diarization : try conv and self-attention embeddings

.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 +1,6 @@
 *.o
 *.a
 .cache/
-.coreml/
-.test/
 .vs/
 .vscode/
 .DS_Store
@@ -12,7 +10,6 @@ build-em/
 build-debug/
 build-release/
 build-static/
-build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
 
@@ -20,7 +17,6 @@ build-sanitize-thread/
 /stream
 /command
 /talk
-/talk-llama
 /bench
 
 arm_neon.h
@@ -35,7 +31,3 @@ 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 1.3.0)
+project(whisper.cpp VERSION 1.2.0)
-
-if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
-    add_compile_options(/utf-8)
-endif ()
 
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -58,8 +54,6 @@ if (APPLE)
     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)
 else()
     option(WHISPER_SUPPORT_OPENBLAS    "whisper: support for OpenBLAS" OFF)
 endif()
@@ -92,33 +86,16 @@ endif()
 
 find_package(Threads REQUIRED)
 
-# on APPLE
+# on APPLE - include Accelerate framework
-if (APPLE)
+if (APPLE AND NOT WHISPER_NO_ACCELERATE)
-    # include Accelerate framework
+    find_library(ACCELERATE_FRAMEWORK Accelerate)
-    if (NOT WHISPER_NO_ACCELERATE)
+    if (ACCELERATE_FRAMEWORK)
-        find_library(ACCELERATE_FRAMEWORK Accelerate)
+        message(STATUS "Accelerate framework found")
 
-        if (ACCELERATE_FRAMEWORK)
+        set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
-            message(STATUS "Accelerate framework found")
+        set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
-
+    else()
-            set(WHISPER_EXTRA_LIBS  ${WHISPER_EXTRA_LIBS}  ${ACCELERATE_FRAMEWORK})
+        message(WARNING "Accelerate framework not found")
-            set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
-        else()
-            message(WARNING "Accelerate framework not found")
-        endif()
-    endif()
-
-    if (WHISPER_COREML)
-        find_library(FOUNDATION_FRAMEWORK Foundation)
-        find_library(COREML_FRAMEWORK CoreML)
-
-        if (COREML_FRAMEWORK)
-            message(STATUS "CoreML framework found")
-
-            set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_COREML)
-        else()
-            message(WARNING "CoreML framework not found")
-        endif()
     endif()
 endif()
 
@@ -195,9 +172,7 @@ else()
             if(NOT WHISPER_NO_FMA)
                 set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
             endif()
-            if(NOT WHISPER_NO_F16C)
+            set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
-                set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
-            endif()
         endif()
     endif()
 endif()
@@ -206,33 +181,6 @@ if (WHISPER_PERF)
     set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF)
 endif()
 
-#
-# whisper.coreml - Core ML support
-#
-
-if (WHISPER_COREML)
-    set(TARGET whisper.coreml)
-
-    add_library(${TARGET}
-        coreml/whisper-encoder.h
-        coreml/whisper-encoder.mm
-        coreml/whisper-encoder-impl.h
-        coreml/whisper-encoder-impl.m
-        )
-
-    include(DefaultTargetOptions)
-
-    target_include_directories(${TARGET} PUBLIC
-        .
-        )
-
-    target_link_libraries(${TARGET} PRIVATE ${FOUNDATION_FRAMEWORK} ${COREML_FRAMEWORK})
-
-    set_target_properties(${TARGET} PROPERTIES
-        COMPILE_FLAGS "-fobjc-arc"
-        )
-endif()
-
 #
 # whisper - this is the main library of the project
 #
@@ -252,10 +200,6 @@ target_include_directories(${TARGET} PUBLIC
     .
     )
 
-if (WHISPER_COREML)
-    target_link_libraries(${TARGET} PRIVATE whisper.coreml)
-endif()
-
 if (MSVC)
     target_link_libraries(${TARGET} PRIVATE ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
 

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

@@ -30,16 +30,10 @@ endif
 # Compile flags
 #
 
-CFLAGS   = -I.              -O3 -DNDEBUG -std=c11   -fPIC
+CFLAGS   = -I.              -O3 -std=c11   -fPIC
-CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
+CXXFLAGS = -I. -I./examples -O3 -std=c++11 -fPIC
 LDFLAGS  =
 
-# ref: https://github.com/ggerganov/whisper.cpp/issues/37
-ifneq ($(wildcard /usr/include/musl/*),)
-	CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
-	CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
-endif
-
 # OS specific
 # TODO: support Windows
 ifeq ($(UNAME_S),Linux)
@@ -77,6 +71,10 @@ 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,$(AVX2_M)))
 			CFLAGS += -mavx2
@@ -88,17 +86,16 @@ ifeq ($(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,$(AVX2_M)))
 			CFLAGS += -mavx2
@@ -110,11 +107,6 @@ ifeq ($(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,$(AVX1_M)))
-				CFLAGS += -mavx
-			endif
 		endif
 	else
 		CFLAGS += -mfma -mf16c -mavx -mavx2
@@ -140,10 +132,6 @@ ifndef WHISPER_NO_ACCELERATE
 		LDFLAGS += -framework Accelerate
 	endif
 endif
-ifdef WHISPER_COREML
-	CXXFLAGS += -DWHISPER_USE_COREML
-	LDFLAGS  += -framework Foundation -framework CoreML
-endif
 ifdef WHISPER_OPENBLAS
 	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
 	LDFLAGS += -lopenblas
@@ -153,19 +141,14 @@ ifdef WHISPER_GPROF
 	CXXFLAGS += -pg
 endif
 ifneq ($(filter aarch64%,$(UNAME_M)),)
-	CFLAGS += -mcpu=native
-	CXXFLAGS += -mcpu=native
 endif
 ifneq ($(filter armv6%,$(UNAME_M)),)
-	# 32-bit Raspberry Pi 1, 2, 3
+	# Raspberry Pi 1, 2, 3
-	CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access
+	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
 endif
 ifneq ($(filter armv7%,$(UNAME_M)),)
-	# 32-bit ARM, for example on Armbian or possibly raspbian
+	# Raspberry Pi 4
-	CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
+	CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
-
-	# 64-bit ARM, use these (TODO: auto-detect 64-bit)
-	# CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
 endif
 ifneq ($(filter armv8%,$(UNAME_M)),)
 	# Raspberry Pi 4
@@ -187,7 +170,7 @@ $(info I CC:       $(CCV))
 $(info I CXX:      $(CXXV))
 $(info )
 
-default: main bench
+default: main
 
 #
 # Build library
@@ -196,29 +179,17 @@ default: main bench
 ggml.o: ggml.c ggml.h
 	$(CC)  $(CFLAGS)   -c ggml.c -o ggml.o
 
-whisper.o: whisper.cpp whisper.h ggml.h
+whisper.o: whisper.cpp whisper.h
 	$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
 
-ifndef WHISPER_COREML
+libwhisper.a: ggml.o whisper.o
-WHISPER_OBJ = whisper.o
+	$(AR) rcs libwhisper.a ggml.o 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
 
-whisper-encoder-impl.o: coreml/whisper-encoder-impl.m coreml/whisper-encoder-impl.h
+libwhisper.so: ggml.o whisper.o
-	$(CXX) -O3 -I . -fobjc-arc -c coreml/whisper-encoder-impl.m -o whisper-encoder-impl.o
+	$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o whisper.o $(LDFLAGS)
-
-WHISPER_OBJ = whisper.o whisper-encoder.o whisper-encoder-impl.o
-endif
-
-libwhisper.a: ggml.o $(WHISPER_OBJ)
-	$(AR) rcs libwhisper.a ggml.o $(WHISPER_OBJ)
-
-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 libwhisper.a libwhisper.so
+	rm -f *.o main stream command talk bench libwhisper.a libwhisper.so
 
 #
 # Examples
@@ -229,24 +200,21 @@ CC_SDL=`sdl2-config --cflags --libs`
 SRC_COMMON = examples/common.cpp
 SRC_COMMON_SDL = examples/common-sdl.cpp
 
-main: examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ)
+main: examples/main/main.cpp $(SRC_COMMON) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ) -o main $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/main/main.cpp $(SRC_COMMON) ggml.o whisper.o -o main $(LDFLAGS)
 	./main -h
 
-bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
+stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o stream $(CC_SDL) $(LDFLAGS)
 
-stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o stream $(CC_SDL) $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o command $(CC_SDL) $(LDFLAGS)
 
-command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o command $(CC_SDL) $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o talk $(CC_SDL) $(LDFLAGS)
 
-talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+bench: examples/bench/bench.cpp ggml.o whisper.o
-	$(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)
+	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o whisper.o -o bench $(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)
 
 #
 # Audio samples

README.md

@@ -4,12 +4,12 @@
 [![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: [v1.3.0](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.3.0) / 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: [v1.2.0](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.2.0) / [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
@@ -58,9 +58,7 @@ the Accelerate framework utilizes the special-purpose AMX coprocessor available
 
