make : fix MUSL Linux build

.gitignore

@@ -1,7 +1,5 @@
 *.o
 *.a
-*.mlmodel
-*.mlmodelc
 .cache/
 .vs/
 .vscode/
@@ -34,5 +32,3 @@ examples/whisper.objc/whisper.objc.xcodeproj/xcuserdata/
 examples/whisper.objc/whisper.objc.xcodeproj/project.xcworkspace/xcuserdata
 
 extra/bench-gg.txt
-
-*.mlmodel*

CMakeLists.txt

@@ -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

@@ -35,7 +35,7 @@ CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
 LDFLAGS  =
 
 # ref: https://github.com/ggerganov/whisper.cpp/issues/37
-ifneq ($(wildcard /usr/include/musl/*),)
+ifneq (,$(findstring musl,$(CCV)))
 	CFLAGS   += -D_POSIX_SOURCE -D_GNU_SOURCE
 	CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
 endif
@@ -138,10 +138,6 @@ 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
@@ -194,23 +190,11 @@ 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 +208,21 @@ 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

@@ -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
@@ -476,7 +463,6 @@ 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)
@@ -486,7 +472,6 @@ in [models](models).
 - [X] Python: | [#9](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 (

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/CMakeLists.txt

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

examples/addon.node/addon.cpp

@@ -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;
@@ -131,7 +130,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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; }
@@ -180,7 +178,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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");
@@ -371,129 +368,6 @@ bool output_csv(struct whisper_context * ctx, const char * fname) {
     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
@@ -788,12 +662,6 @@ 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/talk.llama/.gitignore

@@ -1,2 +0,0 @@
-eleven-labs.py
-audio.mp3

examples/talk.llama/CMakeLists.txt

@@ -1,12 +0,0 @@
-if (WHISPER_SUPPORT_SDL2)
-    # talk.llama
-    set(TARGET talk-llama)
-
-    # TODO: this is temporary
-    #       need to export ggml symbols for MSVC, but too lazy ..
-    add_executable(${TARGET} talk-llama.cpp llama.cpp)
-
-    include(DefaultTargetOptions)
-
-    target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
-endif ()

examples/talk.llama/README.md

@@ -1,2 +0,0 @@
-# talk.llama
-

examples/talk.llama/llama.h

@@ -1,153 +0,0 @@
-#ifndef LLAMA_H
-#define LLAMA_H
-
-#include <stddef.h>
-#include <stdint.h>
-#include <stdbool.h>
-
-#ifdef LLAMA_SHARED
-#    ifdef _WIN32
-#        ifdef LLAMA_BUILD
-#            define LLAMA_API __declspec(dllexport)
-#        else
-#            define LLAMA_API __declspec(dllimport)
-#        endif
-#    else
-#        define LLAMA_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define LLAMA_API
-#endif
-
-#define LLAMA_FILE_VERSION 1
-#define LLAMA_FILE_MAGIC 0x67676d66 // 'ggmf' in hex
-#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-    //
-    // C interface
-    //
-    // TODO: show sample usage
-    //
-
-    struct llama_context;
-
-    typedef int llama_token;
-
-    typedef struct llama_token_data {
-        llama_token id;  // token id
-
-        float p;     // probability of the token
-        float plog;  // log probability of the token
-
-    } llama_token_data;
-
-    typedef void (*llama_progress_callback)(double progress, void *ctx);
-
-    struct llama_context_params {
-        int n_ctx;   // text context
-        int n_parts; // -1 for default
-        int seed;    // RNG seed, 0 for random
-
-        bool f16_kv;     // use fp16 for KV cache
-        bool logits_all; // the llama_eval() call computes all logits, not just the last one
-        bool vocab_only; // only load the vocabulary, no weights
-        bool use_mlock;  // force system to keep model in RAM
-        bool embedding;  // embedding mode only
-
-        // called with a progress value between 0 and 1, pass NULL to disable
-        llama_progress_callback progress_callback;
-        // context pointer passed to the progress callback
-        void * progress_callback_user_data;
-    };
-
-    LLAMA_API struct llama_context_params llama_context_default_params();
-
-    // Various functions for loading a ggml llama model.
-    // Allocate (almost) all memory needed for the model.
-    // Return NULL on failure
-    LLAMA_API struct llama_context * llama_init_from_file(
-                             const char * path_model,
-            struct llama_context_params   params);
-
-    // Frees all allocated memory
-    LLAMA_API void llama_free(struct llama_context * ctx);
-
-    // TODO: not great API - very likely to change
-    // Returns 0 on success
-    LLAMA_API int llama_model_quantize(
-            const char * fname_inp,
-            const char * fname_out,
-                   int   itype,
-                   int   qk);
-
-    // Run the llama inference to obtain the logits and probabilities for the next token.
-    // tokens + n_tokens is the provided batch of new tokens to process
-    // n_past is the number of tokens to use from previous eval calls
-    // Returns 0 on success
-    LLAMA_API int llama_eval(
-            struct llama_context * ctx,
-               const llama_token * tokens,
-                             int   n_tokens,
-                             int   n_past,
-                             int   n_threads);
-
-    // Convert the provided text into tokens.
-    // The tokens pointer must be large enough to hold the resulting tokens.
-    // Returns the number of tokens on success, no more than n_max_tokens
-    // Returns a negative number on failure - the number of tokens that would have been returned
-    // TODO: not sure if correct
-    LLAMA_API int llama_tokenize(
-            struct llama_context * ctx,
-                      const char * text,
-                     llama_token * tokens,
-                             int   n_max_tokens,
-                            bool   add_bos);
-
-    LLAMA_API int llama_n_vocab(struct llama_context * ctx);
-    LLAMA_API int llama_n_ctx  (struct llama_context * ctx);
-    LLAMA_API int llama_n_embd (struct llama_context * ctx);
-
-    // Token logits obtained from the last call to llama_eval()
-    // The logits for the last token are stored in the last row
-    // Can be mutated in order to change the probabilities of the next token
-    // Rows: n_tokens
-    // Cols: n_vocab
-    LLAMA_API float * llama_get_logits(struct llama_context * ctx);
-
-    // Get the embeddings for the input
-    // shape: [n_embd] (1-dimensional)
-    LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
-
-    // Token Id -> String. Uses the vocabulary in the provided context
-    LLAMA_API const char * llama_token_to_str(struct llama_context * ctx, llama_token token);
-
-    // Special tokens
-    LLAMA_API llama_token llama_token_bos();
-    LLAMA_API llama_token llama_token_eos();
-
-    // TODO: improve the last_n_tokens interface ?
-    LLAMA_API llama_token llama_sample_top_p_top_k(
-       struct llama_context * ctx,
-          const llama_token * last_n_tokens_data,
-                        int   last_n_tokens_size,
-                        int   top_k,
-                     double   top_p,
-                     double   temp,
-                     double   repeat_penalty);
-
-    // Performance information
-    LLAMA_API void llama_print_timings(struct llama_context * ctx);
-    LLAMA_API void llama_reset_timings(struct llama_context * ctx);
-
-    // Print system information
-    LLAMA_API const char * llama_print_system_info(void);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif

