rebase on master after whisper_state changes

.github/workflows/build.yml

@@ -265,44 +265,3 @@ jobs:
           popd
           emcmake cmake . -DCMAKE_BUILD_TYPE=${{ matrix.build }}
           make
-
-  ios:
-    runs-on: macos-latest
-
-    strategy:
-      matrix:
-        build: [Release]
-
-    steps:
-      - name: Clone
-        uses: actions/checkout@v1
-
-      - name: Configure
-        run: cp models/for-tests-ggml-base.en.bin models/ggml-base.en.bin
-
-      - name: Build objc example
-        run: xcodebuild -project examples/whisper.objc/whisper.objc.xcodeproj -scheme whisper.objc -configuration ${{ matrix.build }} -sdk iphonesimulator build
-      
-      - name: Build swiftui example
-        run: xcodebuild -project examples/whisper.swiftui/whisper.swiftui.xcodeproj -scheme WhisperCppDemo -configuration ${{ matrix.build }} -sdk iphonesimulator build
-
-  android:
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Clone
-        uses: actions/checkout@v1
-
-      - name: Install Java
-        uses: actions/setup-java@v3
-        with:
-          distribution: zulu
-          java-version: 17
-      
-      - name: Setup Android SDK
-        uses: android-actions/setup-android@v2
-
-      - name: Build
-        run: |
-          cd examples/whisper.android
-          ./gradlew assembleRelease --no-daemon

.gitignore

@@ -1,8 +1,8 @@
 *.o
 *.a
+*.mlmodel
+*.mlmodelc
 .cache/
-.coreml/
-.test/
 .vs/
 .vscode/
 .DS_Store
@@ -12,7 +12,6 @@ build-em/
 build-debug/
 build-release/
 build-static/
-build-cublas/
 build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
@@ -21,9 +20,7 @@ build-sanitize-thread/
 /stream
 /command
 /talk
-/talk-llama
 /bench
-/quantize
 
 arm_neon.h
 sync.sh
@@ -37,7 +34,3 @@ examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
 examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata
 
 extra/bench-gg.txt
-
-models/*.mlmodel
-models/*.mlmodelc
-models/*.mlpackage

CMakeLists.txt

@@ -1,10 +1,6 @@
 cmake_minimum_required (VERSION 3.0)
 
-project(whisper.cpp VERSION 1.4.1)
-
-if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
-    add_compile_options(/utf-8)
-endif ()
+project(whisper.cpp VERSION 1.2.1)
 
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -39,34 +35,32 @@ endif()
 
 # options
 
-option(BUILD_SHARED_LIBS              "whisper: build shared libs" ${BUILD_SHARED_LIBS_DEFAULT})
+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_3RD_PARTY "whisper: enable all compiler warnings in 3rd party libs" OFF)
+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_UNDEFINED     "whisper: enable undefined 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_BUILD_EXAMPLES          "whisper: build examples" ${WHISPER_STANDALONE})
 
-option(WHISPER_SDL2                   "whisper: support for libSDL2" OFF)
+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)
-    option(WHISPER_NO_FMA                "whisper: disable FMA" OFF)
+    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)
+    option(WHISPER_NO_FMA              "whisper: disable FMA" OFF)
 
-    option(WHISPER_COREML                "whisper: enable Core ML framework" OFF)
-    option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback" OFF)
+    option(WHISPER_COREML              "whisper: enable Core ML framework" OFF)
 else()
-    option(WHISPER_OPENBLAS              "whisper: support for OpenBLAS" OFF)
-    option(WHISPER_CUBLAS                "whisper: support for cuBLAS" OFF)
+    option(WHISPER_SUPPORT_OPENBLAS    "whisper: support for OpenBLAS" OFF)
 endif()
 
-option(WHISPER_PERF "whisper: enable perf timings" OFF)
+option(WHISPER_PERF                    "whisper: enable perf timings" OFF)
 
 # sanitizers
 
@@ -121,14 +115,10 @@ if (APPLE)
         else()
             message(WARNING "CoreML framework not found")
         endif()
-
-        if (WHISPER_COREML_ALLOW_FALLBACK)
-            set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_COREML_ALLOW_FALLBACK)
-        endif()
     endif()
 endif()
 
-if (WHISPER_OPENBLAS)
+if (WHISPER_SUPPORT_OPENBLAS)
     find_library(OPENBLAS_LIB
         NAMES openblas libopenblas
         )
@@ -142,31 +132,6 @@ if (WHISPER_OPENBLAS)
     endif()
 endif()
 
-if (WHISPER_CUBLAS)
-    cmake_minimum_required(VERSION 3.17)
-
-    find_package(CUDAToolkit)
-
-    if (CUDAToolkit_FOUND)
-        message(STATUS "cuBLAS found")
-
-        enable_language(CUDA)
-
-        set(GGML_CUDA_SOURCES ggml-cuda.cu ggml-cuda.h)
-
-        add_compile_definitions(GGML_USE_CUBLAS)
-
-        if (WHISPER_STATIC)
-            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
-        else()
-            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
-        endif()
-
-    else()
-        message(WARNING "cuBLAS not found")
-    endif()
-endif()
-
 # compiler flags
 
 if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
@@ -273,7 +238,6 @@ set(TARGET whisper)
 add_library(${TARGET}
     ggml.h
     ggml.c
-    ${GGML_CUDA_SOURCES}
     whisper.h
     whisper.cpp
     )
@@ -303,19 +267,7 @@ if (BUILD_SHARED_LIBS)
 
     target_compile_definitions(${TARGET} PUBLIC
         WHISPER_SHARED
-        GGML_SHARED
         )
-
-    target_compile_definitions(${TARGET} PRIVATE
-        WHISPER_BUILD
-        GGML_BUILD
-        )
-endif()
-
-if (GGML_CUDA_SOURCES)
-    message(STATUS "GGML CUDA sources found, configuring CUDA architecture")
-    set_property(TARGET whisper PROPERTY CUDA_ARCHITECTURES OFF)
-    set_property(TARGET whisper PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto")
 endif()
 
 if (EMSCRIPTEN)

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Georgi Gerganov
+Copyright (c) 2022 Georgi Gerganov
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

Makefile

@@ -1,5 +1,3 @@
-default: main bench quantize
-
 ifndef UNAME_S
 UNAME_S := $(shell uname -s)
 endif
@@ -38,7 +36,7 @@ LDFLAGS  =
 
 # ref: https://github.com/ggerganov/whisper.cpp/issues/37
 ifneq ($(wildcard /usr/include/musl/*),)
-	CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
+	CFLAGS   += -D_POSIX_SOURCE -D_GNU_SOURCE
 	CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
 endif
 
@@ -79,6 +77,10 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 			CFLAGS += -mavx2
 		endif
 	else ifeq ($(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,$(AVX2_M)))
 			CFLAGS += -mavx2
@@ -90,17 +92,16 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 		F16C_M := $(shell grep "f16c " /proc/cpuinfo)
 		ifneq (,$(findstring f16c,$(F16C_M)))
 			CFLAGS += -mf16c
-
-			AVX1_M := $(shell grep "avx " /proc/cpuinfo)
-			ifneq (,$(findstring avx,$(AVX1_M)))
-				CFLAGS += -mavx
-			endif
 		endif
 		SSE3_M := $(shell grep "sse3 " /proc/cpuinfo)
 		ifneq (,$(findstring sse3,$(SSE3_M)))
 			CFLAGS += -msse3
 		endif
 	else ifeq ($(UNAME_S),Haiku)
+		AVX1_M := $(shell sysinfo -cpu | grep "AVX ")
+		ifneq (,$(findstring avx,$(AVX1_M)))
+			CFLAGS += -mavx
+		endif
 		AVX2_M := $(shell sysinfo -cpu | grep "AVX2 ")
 		ifneq (,$(findstring avx2,$(AVX2_M)))
 			CFLAGS += -mavx2
@@ -112,11 +113,6 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
 		F16C_M := $(shell sysinfo -cpu | grep "F16C ")
 		ifneq (,$(findstring f16c,$(F16C_M)))
 			CFLAGS += -mf16c
-
-			AVX1_M := $(shell sysinfo -cpu | grep "AVX ")
-			ifneq (,$(findstring avx,$(AVX1_M)))
-				CFLAGS += -mavx
-			endif
 		endif
 	else
 		CFLAGS += -mfma -mf16c -mavx -mavx2
@@ -125,7 +121,6 @@ endif
 ifeq ($(UNAME_M),amd64)
 	CFLAGS += -mavx -mavx2 -mfma -mf16c
 endif
-
 ifneq ($(filter ppc64%,$(UNAME_M)),)
 	POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
 	ifneq (,$(findstring POWER9,$(POWER9_M)))
@@ -136,7 +131,6 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
 		CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
 	endif
 endif
-
 ifndef WHISPER_NO_ACCELERATE
 	# Mac M1 - include Accelerate framework
 	ifeq ($(UNAME_S),Darwin)
@@ -144,56 +138,30 @@ ifndef WHISPER_NO_ACCELERATE
 		LDFLAGS += -framework Accelerate
 	endif
 endif
-
 ifdef WHISPER_COREML
 	CXXFLAGS += -DWHISPER_USE_COREML
 	LDFLAGS  += -framework Foundation -framework CoreML
-
-ifdef WHISPER_COREML_ALLOW_FALLBACK
-	CXXFLAGS += -DWHISPER_COREML_ALLOW_FALLBACK
 endif
-endif
-
 ifdef WHISPER_OPENBLAS
 	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
 	LDFLAGS += -lopenblas
 endif
-
-ifdef WHISPER_CUBLAS
-	CFLAGS      += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
-	CXXFLAGS    += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
-	LDFLAGS     += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
-	WHISPER_OBJ += ggml-cuda.o
-	NVCC        = nvcc
-	NVCCFLAGS   = --forward-unknown-to-host-compiler -arch=native
-
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
-	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
-endif
-
 ifdef WHISPER_GPROF
 	CFLAGS   += -pg
 	CXXFLAGS += -pg
 endif
-
 ifneq ($(filter aarch64%,$(UNAME_M)),)
 	CFLAGS += -mcpu=native
 	CXXFLAGS += -mcpu=native
 endif
-
 ifneq ($(filter armv6%,$(UNAME_M)),)
-	# 32-bit Raspberry Pi 1, 2, 3
-	CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access
+	# Raspberry Pi 1, 2, 3
+	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
 endif
-
 ifneq ($(filter armv7%,$(UNAME_M)),)
-	# 32-bit ARM, for example on Armbian or possibly raspbian
-	CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
-
-	# 64-bit ARM, use these (TODO: auto-detect 64-bit)
-	# CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
+	# Raspberry Pi 4
+	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
 endif
-
 ifneq ($(filter armv8%,$(UNAME_M)),)
 	# Raspberry Pi 4
 	CFLAGS += -mfp16-format=ieee -mno-unaligned-access
@@ -214,18 +182,20 @@ $(info I CC:       $(CCV))
 $(info I CXX:      $(CXXV))
 $(info )
 
+default: main
+
 #
 # Build library
 #
 
-ggml.o: ggml.c ggml.h ggml-cuda.h
-	$(CC)  $(CFLAGS)   -c $< -o $@
+ggml.o: ggml.c ggml.h
+	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
 
-whisper.o: whisper.cpp whisper.h ggml.h ggml-cuda.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
+whisper.o: whisper.cpp whisper.h
+	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
 
 ifndef WHISPER_COREML
-WHISPER_OBJ += whisper.o
+WHISPER_OBJ = whisper.o
 else
 whisper-encoder.o: coreml/whisper-encoder.mm coreml/whisper-encoder.h
 	$(CXX) -O3 -I . -c coreml/whisper-encoder.mm -o whisper-encoder.o
@@ -233,7 +203,7 @@ whisper-encoder.o: coreml/whisper-encoder.mm coreml/whisper-encoder.h
 whisper-encoder-impl.o: coreml/whisper-encoder-impl.m coreml/whisper-encoder-impl.h
 	$(CXX) -O3 -I . -fobjc-arc -c coreml/whisper-encoder-impl.m -o whisper-encoder-impl.o
 
-WHISPER_OBJ += whisper.o whisper-encoder.o whisper-encoder-impl.o
+WHISPER_OBJ = whisper.o whisper-encoder.o whisper-encoder-impl.o
 endif
 
 libwhisper.a: ggml.o $(WHISPER_OBJ)
@@ -243,7 +213,7 @@ libwhisper.so: ggml.o $(WHISPER_OBJ)
 	$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o $(WHISPER_OBJ) $(LDFLAGS)
 
 clean:
-	rm -f *.o main stream command talk talk-llama bench quantize libwhisper.a libwhisper.so
+	rm -f *.o main stream command talk bench libwhisper.a libwhisper.so
 
 #
 # Examples
@@ -251,19 +221,13 @@ clean:
 
 CC_SDL=`sdl2-config --cflags --libs`
 
-SRC_COMMON     = examples/common.cpp examples/common-ggml.cpp
+SRC_COMMON = examples/common.cpp
 SRC_COMMON_SDL = examples/common-sdl.cpp
 
 main: examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ)
 	$(CXX) $(CXXFLAGS) examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ) -o main $(LDFLAGS)
 	./main -h
 
-bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
-	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
-
-quantize: examples/quantize/quantize.cpp ggml.o $(WHISPER_OBJ) $(SRC_COMMON)
-	$(CXX) $(CXXFLAGS) examples/quantize/quantize.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ) -o quantize $(LDFLAGS)
-
 stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
 	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o stream $(CC_SDL) $(LDFLAGS)
 
@@ -273,8 +237,8 @@ command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(W
 talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
 	$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o talk $(CC_SDL) $(LDFLAGS)
 
-talk-llama: examples/talk-llama/talk-llama.cpp examples/talk-llama/llama.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
-	$(CXX) $(CXXFLAGS) examples/talk-llama/talk-llama.cpp examples/talk-llama/llama.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o talk-llama $(CC_SDL) $(LDFLAGS)
+bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
+	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
 
 #
 # Audio samples

README.md

@@ -1,25 +1,21 @@
 # whisper.cpp
 
-![whisper.cpp](https://user-images.githubusercontent.com/1991296/235238348-05d0f6a4-da44-4900-a1de-d0707e75b763.jpeg)
-
 [![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
 
-Beta: [v1.4.1](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.4.1) / Stable: [v1.2.1](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.1) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
+Stable: [v1.2.1](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.1) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
 
 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, Accelerate framework and [Core ML](https://github.com/ggerganov/whisper.cpp#core-ml-support)
+- Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
 - AVX intrinsics support for x86 architectures
 - VSX intrinsics support for POWER architectures
 - Mixed F16 / F32 precision
-- [4-bit and 5-bit integer quantization support](https://github.com/ggerganov/whisper.cpp#quantization)
 - Low memory usage (Flash Attention)
 - Zero memory allocations at runtime
 - Runs on the CPU
-- [Partial GPU support for NVIDIA via cuBLAS](https://github.com/ggerganov/whisper.cpp#nvidia-gpu-support-via-cublas)
 - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
 
 Supported platforms:
@@ -62,9 +58,7 @@ the Accelerate framework utilizes the special-purpose AMX coprocessor available
 
 ## Quick start
 
-First clone the repository.
-
-Then, download one of the Whisper models converted in [ggml format](models). For example:
+First, download one of the Whisper models converted in [ggml format](models). For example:
 
 ```bash
 bash ./models/download-ggml-model.sh base.en
@@ -229,93 +223,10 @@ make large
 | medium | 1.5 GB | ~1.7 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
 | large  | 2.9 GB | ~3.3 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
 
-## Quantization
-
-`whisper.cpp` supports integer quantization of the Whisper `ggml` models.
-Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.
-
-Here are the steps for creating and using a quantized model:
-
-```bash
-# quantize a model with Q5_0 method
-make quantize
-./quantize models/ggml-base.en.bin models/ggml-base.en-q5_0.bin q5_0
-
-# run the examples as usual, specifying the quantized model file
-./main -m models/ggml-base.en-q5_0.bin ./samples/gb0.wav
-```
-
-## Core ML support
-
-On Apple Silicon devices, the Encoder inference can be executed on the Apple Neural Engine (ANE) via Core ML. This can result in significant
-speed-up - more than x3 faster compared with CPU-only execution. Here are the instructions for generating a Core ML model and using it with `whisper.cpp`:
-
-- Install Python dependencies needed for the creation of the Core ML model:
-
-  ```bash
-  pip install ane_transformers
-  pip install openai-whisper
-  pip install coremltools
-  ```
-
-- Generate a Core ML model. For example, to generate a `base.en` model, use:
-
-  ```bash
-  ./models/generate-coreml-model.sh base.en
-  ```
-
-  This will generate the folder `models/ggml-base.en-encoder.mlmodelc`
-
-- Build `whisper.cpp` with Core ML support:
-
-  ```bash
-  # using Makefile
-  make clean
-  WHISPER_COREML=1 make -j
-
-  # using CMake
-  cd build
-  cmake -DWHISPER_COREML=1 ..
-  ```
-
-- Run the examples as usual. For example:
-
-  ```bash
-  ./main -m models/ggml-base.en.bin -f samples/jfk.wav
-
-  ...
-
-  whisper_init_state: loading Core ML model from 'models/ggml-base.en-encoder.mlmodelc'
-  whisper_init_state: first run on a device may take a while ...
-  whisper_init_state: Core ML model loaded
-
-  system_info: n_threads = 4 / 10 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | COREML = 1 |
-
-  ...
-  ```
-
-  The first run on a device is slow, since the ANE service compiles the Core ML model to some device-specific format.
-  Next runs are faster.
-
-For more information about the Core ML implementation please refer to PR [#566](https://github.com/ggerganov/whisper.cpp/pull/566).
-
-## NVIDIA GPU support via cuBLAS
-
-With NVIDIA cards, the Encoder processing can be offloaded to the GPU to a large extend through cuBLAS.
-First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads
-
-Now build `whisper.cpp` with cuBLAS support:
-
-```
-make clean
-WHISPER_CUBLAS=1 make -j
-```
-
-Run all the examples as usual.
-
 ## Limitations
 
 - Inference only
+- No GPU support (yet)
 
 ## Another example
 
@@ -402,7 +313,7 @@ whisper_print_timings:    total time = 32733.52 ms
 ## 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 continuously.
+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).
 
 ```java
@@ -417,10 +328,6 @@ https://user-images.githubusercontent.com/1991296/194935793-76afede7-cfa8-48d8-a
 Adding the `--print-colors` argument will print the transcribed text using an experimental color coding strategy
 to highlight words with high or low confidence:
 
-```java
-./main -m models/ggml-base.en.bin -f samples/gb0.wav --print-colors
-```
-
 <img width="965" alt="image" src="https://user-images.githubusercontent.com/1991296/197356445-311c8643-9397-4e5e-b46e-0b4b4daa2530.png">
 
 ## Controlling the length of the generated text segments (experimental)
@@ -460,7 +367,7 @@ system_info: n_threads = 4 / 10 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 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]
+[00:00:00.000 --> 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
@@ -573,7 +480,6 @@ in [models](models).
 - [X] Go: [bindings/go](bindings/go) | [#312](https://github.com/ggerganov/whisper.cpp/discussions/312)
 - [X] Ruby: [bindings/ruby](bindings/ruby) | [#507](https://github.com/ggerganov/whisper.cpp/discussions/507)
 - [X] Objective-C / Swift: [ggerganov/whisper.spm](https://github.com/ggerganov/whisper.spm) | [#313](https://github.com/ggerganov/whisper.cpp/discussions/313)
-  - [exPHAT/SwiftWhisper](https://github.com/exPHAT/SwiftWhisper)
 - [X] .NET: | [#422](https://github.com/ggerganov/whisper.cpp/discussions/422)
   - [sandrohanea/whisper.net](https://github.com/sandrohanea/whisper.net)
   - [NickDarvey/whisper](https://github.com/NickDarvey/whisper)
@@ -581,7 +487,6 @@ in [models](models).
   - [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython)
   - [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11)
 - [X] R: [bnosac/audio.whisper](https://github.com/bnosac/audio.whisper)
-- [X] Unity: [macoron/whisper.unity](https://github.com/Macoron/whisper.unity)
 
 ## Examples
 
@@ -595,7 +500,6 @@ Some of the examples are even ported to run in the browser using WebAssembly. Ch
 | [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](examples/talk) | [talk.wasm](examples/talk.wasm) | Talk with a GPT-2 bot |
-| [talk-llama](examples/talk-llama) | | Talk with a LLaMA bot |
 | [whisper.objc](examples/whisper.objc) | | iOS mobile application using whisper.cpp |
 | [whisper.swiftui](examples/whisper.swiftui) | | SwiftUI iOS / macOS application using whisper.cpp |
 | [whisper.android](examples/whisper.android) | | Android mobile application using whisper.cpp |

bindings/go/params.go

@@ -105,10 +105,6 @@ func (p *Params) SetMaxSegmentLength(n int) {
 	p.max_len = C.int(n)
 }
 
-func (p *Params) SetTokenTimestamps(b bool) {
-	p.token_timestamps = toBool(b)
-}
-
 // Set max tokens per segment (0 = no limit)
 func (p *Params) SetMaxTokensPerSegment(n int) {
 	p.max_tokens = C.int(n)

bindings/go/pkg/whisper/context.go

@@ -111,11 +111,6 @@ func (context *context) SetMaxSegmentLength(n uint) {
 	context.params.SetMaxSegmentLength(int(n))
 }
 
-// Set token timestamps flag
-func (context *context) SetTokenTimestamps(b bool) {
-	context.params.SetTokenTimestamps(b)
-}
-
 // Set max tokens per segment (0 = no limit)
 func (context *context) SetMaxTokensPerSegment(n uint) {
 	context.params.SetMaxTokensPerSegment(int(n))
@@ -285,14 +280,10 @@ func toSegment(ctx *whisper.Context, n int) Segment {
 func toTokens(ctx *whisper.Context, n int) []Token {
 	result := make([]Token, ctx.Whisper_full_n_tokens(n))
 	for i := 0; i < len(result); i++ {
-		data := ctx.Whisper_full_get_token_data(n, i)
-
 		result[i] = Token{
-			Id:    int(ctx.Whisper_full_get_token_id(n, i)),
-			Text:  ctx.Whisper_full_get_token_text(n, i),
-			P:     ctx.Whisper_full_get_token_p(n, i),
-			Start: time.Duration(data.T0()) * time.Millisecond * 10,
-			End:   time.Duration(data.T1()) * time.Millisecond * 10,
+			Id:   int(ctx.Whisper_full_get_token_id(n, i)),
+			Text: strings.TrimSpace(ctx.Whisper_full_get_token_text(n, i)),
+			P:    ctx.Whisper_full_get_token_p(n, i),
 		}
 	}
 	return result

bindings/go/pkg/whisper/interface.go

@@ -41,7 +41,6 @@ type Context interface {
 	SetTokenThreshold(float32)    // Set timestamp token probability threshold
 	SetTokenSumThreshold(float32) // Set timestamp token sum probability threshold
 	SetMaxSegmentLength(uint)     // Set max segment length in characters
-	SetTokenTimestamps(bool)      // Set token timestamps flag
 	SetMaxTokensPerSegment(uint)  // Set max tokens per segment (0 = no limit)
 
 	// Process mono audio data and return any errors.
@@ -86,8 +85,7 @@ type Segment struct {
 
 // Token is a text or special token
 type Token struct {
-	Id         int
-	Text       string
-	P          float32
-	Start, End time.Duration
+	Id   int
+	Text string
+	P    float32
 }

bindings/go/whisper.go

@@ -356,7 +356,7 @@ func (ctx *Context) Whisper_full_get_token_id(segment int, token int) Token {
 
 // Get token data for the specified token in the specified segment.
 // This contains probabilities, timestamps, etc.
-func (ctx *Context) Whisper_full_get_token_data(segment int, token int) TokenData {
+func (ctx *Context) whisper_full_get_token_data(segment int, token int) TokenData {
 	return TokenData(C.whisper_full_get_token_data((*C.struct_whisper_context)(ctx), C.int(segment), C.int(token)))
 }
 
