diarization : try conv and self-attention embeddings

.gitignore

@@ -1,7 +1,5 @@
 *.o
 *.a
-*.mlmodel
-*.mlmodelc
 .cache/
 .vs/
 .vscode/
@@ -12,7 +10,6 @@ build-em/
 build-debug/
 build-release/
 build-static/
-build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
 

CMakeLists.txt

@@ -1,6 +1,6 @@
 cmake_minimum_required (VERSION 3.0)
 
-project(whisper.cpp VERSION 1.2.1)
+project(whisper.cpp VERSION 1.2.0)
 
 # Add path to modules
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
@@ -54,8 +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()
@@ -88,33 +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()
 
@@ -191,9 +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()
@@ -202,33 +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
 #
@@ -248,10 +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})
 

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)
@@ -138,10 +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
@@ -151,8 +141,6 @@ ifdef WHISPER_GPROF
 	CXXFLAGS += -pg
 endif
 ifneq ($(filter aarch64%,$(UNAME_M)),)
-	CFLAGS += -mcpu=native
-	CXXFLAGS += -mcpu=native
 endif
 ifneq ($(filter armv6%,$(UNAME_M)),)
 	# Raspberry Pi 1, 2, 3
@@ -194,23 +182,11 @@ ggml.o: ggml.c 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 bench libwhisper.a libwhisper.so
@@ -224,21 +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
 
-stream: examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
+stream: examples/stream/stream.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/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o stream $(CC_SDL) $(LDFLAGS)
 
-command: examples/command/command.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/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o command $(CC_SDL) $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o command $(CC_SDL) $(LDFLAGS)
 
-talk: examples/talk/talk.cpp examples/talk/gpt-2.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/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/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o talk $(CC_SDL) $(LDFLAGS)
 
-bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
+bench: examples/bench/bench.cpp ggml.o whisper.o
-	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o whisper.o -o bench $(LDFLAGS)
 
 #
 # Audio samples

README.md

@@ -4,7 +4,7 @@
 [![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/)
 
-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:
 
@@ -433,19 +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,
@@ -466,7 +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
@@ -476,17 +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)
 - [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)
 
 ## Examples
 

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/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);
     }

bindings/javascript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "whisper.cpp",
-  "version": "1.2.1",
+  "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-encoder-impl.h

@@ -1,142 +0,0 @@
-//
-// CoremlEncoder.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(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
-@interface CoremlEncoderInput : NSObject<MLFeatureProvider>
-
-/// melSegment as 1 × 80 × 3000 3-dimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * melSegment;
-- (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithMelSegment:(MLMultiArray *)melSegment NS_DESIGNATED_INITIALIZER;
-
-@end
-
-
-/// Model Prediction Output Type
-API_AVAILABLE(macos(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
-@interface CoremlEncoderOutput : NSObject<MLFeatureProvider>
-
-/// output as multidimensional array of floats
-@property (readwrite, nonatomic, strong) MLMultiArray * output;
-- (instancetype)init NS_UNAVAILABLE;
-- (instancetype)initWithOutput:(MLMultiArray *)output NS_DESIGNATED_INITIALIZER;
-
-@end
-
-
-/// Class for model loading and prediction
-API_AVAILABLE(macos(10.15), ios(13.0), watchos(6.0), tvos(13.0)) __attribute__((visibility("hidden")))
-@interface CoremlEncoder : NSObject
-@property (readonly, nonatomic, nullable) MLModel * model;
-
-/**
-    URL of the underlying .mlmodelc directory.
-*/
-+ (nullable NSURL *)URLOfModelInThisBundle;
-
-/**
-    Initialize CoremlEncoder instance from an existing MLModel object.
-
-    Usually the application does not use this initializer unless it makes a subclass of CoremlEncoder.
-    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
-*/
-- (instancetype)initWithMLModel:(MLModel *)model NS_DESIGNATED_INITIALIZER;
-
-/**
-    Initialize CoremlEncoder instance with the model in this bundle.
-*/
-- (nullable instancetype)init;
-
-/**
-    Initialize CoremlEncoder instance with the model in this bundle.
-
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Initialize CoremlEncoder instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Initialize CoremlEncoder instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Construct CoremlEncoder instance asynchronously with configuration.
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
-*/
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) __attribute__((visibility("hidden")));
-
-/**
-    Construct CoremlEncoder instance asynchronously with URL of .mlmodelc directory and optional configuration.
-
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param modelURL The model URL.
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
-*/
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) __attribute__((visibility("hidden")));
-
-/**
-    Make a prediction using the standard interface
-    @param input an instance of CoremlEncoderInput to predict from
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as CoremlEncoderOutput
-*/
-- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Make a prediction using the standard interface
-    @param input an instance of CoremlEncoderInput to predict from
-    @param options prediction options
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as CoremlEncoderOutput
-*/
-- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Make a prediction using the convenience interface
-    @param melSegment as 1 × 80 × 3000 3-dimensional array of floats:
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the prediction as CoremlEncoderOutput
-*/
-- (nullable CoremlEncoderOutput *)predictionFromMelSegment:(MLMultiArray *)melSegment error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-
-/**
-    Batch prediction
-    @param inputArray array of CoremlEncoderInput instances to obtain predictions from
-    @param options prediction options
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-    @return the predictions as NSArray<CoremlEncoderOutput *>
-*/
-- (nullable NSArray<CoremlEncoderOutput *> *)predictionsFromInputs:(NSArray<CoremlEncoderInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error;
-@end
-
-NS_ASSUME_NONNULL_END