 ## Quick start
 
-First clone the repository.
+First, download one of the Whisper models converted in [ggml format](models). For example:
-
-Then, download one of the Whisper models converted in [ggml format](models). For example:
 
 ```bash
 bash ./models/download-ggml-model.sh base.en
@@ -225,60 +223,6 @@ make large
 | medium | 1.5 GB | ~1.7 GB | `fd9727b6e1217c2f614f9b698455c4ffd82463b4` |
 | large  | 2.9 GB | ~3.3 GB | `0f4c8e34f21cf1a914c59d8b3ce882345ad349d6` |
 
-## 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).
-  
 ## Limitations
 
 - Inference only
@@ -369,7 +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
@@ -384,10 +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)
@@ -493,19 +433,6 @@ https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a
 
 ---
 
-## Video comparison of different models
-
-Use the [extra/bench-wts.sh](https://github.com/ggerganov/whisper.cpp/blob/master/extra/bench-wts.sh) script to generate a video in the following format:
-
-```java
-./extra/bench-wts.sh samples/jfk.wav
-ffplay ./samples/jfk.wav.all.mp4
-```
-
-https://user-images.githubusercontent.com/1991296/223206245-2d36d903-cf8e-4f09-8c3b-eb9f9c39d6fc.mp4
-
----
-
 ## Benchmarks
 
 In order to have an objective comparison of the performance of the inference across different system configurations,
@@ -526,7 +453,7 @@ The original models are converted to a custom binary format. This allows to pack
 You can download the converted models using the [models/download-ggml-model.sh](models/download-ggml-model.sh) script
 or manually from here:
 
-- https://huggingface.co/ggerganov/whisper.cpp
+- https://huggingface.co/datasets/ggerganov/whisper.cpp
 - https://ggml.ggerganov.com
 
 For more details, see the conversion script [models/convert-pt-to-ggml.py](models/convert-pt-to-ggml.py) or the README
@@ -536,19 +463,13 @@ in [models](models).
 
 - [X] Rust: [tazz4843/whisper-rs](https://github.com/tazz4843/whisper-rs) | [#310](https://github.com/ggerganov/whisper.cpp/discussions/310)
 - [X] Javascript: [bindings/javascript](bindings/javascript) | [#309](https://github.com/ggerganov/whisper.cpp/discussions/309)
-  - React Native (iOS / Android): [whisper.rn](https://github.com/mybigday/whisper.rn)
 - [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)
-- [X] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9)
+- [ ] Python: soon | [WIP](https://github.com/ggerganov/whisper.cpp/issues/9)
-  - [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
 
@@ -562,7 +483,6 @@ 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/examples/go-model-download/main.go

@@ -17,9 +17,9 @@ import (
 // CONSTANTS
 
 const (
-	srcUrl  = "https://huggingface.co/ggerganov/whisper.cpp/resolve/main" // The location of the models
+	srcUrl  = "https://huggingface.co/datasets/ggerganov/whisper.cpp/resolve/main" // The location of the models
-	srcExt  = ".bin"                                                      // Filename extension
+	srcExt  = ".bin"                                                               // Filename extension
-	bufSize = 1024 * 64                                                   // Size of the buffer used for downloading the model
+	bufSize = 1024 * 64                                                            // Size of the buffer used for downloading the model
 )
 
 var (

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)),
+			Id:   int(ctx.Whisper_full_get_token_id(n, i)),
-			Text:  ctx.Whisper_full_get_token_text(n, i),
+			Text: strings.TrimSpace(ctx.Whisper_full_get_token_text(n, i)),
-			P:     ctx.Whisper_full_get_token_p(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,
 		}
 	}
 	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
+	Id   int
-	Text       string
+	Text string
-	P          float32
+	P    float32
-	Start, End time.Duration
 }

bindings/go/pkg/whisper/model.go

@@ -94,7 +94,6 @@ func (model *model) NewContext() (Context, error) {
 	params.SetPrintRealtime(false)
 	params.SetPrintTimestamps(false)
 	params.SetThreads(runtime.NumCPU())
-	params.SetNoContext(true)
 
 	// Return new context
 	return newContext(model, params)

bindings/go/whisper.go

@@ -20,7 +20,7 @@ extern bool callEncoderBegin(void* user_data);
 // Text segment callback
 // Called on every newly generated text segment
 // Use the whisper_full_...() functions to obtain the text segments
-static void whisper_new_segment_cb(struct whisper_context* ctx, struct whisper_state* state, int n_new, void* user_data) {
+static void whisper_new_segment_cb(struct whisper_context* ctx, int n_new, void* user_data) {
     if(user_data != NULL && ctx != NULL) {
         callNewSegment(user_data, n_new);
     }
@@ -29,7 +29,7 @@ static void whisper_new_segment_cb(struct whisper_context* ctx, struct whisper_s
 // Encoder begin callback
 // If not NULL, called before the encoder starts
 // If it returns false, the computation is aborted
-static bool whisper_encoder_begin_cb(struct whisper_context* ctx, struct whisper_state* state, void* user_data) {
+static bool whisper_encoder_begin_cb(struct whisper_context* ctx, void* user_data) {
     if(user_data != NULL && ctx != NULL) {
         return callEncoderBegin(user_data);
     }
@@ -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": "1.3.0",
+  "version": "1.2.0",
   "description": "Whisper speech recognition",
   "main": "whisper.js",
   "scripts": {

bindings/ruby/ext/ruby_whisper.cpp

@@ -199,7 +199,7 @@ static VALUE ruby_whisper_transcribe(int argc, VALUE *argv, VALUE self) {
   {
     static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
 
-    rwp->params.encoder_begin_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, void * user_data) {
+    rwp->params.encoder_begin_callback = [](struct whisper_context * /*ctx*/, void * user_data) {
       bool is_aborted = *(bool*)user_data;
       return !is_aborted;
     };

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

@@ -1,142 +0,0 @@
-//
-// whisper-encoder-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_encoder_implInput : NSObject<MLFeatureProvider>
-
-/// logmel_data as 1 × 80 × 3000 3-dimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * logmel_data;
-- (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithLogmel_data:(MLMultiArray *)logmel_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_encoder_implOutput : NSObject<MLFeatureProvider>
-
-/// output as multidimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * output;
-- (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithOutput:(MLMultiArray *)output 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_encoder_impl : NSObject
-@property (readonly, nonatomic, nullable) MLModel * model;
-
-/**
-    URL of the underlying .mlmodelc directory.
-*/
-+ (nullable NSURL *)URLOfModelInThisBundle;
-
-/**
-    Initialize whisper_encoder_impl instance from an existing MLModel object.
-
-    Usually the application does not use this initializer unless it makes a subclass of whisper_encoder_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_encoder_impl instance with the model in this bundle.
-*/
-- (nullable instancetype)init;
-
-/**
-    Initialize whisper_encoder_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_encoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_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_encoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_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_encoder_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_encoder_impl instance or NSError object.
-*/
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler;
-
-/**
-    Construct whisper_encoder_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_encoder_impl instance or NSError object.
-*/
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler;
-
-/**
-    Make a prediction using the standard interface
-    @param input an instance of whisper_encoder_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_encoder_implOutput
-*/
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)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 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
-*/
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)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 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
-*/
-- (nullable whisper_encoder_implOutput *)predictionFromLogmel_data:(MLMultiArray *)logmel_data error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Batch prediction
-    @param inputArray array of whisper_encoder_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_encoder_implOutput *>
-*/
-- (nullable NSArray<whisper_encoder_implOutput *> *)predictionsFromInputs:(NSArray<whisper_encoder_implInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-@end
-
-NS_ASSUME_NONNULL_END

coreml/whisper-encoder-impl.m

@@ -1,197 +0,0 @@
-//
-// whisper-encoder-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-encoder-impl.h"
-
-@implementation whisper_encoder_implInput
-
-- (instancetype)initWithLogmel_data:(MLMultiArray *)logmel_data {
-    self = [super init];
-    if (self) {
-        _logmel_data = logmel_data;
-    }
-    return self;
-}
-
-- (NSSet<NSString *> *)featureNames {
-    return [NSSet setWithArray:@[@"logmel_data"]];
-}
-
-- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
-    if ([featureName isEqualToString:@"logmel_data"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.logmel_data];
-    }
-    return nil;
-}
-
-@end
-
-@implementation whisper_encoder_implOutput
-
-- (instancetype)initWithOutput:(MLMultiArray *)output {
-    self = [super init];
-    if (self) {
-        _output = output;
-    }
-    return self;
-}
-
-- (NSSet<NSString *> *)featureNames {
-    return [NSSet setWithArray:@[@"output"]];
-}
-
-- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
-    if ([featureName isEqualToString:@"output"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.output];
-    }
-    return nil;
-}
-
-@end
-
-@implementation whisper_encoder_impl
-
-
-/**
-    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; }
-    return [NSURL fileURLWithPath:assetPath];
-}
-
-
-/**
-    Initialize whisper_encoder_impl instance from an existing MLModel object.
-
-    Usually the application does not use this initializer unless it makes a subclass of whisper_encoder_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_encoder_impl instance with the model in this bundle.
-*/
-- (nullable instancetype)init {
-    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
-}
-
-
-/**
-    Initialize whisper_encoder_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_encoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_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_encoder_impl instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for whisper_encoder_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_encoder_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_encoder_impl instance or NSError object.
-*/
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _Nullable model, NSError * _Nullable error))handler {
-    [self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
-              configuration:configuration
-          completionHandler:handler];
-}
-
-
-/**
-    Construct whisper_encoder_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_encoder_impl instance or NSError object.
-*/
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(whisper_encoder_impl * _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];
-            handler(typedModel, nil);
-        } else {
-            handler(nil, error);
-        }
-    }];
-}
-
-- (nullable whisper_encoder_implOutput *)predictionFromFeatures:(whisper_encoder_implInput *)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 {
-    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];
-}
-
-- (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];
-    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 {
-    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];
-    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];
-        [results addObject:result];
-    }
-    return results;
-}
-
-@end

coreml/whisper-encoder.h

@@ -1,22 +0,0 @@
-// Wrapper of the Core ML Whisper Encoder model
-//
-// Code is derived from the work of Github user @wangchou
-// ref: https://github.com/wangchou/callCoreMLFromCpp
-
-#if __cplusplus
-extern "C" {
-#endif
-
-struct whisper_coreml_context;
-
-struct whisper_coreml_context * whisper_coreml_init(const char * path_model);
