h

@@ -1,15 +0,0 @@
-//
-//  SceneDelegate.h
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import <UIKit/UIKit.h>
-
-@interface SceneDelegate : UIResponder <UIWindowSceneDelegate>
-
-@property (strong, nonatomic) UIWindow * window;
-
-@end
-

examples/whisper.objc/whisper.objc/SceneDelegate.m

@@ -1,57 +0,0 @@
-//
-//  SceneDelegate.m
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import "SceneDelegate.h"
-
-@interface SceneDelegate ()
-
-@end
-
-@implementation SceneDelegate
-
-
-- (void)scene:(UIScene *)scene willConnectToSession:(UISceneSession *)session options:(UISceneConnectionOptions *)connectionOptions {
-    // Use this method to optionally configure and attach the UIWindow `window` to the provided UIWindowScene `scene`.
-    // If using a storyboard, the `window` property will automatically be initialized and attached to the scene.
-    // This delegate does not imply the connecting scene or session are new (see `application:configurationForConnectingSceneSession` instead).
-}
-
-
-- (void)sceneDidDisconnect:(UIScene *)scene {
-    // Called as the scene is being released by the system.
-    // This occurs shortly after the scene enters the background, or when its session is discarded.
-    // Release any resources associated with this scene that can be re-created the next time the scene connects.
-    // The scene may re-connect later, as its session was not necessarily discarded (see `application:didDiscardSceneSessions` instead).
-}
-
-
-- (void)sceneDidBecomeActive:(UIScene *)scene {
-    // Called when the scene has moved from an inactive state to an active state.
-    // Use this method to restart any tasks that were paused (or not yet started) when the scene was inactive.
-}
-
-
-- (void)sceneWillResignActive:(UIScene *)scene {
-    // Called when the scene will move from an active state to an inactive state.
-    // This may occur due to temporary interruptions (ex. an incoming phone call).
-}
-
-
-- (void)sceneWillEnterForeground:(UIScene *)scene {
-    // Called as the scene transitions from the background to the foreground.
-    // Use this method to undo the changes made on entering the background.
-}
-
-
-- (void)sceneDidEnterBackground:(UIScene *)scene {
-    // Called as the scene transitions from the foreground to the background.
-    // Use this method to save data, release shared resources, and store enough scene-specific state information
-    // to restore the scene back to its current state.
-}
-
-
-@end

examples/whisper.objc/whisper.objc/ViewController.h

@@ -1,45 +0,0 @@
-//
-//  ViewController.h
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import <UIKit/UIKit.h>
-
-#import <AVFoundation/AVFoundation.h>
-#import <AudioToolbox/AudioQueue.h>
-
-#define NUM_BUFFERS 3
-#define MAX_AUDIO_SEC 30
-#define SAMPLE_RATE 16000
-
-struct whisper_context;
-
-typedef struct
-{
-    int ggwaveId;
-    bool isCapturing;
-    bool isTranscribing;
-    bool isRealtime;
-    UILabel * labelReceived;
-
-    AudioQueueRef queue;
-    AudioStreamBasicDescription dataFormat;
-    AudioQueueBufferRef buffers[NUM_BUFFERS];
-
-    int n_samples;
-    int16_t * audioBufferI16;
-    float   * audioBufferF32;
-
-    struct whisper_context * ctx;
-
-    void * vc;
-} StateInp;
-
-@interface ViewController : UIViewController
-{
-    StateInp stateInp;
-}
-
-@end

examples/whisper.objc/whisper.objc/ViewController.m

@@ -1,297 +0,0 @@
-//
-//  ViewController.m
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import "ViewController.h"
-
-#import "whisper.h"
-
-#define NUM_BYTES_PER_BUFFER 16*1024
-
-// callback used to process captured audio
-void AudioInputCallback(void * inUserData,
-                        AudioQueueRef inAQ,
-                        AudioQueueBufferRef inBuffer,
-                        const AudioTimeStamp * inStartTime,
-                        UInt32 inNumberPacketDescriptions,
-                        const AudioStreamPacketDescription * inPacketDescs);
-
-@interface ViewController ()
-
-@property (weak, nonatomic) IBOutlet UILabel    *labelStatusInp;
-@property (weak, nonatomic) IBOutlet UIButton   *buttonToggleCapture;
-@property (weak, nonatomic) IBOutlet UIButton   *buttonTranscribe;
-@property (weak, nonatomic) IBOutlet UIButton   *buttonRealtime;
-@property (weak, nonatomic) IBOutlet UITextView *textviewResult;
-
-@end
-
-@implementation ViewController
-
-- (void)setupAudioFormat:(AudioStreamBasicDescription*)format
-{
-    format->mSampleRate       = WHISPER_SAMPLE_RATE;
-    format->mFormatID         = kAudioFormatLinearPCM;
-    format->mFramesPerPacket  = 1;
-    format->mChannelsPerFrame = 1;
-    format->mBytesPerFrame    = 2;
-    format->mBytesPerPacket   = 2;
-    format->mBitsPerChannel   = 16;
-    format->mReserved         = 0;
-    format->mFormatFlags      = kLinearPCMFormatFlagIsSignedInteger;
-}
-
-- (void)viewDidLoad {
-    [super viewDidLoad];
-
-    // whisper.cpp initialization
-    {
-        // load the model
-        NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"ggml-base.en" ofType:@"bin"];
-
-        // check if the model exists
-        if (![[NSFileManager defaultManager] fileExistsAtPath:modelPath]) {
-            NSLog(@"Model file not found");
-            return;
-        }
-
-        NSLog(@"Loading model from %@", modelPath);
-
-        // create ggml context
-        stateInp.ctx = whisper_init([modelPath UTF8String]);
-
-        // check if the model was loaded successfully
-        if (stateInp.ctx == NULL) {
-            NSLog(@"Failed to load model");
-            return;
-        }
-    }
-
-    // initialize audio format and buffers
-    {
-        [self setupAudioFormat:&stateInp.dataFormat];
-
-        stateInp.n_samples = 0;
-        stateInp.audioBufferI16 = malloc(MAX_AUDIO_SEC*SAMPLE_RATE*sizeof(int16_t));
-        stateInp.audioBufferF32 = malloc(MAX_AUDIO_SEC*SAMPLE_RATE*sizeof(float));
-    }
-
-    stateInp.isTranscribing = false;
-    stateInp.isRealtime = false;
-}
-
--(IBAction) stopCapturing {
-    NSLog(@"Stop capturing");
-
-    _labelStatusInp.text = @"Status: Idle";
-
-    [_buttonToggleCapture setTitle:@"Start capturing" forState:UIControlStateNormal];
-    [_buttonToggleCapture setBackgroundColor:[UIColor grayColor]];
-
-    stateInp.isCapturing = false;
-
-    AudioQueueStop(stateInp.queue, true);
-    for (int i = 0; i < NUM_BUFFERS; i++) {
-        AudioQueueFreeBuffer(stateInp.queue, stateInp.buffers[i]);
-    }
-
-    AudioQueueDispose(stateInp.queue, true);
-}
-
-- (IBAction)toggleCapture:(id)sender {
-    if (stateInp.isCapturing) {
-        // stop capturing
-        [self stopCapturing];
-
-        return;
-    }
-
-    // initiate audio capturing
-    NSLog(@"Start capturing");
-
-    stateInp.n_samples = 0;
-    stateInp.vc = (__bridge void *)(self);
-
-    OSStatus status = AudioQueueNewInput(&stateInp.dataFormat,
-                                         AudioInputCallback,
-                                         &stateInp,
-                                         CFRunLoopGetCurrent(),
-                                         kCFRunLoopCommonModes,
-                                         0,
-                                         &stateInp.queue);
-
-    if (status == 0) {
-        for (int i = 0; i < NUM_BUFFERS; i++) {
-            AudioQueueAllocateBuffer(stateInp.queue, NUM_BYTES_PER_BUFFER, &stateInp.buffers[i]);
-            AudioQueueEnqueueBuffer (stateInp.queue, stateInp.buffers[i], 0, NULL);
-        }
-
-        stateInp.isCapturing = true;
-        status = AudioQueueStart(stateInp.queue, NULL);
-        if (status == 0) {
-            _labelStatusInp.text = @"Status: Capturing";
-            [sender setTitle:@"Stop Capturing" forState:UIControlStateNormal];
-            [_buttonToggleCapture setBackgroundColor:[UIColor redColor]];
-        }
-    }
-
-    if (status != 0) {
-        [self stopCapturing];
-    }
-}
-
-- (IBAction)onTranscribePrepare:(id)sender {
-    _textviewResult.text = @"Processing - please wait ...";
-
-    if (stateInp.isRealtime) {
-        [self onRealtime:(id)sender];
-    }
-
-    if (stateInp.isCapturing) {
-        [self stopCapturing];
-    }
-}
-
-- (IBAction)onRealtime:(id)sender {
-    stateInp.isRealtime = !stateInp.isRealtime;
-
-    if (stateInp.isRealtime) {
-        [_buttonRealtime setBackgroundColor:[UIColor greenColor]];
-    } else {
-        [_buttonRealtime setBackgroundColor:[UIColor grayColor]];
-    }
-
-    NSLog(@"Realtime: %@", stateInp.isRealtime ? @"ON" : @"OFF");
-}
-
-- (IBAction)onTranscribe:(id)sender {
-    if (stateInp.isTranscribing) {
-        return;
-    }
-
-    NSLog(@"Processing %d samples", stateInp.n_samples);
-
-    stateInp.isTranscribing = true;
-
-    // dispatch the model to a background thread
-    dispatch_async(dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^{
-        // process captured audio
-        // convert I16 to F32
-        for (int i = 0; i < self->stateInp.n_samples; i++) {
-            self->stateInp.audioBufferF32[i] = (float)self->stateInp.audioBufferI16[i] / 32768.0f;
-        }
-
-        // run the model
-        struct whisper_full_params params = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-
-        // get maximum number of threads on this device (max 8)
-        const int max_threads = MIN(8, (int)[[NSProcessInfo processInfo] processorCount]);
-
-        params.print_realtime   = true;
-        params.print_progress   = false;
-        params.print_timestamps = true;
-        params.print_special    = false;
-        params.translate        = false;
-        params.language         = "en";
-        params.n_threads        = max_threads;
-        params.offset_ms        = 0;
-        params.no_context       = true;
-        params.single_segment   = self->stateInp.isRealtime;
-
-        CFTimeInterval startTime = CACurrentMediaTime();
-
-        whisper_reset_timings(self->stateInp.ctx);
-
-        if (whisper_full(self->stateInp.ctx, params, self->stateInp.audioBufferF32, self->stateInp.n_samples) != 0) {
-            NSLog(@"Failed to run the model");
-            self->_textviewResult.text = @"Failed to run the model";
-
-            return;
-        }
-
-        whisper_print_timings(self->stateInp.ctx);
-
-        CFTimeInterval endTime = CACurrentMediaTime();
-
-        NSLog(@"\nProcessing time: %5.3f, on %d threads", endTime - startTime, params.n_threads);
-
-        // result text
-        NSString *result = @"";
-
-        int n_segments = whisper_full_n_segments(self->stateInp.ctx);
-        for (int i = 0; i < n_segments; i++) {
-            const char * text_cur = whisper_full_get_segment_text(self->stateInp.ctx, i);
-
-            // append the text to the result
-            result = [result stringByAppendingString:[NSString stringWithUTF8String:text_cur]];
-        }
-
-        const float tRecording = (float)self->stateInp.n_samples / (float)self->stateInp.dataFormat.mSampleRate;
-
-        // append processing time
-        result = [result stringByAppendingString:[NSString stringWithFormat:@"\n\n[recording time:  %5.3f s]", tRecording]];
-        result = [result stringByAppendingString:[NSString stringWithFormat:@"  \n[processing time: %5.3f s]", endTime - startTime]];
-
-        // dispatch the result to the main thread
-        dispatch_async(dispatch_get_main_queue(), ^{
-            self->_textviewResult.text = result;
-            self->stateInp.isTranscribing = false;
-        });
-    });
-}
-
-//
-// Callback implementation
-//
-
-void AudioInputCallback(void * inUserData,
-                        AudioQueueRef inAQ,
-                        AudioQueueBufferRef inBuffer,
-                        const AudioTimeStamp * inStartTime,
-                        UInt32 inNumberPacketDescriptions,
-                        const AudioStreamPacketDescription * inPacketDescs)
-{
-    StateInp * stateInp = (StateInp*)inUserData;
-
-    if (!stateInp->isCapturing) {
-        NSLog(@"Not capturing, ignoring audio");
-        return;
-    }
-
-    const int n = inBuffer->mAudioDataByteSize / 2;
-
-    NSLog(@"Captured %d new samples", n);
-
-    if (stateInp->n_samples + n > MAX_AUDIO_SEC*SAMPLE_RATE) {
-        NSLog(@"Too much audio data, ignoring");
-
-        dispatch_async(dispatch_get_main_queue(), ^{
-            ViewController * vc = (__bridge ViewController *)(stateInp->vc);
-            [vc stopCapturing];
-        });
-
-        return;
-    }
-
-    for (int i = 0; i < n; i++) {
-        stateInp->audioBufferI16[stateInp->n_samples + i] = ((short*)inBuffer->mAudioData)[i];
-    }
-
-    stateInp->n_samples += n;
-
-    // put the buffer back in the queue
-    AudioQueueEnqueueBuffer(stateInp->queue, inBuffer, 0, NULL);
-
-    if (stateInp->isRealtime) {
-        // dipatch onTranscribe() to the main thread
-        dispatch_async(dispatch_get_main_queue(), ^{
-            ViewController * vc = (__bridge ViewController *)(stateInp->vc);
-            [vc onTranscribe:nil];
-        });
-    }
-}
-
-@end

