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talk.llama
...
llama-podc
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| 18e6fb0287 | |||
| 0f759f125d | |||
| eefed45e37 | |||
| aac1710afb | |||
| 21c1e6afc5 | |||
| a47e812a54 | |||
| 42c6855103 | |||
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| 82637b8e9f | |||
| 4a0deb8b1e |
3
.gitignore
vendored
3
.gitignore
vendored
@ -1,7 +1,5 @@
|
||||
*.o
|
||||
*.a
|
||||
*.mlmodel
|
||||
*.mlmodelc
|
||||
.cache/
|
||||
.vs/
|
||||
.vscode/
|
||||
@ -20,6 +18,7 @@ build-sanitize-thread/
|
||||
/stream
|
||||
/command
|
||||
/talk
|
||||
/talk-llama
|
||||
/bench
|
||||
|
||||
arm_neon.h
|
||||
|
||||
@ -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
|
||||
if (APPLE)
|
||||
# include Accelerate framework
|
||||
if (NOT WHISPER_NO_ACCELERATE)
|
||||
find_library(ACCELERATE_FRAMEWORK Accelerate)
|
||||
# on APPLE - include Accelerate framework
|
||||
if (APPLE AND NOT WHISPER_NO_ACCELERATE)
|
||||
find_library(ACCELERATE_FRAMEWORK Accelerate)
|
||||
if (ACCELERATE_FRAMEWORK)
|
||||
message(STATUS "Accelerate framework found")
|
||||
|
||||
if (ACCELERATE_FRAMEWORK)
|
||||
message(STATUS "Accelerate framework found")
|
||||
|
||||
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
|
||||
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
|
||||
else()
|
||||
message(WARNING "Accelerate framework not found")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
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()
|
||||
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
|
||||
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
|
||||
else()
|
||||
message(WARNING "Accelerate framework not found")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
@ -202,33 +183,6 @@ 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 +202,6 @@ 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} ${CMAKE_THREAD_LIBS_INIT})
|
||||
|
||||
|
||||
64
Makefile
64
Makefile
@ -36,7 +36,7 @@ LDFLAGS =
|
||||
|
||||
# ref: https://github.com/ggerganov/whisper.cpp/issues/37
|
||||
ifneq ($(wildcard /usr/include/musl/*),)
|
||||
CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
|
||||
CFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
|
||||
CXXFLAGS += -D_POSIX_SOURCE -D_GNU_SOURCE
|
||||
endif
|
||||
|
||||
@ -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
|
||||
@ -155,12 +151,15 @@ ifneq ($(filter aarch64%,$(UNAME_M)),)
|
||||
CXXFLAGS += -mcpu=native
|
||||
endif
|
||||
ifneq ($(filter armv6%,$(UNAME_M)),)
|
||||
# Raspberry Pi 1, 2, 3
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
|
||||
# 32-bit Raspberry Pi 1, 2, 3
|
||||
CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access
|
||||
endif
|
||||
ifneq ($(filter armv7%,$(UNAME_M)),)
|
||||
# Raspberry Pi 4
|
||||
CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
|
||||
# 32-bit ARM, for example on Armbian or possibly raspbian
|
||||
CFLAGS += -mfpu=neon -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
|
||||
|
||||
# 64-bit ARM, use these (TODO: auto-detect 64-bit)
|
||||
# CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
|
||||
endif
|
||||
ifneq ($(filter armv8%,$(UNAME_M)),)
|
||||
# Raspberry Pi 4
|
||||
@ -182,7 +181,7 @@ $(info I CC: $(CCV))
|
||||
$(info I CXX: $(CXXV))
|
||||
$(info )
|
||||
|
||||
default: main
|
||||
default: main bench
|
||||
|
||||
#
|
||||
# Build library
|
||||
@ -194,26 +193,14 @@ ggml.o: ggml.c ggml.h
|
||||
whisper.o: whisper.cpp whisper.h
|
||||
$(CXX) $(CXXFLAGS) -c whisper.cpp -o whisper.o
|
||||
|
||||
ifndef WHISPER_COREML
|
||||
WHISPER_OBJ = whisper.o
|
||||
else
|
||||
whisper-encoder.o: coreml/whisper-encoder.mm coreml/whisper-encoder.h
|
||||
$(CXX) -O3 -I . -c coreml/whisper-encoder.mm -o whisper-encoder.o
|
||||
libwhisper.a: ggml.o whisper.o
|
||||
$(AR) rcs libwhisper.a ggml.o whisper.o
|
||||
|
||||
whisper-encoder-impl.o: coreml/whisper-encoder-impl.m coreml/whisper-encoder-impl.h
|
||||
$(CXX) -O3 -I . -fobjc-arc -c coreml/whisper-encoder-impl.m -o whisper-encoder-impl.o
|
||||
|
||||
WHISPER_OBJ = whisper.o whisper-encoder.o whisper-encoder-impl.o
|
||||
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)
|
||||
libwhisper.so: ggml.o whisper.o
|
||||
$(CXX) $(CXXFLAGS) -shared -o libwhisper.so ggml.o whisper.o $(LDFLAGS)
|
||||
|
||||
clean:
|
||||
rm -f *.o main stream command talk bench libwhisper.a libwhisper.so
|
||||
rm -f *.o main stream command talk talk-llama bench libwhisper.a libwhisper.so
|
||||
|
||||
#
|
||||
# Examples
|
||||
@ -224,21 +211,24 @@ 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)
|
||||
$(CXX) $(CXXFLAGS) examples/main/main.cpp $(SRC_COMMON) ggml.o $(WHISPER_OBJ) -o main $(LDFLAGS)
|
||||
main: examples/main/main.cpp $(SRC_COMMON) ggml.o whisper.o
|
||||
$(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)
|
||||
$(CXX) $(CXXFLAGS) examples/stream/stream.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o stream $(CC_SDL) $(LDFLAGS)
|
||||
bench: examples/bench/bench.cpp ggml.o whisper.o
|
||||
$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o whisper.o -o bench $(LDFLAGS)
|
||||
|
||||
command: examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
|
||||
$(CXX) $(CXXFLAGS) examples/command/command.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o command $(CC_SDL) $(LDFLAGS)
|
||||
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.o -o stream $(CC_SDL) $(LDFLAGS)
|
||||
|
||||
talk: examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ)
|
||||
$(CXX) $(CXXFLAGS) examples/talk/talk.cpp examples/talk/gpt-2.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o $(WHISPER_OBJ) -o talk $(CC_SDL) $(LDFLAGS)
|
||||
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.o -o command $(CC_SDL) $(LDFLAGS)
|
||||
|
||||
bench: examples/bench/bench.cpp ggml.o $(WHISPER_OBJ)
|
||||
$(CXX) $(CXXFLAGS) examples/bench/bench.cpp ggml.o $(WHISPER_OBJ) -o bench $(LDFLAGS)
|
||||
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.o -o talk $(CC_SDL) $(LDFLAGS)
|
||||
|
||||
talk-llama: examples/talk-llama/talk-llama.cpp examples/talk-llama/llama.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o
|
||||
$(CXX) $(CXXFLAGS) examples/talk-llama/talk-llama.cpp examples/talk-llama/llama.cpp $(SRC_COMMON) $(SRC_COMMON_SDL) ggml.o whisper.o -o talk-llama $(CC_SDL) $(LDFLAGS)
|
||||
|
||||
#
|
||||
# Audio samples
|
||||
|
||||
@ -313,7 +313,7 @@ whisper_print_timings: total time = 32733.52 ms
|
||||
## Real-time audio input example
|
||||
|
||||
This is a naive example of performing real-time inference on audio from your microphone.