-void whisper_coreml_free(struct whisper_coreml_context * ctx);
-
-void whisper_coreml_encode(
-        const whisper_coreml_context * ctx,
-                               float * mel,
-                               float * out);
-
-#if __cplusplus
-}
-#endif

coreml/whisper-encoder.mm

@@ -1,67 +0,0 @@
-#import "coreml/whisper-encoder.h"
-#import "coreml/whisper-encoder-impl.h"
-
-#import <CoreML/CoreML.h>
-
-#include <stdlib.h>
-
-#if __cplusplus
-extern "C" {
-#endif
-
-struct whisper_coreml_context {
-    const void * data;
-};
-
-struct whisper_coreml_context * whisper_coreml_init(const char * path_model) {
-    NSString * path_model_str = [[NSString alloc] initWithUTF8String:path_model];
-
-    NSURL * url_model = [NSURL fileURLWithPath: path_model_str];
-
-    const void * data = CFBridgingRetain([[whisper_encoder_impl alloc] initWithContentsOfURL:url_model error:nil]);
-
-    if (data == NULL) {
-        return NULL;
-    }
-
-    whisper_coreml_context * ctx = new whisper_coreml_context;
-
-    ctx->data = data;
-
-    return ctx;
-}
-
-void whisper_coreml_free(struct whisper_coreml_context * ctx) {
-    CFRelease(ctx->data);
-    delete ctx;
-}
-
-void whisper_coreml_encode(
-        const whisper_coreml_context * ctx,
-                               float * mel,
-                               float * out) {
-    MLMultiArray * inMultiArray = [
-        [MLMultiArray alloc] initWithDataPointer: mel
-                                           shape: @[@1, @80, @3000]
-                                        dataType: MLMultiArrayDataTypeFloat32
-                                         strides: @[@(240000), @(3000), @1]
-                                     deallocator: nil
-                                           error: nil
-    ];
-
-    whisper_encoder_implOutput * outCoreML = [(__bridge id) ctx->data predictionFromLogmel_data: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));
-}
-
-#if __cplusplus
-}
-#endif

examples/CMakeLists.txt

@@ -63,5 +63,4 @@ else()
     add_subdirectory(command)
     add_subdirectory(bench)
     add_subdirectory(talk)
-    add_subdirectory(talk-llama)
 endif()

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

@@ -1,22 +1,15 @@
-const path = require("path");
+const path = require('path');
-const { whisper } = require(path.join(
+const { whisper } = require(path.join(__dirname, '../../../build/Release/whisper-addon'));
-  __dirname,
-  "../../../build/Release/whisper-addon"
-));
-const { promisify } = require("util");
-
-const whisperAsync = promisify(whisper);
 
 const whisperParamsMock = {
-  language: "en",
+    language: 'en',
-  model: path.join(__dirname, "../../../models/ggml-base.en.bin"),
+    model: path.join(__dirname, '../../../models/ggml-base.en.bin'),
-  fname_inp: path.join(__dirname, "../../../samples/jfk.wav"),
+    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);
+    test("it should receive a non-empty value", () => {
-  });
+        expect(whisper(whisperParamsMock).length).toBeGreaterThan(0);
+    });
 });

examples/addon.node/addon.cpp

@@ -72,7 +72,7 @@ int timestamp_to_sample(int64_t t, int n_samples) {
     return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
 }
 
-void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data) {
+void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
     const auto & params  = *((whisper_print_user_data *) user_data)->params;
     const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
 
@@ -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 };
 
@@ -243,7 +260,7 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
             {
                 static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
 
-                wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, void * user_data) {
+                wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, void * user_data) {
                     bool is_aborted = *(bool*)user_data;
                     return !is_aborted;
                 };
@@ -275,64 +292,51 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
     return 0;
 }
 
-class Worker : public Napi::AsyncWorker {
+Napi::Object whisper(const Napi::CallbackInfo& info) {
- public:
+    Napi::Env env = info.Env();
-  Worker(Napi::Function& callback, whisper_params params)
+    if (info.Length() <= 0 || !info[0].IsObject()) {
-      : Napi::AsyncWorker(callback), params(params) {}
+        Napi::TypeError::New(env, "object expected").ThrowAsJavaScriptException();
-
-  void Execute() override {
-    run(params, result);
-  }
-
-  void OnOK() override {
-    Napi::HandleScope scope(Env());
-    Napi::Object res = Napi::Array::New(Env(), result.size());
-    for (uint64_t i = 0; i < result.size(); ++i) {
-      Napi::Object tmp = Napi::Array::New(Env(), 3);
-      for (uint64_t j = 0; j < 3; ++j) {
-        tmp[j] = Napi::String::New(Env(), result[i][j]);
-      }
-      res[i] = tmp;
     }
-    Callback().Call({Env().Null(), res});
+    whisper_params params;
-  }
+    std::vector<std::vector<std::string>> result;
 
- private:
+    Napi::Object whisper_params = info[0].As<Napi::Object>();
-  whisper_params params;
+    std::string language = whisper_params.Get("language").As<Napi::String>();
-  std::vector<std::vector<std::string>> result;
+    std::string model = whisper_params.Get("model").As<Napi::String>();
-};
+    std::string input = whisper_params.Get("fname_inp").As<Napi::String>();
 
+    params.language = language;
+    params.model = model;
+    params.fname_inp.emplace_back(input);
 
+    // run model
+    run(params, result);
 
-Napi::Value whisper(const Napi::CallbackInfo& info) {
+    fprintf(stderr, "RESULT:\n");
-  Napi::Env env = info.Env();
+    for (auto sentence:result) {
-  if (info.Length() <= 0 || !info[0].IsObject()) {
+        fprintf(stderr, "t0: %s, t1: %s, content: %s \n",
-    Napi::TypeError::New(env, "object expected").ThrowAsJavaScriptException();
+                sentence[0].c_str(), sentence[1].c_str(), sentence[2].c_str());
-  }
+    }
-  whisper_params params;
 
-  Napi::Object whisper_params = info[0].As<Napi::Object>();
+    Napi::Object res = Napi::Array::New(env, result.size());
-  std::string language = whisper_params.Get("language").As<Napi::String>();
+    for (uint64_t i = 0; i < result.size(); ++i) {
-  std::string model = whisper_params.Get("model").As<Napi::String>();
+        Napi::Object tmp = Napi::Array::New(env, 3);
-  std::string input = whisper_params.Get("fname_inp").As<Napi::String>();
+        for (uint64_t j = 0; j < 3; ++j) {
+            tmp[j] = Napi::String::New(env, result[i][j]);
+        }
+        res[i] = tmp;
+    }
 
-  params.language = language;
+    return res;
-  params.model = model;
-  params.fname_inp.emplace_back(input);
-
-  Napi::Function callback = info[1].As<Napi::Function>();
-  Worker* worker = new Worker(callback, params);
-  worker->Queue();
-  return env.Undefined();
 }
 
 
 Napi::Object Init(Napi::Env env, Napi::Object exports) {
-  exports.Set(
+    exports.Set(
-      Napi::String::New(env, "whisper"),
+            Napi::String::New(env, "whisper"),
-      Napi::Function::New(env, whisper)
+            Napi::Function::New(env, whisper)
-  );
+    );
-  return exports;
+    return exports;
 }
 
 NODE_API_MODULE(whisper, Init);

examples/addon.node/index.js

@@ -1,36 +1,27 @@
-const path = require("path");
+const path = require('path');
-const { whisper } = require(path.join(
+const { whisper } = require(path.join(__dirname, '../../build/Release/whisper-addon'));
-  __dirname,
-  "../../build/Release/whisper-addon"
-));
-const { promisify } = require("util");
-
-const whisperAsync = promisify(whisper);
 
 const whisperParams = {
-  language: "en",
+    language: 'en',
-  model: path.join(__dirname, "../../models/ggml-base.en.bin"),
+    model: path.join(__dirname, '../../models/ggml-base.en.bin'),
-  fname_inp: "../../samples/jfk.wav",
+    fname_inp: '',
 };
 
 const arguments = process.argv.slice(2);
 const params = Object.fromEntries(
-  arguments.reduce((pre, item) => {
+    arguments.reduce((pre, item) => {
-    if (item.startsWith("--")) {
+        if (item.startsWith("--")) {
-      return [...pre, item.slice(2).split("=")];
+            return [...pre, item.slice(2).split("=")];
-    }
+        }
-    return pre;
+        return pre;
-  }, [])
+    }, []),
 );
 
 for (const key in params) {
-  if (whisperParams.hasOwnProperty(key)) {
+    if (whisperParams.hasOwnProperty(key)) {
-    whisperParams[key] = params[key];
+        whisperParams[key] = params[key];
-  }
+    }
 }
 
-console.log("whisperParams =", whisperParams);
+console.log('whisperParams =', whisperParams);
-
+console.log(whisper(whisperParams));
-whisperAsync(whisperParams).then((result) => {
-  console.log(`Result from whisper: ${result}`);
-});

examples/main/README.md

@@ -31,7 +31,6 @@ options:
   -osrt,     --output-srt        [false  ] output result in a srt file
   -owts,     --output-words      [false  ] output script for generating karaoke video
   -ocsv,     --output-csv        [false  ] output result in a CSV file
-  -oj,       --output-json       [false  ] output result in a JSON file
   -of FNAME, --output-file FNAME [       ] output file path (without file extension)
   -ps,       --print-special     [false  ] print special tokens
   -pc,       --print-colors      [false  ] print colors

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;
@@ -74,8 +73,6 @@ struct whisper_params {
     bool output_srt     = false;
     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;
@@ -83,7 +80,6 @@ struct whisper_params {
 
     std::string language = "en";
     std::string prompt;
-    std::string font_path = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
     std::string model    = "models/ggml-base.en.bin";
 
     std::vector<std::string> fname_inp = {};
@@ -131,10 +127,7 @@ 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; }
         else if (arg == "-of"   || arg == "--output-file")    { params.fname_out.emplace_back(argv[++i]); }
         else if (arg == "-ps"   || arg == "--print-special")  { params.print_special  = true; }
         else if (arg == "-pc"   || arg == "--print-colors")   { params.print_colors   = true; }
@@ -180,11 +173,8 @@ 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");
-    fprintf(stderr, "  -oj,       --output-json       [%-7s] output result in a JSON file\n",                   params.output_jsn ? "true" : "false");
     fprintf(stderr, "  -of FNAME, --output-file FNAME [%-7s] output file path (without file extension)\n",      "");
     fprintf(stderr, "  -ps,       --print-special     [%-7s] print special tokens\n",                           params.print_special ? "true" : "false");
     fprintf(stderr, "  -pc,       --print-colors      [%-7s] print colors\n",                                   params.print_colors ? "true" : "false");
@@ -203,7 +193,7 @@ struct whisper_print_user_data {
     const std::vector<std::vector<float>> * pcmf32s;
 };
 
-void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, int n_new, void * user_data) {
+void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, void * user_data) {
     const auto & params  = *((whisper_print_user_data *) user_data)->params;
     const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
 
@@ -211,8 +201,8 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
 
     std::string speaker = "";
 
-    int64_t t0 = 0;
+    int64_t t0;
-    int64_t t1 = 0;
+    int64_t t1;
 
     // print the last n_new segments
     const int s0 = n_segments - n_new;
@@ -362,192 +352,28 @@ bool output_csv(struct whisper_context * ctx, const char * fname) {
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
 
     const int n_segments = whisper_full_n_segments(ctx);
-    fout << "start,end,text\n";
     for (int i = 0; i < n_segments; ++i) {
         const char * text = whisper_full_get_segment_text(ctx, i);
         const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
         const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
 
         //need to multiply times returned from whisper_full_get_segment_t{0,1}() by 10 to get milliseconds.
-        fout << 10 * t0 << "," << 10 * t1 << ",\"" << text    << "\"\n";
+        fout << 10 * t0 << ", " << 10 * t1 << ", \"" << text    << "\"\n";
     }
 
     return true;
 }
 
-char *escape_double_quotes(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] == '"') {
-            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] == '"') {
-            escaped[pos++] = '\\';
-            escaped[pos++] = '"';