examples/talk.llama/speak.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-# Usage:
-#  speak.sh <voice_id> <text-to-speak>
-
-# espeak
-# Mac OS: brew install espeak
-# Linux: apt-get install espeak
-#
-#espeak -v en-us+m$1 -s 225 -p 50 -a 200 -g 5 -k 5 "$2"
-
-# for Mac
-say "$2"
-
-# Eleven Labs
-#
-#wd=$(dirname $0)
-#script=$wd/eleven-labs.py
-#python3 $script $1 "$2" >/dev/null 2>&1
-#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 >/dev/null 2>&1

examples/talk.llama/talk-llama.cpp

@@ -1,511 +0,0 @@
-// Talk with AI
-//
-
-#include "common.h"
-#include "common-sdl.h"
-#include "whisper.h"
-#include "llama.h"
-
-#include <cassert>
-#include <cstdio>
-#include <fstream>
-#include <regex>
-#include <string>
-#include <thread>
-#include <vector>
-#include <regex>
-
-std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
-    // initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
-    std::vector<llama_token> res(text.size() + (int)add_bos);
-    int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
-    assert(n >= 0);
-    res.resize(n);
-
-    return res;
-}
-
-// command-line parameters
-struct whisper_params {
-    int32_t n_threads  = std::min(4, (int32_t) std::thread::hardware_concurrency());
-    int32_t voice_ms   = 10000;
-    int32_t capture_id = -1;
-    int32_t max_tokens = 32;
-    int32_t audio_ctx  = 0;
-
-    float vad_thold    = 0.6f;
-    float freq_thold   = 100.0f;
-
-    bool speed_up      = false;
-    bool translate     = false;
-    bool print_special = false;
-    bool print_energy  = false;
-    bool no_timestamps = true;
-
-    std::string person      = "Santa";
-    std::string language    = "en";
-    std::string model_wsp   = "models/ggml-base.en.bin";
-    std::string model_llama = "models/ggml-llama-7B.bin";
-    std::string speak       = "./examples/talk/speak.sh";
-    std::string fname_out;
-};
-
-void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
-
-bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
-    for (int i = 1; i < argc; i++) {
-        std::string arg = argv[i];
-
-        if (arg == "-h" || arg == "--help") {
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-        else if (arg == "-t"   || arg == "--threads")       { params.n_threads     = std::stoi(argv[++i]); }
-        else if (arg == "-vms" || arg == "--voice-ms")      { params.voice_ms      = std::stoi(argv[++i]); }
-        else if (arg == "-c"   || arg == "--capture")       { params.capture_id    = std::stoi(argv[++i]); }
-        else if (arg == "-mt"  || arg == "--max-tokens")    { params.max_tokens    = std::stoi(argv[++i]); }
-        else if (arg == "-ac"  || arg == "--audio-ctx")     { params.audio_ctx     = std::stoi(argv[++i]); }
-        else if (arg == "-vth" || arg == "--vad-thold")     { params.vad_thold     = std::stof(argv[++i]); }
-        else if (arg == "-fth" || arg == "--freq-thold")    { params.freq_thold    = std::stof(argv[++i]); }
-        else if (arg == "-su"  || arg == "--speed-up")      { params.speed_up      = true; }
-        else if (arg == "-tr"  || arg == "--translate")     { params.translate     = true; }
-        else if (arg == "-ps"  || arg == "--print-special") { params.print_special = true; }
-        else if (arg == "-pe"  || arg == "--print-energy")  { params.print_energy  = true; }
-        else if (arg == "-p"   || arg == "--person")        { params.person        = argv[++i]; }
-        else if (arg == "-l"   || arg == "--language")      { params.language      = argv[++i]; }
-        else if (arg == "-mw"  || arg == "--model-whisper") { params.model_wsp     = argv[++i]; }
-        else if (arg == "-ml"  || arg == "--model-llama")   { params.model_llama   = argv[++i]; }
-        else if (arg == "-s"   || arg == "--speak")         { params.speak         = argv[++i]; }
-        else if (arg == "-f"   || arg == "--file")          { params.fname_out     = argv[++i]; }
-        else {
-            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
-            whisper_print_usage(argc, argv, params);
-            exit(0);
-        }
-    }
-
-    return true;
-}
-
-void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
-    fprintf(stderr, "\n");
-    fprintf(stderr, "usage: %s [options]\n", argv[0]);
-    fprintf(stderr, "\n");
-    fprintf(stderr, "options:\n");
-    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
-    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n", params.n_threads);
-    fprintf(stderr, "  -vms N,   --voice-ms N    [%-7d] voice duration in milliseconds\n",              params.voice_ms);
-    fprintf(stderr, "  -c ID,    --capture ID    [%-7d] capture device ID\n",                           params.capture_id);
-    fprintf(stderr, "  -mt N,    --max-tokens N  [%-7d] maximum number of tokens per audio chunk\n",    params.max_tokens);
-    fprintf(stderr, "  -ac N,    --audio-ctx N   [%-7d] audio context size (0 - all)\n",                params.audio_ctx);