coreml/whisper-encoder-impl.m

@@ -1,197 +0,0 @@
-//
-// CoremlEncoder.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 CoremlEncoderInput
-
-- (instancetype)initWithMelSegment:(MLMultiArray *)melSegment {
-    self = [super init];
-    if (self) {
-        _melSegment = melSegment;
-    }
-    return self;
-}
-
-- (NSSet<NSString *> *)featureNames {
-    return [NSSet setWithArray:@[@"melSegment"]];
-}
-
-- (nullable MLFeatureValue *)featureValueForName:(NSString *)featureName {
-    if ([featureName isEqualToString:@"melSegment"]) {
-        return [MLFeatureValue featureValueWithMultiArray:self.melSegment];
-    }
-    return nil;
-}
-
-@end
-
-@implementation CoremlEncoderOutput
-
-- (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 CoremlEncoder
-
-
-/**
-    URL of the underlying .mlmodelc directory.
-*/
-+ (nullable NSURL *)URLOfModelInThisBundle {
-    NSString *assetPath = [[NSBundle bundleForClass:[self class]] pathForResource:@"CoremlEncoder" ofType:@"mlmodelc"];
-    if (nil == assetPath) { os_log_error(OS_LOG_DEFAULT, "Could not load CoremlEncoder.mlmodelc in the bundle resource"); return nil; }
-    return [NSURL fileURLWithPath:assetPath];
-}
-
-
-/**
-    Initialize CoremlEncoder instance from an existing MLModel object.
-
-    Usually the application does not use this initializer unless it makes a subclass of CoremlEncoder.
-    Such application may want to use `-[MLModel initWithContentsOfURL:configuration:error:]` and `+URLOfModelInThisBundle` to create a MLModel object to pass-in.
-*/
-- (instancetype)initWithMLModel:(MLModel *)model {
-    self = [super init];
-    if (!self) { return nil; }
-    _model = model;
-    if (_model == nil) { return nil; }
-    return self;
-}
-
-
-/**
-    Initialize CoremlEncoder instance with the model in this bundle.
-*/
-- (nullable instancetype)init {
-    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle error:nil];
-}
-
-
-/**
-    Initialize CoremlEncoder instance with the model in this bundle.
-
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithConfiguration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    return [self initWithContentsOfURL:(NSURL * _Nonnull)self.class.URLOfModelInThisBundle configuration:configuration error:error];
-}
-
-
-/**
-    Initialize CoremlEncoder instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    MLModel *model = [MLModel modelWithContentsOfURL:modelURL error:error];
-    if (model == nil) { return nil; }
-    return [self initWithMLModel:model];
-}
-
-
-/**
-    Initialize CoremlEncoder instance from the model URL.
-
-    @param modelURL URL to the .mlmodelc directory for CoremlEncoder.
-    @param configuration The model configuration object
-    @param error If an error occurs, upon return contains an NSError object that describes the problem. If you are not interested in possible errors, pass in NULL.
-*/
-- (nullable instancetype)initWithContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    MLModel *model = [MLModel modelWithContentsOfURL:modelURL configuration:configuration error:error];
-    if (model == nil) { return nil; }
-    return [self initWithMLModel:model];
-}
-
-
-/**
-    Construct CoremlEncoder instance asynchronously with configuration.
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
-*/
-+ (void)loadWithConfiguration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler {
-    [self loadContentsOfURL:(NSURL * _Nonnull)[self URLOfModelInThisBundle]