-        } else {
-            escaped[pos++] = str[i];
-        }
-    }
-
-    // no need to set zero due to calloc() being used prior
-
-    return escaped;
-}
-
-bool output_json(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
-    std::ofstream fout(fname);
-    int indent = 0;
-
-    auto doindent = [&]() {
-        for (int i = 0; i < indent; i++) fout << "\t";
-    };
-
-    auto start_arr = [&](const char *name) {
-        doindent();
-        fout << "\"" << name << "\": [\n";
-        indent++;
-    };
-
-    auto end_arr = [&](bool end = false) {
-        indent--;
-        doindent();
-        fout << (end ? "]\n" : "},\n");
-    };
-
-    auto start_obj = [&](const char *name = nullptr) {
-        doindent();
-        if (name) {
-            fout << "\"" << name << "\": {\n";
-        } else {
-            fout << "{\n";
-        }
-        indent++;
-    };
-
-    auto end_obj = [&](bool end = false) {
-        indent--;
-        doindent();
-        fout << (end ? "}\n" : "},\n");
-    };
-
-    auto start_value = [&](const char *name) {
-        doindent();
-        fout << "\"" << name << "\": ";
-    };
-
-    auto value_s = [&](const char *name, const char *val, bool end = false) {
-        start_value(name);
-        char * val_escaped = escape_double_quotes(val);
-        fout << "\"" << val_escaped << (end ? "\"\n" : "\",\n");
-        free(val_escaped);
-    };
-
-    auto end_value = [&](bool end = false) {
-        fout << (end ? "\n" : ",\n");
-    };
-
-    auto value_i = [&](const char *name, const int64_t val, bool end = false) {
-        start_value(name);
-        fout << val;
-        end_value(end);
-    };
-
-    auto value_b = [&](const char *name, const bool val, bool end = false) {
-        start_value(name);
-        fout << (val ? "true" : "false");
-        end_value(end);
-    };
-
-    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);
-    start_obj();
-        value_s("systeminfo", whisper_print_system_info());
-        start_obj("model");
-            value_s("type", whisper_model_type_readable(ctx));
-            value_b("multilingual", whisper_is_multilingual(ctx));
-            value_i("vocab", whisper_model_n_vocab(ctx));
-            start_obj("audio");
-                value_i("ctx", whisper_model_n_audio_ctx(ctx));
-                value_i("state", whisper_model_n_audio_state(ctx));
-                value_i("head", whisper_model_n_audio_head(ctx));
-                value_i("layer", whisper_model_n_audio_layer(ctx), true);
-            end_obj();
-            start_obj("text");
-                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);
-            end_obj();
-            value_i("mels", whisper_model_n_mels(ctx));
-            value_i("f16", whisper_model_f16(ctx), true);
-        end_obj();
-        start_obj("params");
-            value_s("model", params.model.c_str());
-            value_s("language", params.language.c_str());
-            value_b("translate", params.translate, true);
-        end_obj();
-        start_obj("result");
-            value_s("language", whisper_lang_str(whisper_full_lang_id(ctx)), true);
-        end_obj();
-        start_arr("transcription");
-
-            const int n_segments = whisper_full_n_segments(ctx);
-            for (int i = 0; i < n_segments; ++i) {
-                const char * text = whisper_full_get_segment_text(ctx, i);
-                const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
-                const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
-
-                start_obj();
-                    start_obj("timestamps");
-                        value_s("from", to_timestamp(t0, true).c_str());
-                        value_s("to", to_timestamp(t1, true).c_str(), true);
-                    end_obj();
-                    start_obj("offsets");
-                        value_i("from", t0 * 10);
-                        value_i("to", t1 * 10, true);
-                    end_obj();
-                    value_s("text", text, true);
-                end_obj(i == (n_segments - 1));
-            }
-
-        end_arr(true);
-    end_obj(true);
-    return true;
-}
-
 // karaoke video generation
 // outputs a bash script that uses ffmpeg to generate a video with the subtitles
 // TODO: font parameter adjustments
-bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec) {
+bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & /*params*/, float t_sec) {
     std::ofstream fout(fname);
 
     fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
 
-    static const char * font = params.font_path.c_str();
+    // TODO: become parameter
-
+    static const char * font = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
-    std::ifstream fin(font);
-    if (!fin.is_open()) {
-        fprintf(stderr, "%s: font not found at '%s', please specify a monospace font with -fp\n", __func__, font);
-        return false;
-    }
 
     fout << "#!/bin/bash" << "\n";
     fout << "\n";
@@ -650,39 +476,6 @@ 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;
 
@@ -711,6 +504,22 @@ 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];
@@ -774,7 +583,8 @@ 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;
@@ -797,7 +607,7 @@ int main(int argc, char ** argv) {
             {
                 static bool is_aborted = false; // NOTE: this should be atomic to avoid data race
 
-                wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, void * user_data) {
+                wparams.encoder_begin_callback = [](struct whisper_context * /*ctx*/, void * user_data) {
                     bool is_aborted = *(bool*)user_data;
                     return !is_aborted;
                 };
@@ -808,6 +618,8 @@ int main(int argc, char ** argv) {
                 fprintf(stderr, "%s: failed to process audio\n", argv[0]);
                 return 10;
             }
+
+            whisper_full_cluster_segments(ctx);
         }
 
         // output stuff
@@ -843,18 +655,6 @@ int main(int argc, char ** argv) {
                 const auto fname_csv = fname_out + ".csv";
                 output_csv(ctx, fname_csv.c_str());
             }
-
-            // output to JSON file
-            if (params.output_jsn) {
-                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/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, "  -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, "            --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, "            --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, "            --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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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, "  -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);
@@ -291,6 +288,7 @@ int main(int argc, char ** argv) {
             wparams.print_realtime   = false;
             wparams.print_timestamps = !params.no_timestamps;
             wparams.translate        = params.translate;
+            wparams.no_context       = true;
             wparams.single_segment   = !use_vad;
             wparams.max_tokens       = params.max_tokens;
             wparams.language         = params.language.c_str();
@@ -300,8 +298,7 @@ int main(int argc, char ** argv) {
             wparams.speed_up         = params.speed_up;
 
             // disable temperature fallback
-            //wparams.temperature_inc  = -1.0f;
+            wparams.temperature_inc  = -1.0f;
-            wparams.temperature_inc  = params.no_fallback ? 0.0f : wparams.temperature_inc;
 
             wparams.prompt_tokens    = params.no_context ? nullptr : prompt_tokens.data();
             wparams.prompt_n_tokens  = params.no_context ? 0       : prompt_tokens.size();

examples/talk-llama/.gitignore

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

examples/talk-llama/CMakeLists.txt

@@ -1,16 +0,0 @@
-if (WHISPER_SUPPORT_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.h

@@ -1,173 +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 p;     // probability of the token
-        float plog;  // log probability of the token
-
-    } llama_token_data;
-
-    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;
-    };
-
-    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
-    LLAMA_API int llama_model_quantize(
-            const char * fname_inp,
-            const char * fname_out,
-                   int   itype);
-
-    // Returns the KV cache that will contain the context for the
-    // ongoing prediction with the model.
-    LLAMA_API const uint8_t * llama_get_kv_cache(struct llama_context * ctx);
-
-    // Returns the size of the KV cache
-    LLAMA_API size_t llama_get_kv_cache_size(struct llama_context * ctx);
-
-    // Returns the number of tokens in the KV cache
-    LLAMA_API int llama_get_kv_cache_token_count(struct llama_context * ctx);
-
-    // Sets the KV cache containing the current context for the model
-    LLAMA_API void llama_set_kv_cache(
-            struct llama_context * ctx,
-                   const uint8_t * kv_cache,
-                          size_t   n_size,
-                             int   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();
-
-    // TODO: improve the last_n_tokens interface ?
-    LLAMA_API llama_token llama_sample_top_p_top_k(
-       struct llama_context * ctx,
-          const llama_token * last_n_tokens_data,
-                        int   last_n_tokens_size,
-                        int   top_k,
-                      float   top_p,
-                      float   temp,
-                      float   repeat_penalty);
-
-    // 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
-
-#endif // LLAMA_H

examples/talk-llama/llama_internal.h

@@ -1,12 +0,0 @@
-// Internal header to be included by llama.cpp and tests/benchmarks only.
-
-#ifndef LLAMA_INTERNAL_H
-#define LLAMA_INTERNAL_H
-
-#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 // LLAMA_INTERNAL_H

examples/talk-llama/llama_util.h

@@ -1,383 +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
-    #endif
-#endif
-
-#if defined(_WIN32)
-    #define WIN32_LEAN_AND_MEAN
-    #define NOMINMAX
-    #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__
-__attribute__((format(printf, 1, 2)))
-#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) {
-        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);
-        close(fd);
-        if (addr == MAP_FAILED) {
-            throw format("mmap failed: %s", strerror(errno));
-        }
-
-        // 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) {
-        size = file->size;
-
-        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
-
-        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
-        DWORD error = GetLastError();
-        CloseHandle(hFile);
-
-        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());
-        }
-
-        // 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());
-        }
-    }
-
-    ~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 {
-            fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n" MLOCK_SUGGESTION,
-                    size, this->size, std::strerror(errno));
-            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 += size;
-            max_ws_size += size;
-            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;
-    }
-};
-#endif

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

@@ -1,550 +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);
-                            logits[llama_token_eos()] = 0;
-
-                            id = llama_sample_top_p_top_k(ctx_llama,
-                                    embd_inp.data() + std::max(0, n_past - repeat_last_n),
-                                    repeat_last_n, top_k, top_p, temp, repeat_penalty);
-                        }
-
-                        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/gpt-2.cpp

@@ -841,7 +841,6 @@ struct gpt2_context * gpt2_init(const char * path_model) {
 
         if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
             fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
-            delete ctx;
             return nullptr;
         }
 

examples/talk/.gitignore

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

examples/talk/README.md

@@ -31,7 +31,7 @@ To run this, you will need a ggml GPT-2 model: [instructions](https://github.com
 Alternatively, you can simply download the smallest ggml GPT-2 117M model (240 MB) like this:
 