-    fprintf(stderr, "  -vth N,   --vad-thold N   [%-7.2f] voice activity detection threshold\n",        params.vad_thold);
-    fprintf(stderr, "  -fth N,   --freq-thold N  [%-7.2f] high-pass frequency cutoff\n",                params.freq_thold);
-    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",     params.speed_up ? "true" : "false");
-    fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",   params.translate ? "true" : "false");
-    fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                        params.print_special ? "true" : "false");
-    fprintf(stderr, "  -pe,      --print-energy  [%-7s] print sound energy (for debugging)\n",          params.print_energy ? "true" : "false");
-    fprintf(stderr, "  -p NAME,  --person NAME   [%-7s] person name (for prompt selection)\n",          params.person.c_str());
-    fprintf(stderr, "  -l LANG,  --language LANG [%-7s] spoken language\n",                             params.language.c_str());
-    fprintf(stderr, "  -mw FILE, --model-whisper [%-7s] whisper model file\n",                          params.model_wsp.c_str());
-    fprintf(stderr, "  -mg FILE, --model-llama   [%-7s] llama model file\n",                            params.model_llama.c_str());
-    fprintf(stderr, "  -s FILE,  --speak TEXT    [%-7s] command for TTS\n",                             params.speak.c_str());
-    fprintf(stderr, "  -f FNAME, --file FNAME    [%-7s] text output file name\n",                       params.fname_out.c_str());
-    fprintf(stderr, "\n");
-}
-
-std::string transcribe(whisper_context * ctx, const whisper_params & params, const std::vector<float> & pcmf32, float & prob, int64_t & t_ms) {
-    const auto t_start = std::chrono::high_resolution_clock::now();
-
-    prob = 0.0f;
-    t_ms = 0;
-
-    whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
-
-    wparams.print_progress   = false;
-    wparams.print_special    = params.print_special;
-    wparams.print_realtime   = false;
-    wparams.print_timestamps = !params.no_timestamps;
-    wparams.translate        = params.translate;
-    wparams.no_context       = true;
-    wparams.single_segment   = true;
-    wparams.max_tokens       = params.max_tokens;
-    wparams.language         = params.language.c_str();
-    wparams.n_threads        = params.n_threads;
-
-    wparams.audio_ctx        = params.audio_ctx;
-    wparams.speed_up         = params.speed_up;
-
-    if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
-        return "";
-    }
-
-    int prob_n = 0;
-    std::string result;
-
-    const int n_segments = whisper_full_n_segments(ctx);
-    for (int i = 0; i < n_segments; ++i) {
-        const char * text = whisper_full_get_segment_text(ctx, i);
-
-        result += text;
-
-        const int n_tokens = whisper_full_n_tokens(ctx, i);
-        for (int j = 0; j < n_tokens; ++j) {
-            const auto token = whisper_full_get_token_data(ctx, i, j);
-
-            prob += token.p;
-            ++prob_n;
-        }
-    }
-
-    if (prob_n > 0) {
-        prob /= prob_n;
-    }
-
-    const auto t_end = std::chrono::high_resolution_clock::now();
-    t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
-
-    return result;
-}
-
-// need to have leading ' '
-//const std::string k_prompt = R"( Transcript of a dialog, where {1} interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer {1}'s requests immediately and with precision.
-//
-//{0}: Hello, Bob.
-//{1}: Hello {0}. How may I help you today?
-//{0}:)";
-
-const std::string k_prompt = R"( Text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
-{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
-There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
-The transcript only includes text, it does not include markup like HTML and Markdown.
-{1} answers responds with short and concise answers.
-
-{0}{4} Hello, {1}!
-{1}{4} Hello {0}! How may I help you today?
-{0}{4} What time is it?
-{1}{4} It is {2} o'clock.
-{0}{4} What year is it?
-{1}{4} We are in {3}.
-{0}{4} What is a cat?
-{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
-{0}{4} Name a color.
-{1}{4} Blue
-{0}{4})";
-
-int main(int argc, char ** argv) {
-    whisper_params params;
-
-    if (whisper_params_parse(argc, argv, params) == false) {
-        return 1;
-    }
-
-    if (whisper_lang_id(params.language.c_str()) == -1) {
-        fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
-        whisper_print_usage(argc, argv, params);
-        exit(0);
-    }
-
-    // whisper init
-
-    struct whisper_context * ctx_wsp = whisper_init_from_file(params.model_wsp.c_str());
-
-    // llama init
-
-    auto lparams = llama_context_default_params();
-
-    lparams.n_ctx      = 512;
-    lparams.n_parts    = 2; // TODO fix
-    lparams.seed       = 1; // TODO fix
-    lparams.f16_kv     = true;
-
-    struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
-
-    // print some info about the processing