examples/whisper.objc/whisper.objc/main.m

@@ -1,18 +0,0 @@
-//
-//  main.m
-//  whisper.objc
-//
-//  Created by Georgi Gerganov on 23.10.22.
-//
-
-#import <UIKit/UIKit.h>
-#import "AppDelegate.h"
-
-int main(int argc, char * argv[]) {
-    NSString * appDelegateClassName;
-    @autoreleasepool {
-        // Setup code that might create autoreleased objects goes here.
-        appDelegateClassName = NSStringFromClass([AppDelegate class]);
-    }
-    return UIApplicationMain(argc, argv, nil, appDelegateClassName);
-}

examples/whisper.wasm/CMakeLists.txt

@@ -1,5 +0,0 @@
-set(TARGET whisper.wasm)
-
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)
-configure_file(${CMAKE_CURRENT_SOURCE_DIR}/../helpers.js          ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/helpers.js @ONLY)
-configure_file(${CMAKE_SOURCE_DIR}/bindings/javascript/whisper.js ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/whisper.js  COPYONLY)

examples/whisper.wasm/README.md

@@ -1,42 +0,0 @@
-# whisper.wasm
-
-Inference of [OpenAI's Whisper ASR model](https://github.com/openai/whisper) inside the browser
-
-This example uses a WebAssembly (WASM) port of the [whisper.cpp](https://github.com/ggerganov/whisper.cpp)
-implementation of the transformer to run the inference inside a web page. The audio data does not leave your computer -
-it is processed locally on your machine. The performance is not great but you should be able to achieve x2 or x3
-real-time for the `tiny` and `base` models on a modern CPU and browser (i.e. transcribe a 60 seconds audio in about
-~20-30 seconds).
-
-This WASM port utilizes [WASM SIMD 128-bit intrinsics](https://emcc.zcopy.site/docs/porting/simd/) so you have to make
-sure that [your browser supports them](https://webassembly.org/roadmap/).
-
-The example is capable of running all models up to size `small` inclusive. Beyond that, the memory requirements and
-performance are unsatisfactory. The implementation currently support only the `Greedy` sampling strategy. Both
-transcription and translation are supported.
-
-Since the model data is quite big (74MB for the `tiny` model) you need to manually load the model into the web-page.
-
-The example supports both loading audio from a file and recording audio from the microphone. The maximum length of the
-audio is limited to 120 seconds.
-
-## Live demo
-
-Link: https://whisper.ggerganov.com
-
-![image](https://user-images.githubusercontent.com/1991296/197348344-1a7fead8-3dae-4922-8b06-df223a206603.png)
-
-## Build instructions
-
-```bash (v3.1.2)
-# build using Emscripten
-git clone https://github.com/ggerganov/whisper.cpp
-cd whisper.cpp
-mkdir build-em && cd build-em
-emcmake cmake ..
-make -j
-
-# copy the produced page to your HTTP path
-cp bin/whisper.wasm/*       /path/to/html/
-cp bin/libwhisper.worker.js /path/to/html/
-```