-              configuration:configuration
-          completionHandler:handler];
-}
-
-
-/**
-    Construct CoremlEncoder instance asynchronously with URL of .mlmodelc directory and optional configuration.
-
-    Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
-
-    @param modelURL The model URL.
-    @param configuration The model configuration
-    @param handler When the model load completes successfully or unsuccessfully, the completion handler is invoked with a valid CoremlEncoder instance or NSError object.
-*/
-+ (void)loadContentsOfURL:(NSURL *)modelURL configuration:(MLModelConfiguration *)configuration completionHandler:(void (^)(CoremlEncoder * _Nullable model, NSError * _Nullable error))handler {
-    [MLModel loadContentsOfURL:modelURL
-                 configuration:configuration
-             completionHandler:^(MLModel *model, NSError *error) {
-        if (model != nil) {
-            CoremlEncoder *typedModel = [[CoremlEncoder alloc] initWithMLModel:model];
-            handler(typedModel, nil);
-        } else {
-            handler(nil, error);
-        }
-    }];
-}
-
-- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    return [self predictionFromFeatures:input options:[[MLPredictionOptions alloc] init] error:error];
-}
-
-- (nullable CoremlEncoderOutput *)predictionFromFeatures:(CoremlEncoderInput *)input options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    id<MLFeatureProvider> outFeatures = [self.model predictionFromFeatures:input options:options error:error];
-    if (!outFeatures) { return nil; }
-    return [[CoremlEncoderOutput alloc] initWithOutput:(MLMultiArray *)[outFeatures featureValueForName:@"output"].multiArrayValue];
-}
-
-- (nullable CoremlEncoderOutput *)predictionFromMelSegment:(MLMultiArray *)melSegment error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    CoremlEncoderInput *input_ = [[CoremlEncoderInput alloc] initWithMelSegment:melSegment];
-    return [self predictionFromFeatures:input_ error:error];
-}
-
-- (nullable NSArray<CoremlEncoderOutput *> *)predictionsFromInputs:(NSArray<CoremlEncoderInput*> *)inputArray options:(MLPredictionOptions *)options error:(NSError * _Nullable __autoreleasing * _Nullable)error {
-    id<MLBatchProvider> inBatch = [[MLArrayBatchProvider alloc] initWithFeatureProviderArray:inputArray];
-    id<MLBatchProvider> outBatch = [self.model predictionsFromBatch:inBatch options:options error:error];
-    if (!outBatch) { return nil; }
-    NSMutableArray<CoremlEncoderOutput*> *results = [NSMutableArray arrayWithCapacity:(NSUInteger)outBatch.count];
-    for (NSInteger i = 0; i < outBatch.count; i++) {
-        id<MLFeatureProvider> resultProvider = [outBatch featuresAtIndex:i];
-        CoremlEncoderOutput * result = [[CoremlEncoderOutput 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

@@ -1,61 +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([[CoremlEncoder 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
-    ];
-
-    CoremlEncoderOutput * outCoreML = [(__bridge id) ctx->data predictionFromMelSegment:inMultiArray error:nil];
-
-    MLMultiArray * outMA = outCoreML.output;
-
-    memcpy(out, outMA.dataPointer, outMA.count * sizeof(float));
-}
-
-#if __cplusplus
-}
-#endif

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;
 
@@ -260,7 +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;
                 };
@@ -292,64 +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