|
||||
The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continously.
|
||||
The [stream](examples/stream) tool samples the audio every half a second and runs the transcription continuously.
|
||||
More info is available in [issue #10](https://github.com/ggerganov/whisper.cpp/issues/10).
|
||||
|
||||
```java
|
||||
@ -500,6 +500,7 @@ Some of the examples are even ported to run in the browser using WebAssembly. Ch
|
||||
| [stream](examples/stream) | [stream.wasm](examples/stream.wasm) | Real-time transcription of raw microphone capture |
|
||||
| [command](examples/command) | [command.wasm](examples/command.wasm) | Basic voice assistant example for receiving voice commands from the mic |
|
||||
| [talk](examples/talk) | [talk.wasm](examples/talk.wasm) | Talk with a GPT-2 bot |
|
||||
| [talk-llama](examples/talk-llama) | | Talk with a LLaMA bot |
|
||||
| [whisper.objc](examples/whisper.objc) | | iOS mobile application using whisper.cpp |
|
||||
| [whisper.swiftui](examples/whisper.swiftui) | | SwiftUI iOS / macOS application using whisper.cpp |
|
||||
| [whisper.android](examples/whisper.android) | | Android mobile application using whisper.cpp |
|
||||
|
||||
@ -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
|
||||
@ -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
|
||||
@ -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
|
||||
@ -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
|
||||
@ -63,5 +63,5 @@ else()
|
||||
add_subdirectory(command)
|
||||
add_subdirectory(bench)
|
||||
add_subdirectory(talk)
|
||||
add_subdirectory(talk.llama)
|
||||
add_subdirectory(talk-llama)
|
||||
endif()
|
||||
|
||||
@ -1,15 +1,22 @@
|
||||
const path = require('path');
|
||||
const { whisper } = require(path.join(__dirname, '../../../build/Release/whisper-addon'));
|
||||
const path = require("path");
|
||||
const { whisper } = require(path.join(
|
||||
__dirname,
|
||||
"../../../build/Release/whisper-addon"
|
||||
));
|
||||
const { promisify } = require("util");
|
||||
|
||||
const whisperAsync = promisify(whisper);
|
||||
|
||||
const whisperParamsMock = {
|
||||
language: 'en',
|
||||
model: path.join(__dirname, '../../../models/ggml-base.en.bin'),
|
||||
fname_inp: path.join(__dirname, '../../../samples/jfk.wav'),
|
||||
language: "en",
|
||||
model: path.join(__dirname, "../../../models/ggml-base.en.bin"),
|
||||
fname_inp: path.join(__dirname, "../../../samples/jfk.wav"),
|
||||
};
|
||||
|
||||
describe("Run whisper.node", () => {
|
||||
test("it should receive a non-empty value", async () => {
|
||||
let result = await whisperAsync(whisperParamsMock);
|
||||
|
||||
test("it should receive a non-empty value", () => {
|
||||
expect(whisper(whisperParamsMock).length).toBeGreaterThan(0);
|
||||
});
|
||||
expect(result.length).toBeGreaterThan(0);
|
||||
});
|
||||
});
|
||||
|
||||
@ -160,22 +160,6 @@ int run(whisper_params ¶ms, std::vector<std::vector<std::string>> &result) {
|
||||
return 3;
|
||||
}
|
||||
|
||||
// initial prompt
|
||||
std::vector<whisper_token> prompt_tokens;
|
||||
|
||||
if (!params.prompt.empty()) {
|
||||
prompt_tokens.resize(1024);
|
||||
prompt_tokens.resize(whisper_tokenize(ctx, params.prompt.c_str(), prompt_tokens.data(), prompt_tokens.size()));
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "initial prompt: '%s'\n", params.prompt.c_str());
|
||||
fprintf(stderr, "initial tokens: [ ");
|
||||
for (int i = 0; i < (int) prompt_tokens.size(); ++i) {
|
||||
fprintf(stderr, "%d ", prompt_tokens[i]);
|
||||
}
|
||||
fprintf(stderr, "]\n");
|
||||
}
|
||||
|
||||
for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
|
||||
const auto fname_inp = params.fname_inp[f];
|
||||
const auto fname_out = f < (int)params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
|
||||
@ -243,8 +227,7 @@ int run(whisper_params ¶ms, std::vector<std::vector<std::string>> &result) {
|
||||
wparams.greedy.best_of = params.best_of;
|
||||
wparams.beam_search.beam_size = params.beam_size;
|
||||
|
||||
wparams.prompt_tokens = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
|
||||
wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();
|
||||
wparams.initial_prompt = params.prompt.c_str();
|
||||
|
||||
whisper_print_user_data user_data = { ¶ms, &pcmf32s };
|
||||
|
||||
|
||||
@ -8,6 +8,7 @@
|
||||
#include <string>
|
||||
#include <thread>
|
||||
#include <vector>
|
||||
#include <cstring>
|
||||
|
||||
// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
|
||||
// Lowest is red, middle is yellow, highest is green.