 ```
-wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
+wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/datasets/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
 ```
 
 ## TTS

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/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"
+espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
-
-# Mac OS "say" command
-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/eleven-labs.py 
 #
 #wd=$(dirname $0)
 #script=$wd/eleven-labs.py

examples/whisper.android/README.md

@@ -9,4 +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.
 
-<img width="300" alt="image" src="https://user-images.githubusercontent.com/1670775/221613663-a17bf770-27ef-45ab-9a46-a5f99ba65d2a.jpg">
+<img width="300" alt="image" src="https://user-images.githubusercontent.com/1991296/208154256-82d972dc-221b-48c4-bfcb-36ce68602f93.png">

examples/whisper.android/app/src/main/java/com/whispercppdemo/ui/main/MainScreen.kt

@@ -2,7 +2,6 @@ package com.whispercppdemo.ui.main
 
 import androidx.compose.foundation.layout.*
 import androidx.compose.foundation.rememberScrollState
-import androidx.compose.foundation.text.selection.SelectionContainer
 import androidx.compose.foundation.verticalScroll
 import androidx.compose.material3.*
 import androidx.compose.runtime.Composable
@@ -20,7 +19,6 @@ fun MainScreen(viewModel: MainScreenViewModel) {
         canTranscribe = viewModel.canTranscribe,
         isRecording = viewModel.isRecording,
         messageLog = viewModel.dataLog,
-        onBenchmarkTapped = viewModel::benchmark,
         onTranscribeSampleTapped = viewModel::transcribeSample,
         onRecordTapped = viewModel::toggleRecord
     )
@@ -32,7 +30,6 @@ private fun MainScreen(
     canTranscribe: Boolean,
     isRecording: Boolean,
     messageLog: String,
-    onBenchmarkTapped: () -> Unit,
     onTranscribeSampleTapped: () -> Unit,
     onRecordTapped: () -> Unit
 ) {
@@ -48,11 +45,8 @@ private fun MainScreen(
                 .padding(innerPadding)
                 .padding(16.dp)
         ) {
-            Column(verticalArrangement = Arrangement.SpaceBetween) {
+            Row(horizontalArrangement = Arrangement.SpaceBetween) {
-                Row(horizontalArrangement = Arrangement.SpaceBetween, modifier = Modifier.fillMaxWidth()) {
+                TranscribeSampleButton(enabled = canTranscribe, onClick = onTranscribeSampleTapped)
-                    BenchmarkButton(enabled = canTranscribe, onClick = onBenchmarkTapped)
-                    TranscribeSampleButton(enabled = canTranscribe, onClick = onTranscribeSampleTapped)
-                }
                 RecordButton(
                     enabled = canTranscribe,
                     isRecording = isRecording,
@@ -66,16 +60,7 @@ private fun MainScreen(
 
 @Composable
 private fun MessageLog(log: String) {
-    SelectionContainer() {
+    Text(modifier = Modifier.verticalScroll(rememberScrollState()), text = log)
-        Text(modifier = Modifier.verticalScroll(rememberScrollState()), text = log)
-    }
-}
-
-@Composable
-private fun BenchmarkButton(enabled: Boolean, onClick: () -> Unit) {
-    Button(onClick = onClick, enabled = enabled) {
-        Text("Benchmark")
-    }
 }
 
 @Composable

examples/whisper.android/app/src/main/java/com/whispercppdemo/ui/main/MainScreenViewModel.kt

@@ -41,15 +41,10 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
 
     init {
         viewModelScope.launch {
-            printSystemInfo()
             loadData()
         }
     }
 
-    private suspend fun printSystemInfo() {
-        printMessage(String.format("System Info: %s\n", WhisperContext.getSystemInfo()));
-    }
-
     private suspend fun loadData() {
         printMessage("Loading data...\n")
         try {
@@ -86,29 +81,10 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
         //whisperContext = WhisperContext.createContextFromFile(firstModel.absolutePath)
     }
 
-    fun benchmark() = viewModelScope.launch {
-        runBenchmark(6)
-    }
-
     fun transcribeSample() = viewModelScope.launch {
         transcribeAudio(getFirstSample())
     }
 
-    private suspend fun runBenchmark(nthreads: Int) {
-        if (!canTranscribe) {
-            return
-        }
-
-        canTranscribe = false
-
-        printMessage("Running benchmark. This will take minutes...\n")
-        whisperContext?.benchMemory(nthreads)?.let{ printMessage(it) }
-        printMessage("\n")
-        whisperContext?.benchGgmlMulMat(nthreads)?.let{ printMessage(it) }
-
-        canTranscribe = true
-    }
-
     private suspend fun getFirstSample(): File = withContext(Dispatchers.IO) {
         samplesPath.listFiles()!!.first()
     }
@@ -138,14 +114,11 @@ class MainScreenViewModel(private val application: Application) : ViewModel() {
         canTranscribe = false
 
         try {
-            printMessage("Reading wave samples... ")
+            printMessage("Reading wave samples...\n")
             val data = readAudioSamples(file)
-            printMessage("${data.size / (16000 / 1000)} ms\n")
             printMessage("Transcribing data...\n")
-            val start = System.currentTimeMillis()
             val text = whisperContext?.transcribeData(data)
-            val elapsed = System.currentTimeMillis() - start
+            printMessage("Done: $text\n")
-            printMessage("Done ($elapsed ms): $text\n")
         } catch (e: Exception) {
             Log.w(LOG_TAG, e)
             printMessage("${e.localizedMessage}\n")

examples/whisper.android/app/src/main/java/com/whispercppdemo/whisper/LibWhisper.kt

@@ -27,14 +27,6 @@ class WhisperContext private constructor(private var ptr: Long) {
         }
     }
 
-    suspend fun benchMemory(nthreads: Int): String = withContext(scope.coroutineContext) {
-        return@withContext WhisperLib.benchMemcpy(nthreads)
-    }
-
-    suspend fun benchGgmlMulMat(nthreads: Int): String = withContext(scope.coroutineContext) {
-        return@withContext WhisperLib.benchGgmlMulMat(nthreads)
-    }
-
     suspend fun release() = withContext(scope.coroutineContext) {
         if (ptr != 0L) {
             WhisperLib.freeContext(ptr)
@@ -74,10 +66,6 @@ class WhisperContext private constructor(private var ptr: Long) {
             }
             return WhisperContext(ptr)
         }
-
-        fun getSystemInfo(): String {
-            return WhisperLib.getSystemInfo()
-        }
     }
 }
 
@@ -86,7 +74,6 @@ private class WhisperLib {
         init {
             Log.d(LOG_TAG, "Primary ABI: ${Build.SUPPORTED_ABIS[0]}")
             var loadVfpv4 = false
-            var loadV8fp16 = false
             if (isArmEabiV7a()) {
                 // armeabi-v7a needs runtime detection support
                 val cpuInfo = cpuInfo()
@@ -97,24 +84,11 @@ private class WhisperLib {
                         loadVfpv4 = true
                     }
                 }
-            } else if (isArmEabiV8a()) {
-                // ARMv8.2a needs runtime detection support
-                val cpuInfo = cpuInfo()
-                cpuInfo?.let {
-                    Log.d(LOG_TAG, "CPU info: $cpuInfo")
-                    if (cpuInfo.contains("fphp")) {
-                        Log.d(LOG_TAG, "CPU supports fp16 arithmetic")
-                        loadV8fp16 = true
-                    }
-                }
             }
 
             if (loadVfpv4) {
                 Log.d(LOG_TAG, "Loading libwhisper_vfpv4.so")
                 System.loadLibrary("whisper_vfpv4")
-            } else if (loadV8fp16) {
-                Log.d(LOG_TAG, "Loading libwhisper_v8fp16_va.so")
-                System.loadLibrary("whisper_v8fp16_va")
             } else {
                 Log.d(LOG_TAG, "Loading libwhisper.so")
                 System.loadLibrary("whisper")
@@ -129,9 +103,6 @@ private class WhisperLib {
         external fun fullTranscribe(contextPtr: Long, audioData: FloatArray)
         external fun getTextSegmentCount(contextPtr: Long): Int
         external fun getTextSegment(contextPtr: Long, index: Int): String
-        external fun getSystemInfo(): String
-        external fun benchMemcpy(nthread: Int): String
-        external fun benchGgmlMulMat(nthread: Int): String
     }
 }
 
@@ -139,10 +110,6 @@ private fun isArmEabiV7a(): Boolean {
     return Build.SUPPORTED_ABIS[0].equals("armeabi-v7a")
 }
 
-private fun isArmEabiV8a(): Boolean {
-    return Build.SUPPORTED_ABIS[0].equals("arm64-v8a")
-}
-
 private fun cpuInfo(): String? {
     return try {
         File("/proc/cpuinfo").inputStream().bufferedReader().use {

examples/whisper.android/app/src/main/jni/whisper/Android.mk

@@ -13,14 +13,3 @@ ifeq ($(TARGET_ARCH_ABI),armeabi-v7a)
 	LOCAL_CFLAGS += -mfpu=neon-vfpv4
 	include $(BUILD_SHARED_LIBRARY)
 endif
-
-ifeq ($(TARGET_ARCH_ABI),arm64-v8a)
-	include $(CLEAR_VARS)
-	LOCAL_MODULE    := libwhisper_v8fp16_va
-	include $(LOCAL_PATH)/Whisper.mk
-	# Allow building NEON FMA code.
-	# https://android.googlesource.com/platform/ndk/+/master/sources/android/cpufeatures/cpu-features.h
-	LOCAL_CFLAGS += -march=armv8.2-a+fp16
-	include $(BUILD_SHARED_LIBRARY)
-endif
-

examples/whisper.android/app/src/main/jni/whisper/jni.c

@@ -6,7 +6,6 @@
 #include <sys/sysinfo.h>
 #include <string.h>
 #include "whisper.h"
-#include "ggml.h"
 
 #define UNUSED(x) (void)(x)
 #define TAG "JNI"
@@ -215,29 +214,3 @@ Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_getTextSegment(
     jstring string = (*env)->NewStringUTF(env, text);
     return string;
 }
-
-JNIEXPORT jstring JNICALL
-Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_getSystemInfo(
-        JNIEnv *env, jobject thiz
-) {
-    UNUSED(thiz);
-    const char *sysinfo = whisper_print_system_info();
-    jstring string = (*env)->NewStringUTF(env, sysinfo);
-    return string;
-}
-
-JNIEXPORT jstring JNICALL
-Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_benchMemcpy(JNIEnv *env, jobject thiz,
-                                                                      jint n_threads) {
-    UNUSED(thiz);
-    const char *bench_ggml_memcpy = whisper_bench_memcpy_str(n_threads);
-    jstring string = (*env)->NewStringUTF(env, bench_ggml_memcpy);
-}
-
-JNIEXPORT jstring JNICALL
-Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_benchGgmlMulMat(JNIEnv *env, jobject thiz,
-                                                                          jint n_threads) {
-    UNUSED(thiz);
-    const char *bench_ggml_mul_mat = whisper_bench_ggml_mul_mat_str(n_threads);
-    jstring string = (*env)->NewStringUTF(env, bench_ggml_mul_mat);
-}

examples/whisper.objc/README.md

@@ -24,5 +24,3 @@ Also, don't forget to add the `-DGGML_USE_ACCELERATE` compiler flag in Build Pha
 This can significantly improve the performance of the transcription:
 
 <img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">
-
-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.

examples/whisper.objc/whisper.objc.xcodeproj/project.pbxproj

@@ -296,10 +296,6 @@
 				IPHONEOS_DEPLOYMENT_TARGET = 16.0;
 				MTL_ENABLE_DEBUG_INFO = NO;
 				MTL_FAST_MATH = YES;
-				OTHER_CFLAGS = (
-					"-O3",
-					"-DNDEBUG",
-				);
 				SDKROOT = iphoneos;
 				VALIDATE_PRODUCT = YES;
 			};

examples/whisper.swiftui/README.md

@@ -1,18 +1,14 @@
 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.
+5. Select the "release" 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.swiftui/whisper.swiftui.xcodeproj/project.pbxproj

@@ -430,10 +430,6 @@
 				LLVM_LTO = YES;
 				MACOSX_DEPLOYMENT_TARGET = 13.0;
 				MARKETING_VERSION = 1.0;
-				OTHER_CFLAGS = (
-					"-O3",
-					"-DNDEBUG",
-				);