-    {
-        fprintf(stderr, "\n");
-        if (!whisper_is_multilingual(ctx_wsp)) {
-            if (params.language != "en" || params.translate) {
-                params.language = "en";
-                params.translate = false;
-                fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
-            }
-        }
-        fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
-                __func__,
-                params.n_threads,
-                params.language.c_str(),
-                params.translate ? "translate" : "transcribe",
-                params.no_timestamps ? 0 : 1);
-
-        fprintf(stderr, "\n");
-    }
-
-
-    // init audio
-
-    audio_async audio(30*1000);
-    if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
-        fprintf(stderr, "%s: audio.init() failed!\n", __func__);
-        return 1;
-    }
-
-    audio.resume();
-
-    int n_iter = 0;
-
-    bool is_running  = true;
-    bool force_speak = false;
-
-    float prob0 = 0.0f;
-
-    const std::string chat_symb = ":";
-    const std::string bot_name = "LLAMA";
-
-    std::vector<float> pcmf32_cur;
-    std::vector<float> pcmf32_prompt;
-
-    std::string prompt_org = k_prompt;
-    prompt_org = ::replace(prompt_org, "{0}", params.person);
-    prompt_org = ::replace(prompt_org, "{1}", bot_name);
-
-    {
-        // get time string
-        std::string time_str;
-        {
-            time_t t = time(0);
-            struct tm * now = localtime(&t);
-            char buf[128];
-            strftime(buf, sizeof(buf), "%H:%M", now);
-            time_str = buf;
-        }
-        prompt_org = ::replace(prompt_org, "{2}", time_str);
-    }
-
-    {
-        // get year string
-        std::string year_str;
-        {
-            time_t t = time(0);
-            struct tm * now = localtime(&t);
-            char buf[128];
-            strftime(buf, sizeof(buf), "%Y", now);
-            year_str = buf;
-        }
-        prompt_org = ::replace(prompt_org, "{3}", year_str);
-    }
-
-    prompt_org = ::replace(prompt_org, "{4}", chat_symb);
-
-    auto embd_inp = ::llama_tokenize(ctx_llama, prompt_org, true);
-
-    const int n_ctx = llama_n_ctx(ctx_llama);
-
-    printf("\n");
-    printf("%s : initializing - please wait ...\n", __func__);
-
-    if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0, params.n_threads)) {
-        fprintf(stderr, "%s : failed to eval\n", __func__);
-        return 1;
-    }
-
-    //fprintf(stdout, "\n");
-    //fprintf(stdout, "%s", prompt_org.c_str());
-    //fflush(stdout);
-
-    printf("%s : done! start speaking in the microphone\n", __func__);
-    printf("\n");
-    printf("%s%s", params.person.c_str(), chat_symb.c_str());
-    fflush(stdout);
-
-    audio.clear();
-
-    const int n_keep = embd_inp.size();
-    const int voice_id = 2;
-
-    int n_past = n_keep;
-    int n_prev = 64; // TODO arg
-
-    std::vector<llama_token> embd;
-
-    std::vector<std::string> antiprompts = {
-        params.person + chat_symb,
-    };
-
-    // main loop
-    while (is_running) {
-        // handle Ctrl + C
-        is_running = sdl_poll_events();
-
-        if (!is_running) {
-            break;
-        }
-
-        // delay
-        std::this_thread::sleep_for(std::chrono::milliseconds(100));
-
-        int64_t t_ms = 0;
-
-        {
-            audio.get(2000, pcmf32_cur);
-
-            if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1250, params.vad_thold, params.freq_thold, params.print_energy) || force_speak) {
-                //fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
-
-                audio.get(params.voice_ms, pcmf32_cur);
-
-                std::string text_heard;
-
-                if (!force_speak) {
-                    text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prob0, t_ms));
-                }
-
-                // remove text between brackets using regex
-                {
-                    std::regex re("\\[.*?\\]");
-                    text_heard = std::regex_replace(text_heard, re, "");
-                }
-
-                // remove text between brackets using regex
-                {
-                    std::regex re("\\(.*?\\)");
-                    text_heard = std::regex_replace(text_heard, re, "");
-                }
-
-                // remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
-                text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
-
-                // take first line
-                text_heard = text_heard.substr(0, text_heard.find_first_of('\n'));
-
-                // remove leading and trailing whitespace
-                text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
-                text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
-
-                const std::vector<llama_token> tokens = llama_tokenize(ctx_llama, text_heard.c_str(), false);
-
-                if (text_heard.empty() || tokens.empty() || force_speak) {