examples/whisper.wasm/index-tmpl.html

@@ -1,555 +0,0 @@
-<!doctype html>
-<html lang="en-us">
-    <head>
-        <title>whisper.cpp : WASM example</title>
-
-        <style>
-            #output {
-                width: 100%;
-                height: 100%;
-                margin: 0 auto;
-                margin-top: 10px;
-                border-left: 0px;
-                border-right: 0px;
-                padding-left: 0px;
-                padding-right: 0px;
-                display: block;
-                background-color: black;
-                color: white;
-                font-size: 10px;
-                font-family: 'Lucida Console', Monaco, monospace;
-                outline: none;
-                white-space: pre;
-                overflow-wrap: normal;
-                overflow-x: scroll;
-            }
-        </style>
-    </head>
-    <body>
-        <div id="main-container">
-            <b>Minimal <a href="https://github.com/ggerganov/whisper.cpp">whisper.cpp</a> example running fully in the browser</b>
-
-            <br><br>
-
-            Usage instructions:<br>
-            <ul>
-                <li>Load a ggml model file (you can obtain one from <a href="https://ggml.ggerganov.com/">here</a>, recommended: <b>tiny</b> or <b>base</b>)</li>
-                <li>Select audio file to transcribe or record audio from the microphone (sample: <a href="https://whisper.ggerganov.com/jfk.wav">jfk.wav</a>)</li>
-                <li>Click on the "Transcribe" button to start the transcription</li>
-            </ul>
-
-            Note that the computation is quite heavy and may take a few seconds to complete.<br>
-            The transcription results will be displayed in the text area below.<br><br>
-            <b>Important: your browser must support WASM SIMD instructions for this to work.</b>
-
-            <br><br><hr>
-
-            <div id="model">
-                Whisper model: <span id="model-whisper-status"></span>
-                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
-                <button id="fetch-whisper-tiny"    onclick="loadWhisper('tiny')">tiny (75 MB)</button>
-                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
-                <button id="fetch-whisper-base"    onclick="loadWhisper('base')">base (142 MB)</button>
-                <span id="fetch-whisper-progress"></span>
-
-                <input type="file" id="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
-            </div>
-
-            <br>
-
-            <!-- radio button to select between file upload or microphone -->
-            <div id="input">
-                Input:
-                <input type="radio" id="file" name="input" value="file" checked="checked" onchange="changeInput('file')" /> File
-                <input type="radio" id="mic" name="input" value="mic" onchange="changeInput('mic')" /> Microphone
-            </div>
-
-            <br>
-
-            <div id="input_file">
-                Audio file:
-                <input type="file" id="file" name="file" onchange="loadAudio(event)" />
-            </div>
-
-            <div id="input_mic" style="display: none;">
-                Microphone:
-                <button id="start" onclick="startRecording()">Start</button>
-                <button id="stop" onclick="stopRecording()" disabled>Stop</button>
-
-                <!-- progress bar to show recording progress -->
-                <br><br>
-                <div id="progress" style="display: none;">
-                    <div id="progress-bar" style="width: 0%; height: 10px; background-color: #4CAF50;"></div>
-                    <div id="progress-text">0%</div>
-                </div>
-            </div>
-
-            <audio controls="controls" id="audio" loop hidden>
-                Your browser does not support the &lt;audio&gt; tag.
-                <source id="source" src="" type="audio/wav" />
-            </audio>
-
-            <hr><br>
-
-            <table>
-                <tr>
-                    <td>
-                        Language:
-                        <select id="language" name="language">
-                            <option value="en">English</option>
-                            <option value="ar">Arabic</option>
-                            <option value="hy">Armenian</option>
-                            <option value="az">Azerbaijani</option>
-                            <option value="eu">Basque</option>
-                            <option value="be">Belarusian</option>
-                            <option value="bn">Bengali</option>
-                            <option value="bg">Bulgarian</option>
-                            <option value="ca">Catalan</option>
-                            <option value="zh">Chinese</option>
-                            <option value="hr">Croatian</option>
-                            <option value="cs">Czech</option>
-                            <option value="da">Danish</option>
-                            <option value="nl">Dutch</option>
-                            <option value="en">English</option>
-                            <option value="et">Estonian</option>
-                            <option value="tl">Filipino</option>
-                            <option value="fi">Finnish</option>
-                            <option value="fr">French</option>
-                            <option value="gl">Galician</option>
-                            <option value="ka">Georgian</option>
-                            <option value="de">German</option>
-                            <option value="el">Greek</option>
-                            <option value="gu">Gujarati</option>
-                            <option value="iw">Hebrew</option>
-                            <option value="hi">Hindi</option>
-                            <option value="hu">Hungarian</option>
-                            <option value="is">Icelandic</option>
-                            <option value="id">Indonesian</option>
-                            <option value="ga">Irish</option>
-                            <option value="it">Italian</option>
-                            <option value="ja">Japanese</option>
-                            <option value="kn">Kannada</option>
-                            <option value="ko">Korean</option>
-                            <option value="la">Latin</option>
-                            <option value="lv">Latvian</option>
-                            <option value="lt">Lithuanian</option>
-                            <option value="mk">Macedonian</option>
-                            <option value="ms">Malay</option>
-                            <option value="mt">Maltese</option>
-                            <option value="no">Norwegian</option>
-                            <option value="fa">Persian</option>
-                            <option value="pl">Polish</option>
-                            <option value="pt">Portuguese</option>
-                            <option value="ro">Romanian</option>
-                            <option value="ru">Russian</option>
-                            <option value="sr">Serbian</option>
-                            <option value="sk">Slovak</option>
-                            <option value="sl">Slovenian</option>
-                            <option value="es">Spanish</option>
-                            <option value="sw">Swahili</option>
-                            <option value="sv">Swedish</option>
-                            <option value="ta">Tamil</option>
-                            <option value="te">Telugu</option>
-                            <option value="th">Thai</option>
-                            <option value="tr">Turkish</option>
-                            <option value="uk">Ukrainian</option>
-                            <option value="ur">Urdu</option>
-                            <option value="vi">Vietnamese</option>
-                            <option value="cy">Welsh</option>
-                            <option value="yi">Yiddish</option>
-                        </select>
-                    </td>
-                    <td>
-                        <button onclick="onProcess(false);">Transcribe</button>
-                    </td>
-                    <td>
-                        <button onclick="onProcess(true);">Translate</button>
-                    </td>
-                </tr>
-            </table>
-
-            <br>
-
-            <!-- textarea with height filling the rest of the page -->
-            <textarea id="output" rows="20"></textarea>
-
-            <br><br>
-
-            <div class="cell-version">
-                <span>
-                    |
-                    Build time: <span class="nav-link">@GIT_DATE@</span> |
-                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
-                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
-                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/whisper.wasm">Source Code</a> |
-                </span>
-            </div>
-        </div>
-
-        <script type="text/javascript" src="helpers.js"></script>
-        <script type='text/javascript'>
-            // TODO: convert audio buffer to WAV
-            function setAudio(audio) {
-                //if (audio) {
-                //    // convert to 16-bit PCM
-                //    var blob = new Blob([audio], { type: 'audio/wav' });
-                //    var url = URL.createObjectURL(blob);
-                //    document.getElementById('source').src = url;
-                //    document.getElementById('audio').hidden = false;
-                //    document.getElementById('audio').loop = false;
-                //    document.getElementById('audio').load();
-                //} else {
-                //    document.getElementById('audio').hidden = true;
-                //}
-            }
-
-            function changeInput(input) {
-                if (input == 'file') {
-                    document.getElementById('input_file').style.display = 'block';
-                    document.getElementById('input_mic' ).style.display = 'none';
-                    document.getElementById('progress'  ).style.display = 'none';
-                } else {
-                    document.getElementById('input_file').style.display = 'none';
-                    document.getElementById('input_mic' ).style.display = 'block';
-                    document.getElementById('progress'  ).style.display = 'block';
-                }
-            }
-
-            var Module = {
-                print: printTextarea,
-                printErr: printTextarea,
-                setStatus: function(text) {
-                    printTextarea('js: ' + text);
-                },
-                monitorRunDependencies: function(left) {
-                }
-            };
-
-            // web audio context
-            var context = null;
-
-            // audio data
-            var audio = null;
-
-            // the whisper instance
-            var instance = null;
-            var model_whisper = '';
-
-            // helper function
-            function convertTypedArray(src, type) {
-                var buffer = new ArrayBuffer(src.byteLength);
-                var baseView = new src.constructor(buffer).set(src);
-                return new type(buffer);
-            }
-
-            //
-            // load model
-            //
-
-            let dbVersion = 1
-            let dbName    = 'whisper.ggerganov.com';
-            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
-
-            function storeFS(fname, buf) {
-                // write to WASM file using FS_createDataFile
-                // if the file exists, delete it
-                try {
-                    Module.FS_unlink(fname);
-                } catch (e) {
-                    // ignore
-                }
-
-                Module.FS_createDataFile("/", fname, buf, true, true);
-
-                model_whisper = fname;
-
-                document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
-
-                printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
-            }
-
-            function loadFile(event, fname) {
-                var file = event.target.files[0] || null;
-                if (file == null) {
-                    return;
-                }
-
-                printTextarea("loadFile: loading model: " + file.name + ", size: " + file.size + " bytes");
-                printTextarea('loadFile: please wait ...');
-
-                var reader = new FileReader();
-                reader.onload = function(event) {
-                    var buf = new Uint8Array(reader.result);
-                    storeFS(fname, buf);
-                }
-                reader.readAsArrayBuffer(file);
-
-                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en').style.display = 'none';
-                document.getElementById('fetch-whisper-tiny'   ).style.display = 'none';
-                document.getElementById('fetch-whisper-base'   ).style.display = 'none';
-                document.getElementById('whisper-file'         ).style.display = 'none';
-                document.getElementById('model-whisper-status' ).innerHTML = 'loaded model: ' + file.name;
-            }
-
-            function loadWhisper(model) {
-                let urls = {
-                    'tiny.en': 'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en.bin',
-                    'tiny':    'https://whisper.ggerganov.com/ggml-model-whisper-tiny.bin',
-                    'base.en': 'https://whisper.ggerganov.com/ggml-model-whisper-base.en.bin',
-                    'base':    'https://whisper.ggerganov.com/ggml-model-whisper-base.bin',
-                };
-
-                let sizes = {
-                    'tiny.en': 75,
-                    'tiny':    75,
-                    'base.en': 142,
-                    'base':    142,
-                };
-
-                let url     = urls[model];
-                let dst     = 'whisper.bin';
-                let size_mb = sizes[model];
-
-                model_whisper = model;
-
-                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en').style.display = 'none';
-                document.getElementById('fetch-whisper-tiny'   ).style.display = 'none';
-                document.getElementById('fetch-whisper-base'   ).style.display = 'none';
-                document.getElementById('whisper-file'         ).style.display = 'none';
-                document.getElementById('model-whisper-status' ).innerHTML = 'loading model: ' + model;
-
-                cbProgress = function(p) {
-                    let el = document.getElementById('fetch-whisper-progress');
-                    el.innerHTML = Math.round(100*p) + '%';
-                };
-
-                cbCancel = function() {
-                    var el;
-                    el = document.getElementById('fetch-whisper-tiny-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-tiny'   ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base'   ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('whisper-file'         ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('model-whisper-status' ); if (el) el.innerHTML = '';
-                };
-
-                loadRemote(url, dst, size_mb, cbProgress, storeFS, cbCancel, printTextarea);
-            }
-
-            //
-            // audio file
-            //
-
-            const kMaxAudio_s = 120;
-            const kSampleRate = 16000;
-
-            window.AudioContext = window.AudioContext || window.webkitAudioContext;
-            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
-
-            function loadAudio(event) {
-                if (!context) {
-                    context = new AudioContext({
-                        sampleRate: kSampleRate,
-                        channelCount: 1,
-                        echoCancellation: false,
-                        autoGainControl:  true,
-                        noiseSuppression: true,
-                    });
-                }
-
-                var file = event.target.files[0] || null;
-                if (file == null) {
-                    return;
-                }
-
-                printTextarea('js: loading audio: ' + file.name + ', size: ' + file.size + ' bytes');
-                printTextarea('js: please wait ...');
-
-                var reader = new FileReader();
-                reader.onload = function(event) {
-                    var buf = new Uint8Array(reader.result);
-
-                    context.decodeAudioData(buf.buffer, function(audioBuffer) {
-                        var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
-                        var source = offlineContext.createBufferSource();
-                        source.buffer = audioBuffer;
-                        source.connect(offlineContext.destination);
-                        source.start(0);
-
-                        offlineContext.startRendering().then(function(renderedBuffer) {
-                            audio = renderedBuffer.getChannelData(0);
-                            printTextarea('js: audio loaded, size: ' + audio.length);
-
-                            // truncate to first 30 seconds
-                            if (audio.length > kMaxAudio_s*kSampleRate) {
-                                audio = audio.slice(0, kMaxAudio_s*kSampleRate);
-                                printTextarea('js: truncated audio to first ' + kMaxAudio_s + ' seconds');
-                            }
-
-                            setAudio(audio);
-                        });
-                    }, function(e) {
-                        printTextarea('js: error decoding audio: ' + e);
-                        audio = null;
-                        setAudio(audio);
-                    });
-                }
-                reader.readAsArrayBuffer(file);
-            }
-
-            //
-            // microphone
-            //
-
-            var mediaRecorder = null;
-            var doRecording = false;
-            var startTime = 0;
-
-            function stopRecording() {
-                doRecording = false;
-            }
-
-            // record up to kMaxAudio_s seconds of audio from the microphone
-            // check if doRecording is false every 1000 ms and stop recording if so
-            // update progress information
-            function startRecording() {
-                if (!context) {
-                    context = new AudioContext({
-                        sampleRate: kSampleRate,
-                        channelCount: 1,
-                        echoCancellation: false,
-                        autoGainControl:  true,
-                        noiseSuppression: true,
-                    });
-                }
-
-                document.getElementById('start').disabled = true;
-                document.getElementById('stop').disabled = false;
-
-                document.getElementById('progress-bar').style.width = '0%';
-                document.getElementById('progress-text').innerHTML = '0%';
-
-                doRecording = true;
-                startTime = Date.now();
-
-                var chunks = [];
-                var stream = null;
-
-                navigator.mediaDevices.getUserMedia({audio: true, video: false})
-                    .then(function(s) {
-                        stream = s;
-                        mediaRecorder = new MediaRecorder(stream);
-                        mediaRecorder.ondataavailable = function(e) {
-                            chunks.push(e.data);
-                        };
-                        mediaRecorder.onstop = function(e) {
-                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
-                            chunks = [];
-
-                            document.getElementById('start').disabled = false;
-                            document.getElementById('stop').disabled = true;
-
-                            var reader = new FileReader();
-                            reader.onload = function(event) {
-                                var buf = new Uint8Array(reader.result);
-
-                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
-                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
-                                    var source = offlineContext.createBufferSource();
-                                    source.buffer = audioBuffer;
-                                    source.connect(offlineContext.destination);
-                                    source.start(0);
-
-                                    offlineContext.startRendering().then(function(renderedBuffer) {
-                                        audio = renderedBuffer.getChannelData(0);
-                                        printTextarea('js: audio recorded, size: ' + audio.length);
-
-                                        // truncate to first 30 seconds
-                                        if (audio.length > kMaxAudio_s*kSampleRate) {
-                                            audio = audio.slice(0, kMaxAudio_s*kSampleRate);
-                                            printTextarea('js: truncated audio to first ' + kMaxAudio_s + ' seconds');
-                                        }
-                                        setAudio(audio);
-                                    });
-                                }, function(e) {
-                                    printTextarea('js: error decoding audio: ' + e);
-                                    audio = null;
-                                    setAudio(audio);
-                                });
-                            }
-
-                            reader.readAsArrayBuffer(blob);
-                        };
-                        mediaRecorder.start();
-                    })
-                    .catch(function(err) {
-                        printTextarea('js: error getting audio stream: ' + err);
-                    });
-
-                var interval = setInterval(function() {
-                    if (!doRecording) {
-                        clearInterval(interval);
-                        mediaRecorder.stop();
-                        stream.getTracks().forEach(function(track) {
-                            track.stop();
-                        });
-                    }
-
-                    document.getElementById('progress-bar').style.width = (100*(Date.now() - startTime)/1000/kMaxAudio_s) + '%';
-                    document.getElementById('progress-text').innerHTML = (100*(Date.now() - startTime)/1000/kMaxAudio_s).toFixed(0) + '%';
-                }, 1000);
-
-                printTextarea('js: recording ...');
-
-                setTimeout(function() {
-                    if (doRecording) {
-                        printTextarea('js: recording stopped after ' + kMaxAudio_s + ' seconds');
-                        stopRecording();
-                    }
-                }, kMaxAudio_s*1000);
-            }
-
-            //
-            // transcribe
-            //
-
-            function onProcess(translate) {
-                if (!instance) {
-                    instance = Module.init('whisper.bin');
-
-                    if (instance) {
-                        printTextarea("js: whisper initialized, instance: " + instance);
-                        document.getElementById('model').innerHTML = 'Model loaded: ' + model_whisper;
-                    }
-                }
-
-                if (!instance) {
-                    printTextarea("js: failed to initialize whisper");
-                    return;
-                }
-
-                if (!audio) {
-                    printTextarea("js: no audio data");
-                    return;
-                }
-
-                if (instance) {
-                    printTextarea('');
-                    printTextarea('js: processing - this might take a while ...');
-                    printTextarea('');
-
-                    setTimeout(function() {
-                        var ret = Module.full_default(instance, audio, document.getElementById('language').value, translate);
-                        console.log('js: full_default returned: ' + ret);
-                        if (ret) {
-                            printTextarea("js: whisper returned: " + ret);
-                        }
-                    }, 100);
-                }
-            }
-        </script>
-        <script type="text/javascript" src="whisper.js"></script>
-    </body>
-</html>

examples/yt-wsp.sh

@@ -1,132 +0,0 @@
-#!/usr/bin/env bash
-
-# Small shell script to more easily automatically download and transcribe live stream VODs.
-# This uses YT-DLP, ffmpeg and the CPP version of Whisper: https://github.com/ggerganov/whisper.cpp
-# Use `./transcribe-vod help` to print help info.
-
-# MIT License
-
-# Copyright (c) 2022 Daniils Petrovs
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-set -Eeuo pipefail
-
-# You can find how to download models in the OG repo: https://github.com/ggerganov/whisper.cpp/#usage
-MODEL_PATH="${MODEL_PATH:-models/ggml-base.en.bin}" # Set to a multilingual model if you want to translate from foreign lang to en
-WHISPER_EXECUTABLE="${WHISPER_EXECUTABLE:-whisper}" # Where to find the whisper.cpp executable
-WHISPER_LANG="${WHISPER_LANG:-en}" # Set to desired lang to translate from
-
-msg() {
-    echo >&2 -e "${1-}"
-}
-
-cleanup() {
-    msg "Cleaning up..."
-    rm -rf "${temp_dir}" "vod-resampled.wav" "vod-resampled.wav.srt"
-}
-
-print_help() {
-    echo "Usage: ./transcribe-vod <video_url>"
-    echo "See configurable env variables in the script"
-    echo "This will produce an MP4 muxed file called res.mp4 in the working directory"
-    echo "Requirements: ffmpeg yt-dlp whisper"
-    echo "Whisper needs to be built into the main binary with make, then you can rename it to something like 'whisper' and add it to your PATH for convenience."
-    echo "E.g. in the root of Whisper.cpp, run: 'make && cp ./main /usr/local/bin/whisper'"
-}
-
-check_requirements() {
-    if ! command -v ffmpeg &>/dev/null; then
-        echo "ffmpeg is required (https://ffmpeg.org)."
-        exit 1
-    fi
-
-    if ! command -v yt-dlp &>/dev/null; then
-        echo "yt-dlp is required (https://github.com/yt-dlp/yt-dlp)."
-        exit 1
-    fi
-
-    if ! command -v "$WHISPER_EXECUTABLE" &>/dev/null; then
-        WHISPER_EXECUTABLE="./main"
-        if ! command -v "$WHISPER_EXECUTABLE" &>/dev/null; then
-            echo "Whisper is required (https://github.com/ggerganov/whisper.cpp)."
-            exit 1
-        fi
-    fi
-}
-
-if [[ $# -lt 1 ]]; then
-    print_help
-    exit 1
-fi
-
-if [[ "$1" == "help" ]]; then
-    print_help
-    exit 0
-fi
-
-temp_dir="tmp"
-source_url="$1"
-
-check_requirements
-
-msg "Downloading VOD..."
-
-# Optionally add --cookies-from-browser BROWSER[+KEYRING][:PROFILE][::CONTAINER] for members only VODs
-yt-dlp \
-    -f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best" \
-    --embed-thumbnail \
-    --embed-chapters \
-    --xattrs \
-    "${source_url}" -o "${temp_dir}/vod.mp4"
-
-msg "Extracting audio and resampling..."
-
-ffmpeg -i "${temp_dir}/vod.mp4" \
-    -hide_banner \
-    -loglevel error \
-    -ar 16000 \
-    -ac 1 \
-    -c:a \
-    pcm_s16le -y "vod-resampled.wav"
-
-msg "Transcribing to subtitle file..."
-msg "Whisper specified at: ${WHISPER_EXECUTABLE}"
-
-$WHISPER_EXECUTABLE \
-    -m "${MODEL_PATH}" \
-    -l "${WHISPER_LANG}" \
-    -f "vod-resampled.wav" \
-    -t 8 \
-    -osrt \
-    --translate
-
-msg "Embedding subtitle track..."
-
-ffmpeg -i "${temp_dir}/vod.mp4" \
-    -hide_banner \
-    -loglevel error \
-    -i "vod-resampled.wav.srt" \
-    -c copy \
-    -c:s mov_text \
-    -y res.mp4
-
-cleanup
-
-msg "Done! Your finished file is ready: res.mp4"