@@ -407,11 +407,3 @@ func callEncoderBegin(user_data unsafe.Pointer) C.bool {
 	}
 	return true
 }
-
-func (t TokenData) T0() int64 {
-	return int64(t.t0)
-}
-
-func (t TokenData) T1() int64 {
-	return int64(t.t1)
-}

bindings/javascript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "whisper.cpp",
-  "version": "1.4.1",
+  "version": "1.2.1",
   "description": "Whisper speech recognition",
   "main": "whisper.js",
   "scripts": {

coreml/whisper-decoder-impl.h

@@ -1,146 +0,0 @@
-//
-// whisper-decoder-impl.h
-//
-// This file was automatically generated and should not be edited.
-//
-
-#import <Foundation/Foundation.h>
-#import <CoreML/CoreML.h>
-#include <stdint.h>
-#include <os/log.h>
-
-NS_ASSUME_NONNULL_BEGIN
-
-
-/// Model Prediction Input Type
-API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
-@interface whisper_decoder_implInput : NSObject<MLFeatureProvider>
-
-/// token_data as 1 by 1 matrix of 32-bit integers
-@property (readwrite, nonatomic, strong) MLMultiArray * token_data;
-
-/// audio_data as 1 × 384 × 1 × 1500 4-dimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * audio_data;
-- (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data NS_DESIGNATED_INITIALIZER;
-
-@end
-
-
-/// Model Prediction Output Type
-API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
-@interface whisper_decoder_implOutput : NSObject<MLFeatureProvider>
-
-/// var_1346 as multidimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * var_1346;
-- (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithVar_1346:(MLMultiArray *)var_1346 NS_DESIGNATED_INITIALIZER;
-
-@end
-
-
-/// Class for model loading and prediction
-API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
-@interface whisper_decoder_impl : NSObject
-@property (readonly, nonatomic, nullable) MLModel * model;
-
-/**
-    URL of the underlying .mlmodelc directory.
-*/
-+ (nullable NSURL *)URLOfModelInThisBundle;
-
-/**
-    Initialize whisper_decoder_impl instance from an existing MLModel object.
-
-    Usually the application does not use this initializer unless it makes a subclass of whisper_decoder_impl.
-    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
-*/
-- (instancetype)initWithMLModel:(MLModel *)model NS_DESIGNATED_INITIALIZER;
-
-/**
-    Initialize whisper_decoder_impl instance with the model in this bundle.
-*/
-- (nullable instancetype)init;
-
-/**
-    Initialize whisper_decoder_impl instance with the model in this bundle.
-
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Initialize whisper_decoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Initialize whisper_decoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Construct whisper_decoder_impl instance asynchronously with configuration.
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
-*/
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler;
-
-/**
-    Construct whisper_decoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
-
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param modelURL The model URL.
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
-*/
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler;
-
-/**
-    Make a prediction using the standard interface
-    @param input an instance of whisper_decoder_implInput to predict from
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as whisper_decoder_implOutput
-*/
-- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Make a prediction using the standard interface
-    @param input an instance of whisper_decoder_implInput to predict from
-    @param options prediction options
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as whisper_decoder_implOutput
-*/
-- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Make a prediction using the convenience interface
-    @param token_data as 1 by 1 matrix of 32-bit integers:
-    @param audio_data as 1 × 384 × 1 × 1500 4-dimensional array of floats:
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as whisper_decoder_implOutput
-*/
-- (nullable whisper_decoder_implOutput *)predictionFromToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Batch prediction
-    @param inputArray array of whisper_decoder_implInput instances to obtain predictions from
-    @param options prediction options
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the predictions as NSArray<whisper_decoder_implOutput *>
-*/
-- (nullable NSArray<whisper_decoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_decoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-@end
-
-NS_ASSUME_NONNULL_END

coreml/whisper-decoder-impl.m

@@ -1,201 +0,0 @@
-//
-// whisper-decoder-impl.m
-//
-// This file was automatically generated and should not be edited.
-//
-
-#if !__has_feature(objc_arc)
-#error This file must be compiled with automatic reference counting enabled (-fobjc-arc)
-#endif
-
-#import "whisper-decoder-impl.h"
-
-@implementation whisper_decoder_implInput
-
-- (instancetype)initWithToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data {
-    self = [super init];
-    if (self) {
-        _token_data = token_data;
-        _audio_data = audio_data;
-    }
-    return self;
-}
-
-- (NSSet<NSString *> *)featureNames {
-    return [NSSet setWithArray:@[@"token_data", @"audio_data"]];
-}
-
-- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
-    if ([featureName isEqualToString:@"token_data"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.token_data];
-    }
-    if ([featureName isEqualToString:@"audio_data"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.audio_data];
-    }
-    return nil;
-}
-
-@end
-
-@implementation whisper_decoder_implOutput
-
-- (instancetype)initWithVar_1346:(MLMultiArray *)var_1346 {
-    self = [super init];
-    if (self) {
-        _var_1346 = var_1346;
-    }
-    return self;
-}
-
-- (NSSet<NSString *> *)featureNames {
-    return [NSSet setWithArray:@[@"var_1346"]];
-}
-
-- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
-    if ([featureName isEqualToString:@"var_1346"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.var_1346];
-    }
-    return nil;
-}
-
-@end
-
-@implementation whisper_decoder_impl
-
-
-/**
-    URL of the underlying .mlmodelc directory.
-*/
-+ (nullable NSURL *)URLOfModelInThisBundle {
-    NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"whisper_decoder_impl" ofType:@"mlmodelc"];
-    if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load whisper-decoder-impl.mlmodelc in the bundle resource"); return nil; }
-    return [NSURL fileURLWithPath:assetPath];
-}
-
-
-/**
-    Initialize whisper_decoder_impl instance from an existing MLModel object.
-
-    Usually the application does not use this initializer unless it makes a subclass of whisper_decoder_impl.
-    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
-*/
-- (instancetype)initWithMLModel:(MLModel *)model {
-    self = [super init];
-    if (!self) { return nil; }
-    _model = model;
-    if (_model == nil) { return nil; }
-    return self;
-}
-
-
-/**
-    Initialize whisper_decoder_impl instance with the model in this bundle.
-*/
-- (nullable instancetype)init {
-    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
-}
-
-
-/**
-    Initialize whisper_decoder_impl instance with the model in this bundle.
-
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle configuration:configuration error:error];
-}
-
-
-/**
-    Initialize whisper_decoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    MLModel *model = [MLModel modelWithContentsOfURL:modelURL error:error];
-    if (model == nil) { return nil; }
-    return [self initWithMLModel:model];
-}
-
-
-/**
-    Initialize whisper_decoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_decoder_impl.
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    MLModel *model = [MLModel modelWithContentsOfURL:modelURL configuration:configuration error:error];
-    if (model == nil) { return nil; }
-    return [self initWithMLModel:model];
-}
-
-
-/**
-    Construct whisper_decoder_impl instance asynchronously with configuration.
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
-*/
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler {
-    [self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
-              configuration:configuration
-          completionHandler:handler];
-}
-
-
-/**
-    Construct whisper_decoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
-
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param modelURL The model URL.
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_decoder_impl instance or NSError object.
-*/
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_decoder_impl * _Nullable model, NSError * _Nullable error))handler {
-    [MLModel loadContentsOfURL:modelURL
-                 configuration:configuration
-             completionHandler:^(MLModel *model, NSError *error) {
-        if (model != nil) {
-            whisper_decoder_impl *typedModel = [[whisper_decoder_impl alloc] initWithMLModel:model];
-            handler(typedModel, nil);
-        } else {
-            handler(nil, error);
-        }
-    }];
-}
-
-- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    return [self predictionFromFeatures:input options:[[MLPredictionOptions alloc] init] error:error];
-}
-
-- (nullable whisper_decoder_implOutput *)predictionFromFeatures:(whisper_decoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    id<MLFeatureProvider> outFeatures = [self.model predictionFromFeatures:input options:options error:error];
-    if (!outFeatures) { return nil; }
-    return [[whisper_decoder_implOutput alloc] initWithVar_1346:(MLMultiArray *)[outFeatures featureValueForName:@"var_1346"].multiArrayValue];
-}
-
-- (nullable whisper_decoder_implOutput *)predictionFromToken_data:(MLMultiArray *)token_data audio_data:(MLMultiArray *)audio_data error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    whisper_decoder_implInput *input_ = [[whisper_decoder_implInput alloc] initWithToken_data:token_data audio_data:audio_data];
-    return [self predictionFromFeatures:input_ error:error];
-}
-
-- (nullable NSArray<whisper_decoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_decoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    id<MLBatchProvider> inBatch = [[MLArrayBatchProvider alloc] initWithFeatureProviderArray:inputArray];
-    id<MLBatchProvider> outBatch = [self.model predictionsFromBatch:inBatch options:options error:error];
-    if (!outBatch) { return nil; }
-    NSMutableArray<whisper_decoder_implOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
-    for (NSInteger i = 0; i < outBatch.count; i++) {
-        id<MLFeatureProvider> resultProvider = [outBatch featuresAtIndex:i];
-        whisper_decoder_implOutput * result = [[whisper_decoder_implOutput alloc] initWithVar_1346:(MLMultiArray *)[resultProvider featureValueForName:@"var_1346"].multiArrayValue];
-        [results addObject:result];
-    }
-    return results;
-}
-
-@end

coreml/whisper-encoder-impl.h

@@ -1,5 +1,5 @@
 //
-// whisper-encoder-impl.h
+// CoremlEncoder.h
 //
 // This file was automatically generated and should not be edited.
 //
@@ -13,20 +13,20 @@ NS_ASSUME_NONNULL_BEGIN
 
 
 /// Model Prediction Input Type
-API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
-@interface whisper_encoder_implInput : NSObject<MLFeatureProvider>
+API_AVAILABLE(macos(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
+@interface CoremlEncoderInput : NSObject<MLFeatureProvider>
 
-/// logmel_data as 1 × 80 × 3000 3-dimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * logmel_data;
+/// melSegment as 1 × 80 × 3000 3-dimensional array of floats
+@property (readwrite, nonatomic, strong) MLMultiArray * melSegment;
 - (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithLogmel_data:(MLMultiArray *)logmel_data NS_DESIGNATED_INITIALIZER;
+- (instancetype)initWithMelSegment:(MLMultiArray *)melSegment NS_DESIGNATED_INITIALIZER;
 
 @end
 
 
 /// Model Prediction Output Type
-API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
-@interface whisper_encoder_implOutput : NSObject<MLFeatureProvider>
+API_AVAILABLE(macos(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
+@interface CoremlEncoderOutput : NSObject<MLFeatureProvider>
 
 /// output as multidimensional array of floats
 @property (readwrite, nonatomic, strong) MLMultiArray * output;
@@ -37,8 +37,8 @@ API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((v
 
 
 /// Class for model loading and prediction
-API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((visibility("hidden")))
-@interface whisper_encoder_impl : NSObject
+API_AVAILABLE(macos(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
+@interface CoremlEncoder : NSObject
 @property (readonly, nonatomic, nullable) MLModel * model;
 
 /**
@@ -47,20 +47,20 @@ API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((v
 + (nullable NSURL *)URLOfModelInThisBundle;
 
 /**
-    Initialize whisper_encoder_impl instance from an existing MLModel object.
+    Initialize CoremlEncoder instance from an existing MLModel object.
 
-    Usually the application does not use this initializer unless it makes a subclass of whisper_encoder_impl.
+    Usually the application does not use this initializer unless it makes a subclass of CoremlEncoder.
     Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
 */
 - (instancetype)initWithMLModel:(MLModel *)model NS_DESIGNATED_INITIALIZER;
 
 /**
-    Initialize whisper_encoder_impl instance with the model in this bundle.
+    Initialize CoremlEncoder instance with the model in this bundle.
 */
 - (nullable instancetype)init;
 
 /**
-    Initialize whisper_encoder_impl instance with the model in this bundle.
+    Initialize CoremlEncoder instance with the model in this bundle.
 
     @param configuration The model configuration object
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
@@ -68,75 +68,75 @@ API_AVAILABLE(macos(12.0), ios(15.0), watchos(8.0), tvos(15.0)) __attribute__((v
 - (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 
 /**
-    Initialize whisper_encoder_impl instance from the model URL.
+    Initialize CoremlEncoder instance from the model URL.
 
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
 */
 - (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 
 /**
-    Initialize whisper_encoder_impl instance from the model URL.
+    Initialize CoremlEncoder instance from the model URL.
 
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
     @param configuration The model configuration object
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
 */
 - (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 
 /**
-    Construct whisper_encoder_impl instance asynchronously with configuration.
+    Construct CoremlEncoder instance asynchronously with configuration.
     Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
 
     @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
 */
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler;
++ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) __attribute__((visibility("hidden")));
 
 /**
-    Construct whisper_encoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
+    Construct CoremlEncoder instance asynchronously with URL of .mlmodelc directory and optional configuration.
 
     Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
 
     @param modelURL The model URL.
     @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
 */
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler;
++ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) __attribute__((visibility("hidden")));
 
 /**
     Make a prediction using the standard interface
-    @param input an instance of whisper_encoder_implInput to predict from
+    @param input an instance of CoremlEncoderInput to predict from
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as whisper_encoder_implOutput
+    @return the prediction as CoremlEncoderOutput
 */
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 
 /**
     Make a prediction using the standard interface
-    @param input an instance of whisper_encoder_implInput to predict from
+    @param input an instance of CoremlEncoderInput to predict from
     @param options prediction options
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as whisper_encoder_implOutput
+    @return the prediction as CoremlEncoderOutput
 */
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 
 /**
     Make a prediction using the convenience interface
-    @param logmel_data as 1 × 80 × 3000 3-dimensional array of floats:
+    @param melSegment as 1 × 80 × 3000 3-dimensional array of floats:
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as whisper_encoder_implOutput
+    @return the prediction as CoremlEncoderOutput
 */
-- (nullable whisper_encoder_implOutput *)predictionFromLogmel_data:(MLMultiArray *)logmel_data error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+- (nullable CoremlEncoderOutput *)predictionFromMelSegment:(MLMultiArray *)melSegment error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 
 /**
     Batch prediction
-    @param inputArray array of whisper_encoder_implInput instances to obtain predictions from
+    @param inputArray array of CoremlEncoderInput instances to obtain predictions from
     @param options prediction options
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the predictions as NSArray<whisper_encoder_implOutput *>
+    @return the predictions as NSArray<CoremlEncoderOutput *>
 */
-- (nullable NSArray<whisper_encoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_encoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
+- (nullable NSArray<CoremlEncoderOutput *> *)predictionsFromInputs:(NSArray<CoremlEncoderInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
 @end
 
 NS_ASSUME_NONNULL_END

coreml/whisper-encoder-impl.m

@@ -1,5 +1,5 @@
 //
-// whisper-encoder-impl.m
+// CoremlEncoder.m
 //
 // This file was automatically generated and should not be edited.
 //
@@ -10,30 +10,30 @@
 
 #import "whisper-encoder-impl.h"
 
-@implementation whisper_encoder_implInput
+@implementation CoremlEncoderInput
 
-- (instancetype)initWithLogmel_data:(MLMultiArray *)logmel_data {
+- (instancetype)initWithMelSegment:(MLMultiArray *)melSegment {
     self = [super init];
     if (self) {
-        _logmel_data = logmel_data;
+        _melSegment = melSegment;
     }
     return self;
 }
 
 - (NSSet<NSString *> *)featureNames {
-    return [NSSet setWithArray:@[@"logmel_data"]];
+    return [NSSet setWithArray:@[@"melSegment"]];
 }
 
 - (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
-    if ([featureName isEqualToString:@"logmel_data"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.logmel_data];
+    if ([featureName isEqualToString:@"melSegment"]) {
+        return [MLFeatureValue featureValueWithMultiArray:self.melSegment];
     }
     return nil;
 }
 
 @end
 
-@implementation whisper_encoder_implOutput
+@implementation CoremlEncoderOutput
 
 - (instancetype)initWithOutput:(MLMultiArray *)output {
     self = [super init];
@@ -56,23 +56,23 @@
 
 @end
 
-@implementation whisper_encoder_impl
+@implementation CoremlEncoder
 
 
 /**
     URL of the underlying .mlmodelc directory.
 */
 + (nullable NSURL *)URLOfModelInThisBundle {
-    NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"whisper_encoder_impl" ofType:@"mlmodelc"];
-    if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load whisper-encoder-impl.mlmodelc in the bundle resource"); return nil; }
+    NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"CoremlEncoder" ofType:@"mlmodelc"];
+    if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load CoremlEncoder.mlmodelc in the bundle resource"); return nil; }
     return [NSURL fileURLWithPath:assetPath];
 }
 
 
 /**
-    Initialize whisper_encoder_impl instance from an existing MLModel object.
+    Initialize CoremlEncoder instance from an existing MLModel object.
 
-    Usually the application does not use this initializer unless it makes a subclass of whisper_encoder_impl.
+    Usually the application does not use this initializer unless it makes a subclass of CoremlEncoder.
     Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
 */
 - (instancetype)initWithMLModel:(MLModel *)model {
@@ -85,7 +85,7 @@
 
 
 /**
-    Initialize whisper_encoder_impl instance with the model in this bundle.
+    Initialize CoremlEncoder instance with the model in this bundle.
 */
 - (nullable instancetype)init {
     return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
@@ -93,7 +93,7 @@
 
 
 /**
-    Initialize whisper_encoder_impl instance with the model in this bundle.
+    Initialize CoremlEncoder instance with the model in this bundle.
 
     @param configuration The model configuration object
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
@@ -104,9 +104,9 @@
 
 
 /**
-    Initialize whisper_encoder_impl instance from the model URL.
+    Initialize CoremlEncoder instance from the model URL.
 
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
 */
 - (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error {
@@ -117,9 +117,9 @@
 
 
 /**
-    Initialize whisper_encoder_impl instance from the model URL.
+    Initialize CoremlEncoder instance from the model URL.
 
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_impl.
+    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
     @param configuration The model configuration object
     @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
 */
@@ -131,13 +131,13 @@
 
 
 /**
-    Construct whisper_encoder_impl instance asynchronously with configuration.
+    Construct CoremlEncoder instance asynchronously with configuration.
     Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
 
     @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
 */
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler {
++ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler {
     [self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
               configuration:configuration
           completionHandler:handler];
@@ -145,20 +145,20 @@
 
 
 /**
-    Construct whisper_encoder_impl instance asynchronously with URL of .mlmodelc directory and optional configuration.
+    Construct CoremlEncoder instance asynchronously with URL of .mlmodelc directory and optional configuration.
 
     Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
 
     @param modelURL The model URL.
     @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid whisper_encoder_impl instance or NSError object.
+    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
 */
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler {
++ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler {
     [MLModel loadContentsOfURL:modelURL
                  configuration:configuration
              completionHandler:^(MLModel *model, NSError *error) {
         if (model != nil) {
-            whisper_encoder_impl *typedModel = [[whisper_encoder_impl alloc] initWithMLModel:model];
+            CoremlEncoder *typedModel = [[CoremlEncoder alloc] initWithMLModel:model];
             handler(typedModel, nil);
         } else {
             handler(nil, error);
@@ -166,29 +166,29 @@
     }];
 }
 
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
     return [self predictionFromFeatures:input options:[[MLPredictionOptions alloc] init] error:error];
 }
 
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
     id<MLFeatureProvider> outFeatures = [self.model predictionFromFeatures:input options:options error:error];
     if (!outFeatures) { return nil; }
-    return [[whisper_encoder_implOutput alloc] initWithOutput:(MLMultiArray *)[outFeatures featureValueForName:@"output"].multiArrayValue];
+    return [[CoremlEncoderOutput alloc] initWithOutput:(MLMultiArray *)[outFeatures featureValueForName:@"output"].multiArrayValue];
 }
 
-- (nullable whisper_encoder_implOutput *)predictionFromLogmel_data:(MLMultiArray *)logmel_data error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    whisper_encoder_implInput *input_ = [[whisper_encoder_implInput alloc] initWithLogmel_data:logmel_data];
+- (nullable CoremlEncoderOutput *)predictionFromMelSegment:(MLMultiArray *)melSegment error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+    CoremlEncoderInput *input_ = [[CoremlEncoderInput alloc] initWithMelSegment:melSegment];
     return [self predictionFromFeatures:input_ error:error];
 }
 
-- (nullable NSArray<whisper_encoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_encoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
+- (nullable NSArray<CoremlEncoderOutput *> *)predictionsFromInputs:(NSArray<CoremlEncoderInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
     id<MLBatchProvider> inBatch = [[MLArrayBatchProvider alloc] initWithFeatureProviderArray:inputArray];
     id<MLBatchProvider> outBatch = [self.model predictionsFromBatch:inBatch options:options error:error];
     if (!outBatch) { return nil; }
-    NSMutableArray<whisper_encoder_implOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
+    NSMutableArray<CoremlEncoderOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
     for (NSInteger i = 0; i < outBatch.count; i++) {
         id<MLFeatureProvider> resultProvider = [outBatch featuresAtIndex:i];
-        whisper_encoder_implOutput * result = [[whisper_encoder_implOutput alloc] initWithOutput:(MLMultiArray *)[resultProvider featureValueForName:@"output"].multiArrayValue];
+        CoremlEncoderOutput * result = [[CoremlEncoderOutput alloc] initWithOutput:(MLMultiArray *)[resultProvider featureValueForName:@"output"].multiArrayValue];
         [results addObject:result];
     }
     return results;

coreml/whisper-encoder.mm

@@ -18,7 +18,7 @@ struct whisper_coreml_context * whisper_coreml_init(const char * path_model) {
 
     NSURL * url_model = [NSURL fileURLWithPath: path_model_str];
 
-    const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model error:nil]);
+    const void * data = CFBridgingRetain([[CoremlEncoder alloc] initWithContentsOfURL:url_model error:nil]);
 
     if (data == NULL) {
         return NULL;
@@ -49,16 +49,10 @@ void whisper_coreml_encode(
                                            error: nil
     ];
 
-    whisper_encoder_implOutput * outCoreML = [(__bridge id) ctx->data predictionFromLogmel_data:inMultiArray error:nil];
+    CoremlEncoderOutput * outCoreML = [(__bridge id) ctx->data predictionFromMelSegment:inMultiArray error:nil];
 
     MLMultiArray * outMA = outCoreML.output;
 
-    //NSArray<NSNumber *> * shape = outMA.shape;
-    //NSArray<NSNumber *> * strides = outMA.strides;
-
-    //printf("shape:   %ld %ld %ld %ld\n", [shape[0] longValue], [shape[1] longValue], [shape[2] longValue], [shape[3] longValue]);
-    //printf("strides: %ld %ld %ld %ld\n", [strides[0] longValue], [strides[1] longValue], [strides[2] longValue], [strides[3] longValue]);
-
     memcpy(out, outMA.dataPointer, outMA.count * sizeof(float));
 }
 

examples/CMakeLists.txt

@@ -4,7 +4,7 @@ find_package(Threads REQUIRED)
 
 # third-party
 
-if (WHISPER_SDL2)
+if (WHISPER_SUPPORT_SDL2)
     # SDL2
     find_package(SDL2 REQUIRED)
 
@@ -21,17 +21,13 @@ set(TARGET common)
 add_library(${TARGET} STATIC
     common.h
     common.cpp
-    common-ggml.h
-    common-ggml.cpp
     )
 
 include(DefaultTargetOptions)
 
-target_link_libraries(${TARGET} PRIVATE whisper)
-
 set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
-if (WHISPER_SDL2)
+if (WHISPER_SUPPORT_SDL2)
     # common-sdl
 
     set(TARGET common-sdl)
@@ -66,7 +62,5 @@ else()
     add_subdirectory(stream)
     add_subdirectory(command)
     add_subdirectory(bench)
-    add_subdirectory(quantize)
     add_subdirectory(talk)
-    add_subdirectory(talk-llama)
 endif()

examples/addon.node/__test__/whisper.spec.js

@@ -1,23 +1,15 @@
-const path = require("path");
-const { whisper } = require(path.join(
-  __dirname,
-  "../../../build/Release/whisper-addon"
-));
-const { promisify } = require("util");
-
-const whisperAsync = promisify(whisper);
+const path = require('path');
+const { whisper } = require(path.join(__dirname, '../../../build/Release/whisper-addon'));
 
 const whisperParamsMock = {
-  language: "en",
-  model: path.join(__dirname, "../../../models/ggml-base.en.bin"),
-  fname_inp: path.join(__dirname, "../../../samples/jfk.wav"),
+    language: 'en',
+    model: path.join(__dirname, '../../../models/ggml-base.en.bin'),
+    fname_inp: path.join(__dirname, '../../../samples/jfk.wav'),
 };
 
 describe("Run whisper.node", () => {
-    test("it should receive a non-empty value", async () => {
-        let result = await whisperAsync(whisperParamsMock);
 
-        expect(result.length).toBeGreaterThan(0);
-    }, 10000);
+    test("it should receive a non-empty value", () => {
+        expect(whisper(whisperParamsMock).length).toBeGreaterThan(0);
+    });
 });
-

examples/addon.node/addon.cpp

@@ -160,6 +160,22 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
         return 3;
     }
 
+    // initial prompt
+    std::vector<whisper_token> prompt_tokens;
+
+    if (!params.prompt.empty()) {
+        prompt_tokens.resize(1024);
+        prompt_tokens.resize(whisper_tokenize(ctx, params.prompt.c_str(), prompt_tokens.data(), prompt_tokens.size()));
+
+        fprintf(stderr, "\n");
+        fprintf(stderr, "initial prompt: '%s'\n", params.prompt.c_str());
+        fprintf(stderr, "initial tokens: [ ");
+        for (int i = 0; i < (int) prompt_tokens.size(); ++i) {
+            fprintf(stderr, "%d ", prompt_tokens[i]);
+        }
+        fprintf(stderr, "]\n");
+    }
+
     for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
         const auto fname_inp = params.fname_inp[f];
         const auto fname_out = f < (int)params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
@@ -227,7 +243,8 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
             wparams.greedy.best_of        = params.best_of;
             wparams.beam_search.beam_size = params.beam_size;
 
-            wparams.initial_prompt   = params.prompt.c_str();
+            wparams.prompt_tokens     = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
+            wparams.prompt_n_tokens   = prompt_tokens.empty() ? 0       : prompt_tokens.size();
 
             whisper_print_user_data user_data = { &params, &pcmf32s };
 

examples/bench.wasm/CMakeLists.txt

@@ -31,9 +31,9 @@ endif()
 set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
     --bind \
     -s USE_PTHREADS=1 \
-    -s PTHREAD_POOL_SIZE_STRICT=0 \
-    -s INITIAL_MEMORY=2000MB \
-    -s TOTAL_MEMORY=2000MB \
+    -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} \

examples/bench.wasm/index-tmpl.html

@@ -35,15 +35,6 @@
 
             <br><br>
 
-            <b>More examples:</b>
-                <a href="https://whisper.ggerganov.com/">main</a> |
-                <a href="https://whisper.ggerganov.com/bench">bench</a> |
-                <a href="https://whisper.ggerganov.com/stream">stream</a> |
-                <a href="https://whisper.ggerganov.com/command">command</a> |
-                <a href="https://whisper.ggerganov.com/talk">talk</a> |
-
-            <br><br>
-
             <hr>
 
             Select the model you would like to use and click the "Bench" button.<br>
@@ -53,18 +44,11 @@
 
             <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>
-                <button id="fetch-whisper-small-en" onclick="loadWhisper('small.en')">small.en (466 MB)</button>
-                <input type="file" id="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
-                <br><br>
-                Quantized models:<br><br>
-                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
-                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
-                <button id="fetch-whisper-small-en-q5_1"  onclick="loadWhisper('small-en-q5_1')">small.en (Q5_1, 182 MB)</button>
-                <button id="fetch-whisper-medium-en-q5_0" onclick="loadWhisper('medium-en-q5_0')">medium.en (Q5_0, 515 MB)</button>
-                <button id="fetch-whisper-large-q5_0"     onclick="loadWhisper('large-q5_0')">large (Q5_0, 1030 MB)</button>
+                <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="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
             </div>
 