-                    //fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
-                    audio.clear();
-
-                    continue;
-                }
-
-                force_speak = false;
-
-                text_heard.insert(0, 1, ' ');
-                text_heard += "\n" + bot_name + chat_symb;
-                fprintf(stdout, "%s%s%s", "\033[1m", text_heard.c_str(), "\033[0m");
-                fflush(stdout);
-
-                embd = ::llama_tokenize(ctx_llama, text_heard, false);
-
-                // text inference
-                bool done = false;
-                std::string text_to_speak;
-                while (true) {
-                    // predict
-                    if (embd.size() > 0) {
-                        if (n_past + (int) embd.size() > n_ctx) {
-                            n_past = n_keep;
-
-                            // insert n_left/2 tokens at the start of embd from last_n_tokens
-                            embd.insert(embd.begin(), embd_inp.begin() + embd_inp.size() - n_prev, embd_inp.end());
-
-                            //printf("\n---\n");
-                            //printf("resetting: '");
-                            //for (int i = 0; i < (int) embd.size(); i++) {
-                            //    printf("%s", llama_token_to_str(ctx_llama, embd[i]));
-                            //}
-                            //printf("'\n");
-                            //printf("\n---\n");
-                        }
-
-                        if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past, params.n_threads)) {
-                            fprintf(stderr, "%s : failed to eval\n", __func__);
-                            return 1;
-                        }
-                    }
-
-                    //printf("n_iter = %d, n_past = %d, n_ctx = %d, n_keep = %d, n_prev = %d, embd.size() = %d\n", n_iter, n_past, n_ctx, n_keep, n_prev, (int) embd.size());
-
-                    embd_inp.insert(embd_inp.end(), embd.begin(), embd.end());
-                    n_past += embd.size();
-                    embd.clear();
-
-                    if (done) break;
-
-                    {
-                        // out of user input, sample next token
-                        const float top_k          = 5;
-                        const float top_p          = 0.80f;
-                        const float temp           = 0.30f;
-                        const float repeat_penalty = 1.1764f;
-
-                        const int repeat_last_n    = 256;
-
-                        llama_token id = 0;
-
-                        {
-                            //auto logits = llama_get_logits(ctx_llama);
-                            //logits[llama_token_eos()] = 0;
-
-                            id = llama_sample_top_p_top_k(ctx_llama,
-                                    embd_inp.data() + std::max(0, n_past - repeat_last_n),
-                                    repeat_last_n, top_k, top_p, temp, repeat_penalty);
-                        }
-
-                        if (id != llama_token_eos()) {
-                            // add it to the context
-                            embd.push_back(id);
-
-                            text_to_speak += llama_token_to_str(ctx_llama, id);
-
-                            printf("%s", llama_token_to_str(ctx_llama, id));
-                        } else {
-                            // TODO
-                            printf("EOS TOKEN - SHOULD NOT HAPPEN\n");
-                            exit(0);
-                        }
-                    }
-
-                    {
-                        std::string last_output;
-                        for (int i = embd_inp.size() - 16; i < (int) embd_inp.size(); i++) {
-                            last_output += llama_token_to_str(ctx_llama, embd_inp[i]);
-                        }
-                        last_output += llama_token_to_str(ctx_llama, embd[0]);
-
-                        for (std::string & antiprompt : antiprompts) {
-                            if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
-                                done = true;
-                                text_to_speak = ::replace(text_to_speak, antiprompt, "");
-                                fflush(stdout);
-                                break;
-                            }
-                        }
-                    }
-
-                    is_running = sdl_poll_events();
-
-                    if (!is_running) {
-                        break;
-                    }
-                }
-
-                text_to_speak = ::replace(text_to_speak, "\"", "");
-                system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
-
-                audio.clear();
-
-                ++n_iter;
-            }
-        }
-    }
-
-    audio.pause();
-
-    whisper_print_timings(ctx_wsp);
-    whisper_free(ctx_wsp);
-
-    return 0;
-}