 				PRODUCT_BUNDLE_IDENTIFIER = com.whispercppdemo.WhisperCppDemo;
 				PRODUCT_NAME = "$(TARGET_NAME)";
 				SDKROOT = auto;

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/bench-wts.sh

@@ -1,70 +0,0 @@
-# Benchmark word-level timestamps for different models
-#
-# This script takes two arguments
-# - an audio file
-# - [optional] path to a font file
-
-# I'm using "/usr/share/fonts/truetype/freefont/FreeMono.ttf" on Ubuntu
-
-if [ -z "$1" ]; then
-    echo "Usage: $0 <audio file> [font file]"
-    exit 1
-fi
-
-#TODO: Make this a command line parameter
-#models="base small large"
-#models="tiny.en tiny base.en base small.en small medium.en medium large-v1 large"
-models="tiny.en base.en small.en medium.en large"
-
-DURATION=$(ffprobe -i $1 -show_entries format=duration -v quiet -of csv="p=0")
-DURATION=$(printf "%.2f" $DURATION)
-echo "Input file duration: ${DURATION}s"
-
-for model in $models; do
-    echo "Running $model"
-    COMMAND="./main -m models/ggml-$model.bin -owts -f $1 -of $1.$model"
-
-    if [ ! -z "$2" ]; then
-        COMMAND="$COMMAND -fp $2"
-    fi
-    #TODO: Surface errors better
-    # TIMEFMT is for zsh, TIMEFORMAT is for bash
-    EXECTIME=$({ TIMEFMT="%E";TIMEFORMAT=%E; time $COMMAND >/dev/null 2>&1; } 2>&1)
-
-    # Slightly different formats between zsh and bash
-    if [ "${EXECTIME: -1}" == "s" ]; then
-        EXECTIME=${EXECTIME::-1}
-    fi
-
-    RATIO=$(echo "$DURATION / $EXECTIME" | bc -l)
-    RATIO=$(printf "%.2f" $RATIO)
-
-    echo "Execution time: ${EXECTIME}s (${RATIO}x realtime)"
-
-    # If the file already exists, delete it
-    if [ -f $1.mp4 ]; then
-        rm $1.mp4
-    fi
-
-    bash $1.$model.wts >/dev/null 2>&1
-    mv $1.mp4 $1.$model.mp4
-
-    ffmpeg -y -f lavfi -i color=c=black:s=1200x50:d=$DURATION -vf "drawtext=fontfile=$2:fontsize=36:x=10:y=(h-text_h)/2:text='ggml-$model - ${EXECTIME}s (${RATIO}x realtime)':fontcolor=lightgrey" $1.$model.info.mp4 >/dev/null 2>&1
-done
-
-COMMAND="ffmpeg -y"
-for model in $models; do
-    COMMAND="$COMMAND -i $1.$model.info.mp4 -i $1.$model.mp4"
-done
-COMMAND="$COMMAND -filter_complex \""
-COUNT=0
-for model in $models; do
-    COMMAND="$COMMAND[${COUNT}:v][$(($COUNT+1)):v]"
-    COUNT=$((COUNT+2))
-done
-COMMAND="$COMMAND vstack=inputs=${COUNT}[v]\" -map \"[v]\" -map 1:a $1.all.mp4 >/dev/null 2>&1"
-
-echo $COMMAND
-
-# Run the command
-eval $COMMAND

ggml.h

@@ -177,12 +177,11 @@ extern "C" {
 #include <stddef.h>
 #include <stdbool.h>
 
-#define GGML_MAX_DIMS          4
+#define GGML_MAX_DIMS     4
-#define GGML_MAX_NODES         4096
+#define GGML_MAX_NODES    4096
-#define GGML_MAX_PARAMS        16
+#define GGML_MAX_PARAMS   16
-#define GGML_MAX_CONTEXTS      64
+#define GGML_MAX_CONTEXTS 64
-#define GGML_MAX_OPT           4
+#define GGML_MAX_OPT      4
-#define GGML_DEFAULT_N_THREADS 4
 
 #ifdef __ARM_NEON
 // we use the built-in 16-bit float type
@@ -199,14 +198,11 @@ struct ggml_object;
 struct ggml_context;
 
 enum ggml_type {
-    // explicitly numbered values are used in llama.cpp files
-    GGML_TYPE_F32  = 0,
-    GGML_TYPE_F16  = 1,
-    GGML_TYPE_Q4_0 = 2,
-    GGML_TYPE_Q4_1 = 3,
     GGML_TYPE_I8,
     GGML_TYPE_I16,
     GGML_TYPE_I32,
+    GGML_TYPE_F16,
+    GGML_TYPE_F32,
     GGML_TYPE_COUNT,
 };
 
@@ -230,15 +226,12 @@ enum ggml_op {
     GGML_OP_STEP,
     GGML_OP_RELU,
     GGML_OP_GELU,
-    GGML_OP_SILU,
     GGML_OP_NORM, // normalize
-    GGML_OP_RMS_NORM,
 
     GGML_OP_MUL_MAT,
 
     GGML_OP_SCALE,
     GGML_OP_CPY,
-    GGML_OP_CONT,
     GGML_OP_RESHAPE,
     GGML_OP_VIEW,
     GGML_OP_PERMUTE,
@@ -253,35 +246,19 @@ enum ggml_op {
     GGML_OP_FLASH_ATTN,
     GGML_OP_FLASH_FF,
 
-    GGML_OP_MAP_UNARY,
-    GGML_OP_MAP_BINARY,
-
     GGML_OP_COUNT,
 };
 
-
-// ggml object
-struct ggml_object {
-    size_t offs;
-    size_t size;
-
-    struct ggml_object * next;
-
-    char padding[8];
-};
-
-static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
-
 // n-dimensional tensor
 struct ggml_tensor {
     enum ggml_type type;
 
     int    n_dims;
-    int64_t ne[GGML_MAX_DIMS]; // number of elements
+    int    ne[GGML_MAX_DIMS]; // number of elements
-    size_t  nb[GGML_MAX_DIMS]; // stride in bytes:
+    size_t nb[GGML_MAX_DIMS]; // stride in bytes:
-                               // nb[0] = sizeof(type)
+                              // nb[0] = sizeof(type)
-                               // nb[1] = nb[0]   * ne[0] + padding
+                              // nb[1] = nb[0]   * ne[0] + padding
-                               // nb[i] = nb[i-1] * ne[i-1]
+                              // nb[i] = nb[i-1] * ne[i-1]
 
     // compute data
     enum ggml_op op;
@@ -335,7 +312,6 @@ struct ggml_init_params {
     // memory pool
     size_t mem_size;   // bytes
     void * mem_buffer; // if NULL, memory will be allocated internally
-    bool   no_alloc;   // don't allocate memory for the tensor data
 };
 
 void    ggml_time_init(void); // call this once at the beginning of the program
@@ -347,13 +323,10 @@ int64_t ggml_cycles_per_ms(void);
 void ggml_print_object (const struct ggml_object * obj);
 void ggml_print_objects(const struct ggml_context * ctx);
 
-int64_t ggml_nelements(const struct ggml_tensor * tensor);
+int    ggml_nelements(const struct ggml_tensor * tensor);
-size_t  ggml_nbytes   (const struct ggml_tensor * tensor);
+size_t ggml_nbytes   (const struct ggml_tensor * tensor);
-
-int    ggml_blck_size (enum ggml_type type);
-size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
-float  ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
 
+size_t ggml_type_size   (enum ggml_type type);
 size_t ggml_element_size(const struct ggml_tensor * tensor);
 
 struct ggml_context * ggml_init(struct ggml_init_params params);
@@ -367,33 +340,33 @@ struct ggml_tensor * ggml_new_tensor(
         struct ggml_context * ctx,
         enum   ggml_type type,
         int    n_dims,
-        const int64_t *ne);
+        const int *ne);
 
 struct ggml_tensor * ggml_new_tensor_1d(
         struct ggml_context * ctx,
         enum   ggml_type type,
-        int64_t ne0);
+        int    ne0);
 
 struct ggml_tensor * ggml_new_tensor_2d(
         struct ggml_context * ctx,
         enum   ggml_type type,
-        int64_t ne0,
+        int    ne0,
-        int64_t ne1);
+        int    ne1);
 
 struct ggml_tensor * ggml_new_tensor_3d(
         struct ggml_context * ctx,
         enum   ggml_type type,
-        int64_t ne0,
+        int    ne0,
-        int64_t ne1,
+        int    ne1,
-        int64_t ne2);
+        int    ne2);
 
 struct ggml_tensor * ggml_new_tensor_4d(
         struct ggml_context * ctx,
         enum   ggml_type type,
-        int64_t ne0,
+        int    ne0,
-        int64_t ne1,
+        int    ne1,
-        int64_t ne2,
+        int    ne2,
-        int64_t ne3);
+        int    ne3);
 
 struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
 struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
@@ -493,20 +466,12 @@ struct ggml_tensor * ggml_gelu(
         struct ggml_context * ctx,
         struct ggml_tensor  * a);
 
-struct ggml_tensor * ggml_silu(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a);
-
 // normalize along rows
 // TODO: eps is hardcoded to 1e-5 for now
 struct ggml_tensor * ggml_norm(
         struct ggml_context * ctx,
         struct ggml_tensor  * a);
 
-struct ggml_tensor * ggml_rms_norm(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a);
-
 // A: m rows, n columns
 // B: p rows, n columns (i.e. we transpose it internally)
 // result is m columns, p rows
@@ -531,11 +496,6 @@ struct ggml_tensor * ggml_cpy(
         struct ggml_tensor  * a,
         struct ggml_tensor  * b);
 
-// make contiguous
-struct ggml_tensor * ggml_cont(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a);
-
 // return view(a), b specifies the new shape
 // TODO: when we start computing gradient, make a copy instead of view
 struct ggml_tensor * ggml_reshape(
@@ -548,43 +508,33 @@ struct ggml_tensor * ggml_reshape(
 struct ggml_tensor * ggml_reshape_2d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        int64_t               ne0,
+        int                   ne0,
-        int64_t               ne1);
+        int                   ne1);
 
 // return view(a)
 // TODO: when we start computing gradient, make a copy instead of view
 struct ggml_tensor * ggml_reshape_3d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        int64_t               ne0,
+        int                   ne0,
-        int64_t               ne1,
+        int                   ne1,
-        int64_t               ne2);
+        int                   ne2);
 
 // offset in bytes
 struct ggml_tensor * ggml_view_1d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        int64_t               ne0,
+        int                   ne0,
         size_t                offset);
 
 struct ggml_tensor * ggml_view_2d(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
-        int64_t               ne0,
+        int                   ne0,
-        int64_t               ne1,
+        int                   ne1,
         size_t                nb1, // row stride in bytes
         size_t                offset);
 
-struct ggml_tensor * ggml_view_3d(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a,
-        int64_t               ne0,
-        int64_t               ne1,
-        int64_t               ne2,
-        size_t                nb1, // row   stride in bytes
-        size_t                nb2, // slice stride in bytes
-        size_t                offset);
-
 struct ggml_tensor * ggml_permute(
         struct ggml_context * ctx,
         struct ggml_tensor  * a,
@@ -655,21 +605,6 @@ struct ggml_tensor * ggml_flash_ff(
         struct ggml_tensor  * c0,
         struct ggml_tensor  * c1);
 
-// Mapping operations
-typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
-typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
-
-struct ggml_tensor * ggml_map_unary_f32(
-        struct ggml_context        * ctx,
-        struct ggml_tensor         * a,
-        const  ggml_unary_op_f32_t fun);
-
-struct ggml_tensor * ggml_map_binary_f32(
-        struct ggml_context         * ctx,
-        struct ggml_tensor          * a,
-        struct ggml_tensor          * b,
-        const  ggml_binary_op_f32_t fun);
-
 //
 // automatic differentiation
 //
@@ -792,11 +727,14 @@ enum ggml_opt_result ggml_opt(
         struct ggml_tensor * f);
 
 //
-// quantization
+// Temp stuff
 //
 
-size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
+void ggml_svd_reduce_dims(
-size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
+        int ne0,
+        int ne1,
+        float * a,
+        int nd);
 
 //
 // system info
@@ -815,30 +753,6 @@ int ggml_cpu_has_blas(void);
 int ggml_cpu_has_sse3(void);
 int ggml_cpu_has_vsx(void);
 
-
-//
-// Internal types and functions exposed for tests and benchmarks
-//
-
-#ifdef  __cplusplus
-// restrict not standard in C++
-#define GGML_RESTRICT
-#else
-#define GGML_RESTRICT restrict
-#endif
-typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-typedef void (*quantize_row_q_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-typedef void (*vec_dot_q_t)(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
-
-typedef struct {
-    dequantize_row_q_t dequantize_row_q;
-    quantize_row_q_t   quantize_row_q;
-    quantize_row_q_t   quantize_row_q_reference;
-    vec_dot_q_t        vec_dot_q;
-} quantize_fns_t;
-
-quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
-
 #ifdef  __cplusplus
 }
 #endif

models/README.md