             <br>
@@ -176,14 +160,6 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
-                document.getElementById('fetch-whisper-small-en').style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-small-en-q5_1' ).style.display = 'none';
-                document.getElementById('fetch-whisper-medium-en-q5_0').style.display = 'none';
-                document.getElementById('fetch-whisper-large-q5_0'    ).style.display = 'none';
-
                 document.getElementById('whisper-file'         ).style.display = 'none';
                 document.getElementById('model-whisper-status' ).innerHTML = 'loaded model: ' + file.name;
             }
@@ -192,42 +168,19 @@
                 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',
-                    'small.en': 'https://whisper.ggerganov.com/ggml-model-whisper-small.en.bin',
-
-                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
-                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
-                    'small-en-q5_1': 'https://whisper.ggerganov.com/ggml-model-whisper-small.en-q5_1.bin',
-                    'medium-en-q5_0':'https://whisper.ggerganov.com/ggml-model-whisper-medium.en-q5_0.bin',
-                    'large-q5_0':    'https://whisper.ggerganov.com/ggml-model-whisper-large-q5_0.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
-                    'small.en': 466,
-
-                    'tiny-en-q5_1':   31,
-                    'base-en-q5_1':   57,
-                    'small-en-q5_1':  182,
-                    'medium-en-q5_0': 515,
-                    'large-q5_0':     1030,
                 };
 
                 let url     = urls[model];
                 let dst     = 'whisper.bin';
                 let size_mb = sizes[model];
 
-                document.getElementById('fetch-whisper-tiny-en').style.display  = 'none';
-                document.getElementById('fetch-whisper-base-en').style.display  = 'none';
-                document.getElementById('fetch-whisper-small-en').style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-small-en-q5_1' ).style.display = 'none';
-                document.getElementById('fetch-whisper-medium-en-q5_0').style.display = 'none';
-                document.getElementById('fetch-whisper-large-q5_0'    ).style.display = 'none';
-
-                document.getElementById('whisper-file'        ).style.display = 'none';
+                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) {
@@ -237,18 +190,9 @@
 
                 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-small-en'); if (el) el.style.display = 'inline-block';
-
-                    el = document.getElementById('fetch-whisper-tiny-en-q5_1'  ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en-q5_1'  ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-small-en-q5_1' ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-medium-en-q5_0'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-large-q5_0'    ); 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 = '';
+                    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);

examples/command.wasm/emscripten.cpp

@@ -28,6 +28,31 @@ std::string g_transcribed   = "";
 
 std::vector<float> g_pcmf32;
 
+// 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;

examples/command.wasm/index-tmpl.html

@@ -35,15 +35,6 @@
 
             <br><br>
 
-            <b>More examples:</b>
-                <a href="https://whisper.ggerganov.com/">main</a> |
-                <a href="https://whisper.ggerganov.com/bench">bench</a> |
-                <a href="https://whisper.ggerganov.com/stream">stream</a> |
-                <a href="https://whisper.ggerganov.com/command">command</a> |
-                <a href="https://whisper.ggerganov.com/talk">talk</a> |
-
-            <br><br>
-
             <hr>
 
             Select the model you would like to use, click the "Start" button and follow the instructions.
@@ -54,10 +45,6 @@
                 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>
-                <br><br>
-                Quantized models:<br><br>
-                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
-                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
                 <span id="fetch-whisper-progress"></span>
 
                 <!--
@@ -175,17 +162,11 @@
                 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',
-
-                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
-                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
-
-                    'tiny-en-q5_1':   31,
-                    'base-en-q5_1':   57,
                 };
 
                 let url     = urls[model];
@@ -196,10 +177,6 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1').style.display = 'none';
-
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -211,10 +188,6 @@
                     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-en-q5_1'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en-q5_1'); if (el) el.style.display = 'inline-block';
-
                     el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
                 };
 

examples/command/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SDL2)
+if (WHISPER_SUPPORT_SDL2)
     # command
     set(TARGET command)
     add_executable(${TARGET} command.cpp)

examples/command/command.cpp

@@ -163,6 +163,31 @@ std::string transcribe(whisper_context * ctx, const whisper_params & params, con
     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()));
+}
+
 std::vector<std::string> read_allowed_commands(const std::string & fname) {
     std::vector<std::string> allowed_commands;
 

examples/common-ggml.cpp

@@ -1,241 +0,0 @@
-#include "common-ggml.h"
-
-#include <regex>
-#include <map>
-
-static const std::map<std::string, enum ggml_ftype> GGML_FTYPE_MAP = {
-    {"q4_0", GGML_FTYPE_MOSTLY_Q4_0},
-    {"q4_1", GGML_FTYPE_MOSTLY_Q4_1},
-    {"q4_2", GGML_FTYPE_MOSTLY_Q4_2},
-    {"q5_0", GGML_FTYPE_MOSTLY_Q5_0},
-    {"q5_1", GGML_FTYPE_MOSTLY_Q5_1},
-    {"q8_0", GGML_FTYPE_MOSTLY_Q8_0},
-};
-
-void ggml_print_ftypes(FILE * fp) {
-    for (auto it = GGML_FTYPE_MAP.begin(); it != GGML_FTYPE_MAP.end(); it++) {
-        fprintf(fp, "  type = \"%s\" or %d\n", it->first.c_str(), it->second);
-    }
-}
-
-enum ggml_ftype ggml_parse_ftype(const char * str) {
-    enum ggml_ftype ftype;
-    if (str[0] == 'q') {
-        const auto it = GGML_FTYPE_MAP.find(str);
-        if (it == GGML_FTYPE_MAP.end()) {
-            fprintf(stderr, "%s: unknown ftype '%s'\n", __func__, str);
-            return GGML_FTYPE_UNKNOWN;
-        }
-        ftype = it->second;
-    } else {
-        ftype = (enum ggml_ftype) atoi(str);
-    }
-
-    return ftype;
-}
-
-bool ggml_common_quantize_0(
-        std::ifstream & finp,
-        std::ofstream & fout,
-        const ggml_ftype ftype,
-        const std::vector<std::string> & to_quant,
-        const std::vector<std::string> & to_skip) {
-
-    ggml_type qtype = GGML_TYPE_F32;
-
-    switch (ftype) {
-        case GGML_FTYPE_MOSTLY_Q4_0: qtype = GGML_TYPE_Q4_0; break;
-        case GGML_FTYPE_MOSTLY_Q4_1: qtype = GGML_TYPE_Q4_1; break;
-        case GGML_FTYPE_MOSTLY_Q4_2: qtype = GGML_TYPE_Q4_2; break;
-        case GGML_FTYPE_MOSTLY_Q5_0: qtype = GGML_TYPE_Q5_0; break;
-        case GGML_FTYPE_MOSTLY_Q5_1: qtype = GGML_TYPE_Q5_1; break;
-        case GGML_FTYPE_MOSTLY_Q8_0: qtype = GGML_TYPE_Q8_0; break;
-        case GGML_FTYPE_UNKNOWN:
-        case GGML_FTYPE_ALL_F32:
-        case GGML_FTYPE_MOSTLY_F16:
-        case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
-                {
-                    fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype);
-                    return false;
-                }
-    };
-
-    if (!ggml_is_quantized(qtype)) {
-        fprintf(stderr, "%s: invalid quantization type %d (%s)\n", __func__, qtype, ggml_type_name(qtype));
-        return false;
-    }
-
-    size_t total_size_org = 0;
-    size_t total_size_new = 0;
-
-    std::vector<float> work;
-
-    std::vector<uint8_t>     data_u8;
-    std::vector<ggml_fp16_t> data_f16;
-    std::vector<float>       data_f32;
-
-    std::vector<int64_t> hist_all(1 << 4, 0);
-
-    while (true) {
-        int32_t n_dims;
-        int32_t length;
-        int32_t ttype;
-
-        finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-        finp.read(reinterpret_cast<char *>(&length), sizeof(length));
-        finp.read(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
-
-        if (finp.eof()) {
-            break;
-        }
-
-        int32_t nelements = 1;
-        int32_t ne[4] = { 1, 1, 1, 1 };
-        for (int i = 0; i < n_dims; ++i) {
-            finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-            nelements *= ne[i];
-        }
-
-        std::string name(length, 0);
-        finp.read (&name[0], length);
-
-        printf("%64s - [%5d, %5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type) ttype));
-
-        bool quantize = false;
-
-        // check if we should quantize this tensor
-        for (const auto & s : to_quant) {
-            if (std::regex_match(name, std::regex(s))) {
-                quantize = true;
-                break;
-            }
-        }
-
-        // check if we should skip this tensor
-        for (const auto & s : to_skip) {
-            if (std::regex_match(name, std::regex(s))) {
-                quantize = false;
-                break;
-            }
-        }
-
-        // quantize only 2D tensors
-        quantize &= (n_dims == 2);
-
-        if (quantize) {
-            if (ttype != GGML_TYPE_F32 && ttype != GGML_TYPE_F16) {
-                fprintf(stderr, "%s: unsupported ttype %d (%s) for integer quantization\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
-                return false;
-            }
-
-            if (ttype == GGML_TYPE_F16) {
-                data_f16.resize(nelements);
-                finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
-                data_f32.resize(nelements);
-                for (int i = 0; i < nelements; ++i) {
-                    data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
-                }
-            } else {
-                data_f32.resize(nelements);
-                finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
-            }
-
-            ttype = qtype;
-        } else {
-            const int bpe = (ttype == 0) ? sizeof(float) : sizeof(uint16_t);
-
-            data_u8.resize(nelements*bpe);
-            finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
-        }
-
-        fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
-        fout.write(reinterpret_cast<char *>(&length), sizeof(length));
-        fout.write(reinterpret_cast<char *>(&ttype),  sizeof(ttype));
-        for (int i = 0; i < n_dims; ++i) {
-            fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
-        }
-        fout.write(&name[0], length);
-
-        if (quantize) {
-            work.resize(nelements); // for quantization
-
-            size_t cur_size = 0;
-            std::vector<int64_t> hist_cur(1 << 4, 0);
-
-            switch ((ggml_type) ttype) {
-                case GGML_TYPE_Q4_0:
-                    {
-                        cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
-                    } break;
-                case GGML_TYPE_Q4_1:
-                    {
-                        cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
-                    } break;
-                case GGML_TYPE_Q4_2:
-                    {
-                        cur_size = ggml_quantize_q4_2(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
-                    } break;
-                case GGML_TYPE_Q5_0:
-                    {
-                        cur_size = ggml_quantize_q5_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
-                    } break;
-                case GGML_TYPE_Q5_1:
-                    {
-                        cur_size = ggml_quantize_q5_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
-                    } break;
-                case GGML_TYPE_Q8_0:
-                    {
-                        cur_size = ggml_quantize_q8_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
-                    } break;
-                case GGML_TYPE_F32:
-                case GGML_TYPE_F16:
-                case GGML_TYPE_I8:
-                case GGML_TYPE_I16:
-                case GGML_TYPE_I32:
-                case GGML_TYPE_Q8_1:
-                case GGML_TYPE_COUNT:
-                    {
-                        fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
-                        return false;
-                    }
-            }
-
-            fout.write(reinterpret_cast<char *>(work.data()), cur_size);
-            total_size_new += cur_size;
-
-            printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
-            for (int i = 0; i < (int) hist_cur.size(); ++i) {
-                hist_all[i] += hist_cur[i];
-            }
-
-            for (int i = 0; i < (int) hist_cur.size(); ++i) {
-                printf("%5.3f ", hist_cur[i] / (float)nelements);
-            }
-            printf("\n");
-        } else {
-            printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
-            fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
-            total_size_new += data_u8.size();
-        }
-
-        total_size_org += nelements * sizeof(float);
-    }
-
-    printf("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
-    printf("%s: quant size  = %8.2f MB | ftype = %d (%s)\n", __func__, total_size_new/1024.0/1024.0, ftype, ggml_type_name(qtype));
-
-    {
-        int64_t sum_all = 0;
-        for (int i = 0; i < (int) hist_all.size(); ++i) {
-            sum_all += hist_all[i];
-        }
-
-        printf("%s: hist: ", __func__);
-        for (int i = 0; i < (int) hist_all.size(); ++i) {
-            printf("%5.3f ", hist_all[i] / (float)sum_all);
-        }
-        printf("\n");
-    }
-
-    return true;
-}

examples/common-ggml.h

@@ -1,18 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-
-#include <fstream>
-#include <vector>
-#include <string>
-
-enum ggml_ftype ggml_parse_ftype(const char * str);
-
-void ggml_print_ftypes(FILE * fp = stderr);
-
-bool ggml_common_quantize_0(
-        std::ifstream & finp,
-        std::ofstream & fout,
-        const ggml_ftype ftype,
-        const std::vector<std::string> & to_quant,
-        const std::vector<std::string> & to_skip);

examples/common.cpp

@@ -6,86 +6,12 @@
 #include "dr_wav.h"
 
 #include <cmath>
-#include <fstream>
 #include <regex>
 
 #ifndef M_PI
 #define M_PI 3.14159265358979323846
 #endif
 
-bool gpt_params_parse(int argc, char ** argv, gpt_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 == "-p" || arg == "--prompt") {
-            params.prompt = argv[++i];
-        } else if (arg == "-n" || arg == "--n_predict") {
-            params.n_predict = std::stoi(argv[++i]);
-        } else if (arg == "--top_k") {
-            params.top_k = std::stoi(argv[++i]);
-        } else if (arg == "--top_p") {
-            params.top_p = std::stof(argv[++i]);
-        } else if (arg == "--temp") {
-            params.temp = std::stof(argv[++i]);
-        } else if (arg == "-b" || arg == "--batch_size") {
-            params.n_batch = std::stoi(argv[++i]);
-        } else if (arg == "-m" || arg == "--model") {
-            params.model = argv[++i];
-        } else if (arg == "-h" || arg == "--help") {
-            gpt_print_usage(argc, argv, params);
-            exit(0);
-        } else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            gpt_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
-    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, "  -p PROMPT, --prompt PROMPT\n");
-    fprintf(stderr, "                        prompt to start generation with (default: random)\n");
-    fprintf(stderr, "  -n N, --n_predict N   number of tokens to predict (default: %d)\n", params.n_predict);
-    fprintf(stderr, "  --top_k N             top-k sampling (default: %d)\n", params.top_k);
-    fprintf(stderr, "  --top_p N             top-p sampling (default: %.1f)\n", params.top_p);
-    fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", params.temp);
-    fprintf(stderr, "  -b N, --batch_size N  batch size for prompt processing (default: %d)\n", params.n_batch);
-    fprintf(stderr, "  -m FNAME, --model FNAME\n");
-    fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
-    fprintf(stderr, "\n");
-}
-
-std::string gpt_random_prompt(std::mt19937 & rng) {
-    const int r = rng() % 10;
-    switch (r) {
-        case 0: return "So";
-        case 1: return "Once upon a time";
-        case 2: return "When";
-        case 3: return "The";
-        case 4: return "After";
-        case 5: return "If";
-        case 6: return "import";
-        case 7: return "He";
-        case 8: return "She";
-        case 9: return "They";
-        default: return "To";
-    }
-
-    return "The";
-}
-
 std::string trim(const std::string & s) {
     std::regex e("^\\s+|\\s+$");
     return std::regex_replace(s, e, "");
@@ -101,251 +27,6 @@ std::string replace(const std::string & s, const std::string & from, const std::
     return result;
 }
 
-std::map<std::string, int32_t> json_parse(const std::string & fname) {
-    std::map<std::string, int32_t> result;
-
-    // read file into string
-    std::string json;
-    {
-        std::ifstream ifs(fname);
-        if (!ifs) {
-            fprintf(stderr, "Failed to open %s\n", fname.c_str());
-            exit(1);
-        }
-
-        json = std::string((std::istreambuf_iterator<char>(ifs)),
-                (std::istreambuf_iterator<char>()));
-    }
-
-    if (json[0] != '{') {
-        return result;
-    }
-
-    // parse json
-    {
-        bool has_key  = false;
-        bool in_token = false;
-
-        std::string str_key = "";
-        std::string str_val = "";
-
-        int n = json.size();
-        for (int i = 1; i < n; ++i) {
-            if (!in_token) {
-                if (json[i] == ' ') continue;
-                if (json[i] == '"') {
-                    in_token = true;
-                    continue;
-                }
-            } else {
-                if (json[i] == '\\' && i+1 < n) {
-                    if (has_key == false) {
-                        str_key += json[i];
-                    } else {
-                        str_val += json[i];
-                    }
-                    ++i;
-                } else if (json[i] == '"') {
-                    if (has_key == false) {
-                        has_key = true;
-                        ++i;
-                        while (json[i] == ' ') ++i;
-                        ++i; // :
-                        while (json[i] == ' ') ++i;
-                        if (json[i] != '\"') {
-                            while (json[i] != ',' && json[i] != '}') {
-                                str_val += json[i++];
-                            }
-                            has_key = false;
-                        } else {
-                            in_token = true;
-                            continue;
-                        }
-                    } else {
-                        has_key = false;
-                    }
-
-                    str_key = ::replace(str_key, "\\u0120", " " ); // \u0120 -> space
-                    str_key = ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
-                    str_key = ::replace(str_key, "\\\"",    "\""); // \\\"   -> "
-
-                    try {
-                        result[str_key] = std::stoi(str_val);
-                    } catch (...) {
-                        //fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
-
-                    }
-                    str_key = "";
-                    str_val = "";
-                    in_token = false;
-                    continue;
-                }
-                if (has_key == false) {
-                    str_key += json[i];
-                } else {
-                    str_val += json[i];
-                }
-            }
-        }
-    }
-
-    return result;
-}
-
-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;
-}
-
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
-    printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
-
-    vocab.token_to_id = ::json_parse(fname);
-
-    for (const auto & kv : vocab.token_to_id) {
-        vocab.id_to_token[kv.second] = kv.first;
-    }
-
-    printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
-
-    // print the vocabulary
-    //for (auto kv : vocab.token_to_id) {
-    //    printf("'%s' -> %d\n", kv.first.data(), kv.second);
-    //}
-
-    return true;
-}
-
-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);
-
-    {
-        const double scale = 1.0/temp;
-        for (int i = 0; i < n_logits; ++i) {
-            logits_id.push_back(std::make_pair(logits[i]*scale, 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);
-
-    double maxl = -INFINITY;
-    for (const auto & kv : logits_id) {
-        maxl = std::max(maxl, kv.first);
-    }
-
-    // compute probs for the top K tokens
-    std::vector<double> probs;
-    probs.reserve(logits_id.size());
-
-    double sum = 0.0;
-    for (const auto & kv : logits_id) {
-        double p = exp(kv.first - maxl);
-        probs.push_back(p);
-        sum += p;
-    }
-
-    // normalize the probs
-    for (auto & p : probs) {
-        p /= sum;
-    }
-
-    if (top_p < 1.0f) {
-        double cumsum = 0.0f;
-        for (int i = 0; i < top_k; i++) {
-            cumsum += probs[i];
-            if (cumsum >= top_p) {
-                top_k = i + 1;
-                probs.resize(top_k);
-                logits_id.resize(top_k);
-                break;
-            }
-        }
-
-        cumsum = 1.0/cumsum;
-        for (int i = 0; i < (int) probs.size(); i++) {
-            probs[i] *= cumsum;
-        }
-    }
-
-    //printf("\n");
-    //for (int i = 0; i < (int) probs.size(); i++) {
-    //    printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
-    //}
-    //exit(0);
-
-    std::discrete_distribution<> dist(probs.begin(), probs.end());
-    int idx = dist(rng);
-
-    return logits_id[idx].second;
-}
-
 bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) {
     drwav wav;
     std::vector<uint8_t> wav_data; // used for pipe input from stdin
@@ -479,27 +160,3 @@ bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float
 
     return true;
 }
-
-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] + (i > 0 && 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()));
-}

examples/common.h

@@ -1,44 +1,10 @@
-// Various helper functions and utilities
-
 #pragma once
 
-#include <string>
-#include <map>
-#include <vector>
-#include <random>
-#include <thread>
-
+// needs to match WHISPER_SAMPLE_RATE
 #define COMMON_SAMPLE_RATE 16000
 
-//
-// CLI argument parsing
-//
-
-struct gpt_params {
-    int32_t seed      = -1; // RNG seed
-    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t n_predict = 200; // new tokens to predict
-
-    // sampling parameters
-    int32_t top_k = 40;
-    float   top_p = 0.9f;
-    float   temp  = 0.9f;
-
-    int32_t n_batch = 8; // batch size for prompt processing
-
-    std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path
-    std::string prompt;
-};
-
-bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
-
-void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
-
-std::string gpt_random_prompt(std::mt19937 & rng);
-
-//
-// Vocab utils
-//
+#include <vector>
+#include <string>
 
 std::string trim(const std::string & s);
 
@@ -47,52 +13,6 @@ std::string replace(
         const std::string & from,
         const std::string & to);
 
-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;
-};
-
-// poor-man's JSON parsing
-std::map<std::string, int32_t> json_parse(const std::string & fname);
-
-// split text into tokens
-//
-// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
-//
-// Regex (Python):
-// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
-//
-// Regex (C++):
-// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
-//
-std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
-
-// load the tokens from encoder.json
-bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
-
-// sample next token given probabilities for each embedding
-//
-//   - consider only the top K tokens
-//   - from them, consider only the top tokens with cumulative probability > P
-//
-// TODO: not sure if this implementation is correct
-// TODO: temperature is not implemented
-//
-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);
-
-//
-// Audio utils
-//
-
 // Read WAV audio file and store the PCM data into pcmf32
 // The sample rate of the audio must be equal to COMMON_SAMPLE_RATE
 // If stereo flag is set and the audio has 2 channels, the pcmf32s will contain 2 channel PCM
@@ -118,5 +38,3 @@ bool vad_simple(
         float freq_thold,
         bool  verbose);
 
-// compute similarity between two strings using Levenshtein distance
-float similarity(const std::string & s0, const std::string & s1);

examples/helpers.js

@@ -145,15 +145,7 @@ function loadRemote(url, dst, size_mb, cbProgress, cbReady, cbCancel, cbPrint) {
                             var db = event.target.result;
                             var tx = db.transaction(['models'], 'readwrite');
                             var os = tx.objectStore('models');
-
-                            var rq = null;
-                            try {
-                                var rq = os.put(data, url);
-                            } catch (e) {
-                                cbPrint('loadRemote: failed to store "' + url + '" in the IndexedDB: \n' + e);
-                                cbCancel();
-                                return;
-                            }
+                            var rq = os.put(data, url);
 
                             rq.onsuccess = function (event) {
                                 cbPrint('loadRemote: "' + url + '" stored in the IndexedDB');
@@ -188,6 +180,7 @@ function loadRemote(url, dst, size_mb, cbProgress, cbReady, cbCancel, cbPrint) {
 
     rq.onabort = function (event) {
         cbPrint('loadRemote: failed to open IndexedDB: abort');
-        cbCancel();
+
     };
 }
+

examples/main/main.cpp

@@ -8,7 +8,6 @@
 #include <string>
 #include <thread>
 #include <vector>
-#include <cstring>
 
 // Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
 // Lowest is red, middle is yellow, highest is green.
@@ -57,7 +56,7 @@ struct whisper_params {
     int32_t duration_ms  =  0;
     int32_t max_context  = -1;
     int32_t max_len      =  0;
-    int32_t best_of      =  2;
+    int32_t best_of      =  5;
     int32_t beam_size    = -1;
 
     float word_thold    =  0.01f;
@@ -75,7 +74,6 @@ struct whisper_params {
     bool output_wts     = false;
     bool output_csv     = false;
     bool output_jsn     = false;
-    bool output_lrc     = false;
     bool print_special  = false;
     bool print_colors   = false;
     bool print_progress = false;
@@ -131,7 +129,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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 == "-olrc" || arg == "--output-lrc")     { params.output_lrc     = true; }
         else if (arg == "-fp"   || arg == "--font-path")      { params.font_path      = argv[++i]; }
         else if (arg == "-ocsv" || arg == "--output-csv")     { params.output_csv     = true; }
         else if (arg == "-oj"   || arg == "--output-json")    { params.output_jsn     = true; }
@@ -180,7 +177,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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, "  -olrc,     --output-lrc        [%-7s] output result in a lrc file\n",                    params.output_lrc ? "true" : "false");
     fprintf(stderr, "  -owts,     --output-words      [%-7s] output script for generating karaoke video\n",     params.output_wts ? "true" : "false");
     fprintf(stderr, "  -fp,       --font-path         [%-7s] path to a monospace font for karaoke video\n",     params.font_path.c_str());
     fprintf(stderr, "  -ocsv,     --output-csv        [%-7s] output result in a CSV file\n",                    params.output_csv ? "true" : "false");
@@ -211,8 +207,8 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
 
     std::string speaker = "";
 
-    int64_t t0 = 0;
-    int64_t t1 = 0;
+    int64_t t0;
+    int64_t t1;
 
     // print the last n_new segments
     const int s0 = n_segments - n_new;
@@ -352,37 +348,6 @@ bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_
     return true;
 }
 
-char *escape_double_quotes_and_backslashes(const char *str) {
-    if (str == NULL) {
-        return NULL;
-    }
-
-    size_t escaped_length = strlen(str) + 1;
-
-    for (size_t i = 0; str[i] != '\0'; i++) {
-        if (str[i] == '"' || str[i] == '\\') {
-            escaped_length++;
-        }
-    }
-
-    char *escaped = (char *)calloc(escaped_length, 1); // pre-zeroed
-    if (escaped == NULL) {
-        return NULL;
-    }
-
-    size_t pos = 0;
-    for (size_t i = 0; str[i] != '\0'; i++) {
-        if (str[i] == '"' || str[i] == '\\') {
-            escaped[pos++] = '\\';
-        }
-        escaped[pos++] = str[i];
-    }
-
-    // no need to set zero due to calloc() being used prior
-
-    return escaped;
-}
-
 bool output_csv(struct whisper_context * ctx, const char * fname) {
     std::ofstream fout(fname);
     if (!fout.is_open()) {
@@ -398,10 +363,9 @@ bool output_csv(struct whisper_context * ctx, const char * fname) {
         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);
-        char * text_escaped = escape_double_quotes_and_backslashes(text);
 