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()
-        }
     }
 }
 
@@ -129,9 +117,6 @@ 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
     }
 }
 

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

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

examples/whisper.objc/README.md

@@ -24,5 +24,3 @@ Also, don't forget to add the `-DGGML_USE_ACCELERATE` compiler flag in Build Pha
 This can significantly improve the performance of the transcription:
 
 <img width="1072" alt="image" src="https://user-images.githubusercontent.com/1991296/208511239-8d7cdbd1-aa48-41b5-becd-ca288d53cc07.png">
-
-In this project, it also added `-O3 -DNDEBUG` to `Other C Flags`, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.

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

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

examples/whisper.swiftui/README.md

@@ -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;

ggml.h

@@ -198,8 +198,6 @@ struct ggml_object;
 struct ggml_context;
 
 enum ggml_type {
-    GGML_TYPE_Q4_0,
-    GGML_TYPE_Q4_1,
     GGML_TYPE_I8,
     GGML_TYPE_I16,
     GGML_TYPE_I32,
@@ -228,9 +226,7 @@ enum ggml_op {
     GGML_OP_STEP,
     GGML_OP_RELU,
     GGML_OP_GELU,
-    GGML_OP_SILU,
     GGML_OP_NORM, // normalize
-    GGML_OP_RMS_NORM,
 
     GGML_OP_MUL_MAT,
 
@@ -330,10 +326,7 @@ void ggml_print_objects(const struct ggml_context * ctx);
 int    ggml_nelements(const struct ggml_tensor * tensor);
 size_t ggml_nbytes   (const struct ggml_tensor * tensor);
 
-int    ggml_blck_size (enum ggml_type type);
+size_t ggml_type_size   (enum ggml_type type);
-size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
-float  ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
-
 size_t ggml_element_size(const struct ggml_tensor * tensor);
 
 struct ggml_context * ggml_init(struct ggml_init_params params);
@@ -343,9 +336,6 @@ size_t ggml_used_mem(const struct ggml_context * ctx);
 
 size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
 
-bool ggml_mlock_supported(void);
-bool ggml_mlock(struct ggml_context * ctx, char ** err_p);
-
 struct ggml_tensor * ggml_new_tensor(
         struct ggml_context * ctx,
         enum   ggml_type type,
@@ -476,20 +466,12 @@ struct ggml_tensor * ggml_gelu(
         struct ggml_context * ctx,
         struct ggml_tensor  * a);
 
-struct ggml_tensor * ggml_silu(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a);
-
 // normalize along rows
 // TODO: eps is hardcoded to 1e-5 for now
 struct ggml_tensor * ggml_norm(
         struct ggml_context * ctx,
         struct ggml_tensor  * a);
 
-struct ggml_tensor * ggml_rms_norm(
-        struct ggml_context * ctx,
-        struct ggml_tensor  * a);
-
 // A: m rows, n columns
 // B: p rows, n columns (i.e. we transpose it internally)
 // result is m columns, p rows
@@ -744,13 +726,6 @@ enum ggml_opt_result ggml_opt(
         struct ggml_opt_params params,
         struct ggml_tensor * f);
 
-//
-// quantization
-//
-
-size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int qk, int64_t * hist);
-size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int qk, int64_t * hist);
-
 //
 // 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.cpp

@@ -1,8 +1,5 @@
 #define WHISPER_BUILD
 #include "whisper.h"
-#if WHISPER_USE_COREML
-#include "coreml/whisper-encoder.h"
-#endif
 
 #include "ggml.h"
 
@@ -589,10 +586,6 @@ struct whisper_state {
 
     int lang_id = 0; // english by default
 
-#ifdef WHISPER_USE_COREML
-    whisper_coreml_context * ctx_coreml;
-#endif
-
     // [EXPERIMENTAL] token-level timestamps data
     int64_t t_beg = 0;
     int64_t t_last = 0;
@@ -638,13 +631,12 @@ struct whisper_context {
     int64_t t_load_us = 0;
     int64_t t_start_us = 0;
 
+
     ggml_type wtype = ggml_type::GGML_TYPE_F16; // weight type (FP32 or FP16)
 
     whisper_model model;
     whisper_vocab vocab;
     whisper_state * state = nullptr;
-
-    std::string path_model; // populated by whisper_init_from_file()
 };
 
 template<typename T>
@@ -1375,7 +1367,6 @@ static bool whisper_encode_internal(
         }
     }
 
-#ifndef WHISPER_USE_COREML
     struct ggml_tensor * cur;
 
     // convolution + gelu
@@ -1607,7 +1598,7 @@ static bool whisper_encode_internal(
                         ggml_repeat(ctx0, layer.mlp_ln_w, cur),
                         cur),
                     ggml_repeat(ctx0, layer.mlp_ln_b, cur));
-            }
+    }
 
 #ifdef WHISPER_USE_FLASH_FF
             wstate.use_buf(ctx0, 0);
@@ -1647,7 +1638,7 @@ static bool whisper_encode_internal(
                 ggml_repeat(ctx0, layer.mlp_1_b, cur),
                 cur);
 #endif
-        }
+}
 
         wstate.use_buf(ctx0, 3);
 
@@ -1684,13 +1675,6 @@ static bool whisper_encode_internal(
 
         //ggml_graph_print(&gf);
     }
-#else
-    wstate.use_buf(ctx0, -1);
-
-    struct ggml_tensor * cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx);
-
-    whisper_coreml_encode(wstate.ctx_coreml, (float *) mel->data, (float *) cur->data);
-#endif
 
     // cur
     //{
@@ -1858,6 +1842,8 @@ static bool whisper_decode_internal(
 
         // self-attention
         {
+            wstate.use_buf(ctx0, 1);
+
             struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
                     layer.attn_q_w,
                     cur);
@@ -1919,6 +1905,8 @@ static bool whisper_decode_internal(
             // K * Q
             struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
 
+            wstate.use_buf(ctx0, 0);
+
             //struct ggml_tensor * KQ_scaled =
             //    ggml_scale(ctx0,
             //            KQ,
@@ -1927,16 +1915,20 @@ static bool whisper_decode_internal(
 
             struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ, n_past);
 
+            wstate.use_buf(ctx0, 1);
+
             struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
 