|
||||
@ -371,6 +372,39 @@ bool output_csv(struct whisper_context * ctx, const char * fname) {
|
||||
return true;
|
||||
}
|
||||
|
||||
char *escape_double_quotes(const char *str) {
|
||||
if (str == NULL) {
|
||||
return NULL;
|
||||
}
|
||||
|
||||
size_t escaped_length = strlen(str) + 1;
|
||||
|
||||
for (size_t i = 0; str[i] != '\0'; i++) {
|
||||
if (str[i] == '"') {
|
||||
escaped_length++;
|
||||
}
|
||||
}
|
||||
|
||||
char *escaped = (char *)calloc(escaped_length, 1); // pre-zeroed
|
||||
if (escaped == NULL) {
|
||||
return NULL;
|
||||
}
|
||||
|
||||
size_t pos = 0;
|
||||
for (size_t i = 0; str[i] != '\0'; i++) {
|
||||
if (str[i] == '"') {
|
||||
escaped[pos++] = '\\';
|
||||
escaped[pos++] = '"';
|
||||
} else {
|
||||
escaped[pos++] = str[i];
|
||||
}
|
||||
}
|
||||
|
||||
// no need to set zero due to calloc() being used prior
|
||||
|
||||
return escaped;
|
||||
}
|
||||
|
||||
bool output_json(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
|
||||
std::ofstream fout(fname);
|
||||
int indent = 0;
|
||||
@ -414,7 +448,9 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
|
||||
auto value_s = [&](const char *name, const char *val, bool end = false) {
|
||||
start_value(name);
|
||||
fout << "\"" << val << (end ? "\"\n" : "\",\n");
|
||||
char * val_escaped = escape_double_quotes(val);
|
||||
fout << "\"" << val_escaped << (end ? "\"\n" : "\",\n");
|
||||
free(val_escaped);
|
||||
};
|
||||
|
||||
auto end_value = [&](bool end = false) {
|
||||
@ -455,7 +491,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
value_i("ctx", whisper_model_n_text_ctx(ctx));
|
||||
value_i("state", whisper_model_n_text_state(ctx));
|
||||
value_i("head", whisper_model_n_text_head(ctx));
|
||||
value_i("leyer", whisper_model_n_text_layer(ctx), true);
|
||||
value_i("layer", whisper_model_n_text_layer(ctx), true);
|
||||
end_obj();
|
||||
value_i("mels", whisper_model_n_mels(ctx));
|
||||
value_i("f16", whisper_model_f16(ctx), true);
|
||||
@ -477,7 +513,7 @@ bool output_json(struct whisper_context * ctx, const char * fname, const whisper
|
||||
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
|
||||
|
||||
start_obj();
|
||||
start_obj("timestanps");
|
||||
start_obj("timestamps");
|
||||
value_s("from", to_timestamp(t0, true).c_str());
|
||||
value_s("to", to_timestamp(t1, true).c_str(), true);
|
||||
end_obj();
|
||||
@ -639,22 +675,6 @@ int main(int argc, char ** argv) {
|
||||
return 3;
|
||||
}
|
||||
|
||||
// initial prompt
|
||||
std::vector<whisper_token> prompt_tokens;
|
||||
|
||||
if (!params.prompt.empty()) {
|
||||
prompt_tokens.resize(1024);
|
||||
prompt_tokens.resize(whisper_tokenize(ctx, params.prompt.c_str(), prompt_tokens.data(), prompt_tokens.size()));
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "initial prompt: '%s'\n", params.prompt.c_str());
|
||||
fprintf(stderr, "initial tokens: [ ");
|
||||
for (int i = 0; i < (int) prompt_tokens.size(); ++i) {
|
||||
fprintf(stderr, "%d ", prompt_tokens[i]);
|
||||
}
|
||||
fprintf(stderr, "]\n");
|
||||
}
|
||||
|
||||
for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
|
||||
const auto fname_inp = params.fname_inp[f];
|
||||
const auto fname_out = f < (int) params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
|
||||
@ -718,8 +738,7 @@ int main(int argc, char ** argv) {
|
||||
|
||||
wparams.speed_up = params.speed_up;
|
||||
|
||||
wparams.prompt_tokens = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
|
||||
wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();
|
||||
wparams.initial_prompt = params.prompt.c_str();
|
||||
|
||||
wparams.greedy.best_of = params.best_of;
|
||||
wparams.beam_search.beam_size = params.beam_size;
|
||||
|
||||
16
examples/talk-llama/CMakeLists.txt
Normal file
16
examples/talk-llama/CMakeLists.txt
Normal file
@ -0,0 +1,16 @@
|
||||
if (WHISPER_SUPPORT_SDL2)
|
||||
# talk-llama
|
||||
set(TARGET talk-llama)
|
||||
#add_executable(${TARGET} talk-llama.cpp llama.cpp)
|
||||
#target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
|
||||
#target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
|
||||
|
||||
# TODO: this is temporary
|
||||
# need to export ggml symbols for MSVC, but too lazy ..
|
||||
add_executable(${TARGET} talk-llama.cpp llama.cpp ../common.cpp ../common-sdl.cpp ../../ggml.c ../../whisper.cpp)
|
||||
|
||||
target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS} ../../)
|
||||
target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})
|
||||
|
||||
include(DefaultTargetOptions)
|
||||
endif ()
|
||||
36
examples/talk-llama/README.md
Normal file
36
examples/talk-llama/README.md
Normal file
@ -0,0 +1,36 @@
|
||||
# talk-llama
|
||||
|
||||
Talk with an LLaMA AI in your terminal
|
||||
|
||||
[Demo Talk](https://user-images.githubusercontent.com/1991296/228024237-848f998c-c334-46a6-bef8-3271590da83b.mp4)
|
||||
|
||||
## Building
|
||||
|
||||
The `talk-llama` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
|
||||
|
||||
```bash
|
||||
# Install SDL2 on Linux
|
||||
sudo apt-get install libsdl2-dev
|
||||
|
||||
# Install SDL2 on Mac OS
|
||||
brew install sdl2
|
||||
|
||||
# Build the "talk-llama" executable
|
||||
make talk-llama
|
||||
|
||||
# Run it
|
||||
./talk-llama -mw ./models/ggml-small.en.bin -ml ../llama.cpp/models/13B/ggml-model-q4_0.bin -p "Georgi" -t 8
|
||||
```
|
||||
|
||||
- The `-mw` argument specifies the Whisper model that you would like to use. Recommended `base` or `small` for real-time experience
|
||||
- The `-ml` argument specifies the LLaMA model that you would like to use. Read the instructions in https://github.com/ggerganov/llama.cpp for information about how to obtain a `ggml` compatible LLaMA model
|
||||
|
||||
## TTS
|
||||
|
||||
For best experience, this example needs a TTS tool to convert the generated text responses to voice.
|
||||
You can use any TTS engine that you would like - simply edit the [speak.sh](speak.sh) script to your needs.
|
||||
By default, it is configured to use MacOS's `say`, but you can use whatever you wish.