@@ -6,7 +6,7 @@ using the [convert-pt-to-ggml.py](convert-pt-to-ggml.py) script. You can either
 the `ggml` files yourself using the conversion script, or you can use the [download-ggml-model.sh](download-ggml-model.sh)
 script to download the already converted models. Currently, they are hosted on the following locations:
 
-- https://huggingface.co/ggerganov/whisper.cpp
+- https://huggingface.co/datasets/ggerganov/whisper.cpp
 - https://ggml.ggerganov.com
 
 Sample usage:
@@ -23,7 +23,7 @@ You can now use it like this:
 
 A third option to obtain the model files is to download them from Hugging Face:
 
-https://huggingface.co/ggerganov/whisper.cpp/tree/main
+https://huggingface.co/datasets/ggerganov/whisper.cpp/tree/main
 
 ## Available models
 

models/convert-h5-to-ggml.py

@@ -79,11 +79,11 @@ dir_model   = sys.argv[1]
 dir_whisper = sys.argv[2]
 dir_out     = sys.argv[3]
 
-with open(dir_model + "/vocab.json", "r", encoding="utf8") as f:
+with open(dir_model + "/vocab.json", "r") as f:
     encoder = json.load(f)
-with open(dir_model + "/added_tokens.json", "r", encoding="utf8") as f:
+with open(dir_model + "/added_tokens.json", "r") as f:
     encoder_added = json.load(f)
-with open(dir_model + "/config.json", "r", encoding="utf8") as f:
+with open(dir_model + "/config.json", "r") as f:
     hparams = json.load(f)
 
 model = WhisperForConditionalGeneration.from_pretrained(dir_model)

models/convert-pt-to-ggml.py

@@ -39,7 +39,6 @@ import json
 import code
 import torch
 import numpy as np
-import base64
 
 #from transformers import GPTJForCausalLM
 #from transformers import GPT2TokenizerFast
@@ -225,14 +224,18 @@ with np.load(os.path.join(dir_whisper, "whisper/assets", "mel_filters.npz")) as
 #code.interact(local=locals())
 
 multilingual = hparams["n_vocab"] == 51865
-tokenizer = os.path.join(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"
 
-with open(tokenizer, "rb") as f:
+with open(dir_tokenizer + "/vocab.json", "r", encoding="utf8") as f:
-    contents = f.read()
+    tokens = json.load(f)
-    tokens = {base64.b64decode(token): int(rank) for token, rank in (line.split() for line in contents.splitlines() if line)}
 
 # use 16-bit or 32-bit floats
 use_f16 = True
@@ -268,8 +271,9 @@ 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)))
+    text = bytearray([byte_decoder[c] for c in key])
-    fout.write(key)
+    fout.write(struct.pack("i", len(text)))
+    fout.write(text)
 
 for name in list_vars.keys():
     data = list_vars[name].squeeze().numpy()

models/convert-whisper-to-coreml.py

@@ -1,334 +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)
-
-        setattr(self, 'query', nn.Conv2d(n_state, n_state, kernel_size=1))
-        setattr(self, 'key', nn.Conv2d(n_state, n_state, kernel_size=1, bias=False))
-        setattr(self, 'value', nn.Conv2d(n_state, n_state, kernel_size=1))
-        setattr(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)
-
-        setattr(self, 'attn', MultiHeadAttentionANE(n_state, n_head))
-        setattr(self, 'attn_ln', LayerNormANE(n_state))
-
-        setattr(self, 'cross_attn', MultiHeadAttentionANE(n_state, n_head) if cross_attention else None)
-        setattr(self, 'cross_attn_ln', LayerNormANE(n_state) if cross_attention else None)
-
-        n_mlp = n_state * 4
-        setattr(self, 'mlp', nn.Sequential(
-            nn.Conv2d(n_state, n_mlp, kernel_size=1),
-            nn.GELU(),
-            nn.Conv2d(n_mlp, n_state, kernel_size=1)
-        ))
-        setattr(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)
-
-        setattr(self, 'blocks', nn.ModuleList(
-            [ResidualAttentionBlockANE(n_state, n_head) for _ in range(n_layer)]
-        ))
-        setattr(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)
-
-        setattr(self, 'blocks', nn.ModuleList(
-            [ResidualAttentionBlockANE(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
-        ))
-        setattr(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)
-
-        setattr(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,
-        ))
-        setattr(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-coreml-model.sh

@@ -1,82 +0,0 @@
-#!/bin/bash
-
-# This script downloads Whisper model files that have already been converted to Core ML format.
-# This way you don't have to convert them yourself.
-
-src="https://huggingface.co/datasets/ggerganov/whisper.cpp-coreml"
-pfx="resolve/main/ggml"
-
-# get the path of this script
-function get_script_path() {
-    if [ -x "$(command -v realpath)" ]; then
-        echo "$(dirname $(realpath $0))"
-    else
-        local ret="$(cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P)"
-        echo "$ret"
-    fi
-}
-
-models_path="$(get_script_path)"
-
-# Whisper models
-models=( "tiny.en" "tiny" "base.en" "base" "small.en" "small" "medium.en" "medium" "large-v1" "large" )
-
-# list available models
-function list_models {
-    printf "\n"
-    printf "  Available models:"
-    for model in "${models[@]}"; do
-        printf " $model"
-    done
-    printf "\n\n"
-}
-
-if [ "$#" -ne 1 ]; then
-    printf "Usage: $0 <model>\n"
-    list_models
-
-    exit 1
-fi
-
-model=$1
-
-if [[ ! " ${models[@]} " =~ " ${model} " ]]; then
-    printf "Invalid model: $model\n"
-    list_models
-
-    exit 1
-fi
-
-# download Core ML model
-
-printf "Downloading Core ML model $model from '$src' ...\n"
-
-cd $models_path
-
-if [ -f "ggml-$model.mlmodel" ]; then
-    printf "Model $model already exists. Skipping download.\n"
-    exit 0
-fi
-
-if [ -x "$(command -v wget)" ]; then
-    wget --quiet --show-progress -O ggml-$model.mlmodel $src/$pfx-$model.mlmodel
-elif [ -x "$(command -v curl)" ]; then
-    curl -L --output ggml-$model.mlmodel $src/$pfx-$model.mlmodel
-else
-    printf "Either wget or curl is required to download models.\n"
-    exit 1
-fi
-
-
-if [ $? -ne 0 ]; then
-    printf "Failed to download Core ML model $model \n"
-    printf "Please try again later or download the original Whisper model files and convert them yourself.\n"
-    exit 1
-fi
-
-printf "Done! Model '$model' saved in 'models/ggml-$model.mlmodel'\n"
-printf "Run the following command to compile it:\n\n"
-printf "  $ xcrun coremlc compile ./models/ggml-$model.mlmodel ./models\n\n"
-printf "You can now use it like this:\n\n"
-printf "  $ ./main -m models/ggml-$model.bin -f samples/jfk.wav\n"
-printf "\n"

models/download-ggml-model.cmd

@@ -40,7 +40,7 @@ if exist "ggml-%model%.bin" (
   goto :eof
 )
 
-PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-%model%.bin -OutFile ggml-%model%.bin"
+PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://huggingface.co/datasets/ggerganov/whisper.cpp/resolve/main/ggml-%model%.bin -OutFile ggml-%model%.bin"
 
 if %ERRORLEVEL% neq 0 (
   echo Failed to download ggml model %model%

models/download-ggml-model.sh

@@ -6,13 +6,13 @@
 #src="https://ggml.ggerganov.com"
 #pfx="ggml-model-whisper"
 
-src="https://huggingface.co/ggerganov/whisper.cpp"
+src="https://huggingface.co/datasets/ggerganov/whisper.cpp"
 pfx="resolve/main/ggml"
 
 # get the path of this script
 function get_script_path() {
     if [ -x "$(command -v realpath)" ]; then
-        echo "$(dirname "$(realpath "$0")")"
+        echo "$(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

whisper.h

@@ -66,7 +66,6 @@ extern "C" {
     //
 
     struct whisper_context;
-    struct whisper_state;
 
     typedef int whisper_token;
 
@@ -102,20 +101,11 @@ extern "C" {
     WHISPER_API struct whisper_context * whisper_init_from_buffer(void * buffer, size_t buffer_size);
     WHISPER_API struct whisper_context * whisper_init(struct whisper_model_loader * loader);
 
-    // These are the same as the above, but the internal state of the context is not allocated automatically
+    // Frees all memory allocated by the model.
-    // It is the responsibility of the caller to allocate the state using whisper_init_state() (#523)
+    WHISPER_API void whisper_free(struct whisper_context * ctx);
-    WHISPER_API struct whisper_context * whisper_init_from_file_no_state(const char * path_model);
-    WHISPER_API struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size);
-    WHISPER_API struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loader);
-
-    WHISPER_API struct whisper_state * whisper_init_state(struct whisper_context * ctx);
-
-    // Frees all allocated memory
-    WHISPER_API void whisper_free      (struct whisper_context * ctx);
-    WHISPER_API void whisper_free_state(struct whisper_state * state);
 
     // Convert RAW PCM audio to log mel spectrogram.
-    // The resulting spectrogram is stored inside the default state of the provided whisper context.
+    // The resulting spectrogram is stored inside the provided whisper context.
     // Returns 0 on success
     WHISPER_API int whisper_pcm_to_mel(
             struct whisper_context * ctx,
@@ -123,30 +113,17 @@ extern "C" {
                                int   n_samples,
                                int   n_threads);
 
-    WHISPER_API int whisper_pcm_to_mel_with_state(
-            struct whisper_context * ctx,
-              struct whisper_state * state,
-                       const float * samples,
-                               int   n_samples,
-                               int   n_threads);
-
     // Convert RAW PCM audio to log mel spectrogram but applies a Phase Vocoder to speed up the audio x2. 
-    // The resulting spectrogram is stored inside the default state of the provided whisper context.
+    // The resulting spectrogram is stored inside the provided whisper context.
     // Returns 0 on success
     WHISPER_API int whisper_pcm_to_mel_phase_vocoder(
-        struct whisper_context * ctx,
+        struct whisper_context* ctx,
-                   const float * samples,
+        const float* samples,
-                           int   n_samples,
+        int   n_samples,
-                           int   n_threads);
+        int   n_threads);
 
-    WHISPER_API int whisper_pcm_to_mel_phase_vocoder_with_state(
-        struct whisper_context * ctx,
-          struct whisper_state * state,
-                   const float * samples,
-                           int   n_samples,
-                           int   n_threads);
 
-    // This can be used to set a custom log mel spectrogram inside the default state of the provided whisper context.
+    // This can be used to set a custom log mel spectrogram inside the provided whisper context.
     // Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
     // n_mel must be 80
     // Returns 0 on success
@@ -156,14 +133,7 @@ extern "C" {
                                int   n_len,
                                int   n_mel);
 
-    WHISPER_API int whisper_set_mel_with_state(
+    // Run the Whisper encoder on the log mel spectrogram stored inside the provided whisper context.
-            struct whisper_context * ctx,
-              struct whisper_state * state,
-                       const float * data,
-                               int   n_len,
-                               int   n_mel);
-
-    // Run the Whisper encoder on the log mel spectrogram stored inside the default state in the provided whisper context.
     // Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
     // offset can be used to specify the offset of the first frame in the spectrogram.
     // Returns 0 on success
@@ -172,12 +142,6 @@ extern "C" {
                                int   offset,
                                int   n_threads);
 
-    WHISPER_API int whisper_encode_with_state(
-            struct whisper_context * ctx,