         //need to multiply times returned from whisper_full_get_segment_t{0,1}() by 10 to get milliseconds.
-        fout << 10 * t0 << "," << 10 * t1 << ",\"" << text_escaped    << "\"\n";
+        fout << 10 * t0 << "," << 10 * t1 << ",\"" << text    << "\"\n";
     }
 
     return true;
@@ -450,9 +414,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
 
     auto value_s = [&](const char *name, const char *val, bool end = false) {
         start_value(name);
-        char * val_escaped = escape_double_quotes_and_backslashes(val);
-        fout << "\"" << val_escaped << (end ? "\"\n" : "\",\n");
-        free(val_escaped);
+        fout << "\"" << val << (end ? "\"\n" : "\",\n");
     };
 
     auto end_value = [&](bool end = false) {
@@ -493,10 +455,10 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
                 value_i("ctx", whisper_model_n_text_ctx(ctx));
                 value_i("state", whisper_model_n_text_state(ctx));
                 value_i("head", whisper_model_n_text_head(ctx));
-                value_i("layer", whisper_model_n_text_layer(ctx), true);
+                value_i("leyer", whisper_model_n_text_layer(ctx), true);
             end_obj();
             value_i("mels", whisper_model_n_mels(ctx));
-            value_i("ftype", whisper_model_ftype(ctx), true);
+            value_i("f16", whisper_model_f16(ctx), true);
         end_obj();
         start_obj("params");
             value_s("model", params.model.c_str());
@@ -515,7 +477,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
                 const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
 
                 start_obj();
-                    start_obj("timestamps");
+                    start_obj("timestanps");
                         value_s("from", to_timestamp(t0, true).c_str());
                         value_s("to", to_timestamp(t1, true).c_str(), true);
                     end_obj();
@@ -649,39 +611,6 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
     return true;
 }
 
-bool output_lrc(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 << "[by:whisper.cpp]\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 t = whisper_full_get_segment_t0(ctx, i);
-
-        int64_t msec = t * 10;
-        int64_t min = msec / (1000 * 60);
-        msec = msec - min * (1000 * 60);
-        int64_t sec = msec / 1000;
-        msec = msec - sec * 1000;
-
-        char buf[16];
-        snprintf(buf, sizeof(buf), "%02d:%02d.%02d", (int) min, (int) sec, (int) ( msec / 10));
-        std::string timestamp_lrc = std::string(buf);
-
-        fout <<  '[' << timestamp_lrc << ']' << text << "\n";
-    }
-
-    return true;
-}
-
 int main(int argc, char ** argv) {
     whisper_params params;
 
@@ -710,6 +639,22 @@ int main(int argc, char ** argv) {
         return 3;
     }
 
+    // initial prompt
+    std::vector<whisper_token> prompt_tokens;
+
+    if (!params.prompt.empty()) {
+        prompt_tokens.resize(1024);
+        prompt_tokens.resize(whisper_tokenize(ctx, params.prompt.c_str(), prompt_tokens.data(), prompt_tokens.size()));
+
+        fprintf(stderr, "\n");
+        fprintf(stderr, "initial prompt: '%s'\n", params.prompt.c_str());
+        fprintf(stderr, "initial tokens: [ ");
+        for (int i = 0; i < (int) prompt_tokens.size(); ++i) {
+            fprintf(stderr, "%d ", prompt_tokens[i]);
+        }
+        fprintf(stderr, "]\n");
+    }
+
     for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
         const auto fname_inp = params.fname_inp[f];
 		const auto fname_out = f < (int) params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
@@ -773,7 +718,8 @@ int main(int argc, char ** argv) {
 
             wparams.speed_up         = params.speed_up;
 
-            wparams.initial_prompt   = params.prompt.c_str();
+            wparams.prompt_tokens     = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
+            wparams.prompt_n_tokens   = prompt_tokens.empty() ? 0       : prompt_tokens.size();
 
             wparams.greedy.best_of        = params.best_of;
             wparams.beam_search.beam_size = params.beam_size;
@@ -848,12 +794,6 @@ int main(int argc, char ** argv) {
                 const auto fname_jsn = fname_out + ".json";
                 output_json(ctx, fname_jsn.c_str(), params);
             }
-
-            // output to LRC file
-            if (params.output_lrc) {
-                const auto fname_lrc = fname_out + ".lrc";
-                output_lrc(ctx, fname_lrc.c_str());
-            }
         }
     }
 

examples/quantize/CMakeLists.txt

@@ -1,6 +0,0 @@
-set(TARGET quantize)
-add_executable(${TARGET} quantize.cpp)
-
-include(DefaultTargetOptions)
-
-target_link_libraries(${TARGET} PRIVATE common whisper ${CMAKE_THREAD_LIBS_INIT})

examples/quantize/README.md

@@ -1,3 +0,0 @@
-# quantize
-
-Tool for integer quantization of Whisper `ggml` model files

examples/quantize/quantize.cpp

@@ -1,215 +0,0 @@
-#include "ggml.h"
-
-#include "common.h"
-#include "common-ggml.h"
-
-#include <cassert>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <fstream>
-#include <map>
-#include <string>
-#include <vector>
-#include <regex>
-
-// default hparams (Whisper tiny)
-struct whisper_hparams {
-    int32_t n_vocab       = 51864;
-    int32_t n_audio_ctx   = 1500;
-    int32_t n_audio_state = 384;
-    int32_t n_audio_head  = 6;
-    int32_t n_audio_layer = 4;
-    int32_t n_text_ctx    = 448;
-    int32_t n_text_state  = 384;
-    int32_t n_text_head   = 6;
-    int32_t n_text_layer  = 4;
-    int32_t n_mels        = 80;
-    int32_t f16           = 1;
-};
-
-struct whisper_filters {
-    int32_t n_mel;
-    int32_t n_fft;
-
-    std::vector<float> data;
-};
-
-// quantize a model
-bool whisper_model_quantize(const std::string & fname_inp, const std::string & fname_out, ggml_ftype ftype) {
-    gpt_vocab vocab;
-
-    printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
-
-    auto finp = std::ifstream(fname_inp, std::ios::binary);
-    if (!finp) {
-        fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
-        return false;
-    }
-
-    auto fout = std::ofstream(fname_out, std::ios::binary);
-    if (!fout) {
-        fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
-        return false;
-    }
-
-    // verify magic
-    {
-        uint32_t magic;
-        finp.read((char *) &magic, sizeof(magic));
-        if (magic != 0x67676d6c) {
-            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
-            return false;
-        }
-
-        fout.write((char *) &magic, sizeof(magic));
-    }
-
-    whisper_hparams hparams;
-
-    // load hparams
-    {
-        finp.read((char *) &hparams.n_vocab,       sizeof(hparams.n_vocab));
-        finp.read((char *) &hparams.n_audio_ctx,   sizeof(hparams.n_audio_ctx));
-        finp.read((char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
-        finp.read((char *) &hparams.n_audio_head,  sizeof(hparams.n_audio_head));
-        finp.read((char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
-        finp.read((char *) &hparams.n_text_ctx,    sizeof(hparams.n_text_ctx));
-        finp.read((char *) &hparams.n_text_state,  sizeof(hparams.n_text_state));
-        finp.read((char *) &hparams.n_text_head,   sizeof(hparams.n_text_head));
-        finp.read((char *) &hparams.n_text_layer,  sizeof(hparams.n_text_layer));
-        finp.read((char *) &hparams.n_mels,        sizeof(hparams.n_mels));
-        finp.read((char *) &hparams.f16,           sizeof(hparams.f16));
-
-        fprintf(stderr, "%s: n_vocab       = %d\n", __func__, hparams.n_vocab);
-        fprintf(stderr, "%s: n_audio_ctx   = %d\n", __func__, hparams.n_audio_ctx);
-        fprintf(stderr, "%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
-        fprintf(stderr, "%s: n_audio_head  = %d\n", __func__, hparams.n_audio_head);
-        fprintf(stderr, "%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
-        fprintf(stderr, "%s: n_text_ctx    = %d\n", __func__, hparams.n_text_ctx);
-        fprintf(stderr, "%s: n_text_state  = %d\n", __func__, hparams.n_text_state);
-        fprintf(stderr, "%s: n_text_head   = %d\n", __func__, hparams.n_text_head);
-        fprintf(stderr, "%s: n_text_layer  = %d\n", __func__, hparams.n_text_layer);
-        fprintf(stderr, "%s: n_mels        = %d\n", __func__, hparams.n_mels);
-        fprintf(stderr, "%s: f16           = %d\n", __func__, hparams.f16);
-
-        fout.write((char *) &hparams.n_vocab,       sizeof(hparams.n_vocab));
-        fout.write((char *) &hparams.n_audio_ctx,   sizeof(hparams.n_audio_ctx));
-        fout.write((char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
-        fout.write((char *) &hparams.n_audio_head,  sizeof(hparams.n_audio_head));
-        fout.write((char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
-        fout.write((char *) &hparams.n_text_ctx,    sizeof(hparams.n_text_ctx));
-        fout.write((char *) &hparams.n_text_state,  sizeof(hparams.n_text_state));
-        fout.write((char *) &hparams.n_text_head,   sizeof(hparams.n_text_head));
-        fout.write((char *) &hparams.n_text_layer,  sizeof(hparams.n_text_layer));
-        fout.write((char *) &hparams.n_mels,        sizeof(hparams.n_mels));
-        fout.write((char *) &ftype,                 sizeof(hparams.f16));
-    }
-
-    // load mel filters
-    {
-        whisper_filters filters;
-
-        finp.read ((char *) &filters.n_mel, sizeof(filters.n_mel));
-        fout.write((char *) &filters.n_mel, sizeof(filters.n_mel));
-        finp.read ((char *) &filters.n_fft, sizeof(filters.n_fft));
-        fout.write((char *) &filters.n_fft, sizeof(filters.n_fft));
-
-        filters.data.resize(filters.n_mel * filters.n_fft);
-        finp.read ((char *) filters.data.data(), filters.data.size() * sizeof(float));
-        fout.write((char *) filters.data.data(), filters.data.size() * sizeof(float));
-    }
-
-    // load vocab
-    {
-        int32_t n_vocab = 0;
-        finp.read ((char *) &n_vocab, sizeof(n_vocab));
-        fout.write((char *) &n_vocab, sizeof(n_vocab));
-
-        //if (n_vocab != hparams.n_vocab) {
-        //    fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
-        //            __func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
-        //    return false;
-        //}
-
-        std::string word;
-        for (int i = 0; i < n_vocab; i++) {
-            uint32_t len;
-            finp.read ((char *) &len, sizeof(len));
-            fout.write((char *) &len, sizeof(len));
-
-            word.resize(len);
-            finp.read ((char *) word.data(), len);
-            fout.write((char *) word.data(), len);
-
-            vocab.token_to_id[word] = i;
-            vocab.id_to_token[i] = word;
-        }
-    }
-
-    // regexes of tensor names to not be quantized
-    const std::vector<std::string> to_skip = {
-        //"encoder.*",
-        "encoder.conv1.bias",
-        "encoder.conv2.bias",
-        "encoder.positional_embedding",
-        "decoder.positional_embedding",
-    };
-
-    if (!ggml_common_quantize_0(finp, fout, ftype, { ".*" }, to_skip)) {
-        fprintf(stderr, "%s: failed to quantize model '%s'\n", __func__, fname_inp.c_str());
-        return false;
-    }
-
-    finp.close();
-    fout.close();
-
-    return true;
-}
-
-int main(int argc, char ** argv) {
-    if (argc != 4) {
-        fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
-        ggml_print_ftypes(stderr);
-        return 1;
-    }
-
-    // needed to initialize f16 tables
-    {
-        struct ggml_init_params params = { 0, NULL, false };
-        struct ggml_context * ctx = ggml_init(params);
-        ggml_free(ctx);
-    }
-
-    const std::string fname_inp = argv[1];
-    const std::string fname_out = argv[2];
-
-    const ggml_ftype ftype = ggml_parse_ftype(argv[3]);
-
-    const int64_t t_main_start_us = ggml_time_us();
-
-    int64_t t_quantize_us = 0;
-
-    // load the model
-    {
-        const int64_t t_start_us = ggml_time_us();
-
-        if (!whisper_model_quantize(fname_inp, fname_out, ggml_ftype(ftype))) {
-            fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
-            return 1;
-        }
-
-        t_quantize_us = ggml_time_us() - t_start_us;
-    }
-
-    // report timing
-    {
-        const int64_t t_main_end_us = ggml_time_us();
-
-        printf("\n");
-        printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f);
-        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
-    }
-
-    return 0;
-}

examples/stream.wasm/index-tmpl.html

@@ -35,15 +35,6 @@
 
             <br><br>
 
-            <b>More examples:</b>
-                <a href="https://whisper.ggerganov.com/">main</a> |
-                <a href="https://whisper.ggerganov.com/bench">bench</a> |
-                <a href="https://whisper.ggerganov.com/stream">stream</a> |
-                <a href="https://whisper.ggerganov.com/command">command</a> |
-                <a href="https://whisper.ggerganov.com/talk">talk</a> |
-
-            <br><br>
-
             <hr>
 
             Select the model you would like to use, click the "Start" button and start speaking
@@ -54,10 +45,6 @@
                 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>
-                <br><br>
-                Quantized models:<br><br>
-                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
-                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
                 <span id="fetch-whisper-progress"></span>
 
                 <!--
@@ -175,17 +162,11 @@
                 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',
-
-                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
-                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
-
-                    'tiny-en-q5_1':   31,
-                    'base-en-q5_1':   57,
                 };
 
                 let url     = urls[model];
@@ -196,10 +177,6 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1').style.display = 'none';
-
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -211,10 +188,6 @@
                     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-en-q5_1'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en-q5_1'); if (el) el.style.display = 'inline-block';
-
                     el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
                 };
 

examples/stream/CMakeLists.txt

@@ -1,4 +1,4 @@
-if (WHISPER_SDL2)
+if (WHISPER_SUPPORT_SDL2)
     # stream
     set(TARGET stream)
     add_executable(${TARGET} stream.cpp)

examples/stream/stream.cpp

@@ -43,7 +43,6 @@ struct whisper_params {
 
     bool speed_up      = false;
     bool translate     = false;
-    bool no_fallback   = false;
     bool print_special = false;
     bool no_context    = true;
     bool no_timestamps = false;
@@ -74,7 +73,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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 == "-nf"  || arg == "--no-fallback")   { params.no_fallback   = true; }
         else if (arg == "-ps"  || arg == "--print-special") { params.print_special = true; }
         else if (arg == "-kc"  || arg == "--keep-context")  { params.no_context    = false; }
         else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
@@ -96,23 +94,22 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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, "            --keep N        [%-7d] audio to keep from previous step in ms\n",         params.keep_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, "  -nf,      --no-fallback   [%-7s] do not use temperature fallback while decoding\n", params.no_fallback ? "true" : "false");
-    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                           params.print_special ? "true" : "false");
-    fprintf(stderr, "  -kc,      --keep-context  [%-7s] keep context between audio chunks\n",              params.no_context ? "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] text output file name\n",                          params.fname_out.c_str());
+    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, "            --keep N        [%-7d] audio to keep from previous step in ms\n",      params.keep_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, "  -kc,      --keep-context  [%-7s] keep context between audio chunks\n",           params.no_context ? "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] text output file name\n",                       params.fname_out.c_str());
     fprintf(stderr, "\n");
 }
 
@@ -151,7 +148,7 @@ int main(int argc, char ** argv) {
 
     // whisper init
 
-    if (params.language != "auto" && whisper_lang_id(params.language.c_str()) == -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);
@@ -300,8 +297,7 @@ int main(int argc, char ** argv) {
             wparams.speed_up         = params.speed_up;
 
             // disable temperature fallback
-            //wparams.temperature_inc  = -1.0f;
-            wparams.temperature_inc  = params.no_fallback ? 0.0f : wparams.temperature_inc;
+            wparams.temperature_inc  = -1.0f;
 
             wparams.prompt_tokens    = params.no_context ? nullptr : prompt_tokens.data();
             wparams.prompt_n_tokens  = params.no_context ? 0       : prompt_tokens.size();
@@ -383,7 +379,6 @@ int main(int argc, char ** argv) {
                     }
                 }
             }
-            fflush(stdout);
         }
     }
 

examples/talk-llama/.gitignore

@@ -1 +0,0 @@
-audio.mp3

examples/talk-llama/CMakeLists.txt

@@ -1,16 +0,0 @@
-if (WHISPER_SDL2)
-    # talk-llama
-    set(TARGET talk-llama)
-    #add_executable(${TARGET} talk-llama.cpp llama.cpp)
-    #target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
-    #target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
-
-    # TODO: this is temporary
-    #       need to export ggml symbols for MSVC, but too lazy ..
-    add_executable(${TARGET} talk-llama.cpp llama.cpp ../common.cpp ../common-sdl.cpp ../../ggml.c ../../whisper.cpp)
-
-    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS} ../../)
-    target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
-
-    include(DefaultTargetOptions)
-endif ()

examples/talk-llama/README.md

@@ -1,36 +0,0 @@
-# talk-llama
-
-Talk with an LLaMA AI in your terminal
-
-[Demo Talk](https://user-images.githubusercontent.com/1991296/228024237-848f998c-c334-46a6-bef8-3271590da83b.mp4)
-
-## Building
-
-The `talk-llama` 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
-
-# Build the "talk-llama" executable
-make talk-llama
-
-# Run it
-./talk-llama -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/13B/ggml-model-q4_0.bin -p "Georgi" -t 8
-```
-
-- The `-mw` argument specifies the Whisper model that you would like to use. Recommended `base` or `small` for real-time experience
-- The `-ml` argument specifies the LLaMA model that you would like to use. Read the instructions in https://github.com/ggerganov/llama.cpp for information about how to obtain a `ggml` compatible LLaMA model
-
-## TTS
-
-For best experience, this example needs a TTS tool to convert the generated text responses to voice.
-You can use any TTS engine that you would like - simply edit the [speak.sh](speak.sh) script to your needs.
-By default, it is configured to use MacOS's `say`, but you can use whatever you wish.
-
-## Discussion
-
-If you have any feedback, please let "us" know in the following discussion: https://github.com/ggerganov/whisper.cpp/discussions/672?converting=1

examples/talk-llama/eleven-labs.py

@@ -1,23 +0,0 @@
-import sys
-import importlib.util
-
-api_key = "" #Write your https://beta.elevenlabs.io api key here
-if not api_key:
-    print("To use elevenlabs you have to register to https://beta.elevenlabs.io and add your elevenlabs api key to examples/talk-llama/eleven-labs.py")
-    sys.exit()
-
-if importlib.util.find_spec("elevenlabs") is None:
-    print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'")
-    sys.exit()
-
-from elevenlabs import ElevenLabs
-eleven = ElevenLabs(api_key)
-
-# Get a Voice object, by name or UUID
-voice = eleven.voices["Arnold"] #Possible Voices: Adam Antoni Arnold Bella Domi Elli Josh
-
-# Generate the TTS
-audio = voice.generate(str(sys.argv[2:]))
-
-# Save the TTS to a file
-audio.save("audio") 

examples/talk-llama/llama-util.h

@@ -1,433 +0,0 @@
-// Internal header to be included only by llama.cpp.
-// Contains wrappers around OS interfaces.
-
-#ifndef LLAMA_UTIL_H
-#define LLAMA_UTIL_H
-
-#include <cstdio>
-#include <cstdint>
-#include <cerrno>
-#include <cstring>
-#include <cstdarg>
-#include <cstdlib>
-#include <climits>
-
-#include <string>
-#include <vector>
-
-#ifdef __has_include
-    #if __has_include(<unistd.h>)
-        #include <unistd.h>
-        #if defined(_POSIX_MAPPED_FILES)
-            #include <sys/mman.h>
-        #endif
-        #if defined(_POSIX_MEMLOCK_RANGE)
-            #include <sys/resource.h>
-        #endif
-    #endif
-#endif
-
-#if defined(_WIN32)
-    #define WIN32_LEAN_AND_MEAN
-    #ifndef NOMINMAX
-        #define NOMINMAX
-    #endif
-    #include <windows.h>
-    #include <io.h>
-    #include <stdio.h> // for _fseeki64
-#endif
-
-#define LLAMA_ASSERT(x) \
-    do { \
-        if (!(x)) { \
-            fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
-            abort(); \
-        } \
-    } while (0)
-
-#ifdef __GNUC__
-#ifdef __MINGW32__
-__attribute__((format(gnu_printf, 1, 2)))
-#else
-__attribute__((format(printf, 1, 2)))
-#endif
-#endif
-static std::string format(const char * fmt, ...) {
-    va_list ap, ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    int size = vsnprintf(NULL, 0, fmt, ap);
-    LLAMA_ASSERT(size >= 0 && size < INT_MAX);
-    std::vector<char> buf(size + 1);
-    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
-    LLAMA_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-    return std::string(buf.data(), size);
-}
-
-struct llama_file {
-    // use FILE * so we don't have to re-open the file to mmap
-    FILE * fp;
-    size_t size;
-
-    llama_file(const char * fname, const char * mode) {
-        fp = std::fopen(fname, mode);
-        if (fp == NULL) {
-            throw format("failed to open %s: %s", fname, std::strerror(errno));
-        }
-        seek(0, SEEK_END);
-        size = tell();
-        seek(0, SEEK_SET);
-    }
-
-    size_t tell() const {
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        LLAMA_ASSERT(ret != -1); // this really shouldn't fail
-        return (size_t) ret;
-    }
-
-    void seek(size_t offset, int whence) {
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        LLAMA_ASSERT(ret == 0); // same
-    }
-
-    void read_raw(void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        std::size_t ret = std::fread(ptr, size, 1, fp);
-        if (ferror(fp)) {
-            throw format("read error: %s", strerror(errno));
-        }
-        if (ret != 1) {
-            throw std::string("unexpectedly reached end of file");
-        }
-    }
-
-    std::uint32_t read_u32() {
-        std::uint32_t ret;
-        read_raw(&ret, sizeof(ret));
-        return ret;
-    }
-
-    std::string read_string(std::uint32_t len) {
-        std::vector<char> chars(len);
-        read_raw(chars.data(), len);
-        return std::string(chars.data(), len);
-    }
-
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            throw format("write error: %s", strerror(errno));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
-    ~llama_file() {
-        if (fp) {
-            std::fclose(fp);
-        }
-    }
-};
-
-#if defined(_WIN32)
-static std::string llama_format_win_err(DWORD err) {
-    LPSTR buf;
-    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
-                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
-    if (!size) {
-        return "FormatMessageA failed";
-    }
-    std::string ret(buf, size);
-    LocalFree(buf);
-    return ret;
-}
-#endif
-
-struct llama_mmap {
-    void * addr;
-    size_t size;
-
-    llama_mmap(const llama_mmap &) = delete;
-
-#ifdef _POSIX_MAPPED_FILES
-    static constexpr bool SUPPORTED = true;
-
-    llama_mmap(struct llama_file * file, bool prefetch = true) {
-        size = file->size;
-        int fd = fileno(file->fp);
-        int flags = MAP_SHARED;
-#ifdef __linux__
-        flags |= MAP_POPULATE;
-#endif
-        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
-        if (addr == MAP_FAILED) {
-            throw format("mmap failed: %s", strerror(errno));
-        }
-
-        if (prefetch) {
-            // Advise the kernel to preload the mapped memory
-            if (madvise(addr, file->size, MADV_WILLNEED)) {
-                fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n",
-                        strerror(errno));
-            }
-        }
-    }
-
-    ~llama_mmap() {
-        munmap(addr, size);
-    }
-#elif defined(_WIN32)
-    static constexpr bool SUPPORTED = true;
-
-    llama_mmap(struct llama_file * file, bool prefetch = true) {
-        size = file->size;
-
-        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
-
-        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
-        DWORD error = GetLastError();
-
-        if (hMapping == NULL) {
-            throw format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str());
-        }
-
-        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
-        error = GetLastError();
-        CloseHandle(hMapping);
-
-        if (addr == NULL) {
-            throw format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str());
-        }
-
-        #if _WIN32_WINNT >= _WIN32_WINNT_WIN8
-        if (prefetch) {
-            // Advise the kernel to preload the mapped memory
-            WIN32_MEMORY_RANGE_ENTRY range;
-            range.VirtualAddress = addr;
-            range.NumberOfBytes = (SIZE_T)size;
-            if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
-                fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-            }
-        }
-        #else
-        #pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
-        #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
-    }
-
-    ~llama_mmap() {
-        if (!UnmapViewOfFile(addr)) {
-            fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
-                    llama_format_win_err(GetLastError()).c_str());
-        }
-    }
-#else
-    static constexpr bool SUPPORTED = false;
-
-    llama_mmap(struct llama_file *) {
-        throw std::string("mmap not supported");
-    }
-#endif
-};
-
-// Represents some region of memory being locked using mlock or VirtualLock;
-// will automatically unlock on destruction.
-struct llama_mlock {
-    void * addr = NULL;
-    size_t size = 0;
-    bool failed_already = false;
-
-    llama_mlock() {}
-    llama_mlock(const llama_mlock &) = delete;
-
-    ~llama_mlock() {
-        if (size) {
-            raw_unlock(addr, size);
-        }
-    }
-
-    void init(void * addr) {
-        LLAMA_ASSERT(this->addr == NULL && this->size == 0);
-        this->addr = addr;
-    }
-
-    void grow_to(size_t target_size) {
-        LLAMA_ASSERT(addr);
-        if (failed_already) {
-            return;
-        }
-        size_t granularity = lock_granularity();
-        target_size = (target_size + granularity - 1) & ~(granularity - 1);
-        if (target_size > size) {
-            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
-                size = target_size;
-            } else {
-                failed_already = true;
-            }
-        }
-    }
-
-#ifdef _POSIX_MEMLOCK_RANGE
-    static constexpr bool SUPPORTED = true;
-
-    size_t lock_granularity() {
-        return (size_t) sysconf(_SC_PAGESIZE);
-    }
-
-    #ifdef __APPLE__
-        #define MLOCK_SUGGESTION \
-            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
-            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
-    #else
-        #define MLOCK_SUGGESTION \
-            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
-    #endif
-
-    bool raw_lock(const void * addr, size_t size) {
-        if (!mlock(addr, size)) {
-            return true;
-        } else {
-            char* errmsg = std::strerror(errno);
-            bool suggest = (errno == ENOMEM);
-
-            // Check if the resource limit is fine after all
-            struct rlimit lock_limit;
-            if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
-                suggest = false;
-            if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
-                suggest = false;
-
-            fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
-                    size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
-            return false;
-        }
-    }
-
-    #undef MLOCK_SUGGESTION
-
-    void raw_unlock(void * addr, size_t size) {
-        if (munlock(addr, size)) {
-            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
-        }
-    }
-#elif defined(_WIN32)
-    static constexpr bool SUPPORTED = true;
-
-    size_t lock_granularity() {
-        SYSTEM_INFO si;
-        GetSystemInfo(&si);
-        return (size_t) si.dwPageSize;
-    }
-
-    bool raw_lock(void * addr, size_t size) {
-        for (int tries = 1; ; tries++) {
-            if (VirtualLock(addr, size)) {
-                return true;
-            }
-            if (tries == 2) {
-                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
-                        size, this->size, llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-
-            // It failed but this was only the first try; increase the working
-            // set size and try again.
-            SIZE_T min_ws_size, max_ws_size;
-            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
-                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-            // Per MSDN: "The maximum number of pages that a process can lock
-            // is equal to the number of pages in its minimum working set minus
-            // a small overhead."
-            // Hopefully a megabyte is enough overhead:
-            size_t increment = size + 1048576;
-            // The minimum must be <= the maximum, so we need to increase both:
-            min_ws_size += increment;
-            max_ws_size += increment;
-            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
-                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
-                        llama_format_win_err(GetLastError()).c_str());
-                return false;
-            }
-        }
-    }
-
-    void raw_unlock(void * addr, size_t size) {
-        if (!VirtualUnlock(addr, size)) {
-            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
-                    llama_format_win_err(GetLastError()).c_str());
-        }
-    }
-#else
-    static constexpr bool SUPPORTED = false;
-
-    void raw_lock(const void * addr, size_t size) {
-        fprintf(stderr, "warning: mlock not supported on this system\n");
-    }
-
-    void raw_unlock(const void * addr, size_t size) {}
-#endif
-};
-
-// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
-struct llama_buffer {
-    uint8_t * addr = NULL;
-    size_t size = 0;
-
-    void resize(size_t size) {
-        delete[] addr;
-        addr = new uint8_t[size];
-        this->size = size;
-    }
-
-    ~llama_buffer() {
-        delete[] addr;
-    }
-};
-
-#ifdef GGML_USE_CUBLAS
-#include "ggml-cuda.h"
-struct llama_ctx_buffer {
-    uint8_t * addr = NULL;
-    size_t size = 0;
-
-    void resize(size_t size) {
-        if (addr) {
-            ggml_cuda_host_free(addr);
-        }
-        addr = (uint8_t *) ggml_cuda_host_malloc(size);
-        this->size = size;
-    }
-
-    ~llama_ctx_buffer() {
-        if (addr) {
-            ggml_cuda_host_free(addr);
-        }
-    }
-};
-#else
-typedef llama_buffer llama_ctx_buffer;
-#endif
-
-#endif