|
||||
|
||||
## Discussion
|
||||
|
||||
If you have any feedback, please let "us" know in the following discussion: https://github.com/ggerganov/whisper.cpp/discussions/672?converting=1
|
||||
@ -12,6 +12,19 @@
|
||||
#include <cassert>
|
||||
#include <cstring>
|
||||
|
||||
#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
|
||||
#define WIN32_LEAN_AND_MEAN
|
||||
#include <Windows.h>
|
||||
#else
|
||||
#include <sys/types.h>
|
||||
#include <sys/mman.h>
|
||||
#include <unistd.h>
|
||||
#include <fcntl.h>
|
||||
#endif
|
||||
|
||||
#define Min(X, Y) ((Y) > (X) ? (X) : (Y))
|
||||
#define Max(X, Y) ((Y) < (X) ? (X) : (Y))
|
||||
|
||||
#define LLAMA_USE_SCRATCH
|
||||
#define LLAMA_MAX_SCRATCH_BUFFERS 16
|
||||
|
||||
@ -142,6 +155,10 @@ struct llama_model {
|
||||
// the model memory buffer
|
||||
std::vector<uint8_t> buf;
|
||||
|
||||
// model memory mapped file
|
||||
void * mm_addr = NULL;
|
||||
uint64_t mm_length = 0;
|
||||
|
||||
// tensors
|
||||
int n_loaded;
|
||||
std::unordered_map<std::string, struct ggml_tensor *> tensors;
|
||||
@ -165,6 +182,7 @@ struct llama_context {
|
||||
|
||||
int64_t t_load_us = 0;
|
||||
int64_t t_start_us = 0;
|
||||
bool has_evaluated_once = false;
|
||||
|
||||
int64_t t_sample_us = 0;
|
||||
int64_t t_eval_us = 0;
|
||||
@ -206,7 +224,7 @@ struct llama_context {
|
||||
}
|
||||
|
||||
if (buf_last >= 0) {
|
||||
buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
|
||||
buf_max_size[buf_last] = Max(buf_max_size[buf_last], last_size);
|
||||
}
|
||||
|
||||
buf_last = i;
|
||||
@ -246,6 +264,7 @@ static bool kv_cache_init(
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = cache.buf.size();
|
||||
params.mem_buffer = cache.buf.data();
|
||||
params.no_alloc = false;
|
||||
|
||||
cache.ctx = ggml_init(params);
|
||||
|
||||
@ -288,6 +307,58 @@ struct llama_context_params llama_context_default_params() {
|
||||
// model loading
|
||||
//
|
||||
|
||||
static void *mmap_file(const char *fname, uint64_t *mm_length) {
|
||||
#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
|
||||
HANDLE hFile = CreateFileA(fname,
|
||||
GENERIC_READ,
|
||||
FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE,
|
||||
NULL,
|
||||
OPEN_EXISTING,
|
||||
FILE_ATTRIBUTE_NORMAL | FILE_ATTRIBUTE_NOT_CONTENT_INDEXED,
|
||||
NULL);
|
||||
if (hFile == INVALID_HANDLE_VALUE) return 0;
|
||||
LARGE_INTEGER fileSize;
|
||||
fileSize.QuadPart = -1;
|
||||
GetFileSizeEx(hFile, &fileSize);
|
||||
int64_t length = fileSize.QuadPart;
|
||||
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
|
||||
CloseHandle(hFile);
|
||||
if (!hMapping) return 0;
|
||||
void *addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
|
||||
CloseHandle(hMapping);
|
||||
if (!addr) return 0;
|
||||
#else
|
||||
int fd = open(fname, O_RDONLY);
|
||||
if (fd == -1) return 0;
|
||||
int64_t length = lseek(fd, 0, SEEK_END);
|
||||
void *addr = mmap(NULL, length, PROT_READ, MAP_SHARED, fd, 0);
|
||||
close(fd);
|
||||
if (addr == MAP_FAILED) return 0;
|
||||
#endif
|
||||
*mm_length = length;
|
||||
return addr;
|
||||
}
|
||||
|
||||
static void munmap_file(void * addr, size_t length) {
|
||||
#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
|
||||
UnmapViewOfFile(addr);
|
||||
#else
|
||||
munmap(addr, length);
|
||||
#endif
|
||||
}
|
||||
|
||||
static bool report_bad_magic(const char *path, uint32_t got, uint32_t want) {
|
||||
fprintf(stderr,
|
||||
"%s: invalid model file (bad magic [got %#x want %#x])\n"
|
||||
"\tyou most likely need to regenerate your ggml files\n"
|
||||
"\tthe benefit is you'll get 10-100x faster load times\n"
|
||||
"\tsee https://github.com/ggerganov/llama.cpp/issues/91\n"
|
||||
"\tuse convert-pth-to-ggml.py to regenerate from original pth\n"
|
||||
"\tuse migrate-ggml-2023-03-30-pr613.py if you deleted originals\n",
|
||||
path, got, want);
|
||||
return false;
|
||||
}
|
||||
|
||||
static bool llama_model_load(
|
||||
const std::string & fname,
|
||||
llama_context & lctx,
|
||||
@ -299,34 +370,35 @@ static bool llama_model_load(
|
||||
void *progress_callback_user_data) {
|
||||
fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
|
||||
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
lctx.t_start_us = t_start_us;
|
||||
|
||||
std::vector<char> f_buf(1024*1024);
|
||||
lctx.t_start_us = ggml_time_us();
|
||||
|
||||
auto & model = lctx.model;
|
||||
auto & vocab = lctx.vocab;
|
||||
|
||||
auto fin = std::ifstream(fname, std::ios::binary);
|
||||
fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
|
||||
if (!fin) {
|
||||
fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
|
||||
return false;
|
||||
}
|
||||
|
||||
std::vector<char> f_buf(1024*1024);
|
||||
fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
|
||||
|
||||
fin.seekg(0, fin.end);
|
||||
const size_t file_size = fin.tellg();
|
||||
fin.seekg(0);
|
||||
|
||||
// verify magic
|
||||
{
|
||||
uint32_t magic;
|
||||
fin.read((char *) &magic, sizeof(magic));
|
||||
if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
|
||||
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
|
||||
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files or convert them with convert-unversioned-ggml-to-ggml.py!)\n",
|
||||
__func__, fname.c_str());
|
||||
return false;
|
||||
}
|
||||
if (magic != LLAMA_FILE_MAGIC) {
|
||||
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
|
||||
return false;
|
||||
return report_bad_magic(fname.c_str(), magic, LLAMA_FILE_MAGIC);
|
||||
}
|
||||
|
||||
uint32_t format_version;
|
||||
@ -449,43 +521,24 @@ static bool llama_model_load(
|
||||
}
|
||||
}
|
||||
|
||||
// map model into memory
|
||||
char *mm_addr = NULL;
|
||||
model.mm_addr = mmap_file(fname.c_str(), &model.mm_length);
|
||||
if (model.mm_addr == NULL) {
|
||||
fprintf(stderr, "%s: failed to mmap '%s'\n", __func__, fname.c_str());
|
||||
return false;
|
||||
}
|
||||
mm_addr = (char *)model.mm_addr;
|
||||
fprintf(stderr, "%s: ggml map size = %6.2f MB\n", __func__, model.mm_length/(1024.0*1024.0));
|
||||
|
||||
auto & ctx = model.ctx;
|
||||
|
||||
size_t ctx_size = 0;
|
||||
|
||||
{
|
||||
const auto & hparams = model.hparams;
|
||||
|
||||
const int n_embd = hparams.n_embd;
|
||||
const auto &hparams = model.hparams;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_ctx = hparams.n_ctx;