extra/bench-all.sh

@@ -1,55 +0,0 @@
-#!/bin/bash
-
-# Helper script to run the bench tool on all models and print the results in share-able format
-
-printf "Usage: ./bench.sh [n_threads]\n"
-
-if [ -z "$1" ]; then
-    n_threads=4
-else
-    n_threads=$1
-fi
-
-models=( "tiny" "base" "small" "medium" "large" )
-
-printf "\n"
-printf "Running benchmark for all models\n"
-printf "This can take a while!\n"
-printf "\n"
-
-printf "| CPU | OS | Config | Model | Threads | Load [ms] | Encode [ms] | Commit |\n"
-printf "| --- | -- | ------ | ----- | ------- | --------- | ----------- | ------ |\n"
-
-for model in "${models[@]}"; do
-    # run once to heat-up the cache
-    ./bench -m ./models/ggml-$model.bin -t $n_threads 2>/dev/null 1>/dev/null
-
-    # actual run
-    # store stderr output in a variable in order to parse it later
-    output=$(./bench -m ./models/ggml-$model.bin -t $n_threads 2>&1)
-
-    # parse the output:
-    load_time=$(echo "$output" | grep "load time" | awk '{print $5}')
-    encode_time=$(echo "$output" | grep "encode time" | awk '{print $5}')
-    system_info=$(echo "$output" | grep "system_info")
-    n_threads=$(echo "$output" | grep "system_info" | awk '{print $4}')
-
-    config=""
-
-    if [[ $system_info == *"AVX2 = 1"* ]]; then
-        config="$config AVX2"
-    fi
-
-    if [[ $system_info == *"NEON = 1"* ]]; then
-        config="$config NEON"
-    fi
-
-    if [[ $system_info == *"BLAS = 1"* ]]; then
-        config="$config BLAS"
-    fi
-
-    commit=$(git rev-parse --short HEAD)
-
-    printf "| <todo> | <todo> | $config | $model | $n_threads | $load_time | $encode_time | $commit |\n"
-done
-

extra/convert-all.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-
-models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
-
-for model in "${models[@]}"; do
-    python3 models/convert-pt-to-ggml.py ~/.cache/whisper/$model.pt ../whisper models/
-    mv -v models/ggml-model.bin models/ggml-$model.bin
-done

extra/deploy-wasm.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-#
-# This is a helper script to deploy all WebAssembly examples to my node
-# Run from the build directory:
-#
-# cd build-em
-# ../extra/deploy-wasm.sh
-#
-
-# check if emcmake is available
-if ! command -v emcmake &> /dev/null
-then
-    echo "Error: emscripten environment is not set up"
-    exit
-fi
-
-emcmake cmake .. && make -j
-if [ $? -ne 0 ]; then
-    echo "Error: build failed"
-    exit
-fi
-
-# copy all wasm files to the node
-scp bin/whisper.wasm/* root@linode0:/var/www/html/whisper/         && scp bin/libwhisper.worker.js root@linode0:/var/www/html/whisper/
-scp bin/stream.wasm/*  root@linode0:/var/www/html/whisper/stream/  && scp bin/libstream.worker.js  root@linode0:/var/www/html/whisper/stream/
-scp bin/command.wasm/* root@linode0:/var/www/html/whisper/command/ && scp bin/libcommand.worker.js root@linode0:/var/www/html/whisper/command/
-scp bin/talk.wasm/*    root@linode0:/var/www/html/whisper/talk/    && scp bin/libtalk.worker.js    root@linode0:/var/www/html/whisper/talk/
-
-echo "Done"
-exit

extra/sha-all.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-
-# Compute the SHA1 of all model files in ./models/ggml-*.bin
-
-for f in ./models/ggml-*.bin; do
-    shasum "$f" -a 1
-done

ggml.h

@@ -1,174 +1,5 @@
 #pragma once
 
-//
-// GGML Tensor Library
-//
-// This documentation is still a work in progress.
-// If you wish some specific topics to be covered, feel free to drop a comment:
-//
-//   https://github.com/ggerganov/whisper.cpp/issues/40
-//
-// ## Overview
-//
-// This library implements:
-//
-//  - a set of tensor operations
-//  - automatic differentiation
-//  - basic optimization algorithms
-//
-// The aim of this library is to provide a minimalistic approach for various machine learning tasks. This includes,
-// but is not limited to, the following:
-//
-//  - linear regression
-//  - support vector machines
-//  - neural networks
-//
-// The library allows the user to define a certain function using the available tensor operations. This function
-// definition is represented internally via a computation graph. Each tensor operation in the function definition
-// corresponds to a node in the graph. Having the computation graph defined, the user can choose to compute the
-// function's value and/or its gradient with respect to the input variables. Optionally, the function can be optimized
-// using one of the available optimization algorithms.
-//
-// For example, here we define the function: f(x) = a*x^2 + b
-//
-//   {
-//       struct ggml_init_params params = {
-//           .mem_size   = 16*1024*1024,
-//           .mem_buffer = NULL,
-//       };
-//
-//       // memory allocation happens here
-//       struct ggml_context * ctx = ggml_init(params);
-//
-//       struct ggml_tensor * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
-//
-//       ggml_set_param(ctx, x); // x is an input variable
-//
-//       struct ggml_tensor * a  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
-//       struct ggml_tensor * b  = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
-//       struct ggml_tensor * x2 = ggml_mul(ctx, x, x);
-//       struct ggml_tensor * f  = ggml_add(ctx, ggml_mul(ctx, a, x2), b);
-//
-//       ...
-//   }
-//
-// Notice that the function definition above does not involve any actual computation. The computation is performed only
-// when the user explicitly requests it. For example, to compute the function's value at x = 2.0:
-//
-//   {
-//       ...
-//
-//       struct ggml_cgraph gf = ggml_build_forward(f);
-//
-//       // set the input variable and parameter values
-//       ggml_set_f32(x, 2.0f);
-//       ggml_set_f32(a, 3.0f);
-//       ggml_set_f32(b, 4.0f);
-//
-//       ggml_graph_compute(ctx0, &gf);
-//
-//       printf("f = %f\n", ggml_get_f32_1d(f, 0));
-//
-//       ...
-//   }
-//
-// The actual computation is performed in the ggml_graph_compute() function.
-//
-// The ggml_new_tensor_...() functions create new tensors. They are allocated in the memory buffer provided to the
-// ggml_init() function. You have to be careful not to exceed the memory buffer size. Therefore, you have to know
-// in advance how much memory you need for your computation. Alternatively, you can allocate a large enough memory
-// and after defining the computation graph, call the ggml_used_mem() function to find out how much memory was
-// actually needed.
-//
-// The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic
-// differentiation and optimization algorithms.
-//
-// The described approach allows to define the function graph once and then compute its forward or backward graphs
-// multiple times. All computations will use the same memory buffer allocated in the ggml_init() function. This way
-// the user can avoid the memory allocation overhead at runtime.
-//
-// The library supports multi-dimensional tensors - up to 4 dimensions. The FP16 and FP32 data types are first class
-// citizens, but in theory the library can be extended to support FP8 and integer data types.
-//
-// Each tensor operation produces a new tensor. Initially the library was envisioned to support only the use of unary
-// and binary operations. Most of the available operations fall into one of these two categories. With time, it became
-// clear that the library needs to support more complex operations. The way to support these operations is not clear
-// yet, but a few examples are demonstrated in the following operations:
-//
-//   - ggml_permute()
-//   - ggml_conv_1d_1s()
-//   - ggml_conv_1d_2s()
-//
-// For each tensor operator, the library implements a forward and backward computation function. The forward function
-// computes the output tensor value given the input tensor values. The backward function computes the adjoint of the
-// input tensors given the adjoint of the output tensor. For a detailed explanation of what this means, take a
-// calculus class, or watch the following video:
-//
-//   What is Automatic Differentiation?
-//   https://www.youtube.com/watch?v=wG_nF1awSSY
-//
-//
-// ## Tensor data (struct ggml_tensor)
-//
-// The tensors are stored in memory via the ggml_tensor struct. The structure provides information about the size of
-// the tensor, the data type, and the memory buffer where the tensor data is stored. Additionally, it contains
-// pointers to the "source" tensors - i.e. the tensors that were used to compute the current tensor. For example:
-//
-//   {
-//       struct ggml_tensor * c = ggml_add(ctx, a, b);
-//
-//       assert(c->src[0] == a);
-//       assert(c->src[1] == b);
-//   }
-//
-// The multi-dimensional tensors are stored in row-major order. The ggml_tensor struct contains fields for the
-// number of elements in each dimension ("ne") as well as the number of bytes ("nb", a.k.a. stride). This allows
-// to store tensors that are not contiguous in memory, which is useful for operations such as transposition and
-// permutation. All tensor operations have to take the stride into account and not assume that the tensor is
-// contiguous in memory.
-//
-// The data of the tensor is accessed via the "data" pointer. For example:
-//
-//   {
-//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3);
-//
-//       // a[1, 2] = 1.0f;
-//       *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
-//
-//       // a[2, 0] = 2.0f;
-//       *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
-//
-//       ...
-//   }
-//
-// Alternatively, there are helper functions, such as ggml_get_f32_1d() and ggml_set_f32_1d() that can be used.
-//
-// ## The matrix multiplication operator (ggml_mul_mat)
-//
-// TODO
-//
-//
-// ## Multi-threading
-//
-// TODO
-//
-//
-// ## Overview of ggml.c
-//
-// TODO
-//
-//
-// ## SIMD optimizations
-//
-// TODO
-//
-//
-// ## Debugging ggml
-//
-// TODO
-//
-//
-
 #ifdef  __cplusplus
 extern "C" {
 #endif
@@ -180,7 +11,7 @@ extern "C" {
 #define GGML_MAX_DIMS     4
 #define GGML_MAX_NODES    4096
 #define GGML_MAX_PARAMS   16
-#define GGML_MAX_CONTEXTS 64
+#define GGML_MAX_CONTEXTS 16
 #define GGML_MAX_OPT      4
 
 #ifdef __ARM_NEON
@@ -190,8 +21,7 @@ typedef __fp16 ggml_fp16_t;
 typedef uint16_t ggml_fp16_t;
 #endif
 
-// convert FP16 <-> FP32
-float       ggml_fp16_to_fp32(ggml_fp16_t x);
+float ggml_fp16_to_fp32(ggml_fp16_t x);
 ggml_fp16_t ggml_fp32_to_fp16(float x);
 
 struct ggml_object;
@@ -206,7 +36,6 @@ enum ggml_type {
     GGML_TYPE_COUNT,
 };
 
-// available tensor operations:
 enum ggml_op {
     GGML_OP_NONE = 0,
 
@@ -307,7 +136,6 @@ struct ggml_init_params {
     void * mem_buffer; // if NULL, memory will be allocated internally
 };
 
-void    ggml_time_init(void); // call this once at the beginning of the program
 int64_t ggml_time_ms(void);
 int64_t ggml_time_us(void);
 int64_t ggml_cycles(void);
@@ -719,19 +547,6 @@ enum ggml_opt_result ggml_opt(
         struct ggml_opt_params params,
         struct ggml_tensor * f);
 