examples/talk-llama/llama.h

@@ -1,257 +0,0 @@
-#ifndef LLAMA_H
-#define LLAMA_H
-
-#include <stddef.h>
-#include <stdint.h>
-#include <stdbool.h>
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define LLAMA_API __declspec(dllexport)
-#        else
-#            define LLAMA_API __declspec(dllimport)
-#        endif
-#    else
-#        define LLAMA_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define LLAMA_API
-#endif
-
-#define LLAMA_FILE_VERSION 1
-#define LLAMA_FILE_MAGIC 0x67676a74 // 'ggjt' in hex
-#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-    //
-    // C interface
-    //
-    // TODO: show sample usage
-    //
-
-    struct llama_context;
-
-    typedef int llama_token;
-
-    typedef struct llama_token_data {
-        llama_token id;  // token id
-        float logit; // log-odds of the token
-        float p;     // probability of the token
-    } llama_token_data;
-
-    typedef struct llama_token_data_array {
-        llama_token_data * data;
-        size_t size;
-        bool sorted;
-    } llama_token_data_array;
-
-    typedef void (*llama_progress_callback)(float progress, void *ctx);
-
-    struct llama_context_params {
-        int n_ctx;   // text context
-        int n_parts; // -1 for default
-        int seed;    // RNG seed, 0 for random
-
-        bool f16_kv;     // use fp16 for KV cache
-        bool logits_all; // the llama_eval() call computes all logits, not just the last one
-        bool vocab_only; // only load the vocabulary, no weights
-        bool use_mmap;   // use mmap if possible
-        bool use_mlock;  // force system to keep model in RAM
-        bool embedding;  // embedding mode only
-
-        // called with a progress value between 0 and 1, pass NULL to disable
-        llama_progress_callback progress_callback;
-        // context pointer passed to the progress callback
-        void * progress_callback_user_data;
-    };
-
-    // model file types
-    enum llama_ftype {
-        LLAMA_FTYPE_ALL_F32     = 0,
-        LLAMA_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
-        LLAMA_FTYPE_MOSTLY_Q4_2 = 5,  // except 1d tensors
-        // LLAMA_FTYPE_MOSTLY_Q4_3 (6) support has been removed
-        LLAMA_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
-        LLAMA_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
-    };
-
-    LLAMA_API struct llama_context_params llama_context_default_params();
-
-    LLAMA_API bool llama_mmap_supported();
-    LLAMA_API bool llama_mlock_supported();
-
-    // Various functions for loading a ggml llama model.
-    // Allocate (almost) all memory needed for the model.
-    // Return NULL on failure
-    LLAMA_API struct llama_context * llama_init_from_file(
-                             const char * path_model,
-            struct llama_context_params   params);
-
-    // Frees all allocated memory
-    LLAMA_API void llama_free(struct llama_context * ctx);
-
-    // TODO: not great API - very likely to change
-    // Returns 0 on success
-    // nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
-    LLAMA_API int llama_model_quantize(
-            const char * fname_inp,
-            const char * fname_out,
-      enum llama_ftype   ftype,
-            int          nthread);
-
-    // Apply a LoRA adapter to a loaded model
-    // path_base_model is the path to a higher quality model to use as a base for
-    // the layers modified by the adapter. Can be NULL to use the current loaded model.
-    // The model needs to be reloaded before applying a new adapter, otherwise the adapter
-    // will be applied on top of the previous one
-    // Returns 0 on success
-    LLAMA_API int llama_apply_lora_from_file(
-            struct llama_context * ctx,
-                      const char * path_lora,
-                      const char * path_base_model,
-                             int   n_threads);
-
-    // Returns the number of tokens in the KV cache
-    LLAMA_API int llama_get_kv_cache_token_count(struct llama_context * ctx);
-
-    // Sets the current rng seed.
-    LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
-
-    // Returns the size in bytes of the state (rng, logits, embedding and kv_cache)
-    LLAMA_API size_t llama_get_state_size(struct llama_context * ctx);
-
-    // Copies the state to the specified destination address.
-    // Destination needs to have allocated enough memory.
-    // Returns the number of bytes copied
-    LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dest);
-
-    // Set the state reading from the specified address
-    // Returns the number of bytes read
-    LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src);
-
-    // Save/load session file
-    LLAMA_API size_t llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
-    LLAMA_API size_t llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
-
-    // Run the llama inference to obtain the logits and probabilities for the next token.
-    // tokens + n_tokens is the provided batch of new tokens to process
-    // n_past is the number of tokens to use from previous eval calls
-    // Returns 0 on success
-    LLAMA_API int llama_eval(
-            struct llama_context * ctx,
-               const llama_token * tokens,
-                             int   n_tokens,
-                             int   n_past,
-                             int   n_threads);
-
-    // Convert the provided text into tokens.
-    // The tokens pointer must be large enough to hold the resulting tokens.
-    // Returns the number of tokens on success, no more than n_max_tokens
-    // Returns a negative number on failure - the number of tokens that would have been returned
-    // TODO: not sure if correct
-    LLAMA_API int llama_tokenize(
-            struct llama_context * ctx,
-                      const char * text,
-                     llama_token * tokens,
-                             int   n_max_tokens,
-                            bool   add_bos);
-
-    LLAMA_API int llama_n_vocab(struct llama_context * ctx);
-    LLAMA_API int llama_n_ctx  (struct llama_context * ctx);
-    LLAMA_API int llama_n_embd (struct llama_context * ctx);
-
-    // Token logits obtained from the last call to llama_eval()
-    // The logits for the last token are stored in the last row
-    // Can be mutated in order to change the probabilities of the next token
-    // Rows: n_tokens
-    // Cols: n_vocab
-    LLAMA_API float * llama_get_logits(struct llama_context * ctx);
-
-    // Get the embeddings for the input
-    // shape: [n_embd] (1-dimensional)
-    LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
-
-    // Token Id -> String. Uses the vocabulary in the provided context
-    LLAMA_API const char * llama_token_to_str(struct llama_context * ctx, llama_token token);
-
-    // Special tokens
-    LLAMA_API llama_token llama_token_bos();
-    LLAMA_API llama_token llama_token_eos();
-    LLAMA_API llama_token llama_token_nl();
-
-    // Sampling functions
-
-    /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
-    LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float penalty);
-
-    /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
-    LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
-
-    /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-    LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
-
-    /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-    LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep = 1);
-
-    /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-    LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
-
-    /// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
-    LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep = 1);
-
-    /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
-    LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep = 1);
-    LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
-
-    /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-    /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
-    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
-    LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
-
-    /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-    /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-    /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-    /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-    /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
-    LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
-
-    /// @details Selects the token with the highest probability.
-    LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
-
-    /// @details Randomly selects a token from the candidates based on their probabilities.
-    LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
-
-    // Performance information
-    LLAMA_API void llama_print_timings(struct llama_context * ctx);
-    LLAMA_API void llama_reset_timings(struct llama_context * ctx);
-
-    // Print system information
-    LLAMA_API const char * llama_print_system_info(void);
-
-#ifdef __cplusplus
-}
-#endif
-
-// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
-#ifdef LLAMA_API_INTERNAL
-
-#include <vector>
-#include <string>
-struct ggml_tensor;
-
-std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
-
-#endif
-
-#endif // LLAMA_H

examples/talk-llama/prompts/talk-alpaca.txt

@@ -1,23 +0,0 @@
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-
-Write a text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
-{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
-There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
-The transcript only includes text, it does not include markup like HTML and Markdown.
-{1} responds with short and concise answers.
-
-### Response:
-
-{0}{4} Hello, {1}!
-{1}{4} Hello {0}! How may I help you today?
-{0}{4} What time is it?
-{1}{4} It is {2} o'clock.
-{0}{4} What year is it?
-{1}{4} We are in {3}.
-{0}{4} What is a cat?
-{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
-{0}{4} Name a color.
-{1}{4} Blue
-{0}{4}

examples/talk-llama/speak.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-
-# Usage:
-#  speak.sh <voice_id> <text-to-speak>
-
-# espeak
-# Mac OS: brew install espeak
-# Linux: apt-get install espeak
-#
-#espeak -v en-us+m$1 -s 225 -p 50 -a 200 -g 5 -k 5 "$2"
-
-# for Mac
-say "$2"
-
-# Eleven Labs
-# To use it, install the elevenlabs module from pip (pip install elevenlabs), register to https://beta.elevenlabs.io to get an api key and paste it in /examples/talk-llama/eleven-labs.py 
-#
-#wd=$(dirname $0)
-#script=$wd/eleven-labs.py
-#python3 $script $1 "$2" >/dev/null 2>&1
-#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 >/dev/null 2>&1

examples/talk-llama/talk-llama.cpp

@@ -1,576 +0,0 @@
-// Talk with AI
-//
-
-#include "common.h"
-#include "common-sdl.h"
-#include "whisper.h"
-#include "llama.h"
-
-#include <cassert>
-#include <cstdio>
-#include <fstream>
-#include <regex>
-#include <string>
-#include <thread>
-#include <vector>
-#include <regex>
-
-std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
-    // initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
-    std::vector<llama_token> res(text.size() + (int)add_bos);
-    int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
-    assert(n >= 0);
-    res.resize(n);
-
-    return res;
-}
-
-// command-line parameters
-struct whisper_params {
-    int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t voice_ms   = 10000;
-    int32_t capture_id = -1;
-    int32_t max_tokens = 32;
-    int32_t audio_ctx  = 0;
-
-    int32_t n_parts_llama = -1;
-
-    float vad_thold    = 0.6f;
-    float freq_thold   = 100.0f;
-
-    bool speed_up      = false;
-    bool translate     = false;
-    bool print_special = false;
-    bool print_energy  = false;
-    bool no_timestamps = true;
-    bool verbose_prompt = false;
-
-    std::string person      = "Georgi";
-    std::string language    = "en";
-    std::string model_wsp   = "models/ggml-base.en.bin";
-    std::string model_llama = "models/ggml-llama-7B.bin";
-    std::string speak       = "./examples/talk-llama/speak.sh";
-    std::string prompt      = "";
-    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 == "-vms" || arg == "--voice-ms")      { params.voice_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 == "--n-parts-llama")                  { params.n_parts_llama = 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 == "-ps"  || arg == "--print-special") { params.print_special = true; }
-        else if (arg == "-pe"  || arg == "--print-energy")  { params.print_energy  = true; }
-        else if (arg == "--verbose-prompt")                 { params.verbose_prompt = true; }
-        else if (arg == "-p"   || arg == "--person")        { params.person        = argv[++i]; }
-        else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
-        else if (arg == "-mw"  || arg == "--model-whisper") { params.model_wsp     = argv[++i]; }
-        else if (arg == "-ml"  || arg == "--model-llama")   { params.model_llama   = argv[++i]; }
-        else if (arg == "-s"   || arg == "--speak")         { params.speak         = argv[++i]; }
-        else if (arg == "--prompt-file")                    {
-            std::ifstream file(argv[++i]);
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
-            if (params.prompt.back() == '\n') {
-                params.prompt.pop_back();
-            }
-        }
-        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, "  -vms N,   --voice-ms N    [%-7d] voice duration in milliseconds\n",              params.voice_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, "  -p NAME,  --person NAME   [%-7s] person name (for prompt selection)\n",          params.person.c_str());
-    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                             params.language.c_str());
-    fprintf(stderr, "  -mw FILE, --model-whisper [%-7s] whisper model file\n",                          params.model_wsp.c_str());
-    fprintf(stderr, "  -ml FILE, --model-llama   [%-7s] llama model file\n",                            params.model_llama.c_str());
-    fprintf(stderr, "  --n-parts-llama N         [%-7d] num parts in llama model file\n",               params.n_parts_llama);
-    fprintf(stderr, "  -s FILE,  --speak TEXT    [%-7s] command for TTS\n",                             params.speak.c_str());
-    fprintf(stderr, "  --prompt-file FNAME       [%-7s] file with custom prompt to start dialog\n",     "");
-    fprintf(stderr, "  --verbose-prompt          [%-7s] print prompt at start\n",                       params.verbose_prompt ? "true" : "false");
-    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
-    fprintf(stderr, "\n");
-}
-
-std::string transcribe(
-        whisper_context * ctx,
-        const whisper_params & params,
-        const std::vector<float> & pcmf32,
-        const std::string prompt_text,
-        float & prob,
-        int64_t & t_ms) {
-    const auto t_start = std::chrono::high_resolution_clock::now();
-
-    prob = 0.0f;
-    t_ms = 0;
-
-    std::vector<whisper_token> prompt_tokens;
-
-    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-
-    prompt_tokens.resize(1024);
-    prompt_tokens.resize(whisper_tokenize(ctx, prompt_text.c_str(), prompt_tokens.data(), prompt_tokens.size()));
-
-    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.prompt_tokens    = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
-    wparams.prompt_n_tokens  = prompt_tokens.empty() ? 0       : prompt_tokens.size();
-
-    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;
-}
-
-const std::string k_prompt_whisper = R"(A conversation with a person called {1}.)";
-
-const std::string k_prompt_llama = R"(Text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
-{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
-There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
-The transcript only includes text, it does not include markup like HTML and Markdown.
-{1} responds with short and concise answers.
-
-{0}{4} Hello, {1}!
-{1}{4} Hello {0}! How may I help you today?
-{0}{4} What time is it?
-{1}{4} It is {2} o'clock.
-{0}{4} What year is it?
-{1}{4} We are in {3}.
-{0}{4} What is a cat?
-{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
-{0}{4} Name a color.
-{1}{4} Blue
-{0}{4})";
-
-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_wsp = whisper_init_from_file(params.model_wsp.c_str());
-
-    // llama init
-
-    auto lparams = llama_context_default_params();
-
-    // tune these to your liking
-    lparams.n_ctx      = 2048;
-    lparams.seed       = 1;
-    lparams.f16_kv     = true;
-    lparams.n_parts    = params.n_parts_llama;
-
-    struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
-
-    // print some info about the processing
-    {
-        fprintf(stderr, "\n");
-
-        if (!whisper_is_multilingual(ctx_wsp)) {
-            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();
-
-    int n_iter = 0;
-
-    bool is_running  = true;
-    bool force_speak = false;
-
-    float prob0 = 0.0f;
-
-    const std::string chat_symb = ":";
-    const std::string bot_name  = "LLaMA";
-
-    std::vector<float> pcmf32_cur;
-    std::vector<float> pcmf32_prompt;
-
-    const std::string prompt_whisper = ::replace(k_prompt_whisper, "{1}", bot_name);
-
-    // construct the initial prompt for LLaMA inference
-    std::string prompt_llama = params.prompt.empty() ? k_prompt_llama : params.prompt;
-
-    // need to have leading ' '
-    prompt_llama.insert(0, 1, ' ');
-
-    prompt_llama = ::replace(prompt_llama, "{0}", params.person);
-    prompt_llama = ::replace(prompt_llama, "{1}", bot_name);
-
-    {
-        // get time string
-        std::string time_str;
-        {
-            time_t t = time(0);
-            struct tm * now = localtime(&t);
-            char buf[128];
-            strftime(buf, sizeof(buf), "%H:%M", now);
-            time_str = buf;
-        }
-        prompt_llama = ::replace(prompt_llama, "{2}", time_str);
-    }
-
-    {
-        // get year string
-        std::string year_str;
-        {
-            time_t t = time(0);
-            struct tm * now = localtime(&t);
-            char buf[128];
-            strftime(buf, sizeof(buf), "%Y", now);
-            year_str = buf;
-        }
-        prompt_llama = ::replace(prompt_llama, "{3}", year_str);
-    }
-
-    prompt_llama = ::replace(prompt_llama, "{4}", chat_symb);
-
-    // evaluate the initial prompt
-
-    auto embd_inp = ::llama_tokenize(ctx_llama, prompt_llama, true);
-
-    printf("\n");
-    printf("%s : initializing - please wait ...\n", __func__);
-
-    if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0, params.n_threads)) {
-        fprintf(stderr, "%s : failed to eval\n", __func__);
-        return 1;
-    }
-
-    if (params.verbose_prompt) {
-        fprintf(stdout, "\n");
-        fprintf(stdout, "%s", prompt_llama.c_str());
-        fflush(stdout);
-    }
-
-    printf("%s : done! start speaking in the microphone\n", __func__);
-    printf("\n");
-    printf("%s%s", params.person.c_str(), chat_symb.c_str());
-    fflush(stdout);
-
-    // clear audio buffer
-    audio.clear();
-
-    // text inference variables
-    const int voice_id = 2;
-    const int n_keep   = embd_inp.size();
-    const int n_ctx    = llama_n_ctx(ctx_llama);
-
-    int n_past = n_keep;
-    int n_prev = 64; // TODO arg
-
-    std::vector<llama_token> embd;
-
-    // reverse prompts for detecting when it's time to stop speaking
-    std::vector<std::string> antiprompts = {
-        params.person + chat_symb,
-    };
-
-    // main loop
-    while (is_running) {
-        // handle Ctrl + C
-        is_running = sdl_poll_events();
-
-        if (!is_running) {
-            break;
-        }
-
-        // delay
-        std::this_thread::sleep_for(std::chrono::milliseconds(100));
-
-        int64_t t_ms = 0;
-
-        {
-            audio.get(2000, pcmf32_cur);
-
-            if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1250, params.vad_thold, params.freq_thold, params.print_energy) || force_speak) {
-                //fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
-
-                audio.get(params.voice_ms, pcmf32_cur);
-
-                std::string text_heard;
-
-                if (!force_speak) {
-                    text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prompt_whisper, prob0, t_ms));
-                }
-
-                // 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+$"), "");
-
-                const std::vector<llama_token> tokens = llama_tokenize(ctx_llama, text_heard.c_str(), false);
-
-                if (text_heard.empty() || tokens.empty() || force_speak) {
-                    //fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
-                    audio.clear();
-
-                    continue;
-                }
-
-                force_speak = false;
-
-                text_heard.insert(0, 1, ' ');
-                text_heard += "\n" + bot_name + chat_symb;
-                fprintf(stdout, "%s%s%s", "\033[1m", text_heard.c_str(), "\033[0m");
-                fflush(stdout);
-
-                embd = ::llama_tokenize(ctx_llama, text_heard, false);
-
-                // text inference
-                bool done = false;
-                std::string text_to_speak;
-                while (true) {
-                    // predict
-                    if (embd.size() > 0) {
-                        if (n_past + (int) embd.size() > n_ctx) {
-                            n_past = n_keep;
-
-                            // insert n_left/2 tokens at the start of embd from last_n_tokens
-                            embd.insert(embd.begin(), embd_inp.begin() + embd_inp.size() - n_prev, embd_inp.end());
-
-                            //printf("\n---\n");
-                            //printf("resetting: '");
-                            //for (int i = 0; i < (int) embd.size(); i++) {
-                            //    printf("%s", llama_token_to_str(ctx_llama, embd[i]));
-                            //}
-                            //printf("'\n");
-                            //printf("\n---\n");
-                        }
-
-                        if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past, params.n_threads)) {
-                            fprintf(stderr, "%s : failed to eval\n", __func__);
-                            return 1;
-                        }
-                    }
-
-                    //printf("n_iter = %d, n_past = %d, n_ctx = %d, n_keep = %d, n_prev = %d, embd.size() = %d\n", n_iter, n_past, n_ctx, n_keep, n_prev, (int) embd.size());
-
-                    embd_inp.insert(embd_inp.end(), embd.begin(), embd.end());
-                    n_past += embd.size();
-                    embd.clear();
-
-                    if (done) break;
-
-                    {
-                        // out of user input, sample next token
-                        const float top_k          = 5;
-                        const float top_p          = 0.80f;
-                        const float temp           = 0.30f;
-                        const float repeat_penalty = 1.1764f;
-
-                        const int repeat_last_n    = 256;
-
-                        llama_token id = 0;
-
-                        {
-                            auto logits = llama_get_logits(ctx_llama);
-                            auto n_vocab = llama_n_vocab(ctx_llama);
-
-                            logits[llama_token_eos()] = 0;
-
-                            std::vector<llama_token_data> candidates;
-                            candidates.reserve(n_vocab);
-                            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-                                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-                            }
-
-                            llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
-
-                            // apply repeat penalty
-                            const float nl_logit = logits[llama_token_nl()];
-
-                            llama_sample_repetition_penalty(ctx_llama, &candidates_p,
-                                    embd_inp.data() + std::max(0, n_past - repeat_last_n),
-                                    repeat_last_n, repeat_penalty);
-
-                            logits[llama_token_nl()] = nl_logit;
-
-                            if (temp <= 0) {
-                                // Greedy sampling
-                                id = llama_sample_token_greedy(ctx_llama, &candidates_p);
-                            } else {
-                                // Temperature sampling
-                                llama_sample_top_k(ctx_llama, &candidates_p, top_k);
-                                llama_sample_top_p(ctx_llama, &candidates_p, top_p);
-                                llama_sample_temperature(ctx_llama, &candidates_p, temp);
-                                id = llama_sample_token(ctx_llama, &candidates_p);
-                            }
-                        }
-
-                        if (id != llama_token_eos()) {
-                            // add it to the context
-                            embd.push_back(id);
-
-                            text_to_speak += llama_token_to_str(ctx_llama, id);
-
-                            printf("%s", llama_token_to_str(ctx_llama, id));
-                        }
-                    }
-
-                    {
-                        std::string last_output;
-                        for (int i = embd_inp.size() - 16; i < (int) embd_inp.size(); i++) {
-                            last_output += llama_token_to_str(ctx_llama, embd_inp[i]);
-                        }
-                        last_output += llama_token_to_str(ctx_llama, embd[0]);
-
-                        for (std::string & antiprompt : antiprompts) {
-                            if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
-                                done = true;
-                                text_to_speak = ::replace(text_to_speak, antiprompt, "");
-                                fflush(stdout);
-                                break;
-                            }
-                        }
-                    }
-
-                    is_running = sdl_poll_events();
-
-                    if (!is_running) {
-                        break;
-                    }
-                }
-
-                text_to_speak = ::replace(text_to_speak, "\"", "");
-                system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
-
-                audio.clear();
-
-                ++n_iter;
-            }
-        }
-    }
-
-    audio.pause();
-
-    whisper_print_timings(ctx_wsp);
-    whisper_free(ctx_wsp);
-
-    llama_print_timings(ctx_llama);
-    llama_free(ctx_llama);
-
-    return 0;
-}

examples/talk.wasm/CMakeLists.txt

@@ -13,7 +13,6 @@ include(DefaultTargetOptions)
 
 target_link_libraries(${TARGET} PRIVATE
     whisper
-    common
     )
 
 unset(EXTRA_FLAGS)

examples/talk.wasm/gpt-2.cpp

@@ -1,6 +1,4 @@
 #include "ggml.h"
-#include "common-ggml.h"
-
 #include "gpt-2.h"
 