-              struct whisper_state * state,
-                               int   offset,
-                               int   n_threads);
-
     // Run the Whisper decoder to obtain the logits and probabilities for the next token.
     // Make sure to call whisper_encode() first.
     // tokens + n_tokens is the provided context for the decoder.
@@ -191,14 +155,6 @@ extern "C" {
                                int   n_past,
                                int   n_threads);
 
-    WHISPER_API int whisper_decode_with_state(
-            struct whisper_context * ctx,
-              struct whisper_state * state,
-               const whisper_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
@@ -226,7 +182,7 @@ 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,
@@ -234,44 +190,20 @@ extern "C" {
                                int   n_threads,
                              float * lang_probs);
 
-    WHISPER_API int whisper_lang_auto_detect_with_state(
+    WHISPER_API int whisper_n_len          (struct whisper_context * ctx); // mel length
-            struct whisper_context * ctx,
+    WHISPER_API int whisper_n_vocab        (struct whisper_context * ctx);
-              struct whisper_state * state,
+    WHISPER_API int whisper_n_text_ctx     (struct whisper_context * ctx);
-                               int   offset_ms,
+    WHISPER_API int whisper_n_audio_ctx    (struct whisper_context * ctx);
-                               int   n_threads,
+    WHISPER_API int whisper_is_multilingual(struct whisper_context * ctx);
-                             float * lang_probs);
-
-    WHISPER_API int whisper_n_len           (struct whisper_context * ctx); // mel length
-    WHISPER_API int whisper_n_len_from_state(struct whisper_state * state); // mel length
-    WHISPER_API int whisper_n_vocab         (struct whisper_context * ctx);
-    WHISPER_API int whisper_n_text_ctx      (struct whisper_context * ctx);
-    WHISPER_API int whisper_n_audio_ctx     (struct whisper_context * ctx);
-    WHISPER_API int whisper_is_multilingual (struct whisper_context * ctx);
-
-    WHISPER_API int whisper_model_n_vocab      (struct whisper_context * ctx);
-    WHISPER_API int whisper_model_n_audio_ctx  (struct whisper_context * ctx);
-    WHISPER_API int whisper_model_n_audio_state(struct whisper_context * ctx);
-    WHISPER_API int whisper_model_n_audio_head (struct whisper_context * ctx);
-    WHISPER_API int whisper_model_n_audio_layer(struct whisper_context * ctx);
-    WHISPER_API int whisper_model_n_text_ctx   (struct whisper_context * ctx);
-    WHISPER_API int whisper_model_n_text_state (struct whisper_context * ctx);
-    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_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()
     // The logits for the last token are stored in the last row
     // Rows: n_tokens
     // Cols: n_vocab
-    WHISPER_API float * whisper_get_logits           (struct whisper_context * ctx);
+    WHISPER_API float * whisper_get_logits(struct whisper_context * ctx);
-    WHISPER_API float * whisper_get_logits_from_state(struct whisper_state * state);
 
     // Token Id -> String. Uses the vocabulary in the provided context
     WHISPER_API const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token);
-    WHISPER_API const char * whisper_model_type_readable(struct whisper_context * ctx);
-
 
     // Special tokens
     WHISPER_API whisper_token whisper_token_eot (struct whisper_context * ctx);
@@ -286,7 +218,7 @@ extern "C" {
     WHISPER_API whisper_token whisper_token_translate (void);
     WHISPER_API whisper_token whisper_token_transcribe(void);
 
-    // Performance information from the default state.
+    // Performance information
     WHISPER_API void whisper_print_timings(struct whisper_context * ctx);
     WHISPER_API void whisper_reset_timings(struct whisper_context * ctx);
 
@@ -297,33 +229,19 @@ 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
     };
 
     // Text segment callback
     // Called on every newly generated text segment
     // Use the whisper_full_...() functions to obtain the text segments
-    typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data);
+    typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, 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
-    typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, struct whisper_state * state, void * user_data);
+    typedef bool (*whisper_encoder_begin_callback)(struct whisper_context * ctx, void * user_data);
-
-    // Logits filter callback
-    // Can be used to modify the logits before sampling
-    // If not NULL, called after applying temperature to logits
-    typedef void (*whisper_logits_filter_callback)(
-            struct whisper_context * ctx,
-              struct whisper_state * state,
-          const whisper_token_data * tokens,
-                               int   n_tokens,
-                             float * logits,
-                              void * user_data);
 
     // Parameters for the whisper_full() function
     // If you chnage the order or add new parameters, make sure to update the default values in whisper.cpp:
@@ -359,7 +277,6 @@ 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;
 
@@ -395,23 +312,14 @@ 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;
-
-        // called by each decoder to filter obtained logits
-        whisper_logits_filter_callback logits_filter_callback;
-        void * logits_filter_callback_user_data;
     };
 
     WHISPER_API struct whisper_full_params whisper_full_default_params(enum whisper_sampling_strategy strategy);
 
     // Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text
-    // Not thread safe for same context
     // Uses the specified decoding strategy to obtain the text.
     WHISPER_API int whisper_full(
                 struct whisper_context * ctx,
@@ -419,16 +327,7 @@ extern "C" {
                            const float * samples,
                                    int   n_samples);
 
-    WHISPER_API int whisper_full_with_state(
+    // Split the input audio in chunks and process each chunk separately using whisper_full()
-                struct whisper_context * ctx,
-                  struct whisper_state * state,
-            struct whisper_full_params   params,
-                           const float * samples,
-                                   int   n_samples);
-
-    // Split the input audio in chunks and process each chunk separately using whisper_full_with_state()
-    // Result is stored in the default state of the context
-    // Not thread safe if executed in parallel on the same context.
     // It seems this approach can offer some speedup in some cases.
     // However, the transcription accuracy can be worse at the beginning and end of each chunk.
     WHISPER_API int whisper_full_parallel(
@@ -438,56 +337,44 @@ extern "C" {
                                    int   n_samples,
                                    int   n_processors);
 
-    // Number of generated text segments
+    // Number of generated text segments.
     // A segment can be a few words, a sentence, or even a paragraph.
-    WHISPER_API int whisper_full_n_segments           (struct whisper_context * ctx);
+    WHISPER_API int whisper_full_n_segments(struct whisper_context * ctx);
-    WHISPER_API int whisper_full_n_segments_from_state(struct whisper_state * state);
 
-    // Language id associated with the context's default state
+    // Language id associated with the current context
     WHISPER_API int whisper_full_lang_id(struct whisper_context * ctx);
 
-    // Language id associated with the provided state
+    // Get the start and end time of the specified segment.
-    WHISPER_API int whisper_full_lang_id_from_state(struct whisper_state * state);
+    WHISPER_API int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment);
+    WHISPER_API int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment);
 
-    // Get the start and end time of the specified segment
+    // Get the text of the specified segment.
-    WHISPER_API int64_t whisper_full_get_segment_t0           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment);
-    WHISPER_API int64_t whisper_full_get_segment_t0_from_state(struct whisper_state * state, int i_segment);
 
-    WHISPER_API int64_t whisper_full_get_segment_t1           (struct whisper_context * ctx, int i_segment);
+    // Get number of tokens in the specified segment.
-    WHISPER_API int64_t whisper_full_get_segment_t1_from_state(struct whisper_state * state, int i_segment);
+    WHISPER_API int whisper_full_n_tokens(struct whisper_context * ctx, int i_segment);
 
-    // Get the text of the specified segment
+    // Get the token text of the specified token in the specified segment.
-    WHISPER_API const char * whisper_full_get_segment_text           (struct whisper_context * ctx, int i_segment);
+    WHISPER_API const char * whisper_full_get_token_text(struct whisper_context * ctx, int i_segment, int i_token);
-    WHISPER_API const char * whisper_full_get_segment_text_from_state(struct whisper_state * state, int i_segment);
+    WHISPER_API whisper_token whisper_full_get_token_id (struct whisper_context * ctx, int i_segment, int i_token);
 
-    // Get number of tokens in the specified segment
+    // Get token data for the specified token in the specified segment.
-    WHISPER_API int whisper_full_n_tokens           (struct whisper_context * ctx, int i_segment);
-    WHISPER_API int whisper_full_n_tokens_from_state(struct whisper_state * state, int i_segment);
-
-    // Get the token text of the specified token in the specified segment
-    WHISPER_API const char * whisper_full_get_token_text           (struct whisper_context * ctx, int i_segment, int i_token);
-    WHISPER_API const char * whisper_full_get_token_text_from_state(struct whisper_context * ctx, struct whisper_state * state, int i_segment, int i_token);
-
-    WHISPER_API whisper_token whisper_full_get_token_id           (struct whisper_context * ctx, int i_segment, int i_token);
-    WHISPER_API whisper_token whisper_full_get_token_id_from_state(struct whisper_state * state, int i_segment, int i_token);
-
-    // Get token data for the specified token in the specified segment
     // This contains probabilities, timestamps, etc.
-    WHISPER_API whisper_token_data whisper_full_get_token_data           (struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token);
-    WHISPER_API whisper_token_data whisper_full_get_token_data_from_state(struct whisper_state * state, int i_segment, int i_token);
 
-    // Get the probability of the specified token in the specified segment
+    // Get the probability of the specified token in the specified segment.
-    WHISPER_API float whisper_full_get_token_p           (struct whisper_context * ctx, int i_segment, int i_token);
+    WHISPER_API float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token);
-    WHISPER_API float whisper_full_get_token_p_from_state(struct whisper_state * state, int i_segment, int i_token);
 
     ////////////////////////////////////////////////////////////////////////////
 
     // Temporary helpers needed for exposing ggml interface
 
     WHISPER_API int whisper_bench_memcpy(int n_threads);
-    WHISPER_API const char * whisper_bench_memcpy_str(int n_threads);
     WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads);
-    WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads);
+
+    // Temporary experimental API
+
+    WHISPER_API void whisper_full_cluster_segments(struct whisper_context * ctx);
 
 #ifdef __cplusplus
 }