+            wstate.use_buf(ctx0, 0);
+
             struct ggml_tensor * V_trans =
-                ggml_cpy(ctx0,
+                ggml_permute(ctx0,
-                        ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
-                            ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_state, il*n_ctx*ggml_element_size(kv_self.v)*n_state),
-                                ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_state, il*n_ctx*ggml_element_size(kv_self.v)*n_state),
+                            n_state/n_head, n_head, n_past + N),
-                                n_state/n_head, n_head, n_past + N),
+                        1, 2, 0, 3);
-                            1, 2, 0, 3),
+
-                        ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_state/n_head, n_head));
+            wstate.use_buf(ctx0, 1);
 
             struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
 
@@ -1973,6 +1965,8 @@ static bool whisper_decode_internal(
 
             cur = ggml_norm(ctx0, inpCA); // note: we use inpCA here
 
+            wstate.use_buf(ctx0, 1);
+
             // cur = ln_0_w*cur + ln_0_b
             cur = ggml_add(ctx0,
                     ggml_mul(ctx0,
@@ -1983,6 +1977,8 @@ static bool whisper_decode_internal(
 
         // cross-attention
         {
+            wstate.use_buf(ctx0, 0);
+
             struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
                     layer.cross_attn_q_w,
                     cur);
@@ -2006,13 +2002,12 @@ static bool whisper_decode_internal(
                         ggml_view_1d(ctx0, wstate.kv_cross.v, M*n_state, il*M*ggml_element_size(wstate.kv_cross.v)*n_state),
                         n_state/n_head, n_head, M);
 
-            struct ggml_tensor * V_trans =
+            struct ggml_tensor * V_trans = ggml_permute(ctx0, Vcross, 1, 2, 0, 3);
-                ggml_cpy(ctx0,
-                        ggml_permute(ctx0, Vcross, 1, 2, 0, 3),
-                        ggml_new_tensor_3d(ctx0, Vcross->type, M, n_state/n_head, n_head));
 
             // ------
 
+            wstate.use_buf(ctx0, 1);
+
             struct ggml_tensor * Q =
                 ggml_permute(ctx0,
                         ggml_cpy(ctx0,
@@ -2022,6 +2017,8 @@ static bool whisper_decode_internal(
 
             struct ggml_tensor * K = ggml_permute(ctx0, Kcross, 0, 2, 1, 3);
 
+            wstate.use_buf(ctx0, 0);
+
             // K * Q
             struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
 
@@ -2034,10 +2031,16 @@ static bool whisper_decode_internal(
             // no masking for cross-attention
             //struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
 
+            wstate.use_buf(ctx0, 1);
+
             struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ);
 
+            wstate.use_buf(ctx0, 0);
+
             struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
 
+            wstate.use_buf(ctx0, 1);
+
             struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
 
             // cur = KQV_merged.contiguous().view(n_state, N)
@@ -2475,25 +2478,12 @@ static std::vector<whisper_vocab::id> tokenize(const whisper_vocab & vocab, cons
 // interface implementation
 //
 
-#ifdef WHISPER_USE_COREML
-// replace .bin with .mlmodelc
-static std::string whisper_get_coreml_path(std::string path_bin) {
-    auto pos = path_bin.rfind('.');
-    if (pos != std::string::npos) {
-        path_bin = path_bin.substr(0, pos);
-    }
-
-    path_bin += ".mlmodelc";
-
-    return path_bin;
-}
-#endif
-
 struct whisper_state * whisper_init_state(whisper_context * ctx) {
     whisper_state * state = new whisper_state;
 
     const size_t scale = ctx->model.hparams.f16 ? 1 : 2;
 
+
     if (!kv_cache_init(ctx->model.hparams, scale * MEM_REQ_KV_SELF.at(ctx->model.type), state->decoders[0].kv_self, ctx->wtype, ctx->model.hparams.n_text_ctx)) {
         fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
         return nullptr;
@@ -2514,6 +2504,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
         fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
     }
 
+
     state->logits.reserve(ctx->vocab.n_vocab * ctx->model.hparams.n_text_ctx);
 
     state->logits_id.reserve(ctx->model.hparams.n_vocab);
@@ -2533,21 +2524,6 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
 
     state->rng = std::mt19937(0);
 
-#ifdef WHISPER_USE_COREML
-    const auto path_coreml = whisper_get_coreml_path(ctx->path_model);
-
-    fprintf(stderr, "%s: loading Core ML model from '%s'\n", __func__, path_coreml.c_str());
-    fprintf(stderr, "%s: first run on a device may take a while ...\n", __func__);
-
-    state->ctx_coreml = whisper_coreml_init(path_coreml.c_str());
-    if (!state->ctx_coreml) {
-        fprintf(stderr, "%s: failed to load Core ML model from '%s'\n", __func__, path_coreml.c_str());
-        return nullptr;
-    }
-
-    fprintf(stderr, "%s: Core ML model loaded\n", __func__);
-#endif
-
     return state;
 }
 
@@ -2563,7 +2539,6 @@ struct whisper_context * whisper_init_from_file_no_state(const char * path_model
     }
 
     loader.context = &fin;
-
     loader.read = [](void * ctx, void * output, size_t read_size) {
         std::ifstream * fin = (std::ifstream*)ctx;
         fin->read((char *)output, read_size);
@@ -2580,13 +2555,7 @@ struct whisper_context * whisper_init_from_file_no_state(const char * path_model
         fin->close();
     };
 