 #include <cmath>
@@ -16,6 +14,150 @@
 
 /////////////////////// 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;
@@ -23,7 +165,7 @@ struct gpt2_hparams {
     int32_t n_embd  = 768;
     int32_t n_head  = 12;
     int32_t n_layer = 12;
-    int32_t ftype   = 1;
+    int32_t f16     = 1;
 };
 
 struct gpt2_layer {
@@ -45,7 +187,7 @@ struct gpt2_layer {
     struct ggml_tensor * c_mlp_fc_w;
     struct ggml_tensor * c_mlp_fc_b;
 
-    struct ggml_tensor * c_mlp_proj_w;
+    struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
     struct ggml_tensor * c_mlp_proj_b;
 };
 
@@ -56,9 +198,8 @@ struct gpt2_model {
     struct ggml_tensor * ln_f_g;
     struct ggml_tensor * ln_f_b;
 
-    struct ggml_tensor * wte;     // position embedding
-    struct ggml_tensor * wpe;     //    token embedding
-    struct ggml_tensor * lm_head; // language model head
+    struct ggml_tensor * wte; // position embedding
+    struct ggml_tensor * wpe; //    token embedding
 
     std::vector<gpt2_layer> layers;
 
@@ -100,14 +241,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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.ftype,   sizeof(hparams.ftype));
+        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: ftype   = %d\n", __func__, hparams.ftype);
+        printf("%s: f16     = %d\n", __func__, hparams.f16);
     }
 
     // load vocab
@@ -134,14 +275,9 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         }
     }
 
-    // for the big tensors, we have the option to store the data in 16-bit floats or quantized
+    // 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
-    ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
-    if (wtype == GGML_TYPE_COUNT) {
-        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
-                __func__, fname.c_str(), model.hparams.ftype);
-        return false;
-    }
+    const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
 
     auto & ctx = model.ctx;
 
@@ -155,33 +291,32 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         const int n_ctx   = hparams.n_ctx;
         const int n_vocab = hparams.n_vocab;
 
-        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
+        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_sizef(wtype);         // wte
-        ctx_size +=   n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
-        ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype);         // lm_head
+        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_sizef(GGML_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
+        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_sizef(GGML_TYPE_F32)); // ln_2_g
-        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_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_sizef(wtype));         // c_attn_attn_w
-        ctx_size += n_layer*(       3*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_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_sizef(wtype));           // c_attn_proj_w
-        ctx_size += n_layer*(       n_embd*ggml_type_sizef(GGML_TYPE_F32));   // c_attn_proj_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_sizef(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_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_sizef(wtype));         // c_mlp_proj_w
-        ctx_size += n_layer*(         n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_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_sizef(GGML_TYPE_F32); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
+        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
 
@@ -190,11 +325,9 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
     // create the ggml context
     {
-        struct ggml_init_params params = {
-            .mem_size   = ctx_size,
-            .mem_buffer = NULL,
-            .no_alloc   = false,
-        };
+        struct ggml_init_params params;
+        params.mem_size   = ctx_size;
+        params.mem_buffer = NULL;
 
         model.ctx = ggml_init(params);
         if (!model.ctx) {
@@ -217,38 +350,36 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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);
-        model.lm_head = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        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;
-        model.tensors["model/lm_head"] = model.lm_head;
+        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_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.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,           n_embd, 3*n_embd);
-            layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*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_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,           n_embd, 4*n_embd);
-            layer.c_mlp_fc_b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*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  = 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);
+            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;
@@ -266,7 +397,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             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;
+            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;
         }
     }
@@ -294,16 +425,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
     {
         size_t total_size = 0;
 
-        bool has_lm_head = false;
-
         while (true) {
             int32_t n_dims;
             int32_t length;
-            int32_t ttype;
+            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 *>(&ttype),  sizeof(ttype));
+            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
 
             if (fin.eof()) {
                 break;
@@ -332,18 +461,13 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             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(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
+                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
                 return false;
             }
 
-            // for debugging
-            if (0) {
-                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
-            }
+            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
 
-            const size_t bpe = ggml_type_size(ggml_type(ttype));
-
-            if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
+            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;
@@ -351,15 +475,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
 
-            // GPT-2 models share the WTE tensor as the LM head
-            if (name == "model/wte" && has_lm_head == false) {
-                memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor));
-            }
-
-            if (name == "model/lm_head") {
-                has_lm_head = true;
-            }
-
+            //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);
         }
 
@@ -377,7 +493,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 //   - 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 logits for the next token
+//   - embd_w:    the predicted probabilities of the next token
 //
 bool gpt2_eval(
         const gpt2_model & model,
@@ -396,12 +512,12 @@ bool gpt2_eval(
     const int n_head  = hparams.n_head;
     const int n_vocab = hparams.n_vocab;
 
-    static size_t buf_size = 512u*1024*1024;
+    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);
+        printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
 
         // reallocate
         buf_size = buf_size_new;
@@ -412,14 +528,13 @@ bool gpt2_eval(
         }
     }
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_size,
-        /*.mem_buffer =*/ buf,
-        /*.no_alloc   =*/ false,
-    };
+    struct ggml_init_params params;
+    params.mem_size   = buf_size;
+    params.mem_buffer = buf;
 
     struct ggml_context * ctx0 = ggml_init(params);
-    struct ggml_cgraph gf = {};
+
+    struct ggml_cgraph gf = { };
     gf.n_threads = n_threads;
 
     struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
@@ -463,7 +578,7 @@ bool gpt2_eval(
         // [2304, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_attn_attn_w,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
                     cur);
 
             cur = ggml_add(ctx0,
@@ -539,13 +654,11 @@ bool gpt2_eval(
             // 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_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, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
+                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]
@@ -572,7 +685,7 @@ bool gpt2_eval(
         // [768, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_attn_proj_w,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
                     cur);
 
             cur = ggml_add(ctx0,
@@ -609,7 +722,7 @@ bool gpt2_eval(
             // cur = fc_w*cur + fc_b
             // [3072, N]
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_fc_w,
+                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
                     cur);
 
             cur = ggml_add(ctx0,
@@ -629,7 +742,7 @@ bool gpt2_eval(
             // cur = proj_w*cur + proj_b
             // [768, N]
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w,
+                    model.layers[il].c_mlp_proj_w_trans,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -656,12 +769,12 @@ bool gpt2_eval(
     }
 
     // inpL = WTE * inpL
-    // [ 768, 50257] - model.lm_head
+    // [ 768, 50257] - model.wte
     // [ 768, N]     - inpL
-    inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
+    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
 
     // logits -> probs
-    //inpL = ggml_soft_max(ctx0, inpL);
+    inpL = ggml_soft_max(ctx0, inpL);
 
     // run the computation
     ggml_build_forward_expand(&gf, inpL);
@@ -675,7 +788,7 @@ bool gpt2_eval(
     //embd_w.resize(n_vocab*N);
     //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
 
-    // return result just for the last token
+    // 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);
 
@@ -712,7 +825,7 @@ Me too.
     int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
 
     // sampling parameters
-    int32_t top_k = 5;
+    int32_t top_k = 40;
     float   top_p = 0.9f;
     float   temp  = 1.0f;
 };
@@ -720,15 +833,14 @@ Me too.
 struct gpt2_context * gpt2_init(const char * path_model) {
     gpt2_context * ctx = new gpt2_context;
 
-    ctx->rng = std::mt19937(time(nullptr));
+    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__, path_model);
-            delete ctx;
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
             return nullptr;
         }
 
@@ -772,9 +884,9 @@ std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens)
 
     std::string result;
 
-    for (int i = embd.size(); i < (int) embd_inp.size() + n_predict; i++) {
+    for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
         // predict
-        if (!embd.empty()) {
+        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 "";
@@ -801,7 +913,10 @@ std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens)
         result += ctx->vocab.id_to_token[embd[0]];
 
         // end of text token
-        if (embd.back() == 50256) {
+        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;
         }
     }

examples/talk.wasm/gpt-2.h

@@ -2,12 +2,18 @@
 
 // TODO: Change to C-style API and move to ./examples for easy reuse.
 
-#include "common.h"
-
 #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);

examples/talk.wasm/index-tmpl.html

@@ -44,15 +44,6 @@
 
             <br><br>
 
-            <b>More examples:</b>
-                <a href="https://whisper.ggerganov.com/">main</a> |
-                <a href="https://whisper.ggerganov.com/bench">bench</a> |
-                <a href="https://whisper.ggerganov.com/stream">stream</a> |
-                <a href="https://whisper.ggerganov.com/command">command</a> |
-                <a href="https://whisper.ggerganov.com/talk">talk</a> |
-
-            <br><br>
-
             <hr>
 
             Select the models you would like to use and click the "Start" button to begin the conversation
@@ -63,10 +54,6 @@
                 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>
-                <br><br>
-                Quantized models:<br><br>
-                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
-                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
                 <span id="fetch-whisper-progress"></span>
 
                 <!--
@@ -279,17 +266,11 @@
                 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',
-
-                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
-                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
                 };
 
                 let sizes = {
                     'tiny.en': 75,
                     'base.en': 142,
-
-                    'tiny-en-q5_1':   31,
-                    'base-en-q5_1':   57,
                 };
 
                 let url     = urls[model];
@@ -300,10 +281,6 @@
 
                 document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
                 document.getElementById('fetch-whisper-base-en').style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1').style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1').style.display = 'none';
-
                 document.getElementById('model-whisper-status').innerHTML = 'loading "' + model + '" ... ';
 
                 cbProgress = function(p) {
@@ -315,10 +292,6 @@
                     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-en-q5_1'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en-q5_1'); if (el) el.style.display = 'inline-block';
-
                     el = document.getElementById('model-whisper-status');  if (el) el.innerHTML = '';
                 };
 

examples/talk/.gitignore

@@ -1 +1 @@
-audio.mp3
+eleven-labs.py

examples/talk/CMakeLists.txt

@@ -1,8 +1,16 @@
-if (WHISPER_SDL2)
+if (WHISPER_SUPPORT_SDL2)
     # talk
     set(TARGET talk)
-    add_executable(${TARGET} talk.cpp gpt-2.cpp)
-    target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${CMAKE_THREAD_LIBS_INIT})
+    #add_executable(${TARGET} talk.cpp gpt-2.cpp)
+    #target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
+    #target_link_libraries(${TARGET} PRIVATE whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
+
+    # TODO: this is temporary
+    #       need to export ggml symbols for MSVC, but too lazy ..
+    add_executable(${TARGET} talk.cpp gpt-2.cpp ../common.cpp ../common-sdl.cpp ../../ggml.c ../../whisper.cpp)
 
     include(DefaultTargetOptions)
+
+    target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS} ../../)
+    target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
 endif ()

examples/talk/eleven-labs.py

@@ -1,23 +0,0 @@
-import sys
-import importlib.util
-
-api_key = "" #Write your https://beta.elevenlabs.io api key here
-if not api_key:
-    print("To use elevenlabs you have to register to https://beta.elevenlabs.io and add your elevenlabs api key to examples/talk/eleven-labs.py")
-    sys.exit()
-
-if importlib.util.find_spec("elevenlabs") is None:
-    print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'")
-    sys.exit()
-
-from elevenlabs import ElevenLabs
-eleven = ElevenLabs(api_key)
-
-# Get a Voice object, by name or UUID
-voice = eleven.voices["Arnold"] #Possible Voices: Adam Antoni Arnold Bella Domi Elli Josh
-
-# Generate the TTS
-audio = voice.generate(str(sys.argv[2:]))
-
-# Save the TTS to a file
-audio.save("audio") 

examples/talk/gpt-2.cpp

@@ -1,6 +1,4 @@
 #include "ggml.h"
-#include "common-ggml.h"
-
 #include "gpt-2.h"
 
 #include <cmath>
@@ -16,6 +14,150 @@
 
 /////////////////////// 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.empty()) 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.emplace_back(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;
@@ -23,7 +165,7 @@ struct gpt2_hparams {
     int32_t n_embd  = 768;
     int32_t n_head  = 12;
     int32_t n_layer = 12;
-    int32_t ftype   = 1;
+    int32_t f16     = 1;
 };
 
 struct gpt2_layer {
@@ -45,7 +187,7 @@ struct gpt2_layer {
     struct ggml_tensor * c_mlp_fc_w;
     struct ggml_tensor * c_mlp_fc_b;
 
-    struct ggml_tensor * c_mlp_proj_w;
+    struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
     struct ggml_tensor * c_mlp_proj_b;
 };
 
@@ -56,9 +198,8 @@ struct gpt2_model {
     struct ggml_tensor * ln_f_g;
     struct ggml_tensor * ln_f_b;
 
-    struct ggml_tensor * wte;     // position embedding
-    struct ggml_tensor * wpe;     //    token embedding
-    struct ggml_tensor * lm_head; // language model head
+    struct ggml_tensor * wte; // position embedding
+    struct ggml_tensor * wpe; //    token embedding
 
     std::vector<gpt2_layer> layers;
 
@@ -100,14 +241,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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.ftype,   sizeof(hparams.ftype));
+        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: ftype   = %d\n", __func__, hparams.ftype);
+        printf("%s: f16     = %d\n", __func__, hparams.f16);
     }
 
     // load vocab
@@ -127,21 +268,16 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             fin.read((char *) &len, sizeof(len));
 
             word.resize(len);
-            fin.read((char *) word.data(), len);
+            fin.read((char *) &word[0], 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 or quantized
+    // 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
-    ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
-    if (wtype == GGML_TYPE_COUNT) {
-        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
-                __func__, fname.c_str(), model.hparams.ftype);
-        return false;
-    }
+    const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
 
     auto & ctx = model.ctx;
 
@@ -155,33 +291,32 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         const int n_ctx   = hparams.n_ctx;
         const int n_vocab = hparams.n_vocab;
 
-        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
-        ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
+        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_sizef(wtype);         // wte
-        ctx_size +=   n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe
-        ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype);         // lm_head
+        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_sizef(GGML_TYPE_F32)); // ln_1_g
-        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
+        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_sizef(GGML_TYPE_F32)); // ln_2_g
-        ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_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_sizef(wtype));         // c_attn_attn_w
-        ctx_size += n_layer*(       3*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_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_sizef(wtype));           // c_attn_proj_w
-        ctx_size += n_layer*(       n_embd*ggml_type_sizef(GGML_TYPE_F32));   // c_attn_proj_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_sizef(wtype));         // c_mlp_fc_w
-        ctx_size += n_layer*(       4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_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_sizef(wtype));         // c_mlp_proj_w
-        ctx_size += n_layer*(         n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_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_sizef(GGML_TYPE_F32); // memory_k
-        ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
+        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
 
@@ -190,11 +325,9 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
     // create the ggml context
     {
-        struct ggml_init_params params = {
-            .mem_size   = ctx_size,
-            .mem_buffer = NULL,
-            .no_alloc   = false,
-        };
+        struct ggml_init_params params;
+        params.mem_size   = ctx_size;
+        params.mem_buffer = nullptr;
 
         model.ctx = ggml_init(params);
         if (!model.ctx) {
@@ -217,38 +350,36 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
         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);
-        model.lm_head = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        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;
-        model.tensors["model/lm_head"] = model.lm_head;
+        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_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.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,           n_embd, 3*n_embd);
-            layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*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_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,           n_embd, 4*n_embd);
-            layer.c_mlp_fc_b    = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*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  = 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);
+            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;
@@ -266,7 +397,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             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;
+            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;
         }
     }
@@ -294,16 +425,14 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
     {
         size_t total_size = 0;
 
-        bool has_lm_head = false;
-
         while (true) {
             int32_t n_dims;
             int32_t length;
-            int32_t ttype;
+            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 *>(&ttype),  sizeof(ttype));
+            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
 
             if (fin.eof()) {
                 break;
@@ -319,7 +448,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
             std::string name(length, 0);
             fin.read(&name[0], length);
 
-            if (model.tensors.find(name.data()) == model.tensors.end()) {
+            if (model.tensors.find(name) == model.tensors.end()) {
                 fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
                 return false;
             }
@@ -332,18 +461,13 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             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(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]);
+                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
                 return false;
             }
 
-            // for debugging
-            if (0) {
-                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
-            }
+            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
 
-            const size_t bpe = ggml_type_size(ggml_type(ttype));
-
-            if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
+            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;
@@ -351,15 +475,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 
             fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
 
-            // GPT-2 models share the WTE tensor as the LM head
-            if (name == "model/wte" && has_lm_head == false) {
-                memcpy(model.lm_head->data, tensor->data, ggml_nbytes(tensor));
-            }
-
-            if (name == "model/lm_head") {
-                has_lm_head = true;
-            }
-
+            //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);
         }
 
@@ -377,7 +493,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
 //   - 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 logits for the next token
+//   - embd_w:    the predicted probabilities of the next token
 //
 bool gpt2_eval(
         const gpt2_model & model,
@@ -396,12 +512,12 @@ bool gpt2_eval(
     const int n_head  = hparams.n_head;
     const int n_vocab = hparams.n_vocab;
 
-    static size_t buf_size = 512u*1024*1024;
+    static size_t buf_size = 5640ull*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);
+        printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
 
         // reallocate
         buf_size = buf_size_new;
@@ -412,14 +528,13 @@ bool gpt2_eval(
         }
     }
 
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ buf_size,
-        /*.mem_buffer =*/ buf,
-        /*.no_alloc   =*/ false,
-    };
+    struct ggml_init_params params;
+    params.mem_size   = buf_size;
+    params.mem_buffer = buf;
 
     struct ggml_context * ctx0 = ggml_init(params);
-    struct ggml_cgraph gf = {};
+
+    struct ggml_cgraph gf = { };
     gf.n_threads = n_threads;
 
     struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
@@ -463,7 +578,7 @@ bool gpt2_eval(
         // [2304, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_attn_attn_w,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
                     cur);
 
             cur = ggml_add(ctx0,
@@ -539,13 +654,11 @@ bool gpt2_eval(
             // 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_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, model.memory_v->type, n_past + N, n_embd/n_head, n_head));
+                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]
@@ -572,7 +685,7 @@ bool gpt2_eval(
         // [768, N]
         {
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_attn_proj_w,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
                     cur);
 
             cur = ggml_add(ctx0,
@@ -609,7 +722,7 @@ bool gpt2_eval(
             // cur = fc_w*cur + fc_b
             // [3072, N]
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_fc_w,
+                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
                     cur);
 
             cur = ggml_add(ctx0,
@@ -629,7 +742,7 @@ bool gpt2_eval(
             // cur = proj_w*cur + proj_b
             // [768, N]
             cur = ggml_mul_mat(ctx0,
-                    model.layers[il].c_mlp_proj_w,
+                    model.layers[il].c_mlp_proj_w_trans,
                     cur);
 
             cur = ggml_add(ctx0,
@@ -656,12 +769,12 @@ bool gpt2_eval(
     }
 
     // inpL = WTE * inpL
-    // [ 768, 50257] - model.lm_head
+    // [ 768, 50257] - model.wte
     // [ 768, N]     - inpL
-    inpL = ggml_mul_mat(ctx0, model.lm_head, inpL);
+    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
 
     // logits -> probs
-    //inpL = ggml_soft_max(ctx0, inpL);
+    inpL = ggml_soft_max(ctx0, inpL);
 
     // run the computation
     ggml_build_forward_expand(&gf, inpL);
@@ -675,7 +788,7 @@ bool gpt2_eval(
     //embd_w.resize(n_vocab*N);
     //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
 
-    // return result just for the last token
+    // 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);
 

examples/talk/gpt-2.h

@@ -2,12 +2,18 @@
 
 // TODO: Change to C-style API and move to ./examples for easy reuse.
 
-#include "common.h"
-
 #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);

examples/talk/speak.sh

@@ -7,13 +7,9 @@
 # Mac OS: brew install espeak
 # Linux: apt-get install espeak
 #
-#espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
-
-# Mac OS "say" command
-say "$2"
+espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
 
 # Eleven Labs
-# To use it, install the elevenlabs module from pip (pip install elevenlabs), register to https://beta.elevenlabs.io to get an api key and paste it in /examples/talk/eleven-labs.py 
 #
 #wd=$(dirname $0)
 #script=$wd/eleven-labs.py

examples/whisper.android/README.md

@@ -9,6 +9,4 @@ To use:
 5. Select the "release" active build variant, and use Android Studio to run and deploy to your device.
 [^1]: I recommend the tiny or base models for running on an Android device.
 
-(PS: Do not move this android project folder individually to other folders, because this android project folder depends on the files of the whole project.)
-
 <img width="300" alt="image" src="https://user-images.githubusercontent.com/1670775/221613663-a17bf770-27ef-45ab-9a46-a5f99ba65d2a.jpg">

examples/whisper.swiftui/README.md

@@ -1,18 +1,15 @@
 A sample SwiftUI app using [whisper.cpp](https://github.com/ggerganov/whisper.cpp/) to do voice-to-text transcriptions.
 See also: [whisper.objc](https://github.com/ggerganov/whisper.cpp/tree/master/examples/whisper.objc).
 
-**Usage**:
+To use:
 
 1. Select a model from the [whisper.cpp repository](https://github.com/ggerganov/whisper.cpp/tree/master/models).[^1]
-2. Add the model to `whisper.swiftui.demo/Resources/models` **via Xcode**.
+2. Add the model to "whisper.swiftui.demo/Resources/models" via Xcode.
 3. Select a sample audio file (for example, [jfk.wav](https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav)).
-4. Add the sample audio file to `whisper.swiftui.demo/Resources/samples` **via Xcode**.
+4. Add the model to "whisper.swiftui.demo/Resources/samples" via Xcode.
 5. Select the "Release" [^2] build configuration under "Run", then deploy and run to your device.
 
-**Note:** Pay attention to the folder path: `whisper.swiftui.demo/Resources/models` is the appropriate directory to place resources whilst `whisper.swiftui.demo/Models` is related to actual code.
-
 [^1]: I recommend the tiny, base or small models for running on an iOS device.
-
 [^2]: The `Release` build can boost performance of transcription. In this project, it also added `-O3 -DNDEBUG` to `Other C Flags`, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.
 