@@ -73,7 +73,6 @@ struct whisper_params {
     bool output_srt     = false;
     bool output_wts     = false;
     bool output_csv     = false;
-    bool output_jsn     = false;
     bool print_special  = false;
     bool print_colors   = false;
     bool print_progress = false;
@@ -81,7 +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 = {};
@@ -129,9 +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 == "-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; }
@@ -178,9 +174,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -ovtt,     --output-vtt        [%-7s] output result in a vtt file\n",                    params.output_vtt ? "true" : "false");
     fprintf(stderr, "  -osrt,     --output-srt        [%-7s] output result in a srt file\n",                    params.output_srt ? "true" : "false");
     fprintf(stderr, "  -owts,     --output-words      [%-7s] output script for generating karaoke video\n",     params.output_wts ? "true" : "false");
-    fprintf(stderr, "  -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");
@@ -199,7 +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;
 
@@ -358,157 +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;
 }
 
-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);
-        fout << "\"" << val << (end ? "\"\n" : "\",\n");
-    };
-
-    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("leyer", 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("timestanps");
-                        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";
@@ -742,7 +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;
                 };
@@ -753,6 +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
@@ -788,12 +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);
-            }
         }
     }
 

examples/stream/stream.cpp

@@ -288,6 +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();

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/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

@@ -12,15 +12,4 @@ ifeq ($(TARGET_ARCH_ABI),armeabi-v7a)
 	# https://android.googlesource.com/platform/ndk/+/master/sources/android/cpufeatures/cpu-features.h
 	LOCAL_CFLAGS += -mfpu=neon-vfpv4
 	include $(BUILD_SHARED_LIBRARY)
-endif
+endif
-
-ifeq ($(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"
@@ -214,30 +213,4 @@ Java_com_whispercppdemo_whisper_WhisperLib_00024Companion_getTextSegment(
     const char *text = whisper_full_get_segment_text(context, index);
     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

@@ -7,9 +7,8 @@ To use:
 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 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.
 
 [^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-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.c