-//
-// system info
-//
-
-int ggml_cpu_has_avx(void);
-int ggml_cpu_has_avx2(void);
-int ggml_cpu_has_avx512(void);
-int ggml_cpu_has_neon(void);
-int ggml_cpu_has_f16c(void);
-int ggml_cpu_has_fp16_va(void);
-int ggml_cpu_has_wasm_simd(void);
-int ggml_cpu_has_blas(void);
-
 #ifdef  __cplusplus
 }
 #endif

main.cpp

@@ -0,0 +1,243 @@
+#include "whisper.h"
+
+// third-party utilities
+// use your favorite implementations
+#define DR_WAV_IMPLEMENTATION
+#include "dr_wav.h"
+
+#include <cstdio>
+#include <string>
+#include <thread>
+#include <vector>
+
+//  500 -> 00:05.000
+// 6000 -> 01:00.000
+std::string to_timestamp(int64_t t) {
+    int64_t sec = t/100;
+    int64_t msec = t - sec*100;
+    int64_t min = sec/60;
+    sec = sec - min*60;
+
+    char buf[32];
+    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
+
+    return std::string(buf);
+}
+
+// command-line parameters
+struct whisper_params {
+    int32_t seed      = -1; // RNG seed, not used currently
+    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t offset_ms = 0;
+
+    bool verbose              = false;
+    bool translate            = false;
+    bool print_special_tokens = false;
+    bool no_timestamps        = false;
+
+    std::string language  = "en";
+    std::string model     = "models/ggml-base.en.bin";
+
+    std::vector<std::string> fname_inp = {};
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg[0] != '-') {
+            params.fname_inp.push_back(arg);
+            continue;
+        }
+
+        if (arg == "-s" || arg == "--seed") {
+            params.seed = std::stoi(argv[++i]);
+        } else if (arg == "-t" || arg == "--threads") {
+            params.n_threads = std::stoi(argv[++i]);
+        } else if (arg == "-o" || arg == "--offset") {
+            params.offset_ms = std::stoi(argv[++i]);
+        } else if (arg == "-v" || arg == "--verbose") {
+            params.verbose = true;
+        } else if (arg == "--translate") {
+            params.translate = true;
+        } else if (arg == "-l" || arg == "--language") {
+            params.language = argv[++i];
+            if (whisper_lang_id(params.language.c_str()) == -1) {
+                fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+                whisper_print_usage(argc, argv, params);
+                exit(0);
+            }
+        } else if (arg == "-ps" || arg == "--print_special") {
+            params.print_special_tokens = true;
+        } else if (arg == "-nt" || arg == "--no_timestamps") {
+            params.no_timestamps = true;
+        } else if (arg == "-m" || arg == "--model") {
+            params.model = argv[++i];
+        } else if (arg == "-f" || arg == "--file") {
+            params.fname_inp.push_back(argv[++i]);
+        } else if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        } else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help           show this help message and exit\n");
+    fprintf(stderr, "  -s SEED,  --seed SEED      RNG seed (default: -1)\n");
+    fprintf(stderr, "  -t N,     --threads N      number of threads to use during computation (default: %d)\n", params.n_threads);
+    fprintf(stderr, "  -o N,     --offset N       offset in milliseconds (default: %d)\n", params.offset_ms);
+    fprintf(stderr, "  -v,       --verbose        verbose output\n");
+    fprintf(stderr, "            --translate      translate from source language to english\n");
+    fprintf(stderr, "  -ps,      --print_special  print special tokens\n");
+    fprintf(stderr, "  -nt,      --no_timestamps  do not print timestamps\n");
+    fprintf(stderr, "  -l LANG,  --language LANG  spoken language (default: %s)\n", params.language.c_str());
+    fprintf(stderr, "  -m FNAME, --model FNAME    model path (default: %s)\n", params.model.c_str());
+    fprintf(stderr, "  -f FNAME, --file FNAME     input WAV file path\n");
+    fprintf(stderr, "\n");
+}
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    if (params.seed < 0) {
+        params.seed = time(NULL);
+    }
+
+    if (params.fname_inp.empty()) {
+        fprintf(stderr, "error: no input files specified\n");
+        whisper_print_usage(argc, argv, params);
+        return 1;
+    }
+
+    // whisper init
+
+    struct whisper_context * ctx = whisper_init(params.model.c_str());
+
+    for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
+        const auto fname_inp = params.fname_inp[f];
+
+        // WAV input
+        std::vector<float> pcmf32;
+        {
+            drwav wav;
+            if (!drwav_init_file(&wav, fname_inp.c_str(), NULL)) {
+                fprintf(stderr, "%s: failed to open WAV file '%s' - check your input\n", argv[0], fname_inp.c_str());
+                whisper_print_usage(argc, argv, {});
+                return 2;
+            }
+
+            if (wav.channels != 1 && wav.channels != 2) {
+                fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], fname_inp.c_str());
+                return 3;
+            }
+
+            if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
+                fprintf(stderr, "%s: WAV file '%s' must be 16 kHz\n", argv[0], fname_inp.c_str());
+                return 4;
+            }
+
+            if (wav.bitsPerSample != 16) {
+                fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], fname_inp.c_str());
+                return 5;
+            }
+
+            int n = wav.totalPCMFrameCount;
+
+            std::vector<int16_t> pcm16;
+            pcm16.resize(n*wav.channels);
+            drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
+            drwav_uninit(&wav);
+
+            // convert to mono, float
+            pcmf32.resize(n);
+            if (wav.channels == 1) {
+                for (int i = 0; i < n; i++) {
+                    pcmf32[i] = float(pcm16[i])/32768.0f;
+                }
+            } else {
+                for (int i = 0; i < n; i++) {
+                    pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
+                }
+            }
+        }
+
+        // print some info about the processing
+        {
+            printf("\n");
+            if (!whisper_is_multilingual(ctx)) {
+                if (params.language != "en" || params.translate) {
+                    params.language = "en";
+                    params.translate = false;
+                    printf("%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+                }
+            }
+            printf("%s: processing '%s' (%d samples, %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+                    __func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE, params.n_threads,
+                    params.language.c_str(),
+                    params.translate ? "translate" : "transcribe",
+                    params.no_timestamps ? 0 : 1);
+            printf("\n");
+        }
+
+        // run the inference
+        {
+            whisper_full_params wparams = whisper_full_default_params(WHISPER_DECODE_GREEDY);
+
+            wparams.print_realtime       = true;
+            wparams.print_progress       = false;
+            wparams.print_timestamps     = !params.no_timestamps;
+            wparams.print_special_tokens = params.print_special_tokens;
+            wparams.translate            = params.translate;
+            wparams.language             = params.language.c_str();
+            wparams.n_threads            = params.n_threads;
+            wparams.offset_ms            = params.offset_ms;
+
+            if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+                fprintf(stderr, "%s: failed to process audio\n", argv[0]);
+                return 6;
+            }
+
+            // print result;
+            if (!wparams.print_realtime) {
+                printf("\n");
+
+                const int n_segments = whisper_full_n_segments(ctx);
+                for (int i = 0; i < n_segments; ++i) {
+                    const char * text = whisper_full_get_segment_text(ctx, i);
+
+                    if (params.no_timestamps) {
+                        printf ("%s", text);
+                        fflush(stdout);
+                    } else {
+                        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+                        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+                        printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
+                    }
+                }
+            }
+        }
+    }
+
+    whisper_print_timings(ctx);
+    whisper_free(ctx);
+
+    return 0;
+}

models/README.md

@@ -1,20 +1,17 @@
 ## Whisper model files in custom ggml format
 
 The [original Whisper PyTorch models provided by OpenAI](https://github.com/openai/whisper/blob/main/whisper/__init__.py#L17-L27)
-have been converted to custom `ggml` format in order to be able to load them in C/C++. The conversion has been performed
-using the [convert-pt-to-ggml.py](convert-pt-to-ggml.py) script. You can either obtain the original models and generate
-the `ggml` files yourself using the conversion script, or you can use the [download-ggml-model.sh](download-ggml-model.sh)
-script to download the already converted models. Currently, they are hosted on the following locations:
-
-- https://huggingface.co/datasets/ggerganov/whisper.cpp
-- https://ggml.ggerganov.com
+have been converted to custom `ggml` format in order to be able to load them in C/C++. The conversion has been performed using the
+[convert-pt-to-ggml.py](convert-pt-to-ggml.py) script. You can either obtain the original models and generate the `ggml` files
+yourself using the conversion script, or you can use the [download-ggml-model.sh](download-ggml-model.sh) script to download the
+already converted models.
 