|
||||
const int n_vocab = hparams.n_vocab;
|
||||
|
||||
ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
|
||||
|
||||
ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
|
||||
|
||||
ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
|
||||
|
||||
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
|
||||
|
||||
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wq
|
||||
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wk
|
||||
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wv
|
||||
ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // wo
|
||||
|
||||
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ffn_norm
|
||||
|
||||
ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w1
|
||||
ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
|
||||
ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
|
||||
|
||||
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
|
||||
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
|
||||
|
||||
ctx_size += (5 + 10*n_layer)*256; // object overhead
|
||||
|
||||
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
|
||||
fprintf(stderr, "%s: ggml ctx size = %6.2f KB\n", __func__, ctx_size/1024.0);
|
||||
}
|
||||
|
||||
// print memory requirements
|
||||
@ -495,6 +548,7 @@ static bool llama_model_load(
|
||||
// this is the total memory required to run the inference
|
||||
const size_t mem_required =
|
||||
ctx_size +
|
||||
model.mm_length +
|
||||
MEM_REQ_SCRATCH0.at(model.type) +
|
||||
MEM_REQ_SCRATCH1.at(model.type) +
|
||||
MEM_REQ_EVAL.at (model.type);
|
||||
@ -514,6 +568,7 @@ static bool llama_model_load(
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ lctx.model.buf.size(),
|
||||
/*.mem_buffer =*/ lctx.model.buf.data(),
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
|
||||
model.ctx = ggml_init(params);
|
||||
@ -576,234 +631,106 @@ static bool llama_model_load(
|
||||
}
|
||||
}
|
||||
|
||||
const size_t file_offset = fin.tellg();
|
||||
|
||||
fin.close();
|
||||
|
||||
std::vector<uint8_t> tmp;
|
||||
|
||||
if (progress_callback) {
|
||||
progress_callback(0.0, progress_callback_user_data);
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_parts; ++i) {
|
||||
const int part_id = i;
|
||||
//const int part_id = n_parts - i - 1;
|
||||
fprintf(stderr, "%s: loading tensors from '%s'\n", __func__, fname.c_str());
|
||||
|
||||
std::string fname_part = fname;
|
||||
if (i > 0) {
|
||||
fname_part += "." + std::to_string(i);
|
||||
}
|
||||
// load weights
|
||||
{
|
||||
size_t total_size = 0;
|
||||
model.n_loaded = 0;
|
||||
|
||||
fprintf(stderr, "%s: loading model part %d/%d from '%s'\n", __func__, i+1, n_parts, fname_part.c_str());
|
||||
while (true) {
|
||||
int32_t n_dims;
|
||||
int32_t length;
|
||||
int32_t ftype;
|
||||
|
||||
fin = std::ifstream(fname_part, std::ios::binary);
|
||||
fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
|
||||
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
||||
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
||||
|
||||
fin.seekg(0, fin.end);
|
||||
const size_t file_size = fin.tellg();
|
||||
|
||||
fin.seekg(file_offset);
|
||||
|
||||
// load weights
|
||||
{
|
||||
size_t total_size = 0;
|
||||
|
||||
model.n_loaded = 0;
|
||||
|
||||
fprintf(stderr, "%s: ", __func__);
|
||||
|
||||
while (true) {
|
||||
int32_t n_dims;
|
||||
int32_t length;
|
||||
int32_t ftype;
|
||||
|
||||
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
||||
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
||||
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
||||
|
||||
if (fin.eof()) {
|
||||
break;
|
||||
}
|
||||
|
||||
int32_t nelements = 1;
|
||||
int32_t ne[2] = { 1, 1 };
|
||||
for (int i = 0; i < n_dims; ++i) {
|
||||
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
||||
nelements *= ne[i];
|
||||
}
|
||||
|
||||
std::string name(length, 0);
|
||||
fin.read(&name[0], length);
|
||||
|
||||
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
||||
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
|
||||
// split_type = 0: split by columns
|
||||
// split_type = 1: split by rows
|
||||
int split_type = 0;
|
||||
|
||||
// split_type = 0:
|
||||
// regex:
|
||||
// - tok_embeddings.*
|
||||
// - layers.*.attention.wo.weight
|
||||
// - layers.*.feed_forward.w2.weight
|
||||
|
||||
// split_type = 1:
|
||||
// regex:
|
||||
// - output.*
|
||||
// - layers.*.attention.wq.weight
|
||||
// - layers.*.attention.wk.weight
|
||||
// - layers.*.attention.wv.weight
|
||||
// - layers.*.feed_forward.w1.weight
|
||||
// - layers.*.feed_forward.w3.weight
|
||||
if (name.find("tok_embeddings") != std::string::npos) {
|
||||
split_type = 0;
|
||||
} else if (name.find("layers") != std::string::npos) {
|
||||
if (name.find("attention.wo.weight") != std::string::npos) {
|
||||
split_type = 0;
|
||||
} else if (name.find("feed_forward.w2.weight") != std::string::npos) {
|
||||
split_type = 0;
|
||||
} else {
|
||||
split_type = 1;
|
||||
}
|
||||
} else if (name.find("output") != std::string::npos) {
|
||||
split_type = 1;
|
||||
}
|
||||
|
||||
auto tensor = model.tensors[name.data()];
|
||||
|
||||
if (n_dims == 1) {
|
||||
if (ggml_nelements(tensor) != nelements) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (ggml_nelements(tensor)/n_parts != nelements) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
if (n_dims == 1) {
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (split_type == 0) {
|
||||
if (tensor->ne[0]/n_parts != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0]/n_parts, tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
} else {
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1]/n_parts != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1]/n_parts, ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (0) {
|
||||
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
||||
fprintf(stderr, "%24s - [%5d, %5d], type = %6s, split = %d\n", name.data(), ne[0], ne[1], ftype_str[ftype], split_type);
|
||||
}
|
||||
|
||||
size_t bpe = 0;
|
||||
|
||||
switch (ftype) {
|
||||
case 0: bpe = ggml_type_size(GGML_TYPE_F32); break;
|
||||
case 1: bpe = ggml_type_size(GGML_TYPE_F16); break;
|
||||
case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break;
|
||||