-    auto ctx = whisper_init_no_state(&loader);
+    return whisper_init_no_state(&loader);
-
-    if (ctx) {
-        ctx->path_model = path_model;
-    }
-
-    return ctx;
 }
 
 struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size) {
@@ -2711,10 +2680,6 @@ void whisper_free(struct whisper_context * ctx) {
 
         whisper_free_state(ctx->state);
 
-#ifdef WHISPER_USE_COREML
-        whisper_coreml_free(ctx->state->ctx_coreml);
-        ctx->state->ctx_coreml = nullptr;
-#endif
         delete ctx;
     }
 }
@@ -2891,7 +2856,7 @@ int whisper_lang_auto_detect_with_state(
     }
 
     // run the encoder
-    if (whisper_encode_with_state(ctx, state, seek, n_threads) != 0) {
+    if (whisper_encode(ctx, seek, n_threads) != 0) {
         fprintf(stderr, "%s: failed to encode\n", __func__);
         return -6;
     }
@@ -2955,71 +2920,6 @@ int whisper_lang_auto_detect(
     return whisper_lang_auto_detect_with_state(ctx, ctx->state, offset_ms, n_threads, lang_probs);
 }
 
-int whisper_model_n_vocab(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_vocab;
-}
-
-int whisper_model_n_audio_ctx(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_audio_ctx;
-}
-
-int whisper_model_n_audio_state(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_audio_state;
-}
-
-int whisper_model_n_audio_head(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_audio_head;
-}
-
-int whisper_model_n_audio_layer(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_audio_layer;
-}
-
-int whisper_model_n_text_ctx(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_text_ctx;
-}
-
-int whisper_model_n_text_state(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_text_state;
-}
-
-int whisper_model_n_text_head(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_text_head;
-}
-
-int whisper_model_n_text_layer(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_text_layer;
-}
-
-int whisper_model_n_mels(struct whisper_context * ctx) {
-    return ctx->model.hparams.n_mels;
-}
-
-int whisper_model_f16(struct whisper_context * ctx) {
-    return ctx->model.hparams.f16;
-}
-
-int whisper_model_type(struct whisper_context * ctx) {
-    return ctx->model.type;
-}
-
-const char *whisper_model_type_readable(struct whisper_context * ctx) {
-    switch (ctx->model.type) {
-    case e_model::MODEL_TINY:
-        return "tiny";
-    case e_model::MODEL_BASE:
-        return "base";
-    case e_model::MODEL_SMALL:
-        return "small";
-    case e_model::MODEL_MEDIUM:
-        return "medium";
-    case e_model::MODEL_LARGE:
-        return "large";
-    default:
-        return "unknown";
-    }
-}
-
 int whisper_n_len_from_state(struct whisper_state * state) {
     return state->mel.n_len;
 }
@@ -3182,7 +3082,7 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
         /*.max_initial_ts   =*/  1.0f,
         /*.length_penalty   =*/ -1.0f,
 
-        /*.temperature_inc  =*/  0.0f, // TODO: temporary disabled until improve performance
+        /*.temperature_inc  =*/  0.2f,
         /*.entropy_thold    =*/  2.4f,
         /*.logprob_thold    =*/ -1.0f,
         /*.no_speech_thold  =*/  0.6f,
@@ -4651,15 +4551,6 @@ float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int
 //
 
 WHISPER_API int whisper_bench_memcpy(int n_threads) {
-    fputs(whisper_bench_memcpy_str(n_threads), stderr);
-    return 0;
-}
-
-WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
-    static std::string s;
-    s = "";
-    char strbuf[256];
-
     ggml_time_init();
 
     size_t n    = 50;
@@ -4689,8 +4580,7 @@ WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
         src[0] = rand();
     }
 
-    snprintf(strbuf, sizeof(strbuf), "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
+    fprintf(stderr, "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
-    s += strbuf;
 
     // needed to prevent the compile from optimizing the memcpy away
     {
@@ -4698,26 +4588,16 @@ WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
 
         for (size_t i = 0; i < size; i++) sum += dst[i];
 
-        snprintf(strbuf, sizeof(strbuf), "sum:    %s %f\n", sum == -536870910.00 ? "ok" : "error", sum);
+        fprintf(stderr, "sum:    %s %f\n", sum == -536870910.00 ? "ok" : "error", sum);
-        s += strbuf;
     }
 
     free(src);
     free(dst);
 
-    return s.c_str();
-}
-
-WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
-    fputs(whisper_bench_ggml_mul_mat_str(n_threads), stderr);
     return 0;
 }
 
-WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
+WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
-    static std::string s;
-    s = "";
-    char strbuf[256];
-
     ggml_time_init();
 
     const int n_max = 128;
@@ -4793,12 +4673,11 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
             s = ((2.0*N*N*N*n)/tsum)*1e-9;
         }
 
-        snprintf(strbuf, sizeof(strbuf), "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
+        fprintf(stderr, "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f GFLOPS (%3d runs)\n",
             N, N, s_fp16, n_fp16, s_fp32, n_fp32);
-        s += strbuf;
     }
 
-    return s.c_str();
+    return 0;
 }
 
 // =================================================================================================

whisper.h

@@ -248,19 +248,6 @@ extern "C" {
     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
@@ -270,8 +257,6 @@ extern "C" {
 
     // 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);
@@ -477,9 +462,7 @@ extern "C" {
     // 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);
 
 #ifdef __cplusplus
 }