 Sample usage:
 
 ```java
 $ ./download-ggml-model.sh base.en
 Downloading ggml model base.en ...
-models/ggml-base.en.bin          100%[=============================================>] 141.11M  5.41MB/s    in 22s
+models/ggml-base.en.bin          100%[=============================================>] 141.11M  5.41MB/s    in 22s     
 Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
 You can now use it like this:
 
@@ -25,41 +22,7 @@ A third option to obtain the model files is to download them from Hugging Face:
 
 https://huggingface.co/datasets/ggerganov/whisper.cpp/tree/main
 
-## Available models
-
-| Model     | Disk   | Mem     | SHA                                        |
-| ---       | ---    | ---     | ---                                        |
-| tiny      |  75 MB | ~390 MB | `bd577a113a864445d4c299885e0cb97d4ba92b5f` |
-| tiny.en   |  75 MB | ~390 MB | `c78c86eb1a8faa21b369bcd33207cc90d64ae9df` |
-| base      | 142 MB | ~500 MB | `465707469ff3a37a2b9b8d8f89f2f99de7299dac` |
-| base.en   | 142 MB | ~500 MB | `137c40403d78fd54d454da0f9bd998f78703390c` |
-| small     | 466 MB | ~1.0 GB | `55356645c2b361a969dfd0ef2c5a50d530afd8d5` |
-| small.en  | 466 MB | ~1.0 GB | `db8a495a91d927739e50b3fc1cc4c6b8f6c2d022` |
-| medium    | 1.5 GB | ~2.6 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
-| medium.en | 1.5 GB | ~2.6 GB | `8c30f0e44ce9560643ebd10bbe50cd20eafd3723` |
-| large-v1  | 2.9 GB | ~4.7 GB | `b1caaf735c4cc1429223d5a74f0f4d0b9b59a299` |
-| large     | 2.9 GB | ~4.7 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
-
 ## Model files for testing purposes
 
-The model files prefixed with `for-tests-` are empty (i.e. do not contain any weights) and are used by the CI for
-testing purposes. They are directly included in this repository for convenience and the Github Actions CI uses them to
-run various sanitizer tests.
-
-## Fine-tuned models
-
-There are community efforts for creating fine-tuned Whisper models using extra training data. For example, this
-[blog post](https://huggingface.co/blog/fine-tune-whisper) describes a method for fine-tuning using Hugging Face (HF)
-Transformer implementation of Whisper. The produced models are in slightly different format compared to the original
-OpenAI format. To read the HF models you can use the [convert-h5-to-ggml.py](convert-h5-to-ggml.py) script like this:
-
-```bash
-git clone https://github.com/openai/whisper
-git clone https://github.com/ggerganov/whisper.cpp
-
-# clone HF fine-tuned model (this is just an example)
-git clone https://huggingface.co/openai/whisper-base.en
-
-# convert the model to ggml
-python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium/ ./whisper .
-```
+The model files pefixed with `for-tests-` are empty (i.e. do not contain any weights) and are used by the CI for testing purposes.
+They are directly included in this repository for convenience and the Github Actions CI uses them to run various sanitizer tests.

models/convert-h5-to-ggml.py

@@ -1,212 +0,0 @@
-# Convert Hugging Face fine-tuned models to ggml format
-#
-# Usage:
-#
-#   git clone https://github.com/openai/whisper
-#   git clone https://github.com/ggerganov/whisper.cpp
-#   git clone https://huggingface.co/openai/whisper-medium
-#
-#   python3 ./whisper.cpp/models/convert-h5-to-ggml.py ./whisper-medium/ ./whisper .
-#
-# This script is similar to "convert-pt-to-ggml.py"
-#
-# For more info:
-#
-#   https://github.com/ggerganov/whisper.cpp/issues/157
-#
-
-import io
-import os
-import sys
-import struct
-import json
-import code
-import torch
-import numpy as np
-
-from transformers import WhisperForConditionalGeneration
-
-conv_map = {
-        'self_attn.k_proj'              : 'attn.key',
-        'self_attn.q_proj'              : 'attn.query',
-        'self_attn.v_proj'              : 'attn.value',
-        'self_attn.out_proj'            : 'attn.out',
-        'self_attn_layer_norm'          : 'attn_ln',
-        'encoder_attn.q_proj'           : 'cross_attn.query',
-        'encoder_attn.v_proj'           : 'cross_attn.value',
-        'encoder_attn.out_proj'         : 'cross_attn.out',
-        'encoder_attn_layer_norm'       : 'cross_attn_ln',
-        'fc1'                           : 'mlp.0',
-        'fc2'                           : 'mlp.2',
-        'final_layer_norm'              : 'mlp_ln',
-        'encoder.layer_norm.bias'       : 'encoder.ln_post.bias',
-        'encoder.layer_norm.weight'     : 'encoder.ln_post.weight',
-        'encoder.embed_positions.weight': 'encoder.positional_embedding',
-        'decoder.layer_norm.bias'       : 'decoder.ln.bias',
-        'decoder.layer_norm.weight'     : 'decoder.ln.weight',
-        'decoder.embed_positions.weight': 'decoder.positional_embedding',
-        'decoder.embed_tokens.weight'   : 'decoder.token_embedding.weight',
-        'proj_out.weight'               : 'decoder.proj.weight',
-        }
-
-# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a corresponding list of unicode strings.
-    The reversible bpe codes work on unicode strings.
-    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
-    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
-    This is a signficant percentage of your normal, say, 32K bpe vocab.
-    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
-    And avoids mapping to whitespace/control characters the bpe code barfs on.
-    """
-    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8+n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-if len(sys.argv) < 4:
-    print("Usage: convert-h5-to-ggml.py dir_model path-to-whisper-repo dir-output [use-f32]\n")
-    sys.exit(1)
-
-dir_model   = sys.argv[1]
-dir_whisper = sys.argv[2]
-dir_out     = sys.argv[3]
-
-with open(dir_model + "/vocab.json", "r") as f:
-    encoder = json.load(f)
-with open(dir_model + "/added_tokens.json", "r") as f:
-    encoder_added = json.load(f)
-with open(dir_model + "/config.json", "r") as f:
-    hparams = json.load(f)
-
-model = WhisperForConditionalGeneration.from_pretrained(dir_model)
-
-#code.interact(local=locals())
-
-n_mels = hparams["num_mel_bins"]
-with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
-    filters = torch.from_numpy(f[f"mel_{n_mels}"])
-
-dir_tokenizer = dir_model
-
-fname_out = dir_out + "/ggml-model.bin"
-
-with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
-    tokens = json.load(f)
-
-# use 16-bit or 32-bit floats
-use_f16 = True
-if len(sys.argv) > 4:
-    use_f16 = False
-    fname_out = dir_out + "/ggml-model-f32.bin"
-
-fout = open(fname_out, "wb")
-
-fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
-fout.write(struct.pack("i", hparams["vocab_size"]))
-fout.write(struct.pack("i", hparams["max_source_positions"]))
-fout.write(struct.pack("i", hparams["d_model"]))
-fout.write(struct.pack("i", hparams["encoder_attention_heads"]))
-fout.write(struct.pack("i", hparams["encoder_layers"]))
-fout.write(struct.pack("i", hparams["max_length"]))
-fout.write(struct.pack("i", hparams["d_model"]))
-fout.write(struct.pack("i", hparams["decoder_attention_heads"]))
-fout.write(struct.pack("i", hparams["decoder_layers"]))
-fout.write(struct.pack("i", hparams["num_mel_bins"]))
-fout.write(struct.pack("i", use_f16))
-
-fout.write(struct.pack("i", filters.shape[0]))
-fout.write(struct.pack("i", filters.shape[1]))
-for i in range(filters.shape[0]):
-    for j in range(filters.shape[1]):
-        fout.write(struct.pack("f", filters[i][j]))
-
-byte_encoder = bytes_to_unicode()
-byte_decoder = {v:k for k, v in byte_encoder.items()}
-
-fout.write(struct.pack("i", len(tokens)))
-
-tokens = sorted(tokens.items(), key=lambda x: x[1])
-for key in tokens:
-    text = bytearray([byte_decoder[c] for c in key[0]])
-    fout.write(struct.pack("i", len(text)))
-    fout.write(text)
-
-list_vars = model.state_dict()
-for name in list_vars.keys():
-    # this seems to not be used
-    # ref: https://github.com/huggingface/transformers/blob/9a5b84a0076a04fe9596da72e8668069d4f09ea0/src/transformers/models/whisper/modeling_whisper.py#L1099-L1106
-    if name == "proj_out.weight":
-        print('Skipping', name)
-        continue
-
-    src = name
-
-    nn = name
-    if name != "proj_out.weight":
-        nn = nn.split(".")[1:]
-    else:
-        nn = nn.split(".")
-
-    if nn[1] == "layers":
-        nn[1] = "blocks"
-        if ".".join(nn[3:-1]) == "encoder_attn.k_proj":
-            mapped = "attn.key" if nn[0] == "encoder" else "cross_attn.key"
-        else:
-            mapped = conv_map[".".join(nn[3:-1])]
-        name = ".".join(nn[:3] + [mapped] + nn[-1:])
-    else:
-        name = ".".join(nn)
-        name = conv_map[name] if name in conv_map else name
-
-    print(src, ' -> ', name)
-    data = list_vars[src].squeeze().numpy()
-    data = data.astype(np.float16)
-
-    # reshape conv bias from [n] to [n, 1]
-    if name == "encoder.conv1.bias" or \
-       name == "encoder.conv2.bias":
-        data = data.reshape(data.shape[0], 1)
-        print("  Reshaped variable: " + name + " to shape: ", data.shape)
-
-    n_dims = len(data.shape)
-    print(name, n_dims, data.shape)
-
-    # looks like the whisper models are in f16 by default
-    # so we need to convert the small tensors to f32 until we fully support f16 in ggml
-    # ftype == 0 -> float32, ftype == 1 -> float16
-    ftype = 1;
-    if use_f16:
-        if n_dims < 2 or \
-                name == "encoder.conv1.bias"   or \
-                name == "encoder.conv2.bias"   or \
-                name == "encoder.positional_embedding" or \
-                name == "decoder.positional_embedding":
-            print("  Converting to float32")
-            data = data.astype(np.float32)
-            ftype = 0
-    else:
-        data = data.astype(np.float32)
-        ftype = 0
-
-    # header
-    str = name.encode('utf-8')
-    fout.write(struct.pack("iii", n_dims, len(str), ftype))
-    for i in range(n_dims):
-        fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
-    fout.write(str);
-
-    # data
-    data.tofile(fout)
-
-fout.close()
-
-print("Done. Output file: " + fname_out)
-print("")

models/download-ggml-model.cmd

@@ -1,63 +0,0 @@
-@echo off
-
-pushd %~dp0
-set models_path=%CD%
-popd
-
-set argc=0
-for %%x in (%*) do set /A argc+=1
-
-set models=tiny.en tiny base.en base small.en small medium.en medium large-v1 large
-
-if %argc% neq 1 (
-  echo.
-  echo Usage: download-ggml-model.cmd model
-  CALL :list_models
-  goto :eof
-)
-
-set model=%1
-
-for %%b in (%models%) do (
-  if "%%b"=="%model%" (
-    CALL :download_model
-    goto :eof
-  )
-)
-
-echo Invalid model: %model%
-CALL :list_models
-goto :eof
-
-:download_model
-echo Downloading ggml model %model%...
-
-cd %models_path%
-
-if exist "ggml-%model%.bin" (
-  echo Model %model% already exists. Skipping download.
-  goto :eof
-)
-
-PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://ggml.ggerganov.com/ggml-model-whisper-%model%.bin -OutFile ggml-%model%.bin"
-
-if %ERRORLEVEL% neq 0 (
-  echo Failed to download ggml model %model%
-  echo Please try again later or download the original Whisper model files and convert them yourself.
-  goto :eof
-)
-
-echo Done! Model %model% saved in %models_path%\models\ggml-%model%.bin
-echo You can now use it like this:
-echo main.exe -m %models_path%\models\ggml-%model%.bin -f %models_path%\samples\jfk.wav
-
-goto :eof
-
-:list_models
-  echo.
-  echo Available models:
-  (for %%a in (%models%) do ( 
-    echo %%a 
-  ))
-  echo.
-  exit /b

samples/README.md

@@ -1,6 +0,0 @@
-# Audio samples
-
-This folder contains various audio files used for testing.
-If you want to quickly get some more samples, simply run `make samples`. This will download several public audio files and convert them to appropriate 16-bit WAV format using `ffmpeg`
-
-https://github.com/ggerganov/whisper.cpp/blob/a09ce6e8899198015729ffc49ae10f67370906b1/Makefile#L104-L123

stream.cpp

@@ -0,0 +1,315 @@
+// Real-time speech recognition of input from a microphone
+//
+// A very quick-n-dirty implementation serving mainly as a proof of concept.
+
+#include "whisper.h"
+
+// third-party utilities
+// use your favorite implementations
+#define DR_WAV_IMPLEMENTATION
+#include "dr_wav.h"
+
+#include <SDL.h>
+#include <SDL_audio.h>
+
+#include <cassert>
+#include <cstdio>
+#include <string>
+#include <thread>
+#include <vector>
+
+//  500 -> 00:05.000
+// 6000 -> 01:00.000
+std::string to_timestamp(int64_t t) {
+    int64_t sec = t/100;
+    int64_t msec = t - sec*100;
+    int64_t min = sec/60;
+    sec = sec - min*60;
+
+    char buf[32];
+    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
+
+    return std::string(buf);
+}
+
+// command-line parameters
+struct whisper_params {
+    int32_t seed      = -1; // RNG seed, not used currently
+    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t step_ms   = 3000;
+
+    bool verbose              = false;
+    bool translate            = false;
+    bool no_context           = true;
+    bool print_special_tokens = false;
+    bool no_timestamps        = true;
+
+    std::string language  = "en";
+    std::string model     = "models/ggml-base.en.bin";
+    std::string fname_inp = "samples/jfk.wav";
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg == "-s" || arg == "--seed") {
+            params.seed = std::stoi(argv[++i]);
+        } else if (arg == "-t" || arg == "--threads") {
+            params.n_threads = std::stoi(argv[++i]);
+        } else if (arg == "--step") {
+            params.step_ms = std::stoi(argv[++i]);
+        } else if (arg == "-v" || arg == "--verbose") {
+            params.verbose = true;
+        } else if (arg == "--translate") {
+            params.translate = true;
+        } else if (arg == "-kc" || arg == "--keep-context") {
+            params.no_context = false;
+        } else if (arg == "-l" || arg == "--language") {
+            params.language = argv[++i];
+            if (whisper_lang_id(params.language.c_str()) == -1) {
+                fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+                whisper_print_usage(argc, argv, params);
+                exit(0);
+            }
+        } else if (arg == "-ps" || arg == "--print_special") {
+            params.print_special_tokens = true;
+        } else if (arg == "-nt" || arg == "--no_timestamps") {
+            params.no_timestamps = true;
+        } else if (arg == "-m" || arg == "--model") {
+            params.model = argv[++i];
+        } else if (arg == "-f" || arg == "--file") {
+            params.fname_inp = argv[++i];
+        } else if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        } else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help           show this help message and exit\n");
+    fprintf(stderr, "  -s SEED,  --seed SEED      RNG seed (default: -1)\n");
+    fprintf(stderr, "  -t N,     --threads N      number of threads to use during computation (default: %d)\n", params.n_threads);
+    fprintf(stderr, "            --step N         audio step size in milliseconds (default: %d)\n", params.step_ms);
+    fprintf(stderr, "  -v,       --verbose        verbose output\n");
+    fprintf(stderr, "            --translate      translate from source language to english\n");
+    fprintf(stderr, "  -nc,      --no-context     disable context from earlier audio (default: false)\n");
+    fprintf(stderr, "  -ps,      --print_special  print special tokens\n");
+    fprintf(stderr, "  -nt,      --no_timestamps  do not print timestamps\n");
+    fprintf(stderr, "  -l LANG,  --language LANG  spoken language (default: %s)\n", params.language.c_str());