case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break;
|
||||
default:
|
||||
{
|
||||
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
if (n_dims == 1 || n_parts == 1) {
|
||||
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
||||
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (part_id == 0) {
|
||||
fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
|
||||
} else {
|
||||
fin.seekg(ggml_nbytes(tensor), std::ios::cur);
|
||||
}
|
||||
|
||||
total_size += ggml_nbytes(tensor);
|
||||
} else {
|
||||
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)/n_parts) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
||||
__func__, name.data(), ggml_nbytes(tensor)/n_parts, nelements*bpe);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (split_type == 0) {
|
||||
const int np0 = ne[0];
|
||||
|
||||
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
||||
assert(row_size == tensor->nb[1]);
|
||||
|
||||
for (int i1 = 0; i1 < ne[1]; ++i1) {
|
||||
const size_t offset_row = i1*row_size;
|
||||
const size_t offset = offset_row + ((part_id*np0)/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
||||
fin.read(reinterpret_cast<char *>(tensor->data) + offset, row_size/n_parts);
|
||||
}
|
||||
} else {
|
||||
const int np1 = ne[1];
|
||||
|
||||
const size_t row_size = (tensor->ne[0]/ggml_blck_size(tensor->type))*ggml_type_size(tensor->type);
|
||||
|
||||
for (int i1 = 0; i1 < ne[1]; ++i1) {
|
||||
const size_t offset_row = (i1 + part_id*np1)*row_size;
|
||||
fin.read(reinterpret_cast<char *>(tensor->data) + offset_row, row_size);
|
||||
}
|
||||
}
|
||||
|
||||
total_size += ggml_nbytes(tensor)/n_parts;
|
||||
}
|
||||
|
||||
//fprintf(stderr, "%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
||||
model.n_loaded++;
|
||||
|
||||
// progress
|
||||
if (progress_callback) {
|
||||
double current_file_progress = double(size_t(fin.tellg()) - file_offset) / double(file_size - file_offset);
|
||||
double current_progress = (double(i) + current_file_progress) / double(n_parts);
|
||||
progress_callback(current_progress, progress_callback_user_data);
|
||||
}
|
||||
if (model.n_loaded % 8 == 0) {
|
||||
fprintf(stderr, ".");
|
||||
fflush(stderr);
|
||||
}
|
||||
if (fin.eof()) {
|
||||
break;
|
||||
}
|
||||
|
||||
fprintf(stderr, " done\n");
|
||||
int32_t nelements = 1;
|
||||
int32_t ne[2] = { 1, 1 };
|
||||
for (int i = 0; i < n_dims; ++i) {
|
||||
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
||||
nelements *= ne[i];
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
|
||||
if (model.n_loaded == 0) {
|
||||
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
|
||||
} else if (model.n_loaded != (int) model.tensors.size()) {
|
||||
fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
|
||||
std::string name(length, 0);
|
||||
fin.read(&name[0], length);
|
||||
|
||||
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
||||
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
|
||||
auto tensor = model.tensors[name.data()];
|
||||
|
||||
if (ggml_nelements(tensor) != nelements) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
return false;
|
||||
}
|
||||
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
||||
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
|
||||
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
||||
return false;
|
||||
}
|
||||
if (0) {
|
||||
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
||||
fprintf(stderr, "%24s - [%5d, %5d], type = %6s\n", name.data(), ne[0], ne[1], ftype_str[ftype]);
|
||||
}
|
||||
|
||||
switch (ftype) {
|
||||
case 0: // f32
|
||||
case 1: // f16
|
||||
break;
|
||||
case 2: // q4_0
|
||||
case 3: // q4_1
|
||||
assert(ne[0] % 64 == 0);
|
||||
break;
|
||||
default:
|
||||
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
||||
return false;
|
||||
};
|
||||
|
||||
// load the tensor data into memory without copying or reading it
|
||||
size_t offset = fin.tellg();
|
||||
size_t tensor_data_size = ggml_nbytes(tensor);
|
||||
offset = (offset + 31) & -32;
|
||||
tensor->data = mm_addr + offset;
|
||||
fin.seekg(offset + tensor_data_size);
|
||||
total_size += tensor_data_size;
|
||||
model.n_loaded++;
|
||||
|
||||
// progress
|
||||
if (progress_callback) {
|
||||
double current_progress = size_t(fin.tellg()) / double(file_size);
|
||||
progress_callback(current_progress, progress_callback_user_data);
|
||||
}
|
||||
}
|
||||
|
||||
fin.close();
|
||||
|
||||
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
|
||||
if (model.n_loaded == 0) {
|
||||
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
|
||||
} else if (model.n_loaded != (int) model.tensors.size()) {
|
||||
fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
lctx.t_load_us = ggml_time_us() - t_start_us;
|
||||
// loading time will be recalculate after the first eval, so
|
||||
// we take page faults deferred by mmap() into consideration
|
||||
lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
|
||||
|
||||
if (progress_callback) {
|
||||
progress_callback(1.0, progress_callback_user_data);
|
||||
@ -849,6 +776,7 @@ static bool llama_eval_internal(
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ buf_compute.size(),
|
||||
/*.mem_buffer =*/ buf_compute.data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
@ -856,7 +784,7 @@ static bool llama_eval_internal(
|
||||
// for big prompts, if BLAS is enabled, it is better to use only one thread
|
||||
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
|
||||
ggml_cgraph gf = {};
|
||||
gf.n_threads = N > 255 && ggml_cpu_has_blas() ? 1 : n_threads;
|
||||
gf.n_threads = N >= 32 && ggml_cpu_has_blas() ? 1 : n_threads;
|
||||
|
||||
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
||||
@ -922,7 +850,7 @@ static bool llama_eval_internal(
|
||||
struct ggml_tensor * KQ_scaled =
|
||||
ggml_scale(ctx0,
|
||||
KQ,
|
||||
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)));
|
||||
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
||||
@ -1126,7 +1054,7 @@ struct llama_tokenizer {
|
||||
size_t offs = 0;
|
||||
while (offs < text.size()) {
|
||||
llama_sp_symbol sym;
|
||||
size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
|
||||
size_t char_len = Min(text.size() - offs, utf8_len(text[offs]));
|
||||
sym.text = text.c_str() + offs;
|