 ![image](https://user-images.githubusercontent.com/1991296/212539216-0aef65e4-f882-480a-8358-0f816838fd52.png)

examples/whisper.wasm/CMakeLists.txt

@@ -31,9 +31,9 @@ endif()
 set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
     --bind \
     -s USE_PTHREADS=1 \
-    -s PTHREAD_POOL_SIZE_STRICT=0 \
-    -s INITIAL_MEMORY=2000MB \
-    -s TOTAL_MEMORY=2000MB \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s INITIAL_MEMORY=1500MB \
+    -s TOTAL_MEMORY=1500MB \
     -s FORCE_FILESYSTEM=1 \
     -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
     ${EXTRA_FLAGS} \

examples/whisper.wasm/emscripten.cpp

@@ -10,12 +10,6 @@ std::thread g_worker;
 
 std::vector<struct whisper_context *> g_contexts(4, nullptr);
 
-static inline int mpow2(int n) {
-    int p = 1;
-    while (p <= n) p *= 2;
-    return p/2;
-}
-
 EMSCRIPTEN_BINDINGS(whisper) {
     emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
         if (g_worker.joinable()) {
@@ -49,7 +43,7 @@ EMSCRIPTEN_BINDINGS(whisper) {
         }
     }));
 
-    emscripten::function("full_default", emscripten::optional_override([](size_t index, const emscripten::val & audio, const std::string & lang, int nthreads, bool translate) {
+    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();
         }
@@ -72,7 +66,7 @@ EMSCRIPTEN_BINDINGS(whisper) {
         params.print_special    = false;
         params.translate        = translate;
         params.language         = whisper_is_multilingual(g_contexts[index]) ? lang.c_str() : "en";
-        params.n_threads        = std::min(nthreads, std::min(16, mpow2(std::thread::hardware_concurrency())));
+        params.n_threads        = std::min(8, (int) std::thread::hardware_concurrency());
         params.offset_ms        = 0;
 
         std::vector<float> pcmf32;

examples/whisper.wasm/index-tmpl.html

@@ -40,42 +40,21 @@
 
             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:</b>
-                <ul>
-                    <li>your browser must support WASM SIMD instructions for this to work</li>
-                    <li>Firefox cannot load files larger than 256 MB - use Chrome instead</li>
-                </ul>
+            <b>Important: your browser must support WASM SIMD instructions for this to work.</b>
 
-            <b>More examples:</b>
-                <a href="https://whisper.ggerganov.com/">main</a> |
-                <a href="https://whisper.ggerganov.com/bench">bench</a> |
-                <a href="https://whisper.ggerganov.com/stream">stream</a> |
-                <a href="https://whisper.ggerganov.com/command">command</a> |
-                <a href="https://whisper.ggerganov.com/talk">talk</a> |
-
-            <hr>
+            <br><br><hr>
 
             <div id="model">
-                Whisper models: <span id="model-whisper-status"></span><br><br>
+                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>
                 <button id="fetch-whisper-small-en" onclick="loadWhisper('small.en')">small.en (466 MB)</button>
                 <button id="fetch-whisper-small"    onclick="loadWhisper('small')">small (466 MB)</button>
-                <input type="file" id="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
-                <br><br>
-                Quantized models:<br><br>
-                <button id="fetch-whisper-tiny-en-q5_1"   onclick="loadWhisper('tiny-en-q5_1')">tiny.en (Q5_1, 31 MB)</button>
-                <button id="fetch-whisper-tiny-q5_1"      onclick="loadWhisper('tiny-q5_1')">tiny (Q5_1, 31 MB)</button>
-                <button id="fetch-whisper-base-en-q5_1"   onclick="loadWhisper('base-en-q5_1')">base.en (Q5_1, 57 MB)</button>
-                <button id="fetch-whisper-base-q5_1"      onclick="loadWhisper('base-q5_1')">base (Q5_1, 57 MB)</button>
-                <button id="fetch-whisper-small-en-q5_1"  onclick="loadWhisper('small-en-q5_1')">small.en (Q5_1, 182 MB)</button>
-                <button id="fetch-whisper-small-q5_1"     onclick="loadWhisper('small-q5_1')">small (Q5_1, 182 MB)</button><br>
-                <button id="fetch-whisper-medium-en-q5_0" onclick="loadWhisper('medium-en-q5_0')">medium.en (Q5_0, 515 MB)</button>
-                <button id="fetch-whisper-medium-q5_0"    onclick="loadWhisper('medium-q5_0')">medium (Q5_0, 515 MB)</button>
-                <button id="fetch-whisper-large-q5_0"     onclick="loadWhisper('large-q5_0')">large (Q5_0, 1030 MB)</button>
                 <span id="fetch-whisper-progress"></span>
+
+                <input type="file" id="whisper-file" name="file" onchange="loadFile(event, 'whisper.bin')" />
             </div>
 
             <br>
@@ -182,12 +161,6 @@
                             <option value="yi">Yiddish</option>
                         </select>
                     </td>
-                    <!-- Slider to select number of threads between 1 and 16 -->
-                    <td>
-                        Threads:
-                        <input type="range" id="threads" name="threads" min="1" max="16" value="8" onchange="changeThreads(this.value)" />
-                        <span id="threads-value">8</span>
-                    </td>
                     <td>
                         <button onclick="onProcess(false);">Transcribe</button>
                     </td>
@@ -290,13 +263,11 @@
 
                 Module.FS_createDataFile("/", fname, buf, true, true);
 
-                //model_whisper = fname;
+                model_whisper = fname;
 
                 document.getElementById('model-whisper-status').innerHTML = 'loaded "' + model_whisper + '"!';
 
                 printTextarea('storeFS: stored model: ' + fname + ' size: ' + buf.length);
-
-                document.getElementById('model').innerHTML = 'Model fetched: ' + model_whisper;
             }
 
             function loadFile(event, fname) {
@@ -321,17 +292,6 @@
                 document.getElementById('fetch-whisper-tiny'    ).style.display = 'none';
                 document.getElementById('fetch-whisper-base'    ).style.display = 'none';
                 document.getElementById('fetch-whisper-small'   ).style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-tiny-q5_1'     ).style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-base-q5_1'     ).style.display = 'none';
-                document.getElementById('fetch-whisper-small-en-q5_1' ).style.display = 'none';
-                document.getElementById('fetch-whisper-small-q5_1'    ).style.display = 'none';
-                document.getElementById('fetch-whisper-medium-en-q5_0').style.display = 'none';
-                document.getElementById('fetch-whisper-medium-q5_0'   ).style.display = 'none';
-                document.getElementById('fetch-whisper-large-q5_0'    ).style.display = 'none';
-
                 document.getElementById('whisper-file'          ).style.display = 'none';
                 document.getElementById('model-whisper-status'  ).innerHTML = 'loaded model: ' + file.name;
             }
@@ -344,16 +304,6 @@
                     'base':     'https://whisper.ggerganov.com/ggml-model-whisper-base.bin',
                     'small.en': 'https://whisper.ggerganov.com/ggml-model-whisper-small.en.bin',
                     'small':    'https://whisper.ggerganov.com/ggml-model-whisper-small.bin',
-
-                    'tiny-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-tiny.en-q5_1.bin',
-                    'tiny-q5_1':     'https://whisper.ggerganov.com/ggml-model-whisper-tiny-q5_1.bin',
-                    'base-en-q5_1':  'https://whisper.ggerganov.com/ggml-model-whisper-base.en-q5_1.bin',
-                    'base-q5_1':     'https://whisper.ggerganov.com/ggml-model-whisper-base-q5_1.bin',
-                    'small-en-q5_1': 'https://whisper.ggerganov.com/ggml-model-whisper-small.en-q5_1.bin',
-                    'small-q5_1':    'https://whisper.ggerganov.com/ggml-model-whisper-small-q5_1.bin',
-                    'medium-en-q5_0':'https://whisper.ggerganov.com/ggml-model-whisper-medium.en-q5_0.bin',
-                    'medium-q5_0':   'https://whisper.ggerganov.com/ggml-model-whisper-medium-q5_0.bin',
-                    'large-q5_0':    'https://whisper.ggerganov.com/ggml-model-whisper-large-q5_0.bin',
                 };
 
                 let sizes = {
@@ -363,16 +313,6 @@
                     'base':     142,
                     'small.en': 466,
                     'small':    466,
-
-                    'tiny-en-q5_1':   31,
-                    'tiny-q5_1':      31,
-                    'base-en-q5_1':   57,
-                    'base-q5_1':      57,
-                    'small-en-q5_1':  182,
-                    'small-q5_1':     182,
-                    'medium-en-q5_0': 515,
-                    'medium-q5_0':    515,
-                    'large-q5_0':     1030,
                 };
 
                 let url     = urls[model];
@@ -387,19 +327,8 @@
                 document.getElementById('fetch-whisper-tiny'    ).style.display = 'none';
                 document.getElementById('fetch-whisper-base'    ).style.display = 'none';
                 document.getElementById('fetch-whisper-small'   ).style.display = 'none';
-
-                document.getElementById('fetch-whisper-tiny-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-tiny-q5_1'     ).style.display = 'none';
-                document.getElementById('fetch-whisper-base-en-q5_1'  ).style.display = 'none';
-                document.getElementById('fetch-whisper-base-q5_1'     ).style.display = 'none';
-                document.getElementById('fetch-whisper-small-en-q5_1' ).style.display = 'none';
-                document.getElementById('fetch-whisper-small-q5_1'    ).style.display = 'none';
-                document.getElementById('fetch-whisper-medium-en-q5_0').style.display = 'none';
-                document.getElementById('fetch-whisper-medium-q5_0'   ).style.display = 'none';
-                document.getElementById('fetch-whisper-large-q5_0'    ).style.display = 'none';
-
-                document.getElementById('whisper-file'        ).style.display = 'none';
-                document.getElementById('model-whisper-status').innerHTML = 'loading model: ' + model;
+                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');
@@ -408,26 +337,14 @@
 
                 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-small-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('fetch-whisper-small'   ); if (el) el.style.display = 'inline-block';
-
-                    el = document.getElementById('fetch-whisper-tiny-en-q5_1'  ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-tiny-q5_1'     ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-en-q5_1'  ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-base-q5_1'     ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-small-en-q5_1' ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-small-q5_1'    ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-medium-en-q5_0'); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-medium-q5_0'   ); if (el) el.style.display = 'inline-block';
-                    el = document.getElementById('fetch-whisper-large-q5_0'    ); 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 = '';
+                    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);
@@ -437,8 +354,7 @@
             // audio file
             //
 
-            const kMaxAudio_s = 30*60;
-            const kMaxRecording_s = 2*60;
+            const kMaxAudio_s = 120;
             const kSampleRate = 16000;
 
             window.AudioContext = window.AudioContext || window.webkitAudioContext;
@@ -507,7 +423,7 @@
                 doRecording = false;
             }
 
-            // record up to kMaxRecording_s seconds of audio from the microphone
+            // 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() {
@@ -563,9 +479,9 @@
                                         printTextarea('js: audio recorded, size: ' + audio.length);
 
                                         // truncate to first 30 seconds
-                                        if (audio.length > kMaxRecording_s*kSampleRate) {
-                                            audio = audio.slice(0, kMaxRecording_s*kSampleRate);
-                                            printTextarea('js: truncated audio to first ' + kMaxRecording_s + ' 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);
                                     });
@@ -593,31 +509,24 @@
                         });
                     }
 
-                    document.getElementById('progress-bar').style.width = (100*(Date.now() - startTime)/1000/kMaxRecording_s) + '%';
-                    document.getElementById('progress-text').innerHTML = (100*(Date.now() - startTime)/1000/kMaxRecording_s).toFixed(0) + '%';
+                    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 ' + kMaxRecording_s + ' seconds');
+                        printTextarea('js: recording stopped after ' + kMaxAudio_s + ' seconds');
                         stopRecording();
                     }
-                }, kMaxRecording_s*1000);
+                }, kMaxAudio_s*1000);
             }
 
             //
             // transcribe
             //
 
-            var nthreads = 8;
-
-            function changeThreads(value) {
-                nthreads = value;
-                document.getElementById('threads-value').innerHTML = nthreads;
-            }
-
             function onProcess(translate) {
                 if (!instance) {
                     instance = Module.init('whisper.bin');
@@ -644,7 +553,7 @@
                     printTextarea('');
 
                     setTimeout(function() {
-                        var ret = Module.full_default(instance, audio, document.getElementById('language').value, nthreads, translate);
+                        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);

extra/bench-all.sh

@@ -64,10 +64,6 @@ for model in "${models[@]}"; do
         config="$config BLAS"
     fi
 
-    if [[ $system_info == *"COREML = 1"* ]]; then
-        config="$config COREML"
-    fi
-
     commit=$(git rev-parse --short HEAD)
 
     printf "| <todo> | <todo> | $config | $model | $n_threads | $load_time | $encode_time | $commit |\n"

extra/quantize-all.sh

@@ -1,45 +0,0 @@
-#!/bin/bash
-
-printf "Usage: $0 <upload>"
-
-if [ $# -ne 1 ]; then
-    printf "\nError: Invalid number of arguments\n"
-    exit 1
-fi
-
-qtype0="q5_0"
-qtype1="q5_1"
-upload="$1"
-
-cd `dirname $0`
-cd ../
-
-./quantize ./models/ggml-tiny.en.bin   ./models/ggml-tiny.en-${qtype1}.bin ${qtype1}
-./quantize ./models/ggml-tiny.bin      ./models/ggml-tiny-${qtype1}.bin    ${qtype1}
-
-./quantize ./models/ggml-base.en.bin   ./models/ggml-base.en-${qtype1}.bin ${qtype1}
-./quantize ./models/ggml-base.bin      ./models/ggml-base-${qtype1}.bin    ${qtype1}
-
-./quantize ./models/ggml-small.en.bin  ./models/ggml-small.en-${qtype1}.bin ${qtype1}
-./quantize ./models/ggml-small.bin     ./models/ggml-small-${qtype1}.bin    ${qtype1}
-
-./quantize ./models/ggml-medium.en.bin ./models/ggml-medium.en-${qtype0}.bin ${qtype0}
-./quantize ./models/ggml-medium.bin    ./models/ggml-medium-${qtype0}.bin    ${qtype0}
-
-./quantize ./models/ggml-large.bin     ./models/ggml-large-${qtype0}.bin ${qtype0}
-
-if [ "$upload" == "1" ]; then
-    scp ./models/ggml-tiny.en-${qtype1}.bin   root@linode0:/mnt/Data/ggml/ggml-model-whisper-tiny.en-${qtype1}.bin
-    scp ./models/ggml-tiny-${qtype1}.bin      root@linode0:/mnt/Data/ggml/ggml-model-whisper-tiny-${qtype1}.bin
-
-    scp ./models/ggml-base.en-${qtype1}.bin   root@linode0:/mnt/Data/ggml/ggml-model-whisper-base.en-${qtype1}.bin
-    scp ./models/ggml-base-${qtype1}.bin      root@linode0:/mnt/Data/ggml/ggml-model-whisper-base-${qtype1}.bin
-
-    scp ./models/ggml-small.en-${qtype1}.bin  root@linode0:/mnt/Data/ggml/ggml-model-whisper-small.en-${qtype1}.bin
-    scp ./models/ggml-small-${qtype1}.bin     root@linode0:/mnt/Data/ggml/ggml-model-whisper-small-${qtype1}.bin
-
-    scp ./models/ggml-medium.en-${qtype0}.bin root@linode0:/mnt/Data/ggml/ggml-model-whisper-medium.en-${qtype0}.bin
-    scp ./models/ggml-medium-${qtype0}.bin    root@linode0:/mnt/Data/ggml/ggml-model-whisper-medium-${qtype0}.bin
-
-    scp ./models/ggml-large-${qtype0}.bin     root@linode0:/mnt/Data/ggml/ggml-model-whisper-large-${qtype0}.bin
-fi

extra/sync-ggml.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-cp -rpv ../ggml/src/ggml.c               ./ggml.c
-cp -rpv ../ggml/src/ggml-cuda.cu         ./ggml-cuda.cu
-cp -rpv ../ggml/src/ggml-cuda.h          ./ggml-cuda.h
-cp -rpv ../ggml/include/ggml/ggml.h      ./ggml.h
-cp -rpv ../ggml/examples/common.h        ./examples/common.h
-cp -rpv ../ggml/examples/common.cpp      ./examples/common.cpp
-cp -rpv ../ggml/examples/common-ggml.h   ./examples/common-ggml.h
-cp -rpv ../ggml/examples/common-ggml.cpp ./examples/common-ggml.cpp

ggml-cuda.cu

@@ -1,365 +0,0 @@
-#include <stdint.h>
-#include <stdio.h>
-#include <cuda_fp16.h>
-#include <atomic>
-#include "ggml-cuda.h"
-
-typedef uint16_t ggml_fp16_t;
-static_assert(sizeof(__half) == sizeof(ggml_fp16_t), "wrong fp16 size");
-
-#define QK4_0 32
-typedef struct {
-    float   d;              // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(float) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-typedef struct {
-    float   d;              // delta
-    float   m;              // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == sizeof(float) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK4_2 16
-typedef struct {
-    __half  d;              // delta
-    uint8_t qs[QK4_2 / 2];  // nibbles / quants
-} block_q4_2;
-static_assert(sizeof(block_q4_2) == sizeof(ggml_fp16_t) + QK4_2 / 2, "wrong q4_2 block size/padding");
-
-#define QK5_0 32
-typedef struct {
-    __half d;               // delta
-    uint8_t qh[4];          // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2];  // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-typedef struct {
-    __half d;               // delta
-    __half m;               // min
-    uint32_t qh;            // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2];  // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-typedef struct {
-    float   d;              // delta
-    int8_t  qs[QK8_0];      // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
-
-static __global__ void dequantize_block_q4_0(const void * vx, float * y) {
-    const block_q4_0 * x = (const block_q4_0 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-
-    const uint8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK4_0; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vi0 = vi & 0xf;
-        const int8_t vi1 = vi >> 4;
-
-        const float v0 = (vi0 - 8)*d;
-        const float v1 = (vi1 - 8)*d;
-
-        y[i*QK4_0 + l + 0] = v0;
-        y[i*QK4_0 + l + 1] = v1;
-    }
-}
-
-static __global__ void dequantize_block_q4_1(const void * vx, float * y) {
-    const block_q4_1 * x = (const block_q4_1 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-    const float m = x[i].m;
-
-    const uint8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK4_1; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vi0 = vi & 0xf;
-        const int8_t vi1 = vi >> 4;
-
-        const float v0 = vi0*d + m;
-        const float v1 = vi1*d + m;
-
-        y[i*QK4_1 + l + 0] = v0;
-        y[i*QK4_1 + l + 1] = v1;
-    }
-}
-
-static __global__ void dequantize_block_q4_2(const void * vx, float * y) {
-    const block_q4_2 * x = (const block_q4_2 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-
-    const uint8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK4_2; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vi0 = vi & 0xf;
-        const int8_t vi1 = vi >> 4;
-
-        const float v0 = (vi0 - 8)*d;
-        const float v1 = (vi1 - 8)*d;
-
-        y[i*QK4_2 + l + 0] = v0;
-        y[i*QK4_2 + l + 1] = v1;
-    }
-}
-
-static __global__ void dequantize_block_q5_0(const void * vx, float * y) {
-    const block_q5_0 * x = (const block_q5_0 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-
-    const uint8_t * pp = x[i].qs;
-
-    uint32_t qh;
-    memcpy(&qh, x[i].qh, sizeof(qh));
-
-    for (int l = 0; l < QK5_0; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
-        const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
-
-        const int8_t vi0 = ((vi & 0xf) | vh0);
-        const int8_t vi1 = ((vi >>  4) | vh1);
-
-        const float v0 = (vi0 - 16)*d;
-        const float v1 = (vi1 - 16)*d;
-
-        y[i*QK5_0 + l + 0] = v0;
-        y[i*QK5_0 + l + 1] = v1;
-    }
-}
-
-static __global__ void dequantize_block_q5_1(const void * vx, float * y) {
-    const block_q5_1 * x = (const block_q5_1 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-    const float m = x[i].m;
-
-    const uint8_t * pp = x[i].qs;
-
-    const uint32_t qh = x[i].qh;
-
-    for (int l = 0; l < QK5_1; l += 2) {
-        const uint8_t vi = pp[l/2];
-
-        const int8_t vh0 = ((qh & (1 << (l + 0))) >> (l + 0)) << 4;
-        const int8_t vh1 = ((qh & (1 << (l + 1))) >> (l + 1)) << 4;
-
-        const int8_t vi0 = (vi & 0xf) | vh0;
-        const int8_t vi1 = (vi >>  4) | vh1;
-
-        const float v0 = vi0*d + m;
-        const float v1 = vi1*d + m;
-
-        y[i*QK5_1 + l + 0] = v0;
-        y[i*QK5_1 + l + 1] = v1;
-    }
-}
-
-static __global__ void dequantize_block_q8_0(const void * vx, float * y) {
-    const block_q8_0 * x = (const block_q8_0 *) vx;
-
-    const int i = blockIdx.x;
-
-    const float d = x[i].d;
-
-    const int8_t * pp = x[i].qs;
-
-    for (int l = 0; l < QK8_0; l++) {
-        const int8_t vi = pp[l];
-
-        y[i*QK8_0 + l] = vi*d;
-    }
-}
-
-void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK4_0;
-    dequantize_block_q4_0<<<nb, 1, 0, stream>>>(vx, y);
-}
-
-void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK4_1;
-    dequantize_block_q4_1<<<nb, 1, 0, stream>>>(vx, y);
-}
-
-void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK4_2;
-    dequantize_block_q4_2<<<nb, 1, 0, stream>>>(vx, y);
-}
-
-void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK5_0;
-    dequantize_block_q5_0<<<nb, 1, 0, stream>>>(vx, y);
-}
-
-void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK5_1;
-    dequantize_block_q5_1<<<nb, 1, 0, stream>>>(vx, y);
-}
-
-void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream) {
-    const int nb = k / QK8_0;
-    dequantize_block_q8_0<<<nb, 1, 0, stream>>>(vx, y);
-}
-
-dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(ggml_type type) {
-    switch (type) {
-        case GGML_TYPE_Q4_0:
-            return dequantize_row_q4_0_cuda;
-        case GGML_TYPE_Q4_1:
-            return dequantize_row_q4_1_cuda;
-        case GGML_TYPE_Q4_2:
-            return dequantize_row_q4_2_cuda;
-        case GGML_TYPE_Q5_0:
-            return dequantize_row_q5_0_cuda;
-        case GGML_TYPE_Q5_1:
-            return dequantize_row_q5_1_cuda;
-        case GGML_TYPE_Q8_0:
-            return dequantize_row_q8_0_cuda;
-        default:
-            return nullptr;
-    }
-}
-
-// buffer pool for cuda
-#define MAX_CUDA_BUFFERS 16
-
-struct scoped_spin_lock {
-    std::atomic_flag& lock;
-    scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
-        while (lock.test_and_set(std::memory_order_acquire)) {
-            ; // spin
-        }
-    }
-    ~scoped_spin_lock() {
-        lock.clear(std::memory_order_release);
-    }
-    scoped_spin_lock(const scoped_spin_lock&) = delete;
-    scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
-};
-
-struct cuda_buffer {
-    void * ptr = nullptr;
-    size_t size = 0;
-};
-
-static cuda_buffer g_cuda_buffer_pool[MAX_CUDA_BUFFERS];
-static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
-
-void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
-        cuda_buffer& b = g_cuda_buffer_pool[i];
-        if (b.size >= size && b.ptr != nullptr) {
-            void * ptr = b.ptr;
-            *actual_size = b.size;
-            b.ptr = nullptr;
-            b.size = 0;
-            return ptr;
-        }
-    }
-    void * ptr;
-    CUDA_CHECK(cudaMalloc((void **) &ptr, size));
-    *actual_size = size;
-    return ptr;
-}
-
-void ggml_cuda_pool_free(void * ptr, size_t size) {
-    scoped_spin_lock lock(g_cuda_pool_lock);
-
-    for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
-        cuda_buffer& b = g_cuda_buffer_pool[i];
-        if (b.ptr == nullptr) {
-            b.ptr = ptr;
-            b.size = size;
-            return;
-        }
-    }
-    fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
-    CUDA_CHECK(cudaFree(ptr));
-}
-
-cublasHandle_t g_cublasH = nullptr;
-cudaStream_t g_cudaStream = nullptr;
-cudaStream_t g_cudaStream2 = nullptr;
-cudaEvent_t g_cudaEvent = nullptr;
-
-void ggml_init_cublas() {
-    if (g_cublasH == nullptr) {
-        // create cublas handle, bind a stream
-        CUBLAS_CHECK(cublasCreate(&g_cublasH));
-        CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream, cudaStreamNonBlocking));
-        CUBLAS_CHECK(cublasSetStream(g_cublasH, g_cudaStream));
-
-        // create additional stream and event for synchronization
-        CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStream2, cudaStreamNonBlocking));
-        CUDA_CHECK(cudaEventCreateWithFlags(&g_cudaEvent, cudaEventDisableTiming));
-
-        // configure logging to stdout
-        // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, NULL));
-    }
-}
-
-cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream) {
-    const uint64_t ne0 = src->ne[0];
-    const uint64_t ne1 = src->ne[1];
-    const uint64_t nb0 = src->nb[0];
-    const uint64_t nb1 = src->nb[1];
-    const uint64_t nb2 = src->nb[2];
-    const uint64_t nb3 = src->nb[3];
-    const enum ggml_type type = src->type;
-    const size_t ts = ggml_type_size(type);
-    const size_t bs = ggml_blck_size(type);
-
-    const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3);
-    if (nb0 == ts && nb1 == ts*ne0/bs) {
-        return cudaMemcpyAsync(dst, x, ne1*nb1, cudaMemcpyHostToDevice, stream);
-    } else if (nb0 == ts) {
-        return cudaMemcpy2DAsync(dst, ts*ne0/bs, x, nb1, ts*ne0/bs, ne1, cudaMemcpyHostToDevice, stream);
-    } else {
-        for (uint64_t i1 = 0; i1 < ne1; i1++) {
-            const void * rx = (const void *) ((const char *) x + i1*nb1);
-            void * rd = (void *) ((char *) dst + i1*ts*ne0/bs);
-            // pretend the row is a matrix with cols=1
-            cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, cudaMemcpyHostToDevice, stream);
-            if (r != cudaSuccess) return r;
-        }
-        return cudaSuccess;
-    }
-}
-
-void * ggml_cuda_host_malloc(size_t size) {
-    void * ptr;
-    CUDA_CHECK(cudaMallocHost((void **) &ptr, size));
-    return ptr;
-}
-
-void ggml_cuda_host_free(void * ptr) {
-    CUDA_CHECK(cudaFreeHost(ptr));
-}

ggml-cuda.h

@@ -1,54 +0,0 @@
-#include <cublas_v2.h>
-#include <cuda_runtime.h>
-#include "ggml.h"
-
-#ifdef  __cplusplus
-extern "C" {
-#endif
-
-#define CUDA_CHECK(err)                                                                 \
-    do {                                                                                \
-        cudaError_t err_ = (err);                                                       \
-        if (err_ != cudaSuccess) {                                                      \
-            fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__,   \
-                cudaGetErrorString(err_));                                              \
-            exit(1);                                                                    \
-        }                                                                               \
-    } while (0)
-
-#define CUBLAS_CHECK(err)                                                               \
-    do {                                                                                \
-        cublasStatus_t err_ = (err);                                                    \
-        if (err_ != CUBLAS_STATUS_SUCCESS) {                                            \
-            fprintf(stderr, "cuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__);    \
-            exit(1);                                                                    \
-        }                                                                               \
-    } while (0)
-
-extern cublasHandle_t g_cublasH;
-extern cudaStream_t g_cudaStream;
-extern cudaStream_t g_cudaStream2;
-extern cudaEvent_t g_cudaEvent;
-
-void   ggml_init_cublas(void);
-void * ggml_cuda_host_malloc(size_t size);
-void   ggml_cuda_host_free(void * ptr);
-
-void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size);
-void   ggml_cuda_pool_free(void * ptr, size_t size);
-
-void dequantize_row_q4_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
-void dequantize_row_q4_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
-void dequantize_row_q4_2_cuda(const void * vx, float * y, int k, cudaStream_t stream);
-void dequantize_row_q5_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
-void dequantize_row_q5_1_cuda(const void * vx, float * y, int k, cudaStream_t stream);
-void dequantize_row_q8_0_cuda(const void * vx, float * y, int k, cudaStream_t stream);
-
-cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cudaStream_t stream);
-
-typedef void (*dequantize_row_q_cuda_t)(const void * x, float * y, int k, cudaStream_t stream);
-dequantize_row_q_cuda_t ggml_get_dequantize_row_q_cuda(enum ggml_type type);
-
-#ifdef  __cplusplus
-}
-#endif

models/convert-h5-to-ggml.py

@@ -23,7 +23,6 @@ import json
 import code
 import torch
 import numpy as np
-from pathlib import Path
 
 from transformers import WhisperForConditionalGeneration
 
@@ -76,13 +75,16 @@ 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   = Path(sys.argv[1])
-dir_whisper = Path(sys.argv[2])
-dir_out     = Path(sys.argv[3])
+dir_model   = sys.argv[1]
+dir_whisper = sys.argv[2]
+dir_out     = sys.argv[3]
 