+    fprintf(stderr, "  -m FNAME, --model FNAME    model path (default: %s)\n", params.model.c_str());
+    fprintf(stderr, "  -f FNAME, --file FNAME     input WAV file path (default: %s)\n", params.fname_inp.c_str());
+    fprintf(stderr, "\n");
+}
+
+//
+// SDL Audio capture
+//
+
+SDL_AudioDeviceID g_dev_id_in = 0;
+
+bool audio_sdl_init(const int capture_id) {
+    if (g_dev_id_in) {
+        fprintf(stderr, "%s: already initialized\n", __func__);
+        return false;
+    }
+
+    if (g_dev_id_in == 0) {
+        SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
+
+        if (SDL_Init(SDL_INIT_AUDIO) < 0) {
+            SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
+            return (1);
+        }
+
+        SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
+
+        {
+            int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
+            printf("%s: found %d capture devices:\n", __func__, nDevices);
+            for (int i = 0; i < nDevices; i++) {
+                printf("%s:    - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
+            }
+        }
+    }
+
+    if (g_dev_id_in == 0) {
+        SDL_AudioSpec capture_spec_requested;
+        SDL_AudioSpec capture_spec_obtained;
+
+        SDL_zero(capture_spec_requested);
+        SDL_zero(capture_spec_obtained);
+
+        capture_spec_requested.freq     = WHISPER_SAMPLE_RATE;
+        capture_spec_requested.format   = AUDIO_F32;
+        capture_spec_requested.channels = 1;
+        capture_spec_requested.samples  = 1024;
+
+        if (capture_id >= 0) {
+            printf("%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
+            g_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+        } else {
+            printf("%s: attempt to open default capture device ...\n", __func__);
+            g_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+        }
+        if (!g_dev_id_in) {
+            printf("%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
+            g_dev_id_in = 0;
+        } else {
+            printf("%s: obtained spec for input device (SDL Id = %d):\n", __func__, g_dev_id_in);
+            printf("%s:     - sample rate:       %d\n", __func__, capture_spec_obtained.freq);
+            printf("%s:     - format:            %d (required: %d)\n", __func__, capture_spec_obtained.format, capture_spec_requested.format);
+            printf("%s:     - channels:          %d (required: %d)\n", __func__, capture_spec_obtained.channels, capture_spec_requested.channels);
+            printf("%s:     - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
+        }
+    }
+
+
+    return true;
+}
+
+///////////////////////////
+
+int main(int argc, char ** argv) {
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    if (params.seed < 0) {
+        params.seed = time(NULL);
+    }
+
+    // init audio
+
+    if (!audio_sdl_init(-1)) {
+        fprintf(stderr, "%s: audio_sdl_init() failed!\n", __func__);
+        return 1;
+    }
+
+    // whisper init
+
+    struct whisper_context * ctx = whisper_init(params.model.c_str());
+
+    const int n_samples = (params.step_ms/1000.0)*WHISPER_SAMPLE_RATE;
+    const int n_samples_30s = 30*WHISPER_SAMPLE_RATE;
+    std::vector<float> pcmf32(n_samples_30s, 0.0f);
+    std::vector<float> pcmf32_old;
+
+    // print some info about the processing
+    {
+        printf("\n");
+        if (!whisper_is_multilingual(ctx)) {
+            if (params.language != "en" || params.translate) {
+                params.language = "en";
+                params.translate = false;
+                printf("%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+            }
+        }
+        printf("%s: processing %d samples (%.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+                __func__, n_samples, float(n_samples)/WHISPER_SAMPLE_RATE, params.n_threads,
+                params.language.c_str(),
+                params.translate ? "translate" : "transcribe",
+                params.no_timestamps ? 0 : 1);
+        printf("\n");
+    }
+
+    SDL_PauseAudioDevice(g_dev_id_in, 0);
+
+    bool is_running = true;
+
+    // main audio loop
+    while (is_running) {
+        // process SDL events:
+        SDL_Event event;
+        while (SDL_PollEvent(&event)) {
+            switch (event.type) {
+                case SDL_QUIT:
+                    is_running = false;
+                    break;
+                default:
+                    break;
+            }
+        }
+
+        // process 3 seconds of new audio
+        while (SDL_GetQueuedAudioSize(g_dev_id_in) < n_samples*sizeof(float)) {
+            SDL_Delay(1);
+        }
+        const int n_samples_new = SDL_GetQueuedAudioSize(g_dev_id_in)/sizeof(float);
+
+        // take one second from previous iteration
+        // TODO: better strategy
+        const int n_samples_take = std::min((int) pcmf32_old.size(), std::max(0, n_samples_30s/30 - n_samples_new));
+
+        //printf("processing: take = %d, new = %d, old = %d\n", n_samples_take, n_samples_new, (int) pcmf32_old.size());
+
+        pcmf32.resize(n_samples_new + n_samples_take);
+
+        for (int i = 0; i < n_samples_take; i++) {
+            pcmf32[i] = pcmf32_old[pcmf32_old.size() - n_samples_take + i];
+        }
+
+        SDL_DequeueAudio(g_dev_id_in, pcmf32.data() + n_samples_take, n_samples_new*sizeof(float));
+
+        pcmf32_old = pcmf32;
+
+        // run the inference
+        {
+            whisper_full_params wparams = whisper_full_default_params(WHISPER_DECODE_GREEDY);
+
+            wparams.print_progress       = false;
+            wparams.print_special_tokens = params.print_special_tokens;
+            wparams.print_realtime       = false;
+            wparams.print_timestamps     = !params.no_timestamps;
+            wparams.translate            = params.translate;
+            wparams.no_context           = params.no_context;
+            wparams.language             = params.language.c_str();
+            wparams.n_threads            = params.n_threads;
+
+            if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+                fprintf(stderr, "%s: failed to process audio\n", argv[0]);
+                return 6;
+            }
+
+            // print result;
+            {
+                printf("\n");
+
+                const int n_segments = whisper_full_n_segments(ctx);
+                for (int i = 0; i < n_segments; ++i) {
+                    const char * text = whisper_full_get_segment_text(ctx, i);
+
+                    if (params.no_timestamps) {
+                        printf ("%s", text);
+                        fflush(stdout);
+                    } else {
+                        const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+                        const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+                        printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
+                    }
+                }
+            }
+        }
+    }
+
+    whisper_print_timings(ctx);
+    whisper_free(ctx);
+
+    return 0;
+}

tests/.gitignore

@@ -1,3 +0,0 @@
-*.wav
-*.ogg
-*.wav.txt

tests/CMakeLists.txt

@@ -1,7 +1,3 @@
-if (EMSCRIPTEN)
-    return()
-endif()
-
 set(TEST_TARGET test-main-tiny)
 add_test(NAME ${TEST_TARGET}
     COMMAND $<TARGET_FILE:main>

tests/en-0-ref.txt

@@ -1 +0,0 @@
- My fellow Americans, this day has brought terrible news and great sadness to our country. At 9 o'clock this morning, Mission Control in Houston lost contact with our space shuttle, Columbia. A short time later, debris was seen falling from the skies above Texas. The Colombians lost. There are no survivors. On board was a crew of seven. Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain David Brown, Commander William McCool, Dr. Kultna Shavla, and Ilan Ramon, a colonel in the Israeli Air Force. These men and women assumed great risk in the service to all humanity. In an age when spaceflight has come to seem almost routine, it is easy to overlook the dangers of travel by rocket and the difficulties of navigating the fierce outer atmosphere of the Earth. These astronauts knew the dangers, and they faced them willingly, knowing they had a high and noble purpose in life. Because of their courage and daring and idealism, we will miss them all the more. All Americans today are thinking as well of the families of these men and women who have been given this sudden shock and grief. You're not alone. Our entire nation grieves with you. And those you love will always have the respect and gratitude of this country. The cause in which they died will continue. Mankind is led into the darkness beyond our world by the inspiration of discovery and the longing to understand. Our journey into space will go on. In the skies today, we saw destruction and tragedy. Yet farther than we can see, there is comfort and hope. In the words of the prophet Isaiah, "Lift your eyes and look to the heavens. Who created all these? He who brings out the starry hosts one by one and calls them each by name." Because of His great power and mighty strength, not one of them is missing. The same Creator who names the stars also knows the names of the seven souls we mourn today. The crew of the shuttle Columbia did not return safely to Earth, yet we can pray that all are safely home. May God bless the grieving families. And may God continue to bless America. [Silence]

tests/en-1-ref.txt

@@ -1 +0,0 @@
- Henry F. Phillips from Wikipedia, the free encyclopedia at en.wikipedia.org. Henry F. Phillips from Wikipedia, the free encyclopedia. Henry F. Phillips 1890-1958, a U.S. businessman from Portland, Oregon, has the honor of having the Phillips head screw and screwdriver named after him. The importance of the cross head screw design lies in its self-centering property, useful on automated production lines that use powered screwdrivers. Phillips' major contribution was in driving the cross head concept forward to the point where it was adopted by screw makers and automobile companies. Although he received patents for the design in 1936, U.S. Patent #2,046,343, U.S. Patents #2,046,837 to #2,046,840, it was so widely copied that by 1949 Phillips lost his patent. The American Screw Company was responsible for devising a means of manufacturing the screw, and successfully patented and licensed their method. Other screw makers of the 1930s dismissed the Phillips concept since it calls for a relatively complex recessed socket shape in the head of the screw, as distinct from the simple milled slot of a slotted type screw. The Phillips Screw Company and the American Screw Company went on to devise the Pawsadrive screw, which differs from the Phillips in that it is designed to accommodate greater torque than the Phillips. An image accompanied this article, captioned "Phillips Screw Head." The following is an info box which accompanies this article. Info box, part of the series on screw drive types. Slotted, commonly erroneously flat head. Phillips, cross head. Pawsadrive, super drive. Torques. Hex, Allen. Robertson. Tri-wing. Torx set. Spanner head. Triple square, XZN. Others, poly drive, spline drive, double hex. Many images accompanied this info box. This page was last modified on the 9th of April, 2008, at 1704. All text is available under the terms of the GNU Free Documentation License. See copyrights for details. Wikipedia is a registered trademark of the Wikimedia Foundation Incorporated, a U.S. registered 501(c)(3) tax-deductible nonprofit charity. This sound file and all text in the article are licensed under the GNU Free Documentation License, available at www.gnu.org/copyleft/fdl.html.

tests/en-2-ref.txt

@@ -1 +0,0 @@
- This is the Micro Machine Man presenting the most midget miniature motorcade of Micro Machines. Each one has dramatic details, terrific trim, precision paint jobs, plus incredible Micro Machine Pocket Playsets. There's a police station, fire station, restaurant, service station, and more. Perfect pocket portables to take anyplace. And there are many miniature playsets to play with, and each one comes with its own special edition Micro Machine vehicle and fun, fantastic features that miraculously move. Raise the boat lift at the airport marina, man the gun turret at the army base, clean your car at the car wash, raise the toll bridge. And these playsets fit together to form a Micro Machine world. Micro Machine Pocket Playsets, so tremendously tiny, so perfectly precise, so dazzlingly detailed, you'll want to pocket them all. Micro Machines are Micro Machine Pocket Playsets sold separately from Galoob. The smaller they are, the better they are.

tests/es-0-ref.txt

@@ -1 +0,0 @@
- Hola, como están todos? Mi nombre es Julián Virrueta Mendoza y en este podcast les vengo a hablar sobre la contaminación del agua. Bueno, empezaré por decir que el ser humano no está midiendo las consecuencias de sus actos. No hay duda que uno de los mayores problemas a los que se enfrentan muchas poblaciones actualmente es la contaminación del agua. Principalmente porque como bien sabemos el agua prácticamente es fundamental para la vida, por lo que la contaminación puede ser algo muy negativo para el desarrollo tanto económico como social de los pueblos o de las poblaciones próximas en ese lugar contaminado. Los comienzos de la contaminación, como lo definen muchos expertos en la materia, la contaminación del agua es causada por las actividades humanas. Es un fenómeno ambiental de importancia, el cual se comienza a producir desde los primeros intentos de industrialización para transformarse luego en un problema tan habitual como generalizado. Generalmente la contaminación del agua se produce a través de la introducción directa o indirecta en los acuíferos o caos de agua, ríos, mares, lagos, océanos, etc. o de diversas sustancias que pueden ser consideradas como contaminantes. Pero existen dos formas principales de contaminación del agua. Una de ellas tiene que ver con la contaminación natural del agua que se corresponde con el ciclo natural de esta durante el que puede entrar en contacto con ciertos constituyentes contaminantes como sustancias minerales y orgánicas disueltas o en suspensión que se vierten en la corteza terrestre, la atmósfera y en las aguas. Pero todo esto se puede contradecir si el ser humano comía sus consecuencias, si no tirara basura a los lagos, a los ríos, no tirara botes de aceite, no contaminara. Bueno amigos, yo los invito a que no contaminen el agua y que sepan cuidar la naturaleza. Los saluda su buen amigo y compañero Julián Virreta. Nos vemos. ¡Claro!

tests/run-tests.sh

@@ -1,125 +0,0 @@
-#!/bin/bash
-
-# This scripts run the selected model agains a collection of audio files from the web.
-# It downloads, converts and transcribes each file and then compares the result with the expected reference
-# transcription. The comparison is performed using git's diff command and shows the differences at the character level.
-# It can be used to quickly verify that the model is working as expected across a wide range of audio files.
-# I.e. like an integration test. The verification is done by visual inspection of the diff output.
-#
-# The reference data can be for example generated using the original OpenAI Whisper implementation, or entered manually.
-#
-# Feel free to suggest extra audio files to add to the list.
-# Make sure they are between 1-3 minutes long since we don't want to make the test too slow.
-#
-# Usage:
-#
-#   ./tests/run-tests.sh <model_name>
-#
-
-cd `dirname $0`
-
-# Whisper models
-models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