@@ -8517,6 +8517,195 @@ enum ggml_opt_result ggml_opt(
 
 ////////////////////////////////////////////////////////////////////////////////
 
+void ggml_svd_reduce_dims(
+        int ne0,
+        int ne1,
+        float * a,
+        int nd) {
+    int n = ne1;
+    int m = ne0;
+
+    float * A = a;
+    float * A0 = (float *) malloc(n * m * sizeof(float));
+
+    // average vector
+    //float * M = (float *) malloc(m * sizeof(float));
+
+    //{
+    //    for (int j = 0; j < m; ++j) {
+    //        M[j] = 0.0f;
+    //    }
+    //    for (int i = 0; i < n; ++i) {
+    //        for (int j = 0; j < m; ++j) {
+    //            M[j] += A[i * m + j];
+    //        }
+    //    }
+    //    for (int j = 0; j < m; ++j) {
+    //        M[j] /= (float) n;
+    //    }
+    //}
+
+    //// subtract average vector
+    //for (int i = 0; i < n; ++i) {
+    //    for (int j = 0; j < m; ++j) {
+    //        A[i * m + j] -= M[j];
+    //    }
+    //}
+
+    //free(M);
+
+    memcpy(A0, A, n * m * sizeof(float));
+
+    // print A
+    //printf("A:\n");
+    //for (int i = 0; i < n; ++i) {
+    //    printf("col %d : ", i);
+    //    for (int j = 0; j < m; ++j) {
+    //        printf("%9.5f ", A[i * m + j]);
+    //    }
+    //    printf("\n");
+    //}
+    //printf("\n");
+
+    // SVD
+    // A = U * S * V^T
+
+    float * U = (float *) malloc(n * m * sizeof(float));
+    float * S = (float *) malloc(n * sizeof(float));
+    float * V = (float *) malloc(n * n * sizeof(float));
+
+    int lda = m;
+    int ldu = m;
+    int ldvt = n;
+
+    float work_size;
+    int lwork = -1;
+    int info = 0;
+
+    sgesvd_("S", "S", &m, &n, A, &lda, S, U, &ldu, V, &ldvt, &work_size, &lwork, &info);
+
+    lwork = (int) work_size;
+
+    //printf("work_size = %f, info = %d, lwork = %d\n", work_size, info, lwork);
+
+    float * work = (float *) malloc(lwork * sizeof(float));
+
+    sgesvd_("S", "S", &m, &n, A, &lda, S, U, &ldu, V, &ldvt, work, &lwork, &info);
+
+    free(work);
+
+    // print U
+    //printf("U:\n");
+    //for (int i = 0; i < n; ++i) {
+    //    printf("col %d : ", i);
+    //    for (int j = 0; j < m; ++j) {
+    //        printf("%9.5f ", U[i * m + j]);
+    //    }
+    //    printf("\n");
+    //}
+    //printf("\n");
+
+    // normalize S
+    {
+        double sum = 0.0;
+        for (int i = 0; i < n; ++i) {
+            sum += S[i];
+        }
+        sum *= sqrt((double) m);
+        for (int i = 0; i < n; ++i) {
+            S[i] /= sum;
+        }
+    }
+
+    // print S
+    printf("S:\n");
+    for (int i = 0; i < n; ++i) {
+        printf("- %d = %9.5f\n", i, S[i]);
+    }
+    printf("\n");
+
+    // print V
+    //printf("V:\n");
+    //for (int i = 0; i < n; ++i) {
+    //    printf("col %d : ", i);
+    //    for (int j = 0; j < n; ++j) {
+    //        printf("%9.5f ", V[i * n + j]);
+    //    }
+    //    printf("\n");
+    //}
+    //printf("\n");
+
+    // print A
+    //printf("A:\n");
+    //for (int i = 0; i < n; ++i) {
+    //    printf("col %d : ", i);
+    //    for (int j = 0; j < m; ++j) {
+    //        printf("%9.5f ", A[i * m + j]);
+    //    }
+    //    printf("\n");
+    //}
+    //printf("\n");
+
+    // compute singular vectors in U
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < m; ++j) {
+            U[i * m + j] *= S[i];
+        }
+    }
+
+    // normalize U
+    for (int i = 0; i < n; ++i) {
+        double sum = 0.0;
+        for (int j = 0; j < m; ++j) {
+            sum += U[i * m + j] * U[i * m + j];
+        }
+        sum = sqrt(sum);
+        for (int j = 0; j < m; ++j) {
+            U[i * m + j] /= sum*sqrt((double) m);
+        }
+    }
+
+    // print U
+    //printf("U:\n");
+    //for (int i = 0; i < n; ++i) {
+    //    printf("col %d : ", i);
+    //    for (int j = 0; j < m; ++j) {
+    //        printf("%9.5f ", U[i * m + j]);
+    //    }
+    //    printf("\n");
+    //}
+    //printf("\n");
+
+    // project A0 onto U
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < nd; ++j) {
+            A[i * nd + j] = 0.0f;
+            //if (j == 0) continue;
+            for (int k = 0; k < m; ++k) {
+                A[i * nd + j] += A0[i * m + k] * U[j * m + k];
+            }
+        }
+    }
+
+    // print A
+    //printf("A:\n");
+    //for (int i = 0; i < n; ++i) {
+    //    printf("col %d : ", i);
+    //    for (int j = 0; j < n; ++j) {
+    //        printf("%9.5f ", A[i * n + j]);
+    //    }
+    //    printf("\n");
+    //}
+    //printf("\n");
+
+    free(U);
+    free(S);
+    free(V);
+    free(A0);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+
 int ggml_cpu_has_avx(void) {
 #if defined(__AVX__)
     return 1;

ggml.h

@@ -726,6 +726,16 @@ enum ggml_opt_result ggml_opt(
         struct ggml_opt_params params,
         struct ggml_tensor * f);
 
+//
+// Temp stuff
+//
+
+void ggml_svd_reduce_dims(
+        int ne0,
+        int ne1,
+        float * a,
+        int nd);
+
 //
 // system info
 //

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/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

@@ -6,7 +6,7 @@
 #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

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(
+    // Convert RAW PCM audio to log mel spectrogram but applies a Phase Vocoder to speed up the audio x2. 
-            struct whisper_context * ctx,
+    // The resulting spectrogram is stored inside the provided whisper context.
-              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.
     // 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
@@ -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);
 
@@ -304,23 +236,12 @@ extern "C" {
     // 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);
 
     // 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:
@@ -394,16 +315,11 @@ extern "C" {
         // 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,
@@ -411,16 +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(
@@ -430,56 +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
 }