||||
sym.n = char_len;
|
||||
offs += char_len;
|
||||
@ -1240,12 +1168,12 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
|
||||
// sampling
|
||||
//
|
||||
|
||||
static void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
|
||||
static void sample_top_k(std::vector<std::pair<float, llama_vocab::id>> & logits_id, int top_k) {
|
||||
// find the top k tokens
|
||||
std::partial_sort(
|
||||
logits_id.begin(),
|
||||
logits_id.begin() + top_k, logits_id.end(),
|
||||
[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
|
||||
[](const std::pair<float, llama_vocab::id> & a, const std::pair<float, llama_vocab::id> & b) {
|
||||
return a.first > b.first;
|
||||
});
|
||||
|
||||
@ -1256,9 +1184,9 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
llama_context & lctx,
|
||||
const std::vector<llama_vocab::id> & last_n_tokens,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
double repeat_penalty) {
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty) {
|
||||
auto & rng = lctx.rng;
|
||||
|
||||
const int n_logits = lctx.model.hparams.n_vocab;
|
||||
@ -1266,17 +1194,17 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
const auto & logits = lctx.logits;
|
||||
const auto * plogits = logits.data() + logits.size() - n_logits;
|
||||
|
||||
std::vector<std::pair<double, llama_vocab::id>> logits_id;
|
||||
std::vector<std::pair<float, llama_vocab::id>> logits_id;
|
||||
logits_id.reserve(n_logits);
|
||||
|
||||
{
|
||||
const double scale = 1.0/temp;
|
||||
const float scale = 1.0f/temp;
|
||||
for (int i = 0; i < n_logits; ++i) {
|
||||
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
||||
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
||||
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
||||
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
||||
if (plogits[i] < 0.0) {
|
||||
if (plogits[i] < 0.0f) {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
||||
@ -1289,18 +1217,18 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
|
||||
sample_top_k(logits_id, top_k);
|
||||
|
||||
double maxl = -std::numeric_limits<double>::infinity();
|
||||
float maxl = -std::numeric_limits<float>::infinity();
|
||||
for (const auto & kv : logits_id) {
|
||||
maxl = std::max(maxl, kv.first);
|
||||
maxl = Max(maxl, kv.first);
|
||||
}
|
||||
|
||||
// compute probs for the top k tokens
|
||||
std::vector<double> probs;
|
||||
std::vector<float> probs;
|
||||
probs.reserve(logits_id.size());
|
||||
|
||||
double sum = 0.0;
|
||||
for (const auto & kv : logits_id) {
|
||||
double p = exp(kv.first - maxl);
|
||||
const float p = expf(kv.first - maxl);
|
||||
probs.push_back(p);
|
||||
sum += p;
|
||||
}
|
||||
@ -1310,8 +1238,8 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
p /= sum;
|
||||
}
|
||||
|
||||
if (top_p < 1.0f) {
|
||||
double cumsum = 0.0f;
|
||||
if (top_p < 1.0) {
|
||||
double cumsum = 0.0;
|
||||
for (int i = 0; i < (int) probs.size(); i++) {
|
||||
cumsum += probs[i];
|
||||
if (cumsum >= top_p) {
|
||||
@ -1345,7 +1273,7 @@ static llama_vocab::id llama_sample_top_p_top_k(
|
||||
//
|
||||
|
||||
// TODO: reuse code from the llama_model_load() somehow
|
||||
bool llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype, int qk) {
|
||||
static bool llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype) {
|
||||
ggml_type type = GGML_TYPE_Q4_1;
|
||||
|
||||
switch (itype) {
|
||||
@ -1385,8 +1313,7 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
return false;
|
||||
}
|
||||
if (magic != LLAMA_FILE_MAGIC) {
|
||||
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
|
||||
return false;
|
||||
return report_bad_magic(fname_inp.c_str(), magic, LLAMA_FILE_MAGIC);
|
||||
}
|
||||
|
||||
fout.write((char *) &magic, sizeof(magic));
|
||||
@ -1444,7 +1371,7 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
return false;
|
||||
}
|
||||
|
||||
std::string word;
|
||||
std::vector<char> word(32);
|
||||
vocab.id_to_token.resize(n_vocab);
|
||||
for (int i = 0; i < n_vocab; i++) {
|
||||
uint32_t len;
|
||||
@ -1452,17 +1379,17 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
fout.write((char *) &len, sizeof(len));
|
||||
|
||||
word.resize(len);
|
||||
finp.read ((char *) word.data(), len);
|
||||
fout.write((char *) word.data(), len);
|
||||
finp.read ((char *) &word[0], len);
|
||||
fout.write((char *) &word[0], len);
|
||||
|
||||
float score;
|
||||
finp.read ((char *) &score, sizeof(score));
|
||||
fout.write((char *) &score, sizeof(score));
|
||||
|
||||
vocab.token_to_id[word] = i;
|
||||
vocab.token_to_id[word.data()] = i;
|
||||
|
||||
auto &tok_score = vocab.id_to_token[i];
|
||||
tok_score.tok = word;
|
||||
tok_score.tok = word.data();
|
||||
tok_score.score = score;
|
||||
}
|
||||
}
|
||||
@ -1503,6 +1430,13 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
std::string name(length, 0);
|
||||
finp.read (&name[0], length);
|
||||
|
||||
{
|
||||
// ensure tensor data is aligned
|
||||
uint64_t offset = finp.tellg();
|
||||
offset = (offset + 31) & -32;
|
||||
finp.seekg(offset);
|
||||
}
|
||||
|
||||
{
|
||||
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
||||
printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
|
||||
@ -1558,6 +1492,13 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
}
|
||||
fout.write(&name[0], length);
|
||||
|
||||
{
|
||||
// ensure tensor data is aligned
|
||||
uint64_t offset = fout.tellp();
|
||||
offset = (offset + 31) & -32;
|
||||
fout.seekp(offset);
|
||||
}
|
||||
|
||||
if (quantize) {
|
||||
printf("quantizing .. ");
|
||||
work.resize(nelements); // for quantization
|
||||
@ -1568,11 +1509,11 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
switch (type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
{
|
||||
cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data());
|
||||
cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
|
||||
} break;
|
||||
case GGML_TYPE_Q4_1:
|
||||
{
|
||||
cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], qk, hist_cur.data());
|
||||
cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
@ -1590,7 +1531,7 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
}
|
||||
|
||||
for (int i = 0; i < (int) hist_cur.size(); ++i) {
|
||||
printf("%5.3f ", hist_cur[i] / (float)nelements);
|
||||