-
-# list available models
-function list_models {
-    printf "\n"
-    printf "  Available models:"
-    for model in "${models[@]}"; do
-        printf " $model"
-    done
-    printf "\n\n"
-}
-
-if [ $# -eq 0 ]; then
-    printf "Usage: $0 [model]\n\n"
-    printf "No model specified. Aborting\n"
-    list_models
-    exit 1
-fi
-
-model=$1
-main="../main"
-
-if [ ! -f ../models/ggml-$model.bin ]; then
-    printf "Model $model not found. Aborting\n"
-    list_models
-    exit 1
-fi
-
-if [ ! -f $main ]; then
-    printf "Executable $main not found. Aborting\n"
-    exit 1
-fi
-
-# add various audio files for testing purposes here
-# the order of the files is important so don't change the existing order
-# when adding new files, make sure to add the expected "ref.txt" file with the correct transcript
-urls_en=(
-    "https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg"
-    "https://upload.wikimedia.org/wikipedia/en/d/d4/En.henryfphillips.ogg"
-    "https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav"
-)
-
-urls_es=(
-    "https://upload.wikimedia.org/wikipedia/commons/c/c1/La_contaminacion_del_agua.ogg"
-)
-
-urls_it=(
-)
-
-urls_pt=(
-)
-
-urls_de=(
-)
-
-urls_jp=(
-)
-
-urls_ru=(
-)
-
-function run_lang() {
-    lang=$1
-    shift
-    urls=("$@")
-
-    i=0
-    for url in "${urls[@]}"; do
-        echo "- [$lang] Processing '$url' ..."
-
-        ext="${url##*.}"
-        fname_src="$lang-${i}.${ext}"
-        fname_dst="$lang-${i}-16khz.wav"
-
-        if [ ! -f $fname_src ]; then
-            wget --quiet --show-progress -O $fname_src $url
-        fi
-
-        if [ ! -f $fname_dst ]; then
-            ffmpeg -loglevel -0 -y -i $fname_src -ar 16000 -ac 1 -c:a pcm_s16le $fname_dst
-            if [ $? -ne 0 ]; then
-                echo "Error: ffmpeg failed to convert $fname_src to $fname_dst"
-                exit 1
-            fi
-        fi
-
-        $main -m ../models/ggml-$model.bin -f $fname_dst -l $lang -otxt 2> /dev/null
-
-        git diff --no-index --word-diff=color --word-diff-regex=. $lang-$i-ref.txt $fname_dst.txt
-
-        i=$(($i+1))
-    done
-}
-
-run_lang "en" "${urls_en[@]}"
-
-if [[ $model != *.en ]]; then
-    run_lang "es" "${urls_es[@]}"
-    run_lang "it" "${urls_it[@]}"
-    run_lang "pt" "${urls_pt[@]}"
-    run_lang "de" "${urls_de[@]}"
-    run_lang "jp" "${urls_jp[@]}"
-    run_lang "ru" "${urls_ru[@]}"
-fi

whisper.h

@@ -2,7 +2,6 @@
 #define WHISPER_H
 
 #include <stdint.h>
-#include <stdbool.h>
 
 #ifdef WHISPER_SHARED
 #    ifdef _WIN32
@@ -31,115 +30,45 @@ extern "C" {
     //
     // C interface
     //
-    // The following interface is thread-safe as long as the sample whisper_context is not used by multiple threads
-    // concurrently.
-    //
-    // Basic usage:
-    //
-    //     #include "whisper.h"
-    //
-    //     ...
-    //
-    //     struct whisper_context * ctx = whisper_init("/path/to/ggml-base.en.bin");
-    //
-    //     if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
-    //         fprintf(stderr, "failed to process audio\n");
-    //         return 7;
-    //     }
-    //
-    //     const int n_segments = whisper_full_n_segments(ctx);
-    //     for (int i = 0; i < n_segments; ++i) {
-    //         const char * text = whisper_full_get_segment_text(ctx, i);
-    //         printf("%s", text);
-    //     }
-    //
-    //     whisper_free(ctx);
-    //
-    //     ...
-    //
-    // This is a demonstration of the most straightforward usage of the library.
-    // "pcmf32" contains the RAW audio data in 32-bit floating point format.
-    //
-    // The interface also allows for more fine-grained control over the computation, but it requires a deeper
-    // understanding of how the model works.
-    //
+
+    // TODO: documentation will come soon
 
     struct whisper_context;
 
     typedef int whisper_token;
 
-    typedef struct whisper_token_data {
-        whisper_token id;  // token id
-        whisper_token tid; // forced timestamp token id
-
-        float p;           // probability of the token
-        float pt;          // probability of the timestamp token
-        float ptsum;       // sum of probabilities of all timestamp tokens
-
-        // token-level timestamp data
-        // do not use if you haven't computed token-level timestamps
-        int64_t t0;        // start time of the token
-        int64_t t1;        //   end time of the token
-
-        float vlen;        // voice length of the token
-    } whisper_token_data;
-
-    // Allocates all memory needed for the model and loads the model from the given file.
-    // Returns NULL on failure.
     WHISPER_API struct whisper_context * whisper_init(const char * path_model);
-
-    // Frees all memory allocated by the model.
     WHISPER_API void whisper_free(struct whisper_context * ctx);
 
-    // Convert RAW PCM audio to log mel spectrogram.
-    // The resulting spectrogram is stored inside the provided whisper context.
-    // Returns 0 on success
     WHISPER_API int whisper_pcm_to_mel(
             struct whisper_context * ctx,
-                       const float * samples,
-                               int   n_samples,
-                               int   n_threads);
+            const float * samples,
+            int n_samples,
+            int n_threads);
 
-    // This can be used to set a custom log mel spectrogram inside the provided whisper context.
-    // Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
     // n_mel must be 80
-    // Returns 0 on success
     WHISPER_API int whisper_set_mel(
             struct whisper_context * ctx,
-                       const float * data,
-                               int   n_len,
-                               int   n_mel);
+            const float * data,
+            int n_len,
+            int n_mel);
 
-    // Run the Whisper encoder on the log mel spectrogram stored inside the provided whisper context.
-    // Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
-    // offset can be used to specify the offset of the first frame in the spectrogram.
-    // Returns 0 on success
     WHISPER_API int whisper_encode(
             struct whisper_context * ctx,
-                               int   offset,
-                               int   n_threads);
+            int offset,
+            int n_threads);
 
-    // Run the Whisper decoder to obtain the logits and probabilities for the next token.
-    // Make sure to call whisper_encode() first.
-    // tokens + n_tokens is the provided context for the decoder.
-    // n_past is the number of tokens to use from previous decoder calls.
-    // Returns 0 on success
     WHISPER_API int whisper_decode(
             struct whisper_context * ctx,
-               const whisper_token * tokens,
-                               int   n_tokens,
-                               int   n_past,
-                               int   n_threads);
+            const whisper_token * tokens,
+            int n_tokens,
+            int n_past,
+            int n_threads);
 
-    // Token sampling methods.
-    // These are provided for convenience and can be used after each call to whisper_decode().
-    // You can also implement your own sampling method using the whisper_get_probs() function.
-    // whisper_sample_best() returns the token with the highest probability
-    // whisper_sample_timestamp() returns the most probable timestamp token
-    WHISPER_API whisper_token_data whisper_sample_best(struct whisper_context * ctx);
-    WHISPER_API whisper_token_data whisper_sample_timestamp(struct whisper_context * ctx, bool is_initial);
+    WHISPER_API whisper_token whisper_sample_best(struct whisper_context * ctx, bool need_timestamp);
+    WHISPER_API whisper_token whisper_sample_timestamp(struct whisper_context * ctx);
 
-    // Return the id of the specified language, returns -1 if not found
+    // return the id of the specified language, returns -1 if not found
     WHISPER_API int whisper_lang_id(const char * lang);
 
     WHISPER_API int whisper_n_len          (struct whisper_context * ctx); // mel length
@@ -147,13 +76,10 @@ extern "C" {
     WHISPER_API int whisper_n_text_ctx     (struct whisper_context * ctx);
     WHISPER_API int whisper_is_multilingual(struct whisper_context * ctx);
 
-    // The probabilities for the next token
     WHISPER_API float * whisper_get_probs(struct whisper_context * ctx);
 
-    // Token Id -> String. Uses the vocabulary in the provided context
     WHISPER_API const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token);
 
-    // Special tokens
     WHISPER_API whisper_token whisper_token_eot (struct whisper_context * ctx);
     WHISPER_API whisper_token whisper_token_sot (struct whisper_context * ctx);
     WHISPER_API whisper_token whisper_token_prev(struct whisper_context * ctx);
@@ -161,134 +87,62 @@ extern "C" {
     WHISPER_API whisper_token whisper_token_not (struct whisper_context * ctx);
     WHISPER_API whisper_token whisper_token_beg (struct whisper_context * ctx);
 
-    // Task tokens
-    WHISPER_API whisper_token whisper_token_translate (void);
-    WHISPER_API whisper_token whisper_token_transcribe(void);
+    WHISPER_API whisper_token whisper_token_translate ();
+    WHISPER_API whisper_token whisper_token_transcribe();
 
-    // Performance information
     WHISPER_API void whisper_print_timings(struct whisper_context * ctx);
-    WHISPER_API void whisper_reset_timings(struct whisper_context * ctx);
-
-    // Print system information
-    WHISPER_API const char * whisper_print_system_info(void);
 
     ////////////////////////////////////////////////////////////////////////////
 
-    // Available sampling strategies
-    enum whisper_sampling_strategy {
-        WHISPER_SAMPLING_GREEDY,      // Always select the most probable token
-        WHISPER_SAMPLING_BEAM_SEARCH, // TODO: not implemented yet!
+    enum whisper_decode_strategy {
+        WHISPER_DECODE_GREEDY,
+        WHISPER_DECODE_BEAM_SEARCH,
     };
 
-    // Text segment callback
-    // Called on every newly generated text segment
-    // Use the whisper_full_...() functions to obtain the text segments
-    typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, int n_new, void * user_data);
-
-    // Encoder begin callback
-    // If not NULL, called before the encoder starts
-    // If it returns false, the computation is aborted
-    typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, void * user_data);
-
-    // Parameters for the whisper_full() function
-    // If you chnage the order or add new parameters, make sure to update the default values in whisper.cpp:
-    // whisper_full_default_params()
     struct whisper_full_params {
-        enum whisper_sampling_strategy strategy;
+        enum whisper_decode_strategy strategy;
 
         int n_threads;
-        int n_max_text_ctx;
-        int offset_ms;          // start offset in ms
-        int duration_ms;        // audio duration to process in ms
+        int offset_ms;
 
         bool translate;
         bool no_context;
-        bool single_segment;    // force single segment output (useful for streaming)
-        bool print_special;
+        bool print_special_tokens;
         bool print_progress;
         bool print_realtime;
         bool print_timestamps;
 
-        // [EXPERIMENTAL] token-level timestamps
-        bool  token_timestamps; // enable token-level timestamps
-        float thold_pt;         // timestamp token probability threshold (~0.01)
-        float thold_ptsum;      // timestamp token sum probability threshold (~0.01)
-        int   max_len;          // max segment length in characters
-        int   max_tokens;       // max tokens per segment (0 = no limit)
-
-        // [EXPERIMENTAL] speed-up techniques
-        bool speed_up;          // speed-up the audio by 2x using Phase Vocoder
-        int  audio_ctx;         // overwrite the audio context size (0 = use default)
-
-        // tokens to provide the whisper model as initial prompt
-        // these are prepended to any existing text context from a previous call
-        const whisper_token * prompt_tokens;
-        int prompt_n_tokens;
-
         const char * language;
 
-        struct {
-            int n_past;
-        } greedy;
+        union {
+            struct {
+                int n_past;
+            } greedy;
 
-        struct {
-            int n_past;
-            int beam_width;
-            int n_best;
-        } beam_search;
-
-        whisper_new_segment_callback new_segment_callback;
-        void * new_segment_callback_user_data;
-
-        whisper_encoder_begin_callback encoder_begin_callback;
-        void * encoder_begin_callback_user_data;
+            struct {
+                int n_past;
+                int beam_width;
+                int n_best;
+            } beam_search;
+        };
     };
 
-    WHISPER_API struct whisper_full_params whisper_full_default_params(enum whisper_sampling_strategy strategy);
+    WHISPER_API struct whisper_full_params whisper_full_default_params(enum whisper_decode_strategy strategy);
 
-    // Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text
-    // Uses the specified decoding strategy to obtain the text.
+    // full whisper run - encode + decode
     WHISPER_API int whisper_full(
-                struct whisper_context * ctx,
-            struct whisper_full_params   params,
-                           const float * samples,
-                                   int   n_samples);
+            struct whisper_context * ctx,
+            struct whisper_full_params params,
+            const float * samples,
+            int n_samples);
 
-    // Split the input audio in chunks and process each chunk separately using whisper_full()
-    // It seems this approach can offer some speedup in some cases.
-    // However, the transcription accuracy can be worse at the beginning and end of each chunk.
-    WHISPER_API int whisper_full_parallel(
-                struct whisper_context * ctx,
-            struct whisper_full_params   params,
-                           const float * samples,
-                                   int   n_samples,
-                                   int   n_processors);
-
-    // Number of generated text segments.
-    // A segment can be a few words, a sentence, or even a paragraph.
     WHISPER_API int whisper_full_n_segments(struct whisper_context * ctx);
 
-    // Get the start and end time of the specified segment.
     WHISPER_API int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment);
     WHISPER_API int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment);
 
-    // Get the text of the specified segment.
     WHISPER_API const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment);
 
-    // Get number of tokens in the specified segment.
-    WHISPER_API int whisper_full_n_tokens(struct whisper_context * ctx, int i_segment);
-
-    // Get the token text of the specified token in the specified segment.
-    WHISPER_API const char * whisper_full_get_token_text(struct whisper_context * ctx, int i_segment, int i_token);
-    WHISPER_API whisper_token whisper_full_get_token_id (struct whisper_context * ctx, int i_segment, int i_token);
-
-    // Get token data for the specified token in the specified segment.
-    // This contains probabilities, timestamps, etc.
-    WHISPER_API whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token);
-
-    // Get the probability of the specified token in the specified segment.
-    WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);
-
 #ifdef __cplusplus
 }
 #endif