-encoder = json.load((dir_model / "vocab.json").open("r", encoding="utf8"))
-encoder_added = json.load((dir_model / "added_tokens.json").open( "r", encoding="utf8"))
-hparams = json.load((dir_model / "config.json").open("r", encoding="utf8") )
+with open(dir_model + "/vocab.json", "r", encoding="utf8") as f:
+    encoder = json.load(f)
+with open(dir_model + "/added_tokens.json", "r", encoding="utf8") as f:
+    encoder_added = json.load(f)
+with open(dir_model + "/config.json", "r", encoding="utf8") as f:
+    hparams = json.load(f)
 
 model = WhisperForConditionalGeneration.from_pretrained(dir_model)
 
@@ -94,15 +96,16 @@ with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as
 
 dir_tokenizer = dir_model
 
-fname_out = dir_out / "ggml-model.bin"
+fname_out = dir_out + "/ggml-model.bin"
 
-tokens = json.load(open(dir_tokenizer / "vocab.json", "r", encoding="utf8"))
+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"
+    fname_out = dir_out + "/ggml-model-f32.bin"
 
 fout = open(fname_out, "wb")
 
@@ -168,9 +171,10 @@ for name in list_vars.keys():
     data = data.astype(np.float16)
 
     # reshape conv bias from [n] to [n, 1]
-    if name in ["encoder.conv1.bias", "encoder.conv2.bias"]:
+    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)
+        print("  Reshaped variable: " + name + " to shape: ", data.shape)
 
     n_dims = len(data.shape)
     print(name, n_dims, data.shape)
@@ -178,7 +182,7 @@ for name in list_vars.keys():
     # 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
+    ftype = 1;
     if use_f16:
         if n_dims < 2 or \
                 name == "encoder.conv1.bias"   or \
@@ -193,16 +197,16 @@ for name in list_vars.keys():
         ftype = 0
 
     # header
-    str_ = name.encode('utf-8')
-    fout.write(struct.pack("iii", n_dims, len(str_), ftype))
+    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_)
+    fout.write(str);
 
     # data
     data.tofile(fout)
 
 fout.close()
 
-print("Done. Output file: " , fname_out)
+print("Done. Output file: " + fname_out)
 print("")

models/convert-pt-to-ggml.py

@@ -39,8 +39,7 @@ import json
 import code
 import torch
 import numpy as np
-import base64
-from pathlib import Path
+
 #from transformers import GPTJForCausalLM
 #from transformers import GPT2TokenizerFast
 
@@ -194,17 +193,17 @@ if len(sys.argv) < 4:
     print("Usage: convert-pt-to-ggml.py model.pt path-to-whisper-repo dir-output [use-f32]\n")
     sys.exit(1)
 
-fname_inp   = Path(sys.argv[1])
-dir_whisper = Path(sys.argv[2])
-dir_out     = Path(sys.argv[3])
+fname_inp   = sys.argv[1]
+dir_whisper = sys.argv[2]
+dir_out     = sys.argv[3]
 
 # try to load PyTorch binary data
 try:
     model_bytes = open(fname_inp, "rb").read()
     with io.BytesIO(model_bytes) as fp:
         checkpoint = torch.load(fp, map_location="cpu")
-except Exception:
-    print("Error: failed to load PyTorch model file:" , fname_inp)
+except:
+    print("Error: failed to load PyTorch model file: %s" % fname_inp)
     sys.exit(1)
 
 hparams = checkpoint["dims"]
@@ -218,29 +217,33 @@ list_vars = checkpoint["model_state_dict"]
 
 # load mel filters
 n_mels = hparams["n_mels"]
-with np.load(dir_whisper / "whisper" / "assets" / "mel_filters.npz") as f:
+with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as f:
     filters = torch.from_numpy(f[f"mel_{n_mels}"])
     #print (filters)
 
 #code.interact(local=locals())
 
 multilingual = hparams["n_vocab"] == 51865
-tokenizer = dir_whisper / "whisper" / "assets" / (multilingual and "multilingual.tiktoken" or "gpt2.tiktoken")
+dir_tokenizer = os.path.join(dir_whisper, "whisper/assets", 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))
 
 # output in the same directory as the model
-fname_out = dir_out / "ggml-model.bin"
+fname_out = dir_out + "/ggml-model.bin"
 
-with open(tokenizer, "rb") as f:
-    contents = f.read()
-    tokens = {base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)}
+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"
+    fname_out = dir_out + "/ggml-model-f32.bin"
 
-fout = fname_out.open("wb")
+fout = open(fname_out, "wb")
 
 fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
 fout.write(struct.pack("i", hparams["n_vocab"]))
@@ -268,24 +271,26 @@ byte_decoder = {v:k for k, v in byte_encoder.items()}
 fout.write(struct.pack("i", len(tokens)))
 
 for key in tokens:
-    fout.write(struct.pack("i", len(key)))
-    fout.write(key)
+    text = bytearray([byte_decoder[c] for c in key])
+    fout.write(struct.pack("i", len(text)))
+    fout.write(text)
 
 for name in list_vars.keys():
     data = list_vars[name].squeeze().numpy()
-    print("Processing variable: " , name ,  " with shape: ", data.shape)
+    print("Processing variable: " + name + " with shape: ", data.shape)
 
     # reshape conv bias from [n] to [n, 1]
-    if name in ["encoder.conv1.bias", "encoder.conv2.bias"]:
+    if name == "encoder.conv1.bias" or \
+       name == "encoder.conv2.bias":
         data = data.reshape(data.shape[0], 1)
-        print(f"  Reshaped variable: {name} to shape: ", data.shape)
+        print("  Reshaped variable: " + name + " to shape: ", data.shape)
 
-    n_dims = len(data.shape)
+    n_dims = len(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
+    ftype = 1;
     if use_f16:
         if n_dims < 2 or \
                 name == "encoder.conv1.bias"   or \
@@ -306,16 +311,16 @@ for name in list_vars.keys():
     #        data = data.transpose()
 
     # header
-    str_ = name.encode('utf-8')
-    fout.write(struct.pack("iii", n_dims, len(str_), ftype))
+    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_)
+    fout.write(str);
 
     # data
     data.tofile(fout)
 
 fout.close()
 
-print("Done. Output file: " , fname_out)
+print("Done. Output file: " + fname_out)
 print("")

models/convert-whisper-to-coreml.py

@@ -1,331 +0,0 @@
-import argparse
-import torch
-import torch.nn.functional as F
-import coremltools as ct
-
-from torch import Tensor
-from torch import nn
-from typing import Dict
-from typing import Optional
-from ane_transformers.reference.layer_norm import LayerNormANE as LayerNormANEBase
-from coremltools.models.neural_network.quantization_utils import quantize_weights
-from whisper.model import Whisper, AudioEncoder, TextDecoder, ResidualAttentionBlock, MultiHeadAttention, ModelDimensions
-from whisper import load_model
-
-# Use for changing dim of input in encoder and decoder embeddings
-def linear_to_conv2d_map(state_dict, prefix, local_metadata, strict,
-                         missing_keys, unexpected_keys, error_msgs):
-    """
-    Unsqueeze twice to map nn.Linear weights to nn.Conv2d weights
-    """
-    for k in state_dict:
-        is_attention = all(substr in k for substr in ['attn', '.weight'])
-        is_mlp = any(k.endswith(s) for s in ['mlp.0.weight', 'mlp.2.weight'])
-
-        if (is_attention or is_mlp) and len(state_dict[k].shape) == 2:
-            state_dict[k] = state_dict[k][:, :, None, None]
-
-
-def correct_for_bias_scale_order_inversion(state_dict, prefix, local_metadata,
-                                           strict, missing_keys,
-                                           unexpected_keys, error_msgs):
-    state_dict[prefix + 'bias'] = state_dict[prefix + 'bias'] / state_dict[prefix + 'weight']
-    return state_dict
-
-class LayerNormANE(LayerNormANEBase):
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._register_load_state_dict_pre_hook(
-            correct_for_bias_scale_order_inversion)
-
-class MultiHeadAttentionANE(MultiHeadAttention):
-    def __init__(self, n_state: int, n_head: int):
-        super().__init__(n_state, n_head)
-        self.query =  nn.Conv2d(n_state, n_state, kernel_size=1)
-        self.key = nn.Conv2d(n_state, n_state, kernel_size=1, bias=False)
-        self.value = nn.Conv2d(n_state, n_state, kernel_size=1)
-        self.out = nn.Conv2d(n_state, n_state, kernel_size=1)
-
-    def forward(self,
-                x: Tensor,
-                xa: Optional[Tensor] = None,
-                mask: Optional[Tensor] = None,
-                kv_cache: Optional[dict] = None):
-
-        q = self.query(x)
-
-        if kv_cache is None or xa is None or self.key not in kv_cache:
-            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
-            # otherwise, perform key/value projections for self- or cross-attention as usual.
-            k = self.key(x if xa is None else xa)
-            v = self.value(x if xa is None else xa)
-
-        else:
-            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
-            k = kv_cache[self.key]
-            v = kv_cache[self.value]
-
-        wv, qk = self.qkv_attention_ane(q, k, v, mask)
-
-        return self.out(wv), qk
-
-    def qkv_attention_ane(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
-
-        _, dim, _, seqlen = q.size()
-
-        dim_per_head = dim // self.n_head
-
-        scale = float(dim_per_head)**-0.5
-
-        q = q * scale
-
-        mh_q = q.split(dim_per_head, dim=1)
-        mh_k = k.transpose(1,3).split(dim_per_head, dim=3)
-        mh_v = v.split(dim_per_head, dim=1)
-
-        mh_qk = [
-            torch.einsum('bchq,bkhc->bkhq', [qi, ki])
-            for qi, ki in zip(mh_q, mh_k)
-        ]  # (batch_size, max_seq_length, 1, max_seq_length) * n_heads
-
-        if mask is not None:
-            for head_idx in range(self.n_head):
-                mh_qk[head_idx] = mh_qk[head_idx] + mask[:, :seqlen, :, :seqlen]
-
-        attn_weights = [aw.softmax(dim=1) for aw in mh_qk]  # (batch_size, max_seq_length, 1, max_seq_length) * n_heads
-        attn = [torch.einsum('bkhq,bchk->bchq', wi, vi) for wi, vi in zip(attn_weights, mh_v)]  # (batch_size, dim_per_head, 1, max_seq_length) * n_heads
-        attn = torch.cat(attn, dim=1)  # (batch_size, dim, 1, max_seq_length)
-
-        return attn, torch.cat(mh_qk, dim=1).float().detach()
-
-
-class ResidualAttentionBlockANE(ResidualAttentionBlock):
-    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
-        super().__init__(n_state, n_head, cross_attention)
-        self.attn =  MultiHeadAttentionANE(n_state, n_head)
-        self.attn_ln = LayerNormANE(n_state)
-        self.cross_attn =  MultiHeadAttentionANE(n_state, n_head) if cross_attention else None
-        self.cross_attn_ln =  LayerNormANE(n_state) if cross_attention else None
-
-        n_mlp = n_state * 4
-        self.mlp =  nn.Sequential(
-            nn.Conv2d(n_state, n_mlp, kernel_size=1),
-            nn.GELU(),
-            nn.Conv2d(n_mlp, n_state, kernel_size=1)
-        )
-        self.mlp_ln = LayerNormANE(n_state)
-
-
-class AudioEncoderANE(AudioEncoder):
-    def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
-        super().__init__(n_mels, n_ctx, n_state, n_head, n_layer)
-
-        self.blocks = nn.ModuleList(
-            [ResidualAttentionBlockANE(n_state, n_head) for _ in range(n_layer)]
-        )
-        self.ln_post = LayerNormANE(n_state)
-
-    def forward(self, x: Tensor):
-        """
-        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
-            the mel spectrogram of the audio
-        """
-        x = F.gelu(self.conv1(x))
-        x = F.gelu(self.conv2(x))
-
-        assert x.shape[1:] == self.positional_embedding.shape[::-1], "incorrect audio shape"
-
-        # Add positional embedding and add dummy dim for ANE
-        x = (x + self.positional_embedding.transpose(0,1)).to(x.dtype).unsqueeze(2)
-
-        for block in self.blocks:
-            x = block(x)
-
-        x = self.ln_post(x)
-
-        # """
-        # TODO:
-        # I think we need to transpose the result here to make it fit whisper.cpp memory order.
-        # However, even doing this, the results are still wrong. Kind of less wrong compared to
-        # not transposing, but still wrong.
-
-        # Also, I don't know why the original OpenAI implementation does not need to transpose
-
-        # transpose to (batch_size, n_ctx, n_state)
-        # x : torch.Tensor, shape = (batch_size, n_state, 1, n_ctx)
-
-        # """
-        # x = x.transpose(1,3)
-
-        return x
-
-class TextDecoderANE(TextDecoder):
-
-    def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
-        super().__init__(n_vocab, n_ctx, n_state, n_head, n_layer)
-
-        self.blocks= nn.ModuleList(
-            [ResidualAttentionBlockANE(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
-        )
-        self.ln= LayerNormANE(n_state)
-
-    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
-        """
-        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
-            the text tokens
-        xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
-            the encoded audio features to be attended on
-        """
-        offset = next(iter(kv_cache.values())).shape[3] if kv_cache else 0
-        x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
-        x = x.to(xa.dtype)
-
-        # Reformat for ANE
-        mask = self.mask[None, None, :, :].permute(0,3,1,2)
-        x = x.transpose(1,2).unsqueeze(2)
-
-        for block in self.blocks:
-            x = block(x, xa, mask=mask, kv_cache=kv_cache)
-
-        x = self.ln(x)
-
-        # Reformat back from ANE
-        x = x.permute(0,2,3,1).squeeze(0)
-
-        # ANE can only load tensors with dim size of at most 16,384 - whisper uses 51,864 (en) or 51,865 (multi-lang) tokens so we need to compute in chunks
-        if self.token_embedding.weight.shape[0] == 51865:
-            # split in 11 chunks - 4715 each
-            splits = self.token_embedding.weight.split(self.token_embedding.weight.shape[0]//11, dim=0)
-            logits = torch.cat([torch.einsum('bid,jd->bij', x, split) for split in splits]).view(*x.shape[:2], -1)
-        else:
-            # split in 12 chunks - 4322 each
-            assert(self.token_embedding.weight.shape[0] == 51864)
-            splits = self.token_embedding.weight.split(self.token_embedding.weight.shape[0]//12, dim=0)
-            logits = torch.cat([torch.einsum('bid,jd->bij', x, split) for split in splits]).view(*x.shape[:2], -1)
-
-        return logits
-
-class WhisperANE(Whisper):
-    def __init__(self, dims: ModelDimensions):
-        super().__init__(dims)
-
-        self.encoder = AudioEncoderANE(
-            self.dims.n_mels,
-            self.dims.n_audio_ctx,
-            self.dims.n_audio_state,
-            self.dims.n_audio_head,
-            self.dims.n_audio_layer,
-        )
-        self.decoder = TextDecoderANE(
-            self.dims.n_vocab,
-            self.dims.n_text_ctx,
-            self.dims.n_text_state,
-            self.dims.n_text_head,
-            self.dims.n_text_layer,
-        )
-
-        self._register_load_state_dict_pre_hook(linear_to_conv2d_map)
-
-    def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
-        return self.decoder(tokens, self.encoder(mel))
-
-    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
-        cache = {**cache} if cache is not None else {}
-        hooks = []
-
-        def save_to_cache(module, _, output):
-            if module not in cache or output.shape[3] > self.decoder.positional_embedding.shape[0]:
-                cache[module] = output  # save as-is, for the first token or cross attention
-            else:
-                cache[module] = torch.cat([cache[module], output], dim=3).detach()
-            return cache[module]
-
-        def install_hooks(layer: nn.Module):
-            if isinstance(layer, MultiHeadAttentionANE):
-                hooks.append(layer.key.register_forward_hook(save_to_cache))
-                hooks.append(layer.value.register_forward_hook(save_to_cache))
-
-        self.decoder.apply(install_hooks)
-        return cache, hooks
-
-def convert_encoder(hparams, model, quantize=False):
-    model.eval()
-
-    input_shape = (1, 80, 3000)
-    input_data = torch.randn(input_shape)
-    traced_model = torch.jit.trace(model, input_data)
-
-    model = ct.convert(
-        traced_model,
-        convert_to=None if quantize else "mlprogram", # convert will fail if weights are quantized, not sure why
-        inputs=[ct.TensorType(name="logmel_data", shape=input_shape)],
-        outputs=[ct.TensorType(name="output")],
-        compute_units=ct.ComputeUnit.ALL
-    )
-
-    if quantize:
-        model = quantize_weights(model, nbits=16)
-
-    return model
-
-def convert_decoder(hparams, model, quantize=False):
-    model.eval()
-
-    tokens_shape = (1, 1)
-    audio_shape = (1, hparams.n_audio_state, 1, 1500)
-
-    audio_data = torch.randn(audio_shape)
-    token_data = torch.randint(50257, tokens_shape).long()
-    traced_model = torch.jit.trace(model, (token_data, audio_data))
-
-    model = ct.convert(
-        traced_model,
-        convert_to=None if quantize else "mlprogram", # convert will fail if weights are quantized, not sure why
-        inputs=[
-            ct.TensorType(name="token_data", shape=tokens_shape, dtype=int),
-            ct.TensorType(name="audio_data", shape=audio_shape)
-        ]
-    )
-
-    if quantize:
-        model = quantize_weights(model, nbits=16)
-
-    return model
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--model", type=str, help="model to convert (e.g. tiny, tiny.en, base, base.en, small, small.en, medium, medium.en, large)", required=True)
-    parser.add_argument("--encoder-only", type=bool, help="only convert encoder", default=False)
-    parser.add_argument("--quantize",     type=bool, help="quantize weights to F16", default=False)
-    parser.add_argument("--optimize-ane", type=bool, help="optimize for ANE execution (currently broken)", default=False)
-    args = parser.parse_args()
-
-    if args.model not in ["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"]:
-        raise ValueError("Invalid model name")
-
-    whisper = load_model(args.model).cpu()
-    hparams = whisper.dims
-    print(hparams)
-
-    if args.optimize_ane:
-        whisperANE = WhisperANE(hparams).eval()
-        whisperANE.load_state_dict(whisper.state_dict())
-
-        encoder = whisperANE.encoder
-        decoder = whisperANE.decoder
-    else:
-        encoder = whisper.encoder
-        decoder = whisper.decoder
-
-    # Convert encoder
-    encoder = convert_encoder(hparams, encoder, quantize=args.quantize)
-    encoder.save(f"models/coreml-encoder-{args.model}.mlpackage")
-
-    if args.encoder_only is False:
-        # Convert decoder
-        decoder = convert_decoder(hparams, decoder, quantize=args.quantize)
-        decoder.save(f"models/coreml-decoder-{args.model}.mlpackage")
-
-    print("done converting")

models/download-ggml-model.sh

@@ -12,7 +12,7 @@ pfx="resolve/main/ggml"
 # get the path of this script
 function get_script_path() {
     if [ -x "$(command -v realpath)" ]; then
-        echo "$(dirname "$(realpath "$0")")"
+        echo "$(dirname $(realpath $0))"
     else
         local ret="$(cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P)"
         echo "$ret"

models/generate-coreml-interface.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-#
-# This generates:
-#   - coreml/whisper-encoder-impl.h and coreml/whisper-encoder-impl.m
-#   - coreml/whisper-decoder-impl.h and coreml/whisper-decoder-impl.m
-#
-
-wd=$(dirname "$0")
-cd "$wd/../"
-
-python3 models/convert-whisper-to-coreml.py --model tiny.en
-
-mv -v models/coreml-encoder-tiny.en.mlpackage models/whisper-encoder-impl.mlpackage
-xcrun coremlc generate models/whisper-encoder-impl.mlpackage coreml/
-mv coreml/whisper_encoder_impl.h coreml/whisper-encoder-impl.h
-mv coreml/whisper_encoder_impl.m coreml/whisper-encoder-impl.m
-sed -i '' 's/whisper_encoder_impl\.h/whisper-encoder-impl.h/g' coreml/whisper-encoder-impl.m
-sed -i '' 's/whisper_encoder_impl\.m/whisper-encoder-impl.m/g' coreml/whisper-encoder-impl.m
-sed -i '' 's/whisper_encoder_impl\.h/whisper-encoder-impl.h/g' coreml/whisper-encoder-impl.h
-
-mv -v models/coreml-decoder-tiny.en.mlpackage models/whisper-decoder-impl.mlpackage
-xcrun coremlc generate models/whisper-decoder-impl.mlpackage coreml/
-mv coreml/whisper_decoder_impl.h coreml/whisper-decoder-impl.h
-mv coreml/whisper_decoder_impl.m coreml/whisper-decoder-impl.m
-sed -i '' 's/whisper_decoder_impl\.h/whisper-decoder-impl.h/g' coreml/whisper-decoder-impl.m
-sed -i '' 's/whisper_decoder_impl\.m/whisper-decoder-impl.m/g' coreml/whisper-decoder-impl.m
-sed -i '' 's/whisper_decoder_impl\.h/whisper-decoder-impl.h/g' coreml/whisper-decoder-impl.h
-
-rm -rfv models/whisper-encoder-impl.mlpackage models/whisper-decoder-impl.mlpackage

models/generate-coreml-model.sh

@@ -1,25 +0,0 @@
-#!/bin/bash
-
-# Usage: ./generate-coreml-model.sh <model-name>
-if [ $# -eq 0 ]
-  then
-    echo "No model name supplied"
-    echo "Usage: ./generate-coreml-model.sh <model-name>"
-    exit 1
-fi
-
-mname="$1"
-
-wd=$(dirname "$0")
-cd "$wd/../"
-
-python3 models/convert-whisper-to-coreml.py --model $mname --encoder-only True
-
-xcrun coremlc compile models/coreml-encoder-${mname}.mlpackage models/
-rm -rf models/ggml-${mname}-encoder.mlmodelc
-mv -v models/coreml-encoder-${mname}.mlmodelc models/ggml-${mname}-encoder.mlmodelc
-
-# TODO: decoder (sometime in the future maybe)
-#xcrun coremlc compile models/whisper-decoder-${mname}.mlpackage models/
-#rm -rf models/ggml-${mname}-decoder.mlmodelc
-#mv -v models/coreml_decoder_${mname}.mlmodelc models/ggml-${mname}-decoder.mlmodelc

tests/run-tests.sh

@@ -13,7 +13,7 @@
 #
 # Usage:
 #
-#   ./tests/run-tests.sh <model_name> [threads]
+#   ./tests/run-tests.sh <model_name>
 #
 
 cd `dirname $0`
@@ -32,7 +32,7 @@ function list_models {
 }
 
 if [ $# -eq 0 ]; then
-    printf "Usage: $0 [model] [threads]\n\n"
+    printf "Usage: $0 [model]\n\n"
     printf "No model specified. Aborting\n"
     list_models
     exit 1
@@ -41,11 +41,6 @@ fi
 model=$1
 main="../main"
 
-threads=""
-if [ $# -eq 2 ]; then
-    threads="-t $2"
-fi
-
 if [ ! -f ../models/ggml-$model.bin ]; then
     printf "Model $model not found. Aborting\n"
     list_models
@@ -110,7 +105,7 @@ function run_lang() {
             fi
         fi
 
-        $main -m ../models/ggml-$model.bin $threads -f $fname_dst -l $lang -otxt 2> /dev/null
+        $main -m ../models/ggml-$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
 

whisper.h

@@ -226,7 +226,7 @@ extern "C" {
     // Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first
     // Returns the top language id or negative on failure
     // If not null, fills the lang_probs array with the probabilities of all languages
-    // The array must be whisper_lang_max_id() + 1 in size
+    // The array must be whispe_lang_max_id() + 1 in size
     // ref: https://github.com/openai/whisper/blob/main/whisper/decoding.py#L18-L69
     WHISPER_API int whisper_lang_auto_detect(
             struct whisper_context * ctx,
@@ -258,7 +258,7 @@ extern "C" {
     WHISPER_API int whisper_model_n_text_head  (struct whisper_context * ctx);
     WHISPER_API int whisper_model_n_text_layer (struct whisper_context * ctx);
     WHISPER_API int whisper_model_n_mels       (struct whisper_context * ctx);
-    WHISPER_API int whisper_model_ftype        (struct whisper_context * ctx);
+    WHISPER_API int whisper_model_f16          (struct whisper_context * ctx);
     WHISPER_API int whisper_model_type         (struct whisper_context * ctx);
 
     // Token logits obtained from the last call to whisper_decode()
@@ -297,7 +297,7 @@ extern "C" {
 
     // Available sampling strategies
     enum whisper_sampling_strategy {
-        WHISPER_SAMPLING_GREEDY,      // similar to OpenAI's GreedyDecoder
+        WHISPER_SAMPLING_GREEDY,      // similar to OpenAI's GreefyDecoder
         WHISPER_SAMPLING_BEAM_SEARCH, // similar to OpenAI's BeamSearchDecoder
     };
 
@@ -306,9 +306,6 @@ extern "C" {
     // Use the whisper_full_...() functions to obtain the text segments
     typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data);
 
-    // Progress callback
-    typedef void (*whisper_progress_callback)(struct whisper_context * ctx, struct whisper_state * state, int progress, void * user_data);
-
     // Encoder begin callback
     // If not NULL, called before the encoder starts
     // If it returns false, the computation is aborted
@@ -359,7 +356,6 @@ extern "C" {
 
         // tokens to provide to the whisper decoder as initial prompt
         // these are prepended to any existing text context from a previous call
-        const char * initial_prompt;
         const whisper_token * prompt_tokens;
         int prompt_n_tokens;
 
@@ -395,10 +391,6 @@ extern "C" {
         whisper_new_segment_callback new_segment_callback;
         void * new_segment_callback_user_data;
 
-        // called on each progress update
-        whisper_progress_callback progress_callback;
-        void * progress_callback_user_data;
-
         // called each time before the encoder starts
         whisper_encoder_begin_callback encoder_begin_callback;
         void * encoder_begin_callback_user_data;