printf("%5.3f ", hist_cur[i] / float(nelements));
|
||||
}
|
||||
printf("\n");
|
||||
} else {
|
||||
@ -1613,7 +1554,7 @@ bool llama_model_quantize_internal(const std::string & fname_inp, const std::str
|
||||
|
||||
printf("%s: hist: ", __func__);
|
||||
for (int i = 0; i < (int) hist_all.size(); ++i) {
|
||||
printf("%5.3f ", hist_all[i] / (float)sum_all);
|
||||
printf("%5.3f ", hist_all[i] / float(sum_all));
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
@ -1655,7 +1596,10 @@ struct llama_context * llama_init_from_file(
|
||||
|
||||
if (params.use_mlock) {
|
||||
char *err;
|
||||
if (!ggml_mlock(ctx->model.ctx, &err)) {
|
||||
if (!ggml_mlock(ctx->model.ctx,
|
||||
ctx->model.mm_addr,
|
||||
ctx->model.mm_length,
|
||||
&err)) {
|
||||
fprintf(stderr, "%s\n", err);
|
||||
free(err);
|
||||
llama_free(ctx);
|
||||
@ -1705,15 +1649,18 @@ void llama_free(struct llama_context * ctx) {
|
||||
ggml_free(ctx->model.ctx);
|
||||
}
|
||||
|
||||
if (ctx->model.mm_addr) {
|
||||
munmap_file(ctx->model.mm_addr, ctx->model.mm_length);
|
||||
}
|
||||
|
||||
delete ctx;
|
||||
}
|
||||
|
||||
int llama_model_quantize(
|
||||
const char * fname_inp,
|
||||
const char * fname_out,
|
||||
int itype,
|
||||
int qk) {
|
||||
if (!llama_model_quantize_internal(fname_inp, fname_out, itype, qk)) {
|
||||
int itype) {
|
||||
if (!llama_model_quantize_internal(fname_inp, fname_out, itype)) {
|
||||
fprintf(stderr, "%s: failed to quantize\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
@ -1731,7 +1678,11 @@ int llama_eval(
|
||||
fprintf(stderr, "%s: failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
// get a more accurate load time, upon first eval
|
||||
if (!ctx->has_evaluated_once) {
|
||||
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
|
||||
ctx->has_evaluated_once = true;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
@ -1796,9 +1747,9 @@ llama_token llama_sample_top_p_top_k(
|
||||
const llama_token * last_n_tokens_data,
|
||||
int last_n_tokens_size,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
double repeat_penalty) {
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
llama_token result = 0;
|
||||
@ -1824,21 +1775,20 @@ llama_token llama_sample_top_p_top_k(
|
||||
void llama_print_timings(struct llama_context * ctx) {
|
||||
const int64_t t_end_us = ggml_time_us();
|
||||
|
||||
const int32_t n_sample = std::max(1, ctx->n_sample);
|
||||
const int32_t n_eval = std::max(1, ctx->n_eval);
|
||||
const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
|
||||
const int32_t n_sample = Max(1, ctx->n_sample);
|
||||
const int32_t n_eval = Max(1, ctx->n_eval);
|
||||
const int32_t n_p_eval = Max(1, ctx->n_p_eval);
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
|
||||
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample);
|
||||
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3f * ctx->t_p_eval_us, n_p_eval, 1e-3f * ctx->t_p_eval_us / n_p_eval);
|
||||
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us, n_eval, 1e-3f * ctx->t_eval_us / n_eval);
|
||||
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
|
||||
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
|
||||
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample);
|
||||
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval);
|
||||
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval);
|
||||
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
|
||||
}
|
||||
|
||||
void llama_reset_timings(struct llama_context * ctx) {
|
||||
ctx->t_start_us = ggml_time_us();
|
||||
|
||||
ctx->t_sample_us = ctx->n_sample = 0;
|
||||
ctx->t_eval_us = ctx->n_eval = 0;
|
||||
ctx->t_p_eval_us = ctx->n_p_eval = 0;
|
||||
@ -6,7 +6,7 @@
|
||||
#include <stdbool.h>
|
||||
|
||||
#ifdef LLAMA_SHARED
|
||||
# ifdef _WIN32
|
||||
# if defined(_WIN32) && !defined(__MINGW32__)
|
||||
# ifdef LLAMA_BUILD
|
||||
# define LLAMA_API __declspec(dllexport)
|
||||
# else
|
||||
@ -20,7 +20,7 @@
|
||||
#endif
|
||||
|
||||
#define LLAMA_FILE_VERSION 1
|
||||
#define LLAMA_FILE_MAGIC 0x67676d66 // 'ggmf' in hex
|
||||
#define LLAMA_FILE_MAGIC 0x67676a74 // 'ggjt' in hex
|
||||
#define LLAMA_FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
|
||||
|
||||
#ifdef __cplusplus
|
||||
@ -45,7 +45,7 @@ extern "C" {
|
||||
|
||||
} llama_token_data;
|
||||
|
||||
typedef void (*llama_progress_callback)(double progress, void *ctx);
|
||||
typedef void (*llama_progress_callback)(float progress, void *ctx);
|
||||
|
||||
struct llama_context_params {
|
||||
int n_ctx; // text context
|
||||
@ -81,8 +81,7 @@ extern "C" {
|
||||
LLAMA_API int llama_model_quantize(
|
||||
const char * fname_inp,
|
||||
const char * fname_out,
|
||||
int itype,
|
||||
int qk);
|
||||
int itype);
|
||||
|
||||
// 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
|
||||
@ -135,9 +134,9 @@ extern "C" {
|
||||
const llama_token * last_n_tokens_data,
|
||||
int last_n_tokens_size,
|
||||
int top_k,
|
||||
double top_p,
|
||||
double temp,
|
||||
double repeat_penalty);
|
||||
float top_p,
|
||||
float temp,
|
||||
float repeat_penalty);
|
||||
|
||||
// Performance information
|
||||
LLAMA_API void llama_print_timings(struct llama_context * ctx);
|
||||
23
examples/talk-llama/prompts/talk-alpaca.txt
Normal file
23
examples/talk-llama/prompts/talk-alpaca.txt
Normal file
@ -0,0 +1,23 @@
|
||||
Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
||||
|
||||
### Instruction:
|
||||
|
||||
Write a text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
|
||||
{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
|
||||
There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
|
||||
The transcript only includes text, it does not include markup like HTML and Markdown.
|
||||
{1} responds with short and concise answers.
|
||||
|
||||
### Response:
|
||||
|
||||
{0}{4} Hello, {1}!
|
||||
{1}{4} Hello {0}! How may I help you today?
|
||||
{0}{4} What time is it?
|
||||
{1}{4} It is {2} o'clock.
|
||||
{0}{4} What year is it?
|
||||
{1}{4} We are in {3}.
|
||||
{0}{4} What is a cat?
|
||||
{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
|
||||
{0}{4} Name a color.
|
||||
{1}{4} Blue
|
||||
{0}{4}
|
||||
@ -10,7 +10,15 @@
|
||||
#espeak -v en-us+m$1 -s 225 -p 50 -a 200 -g 5 -k 5 "$2"
|
||||
|
||||
# for Mac
|
||||
say "$2"
|
||||
if [ "$1" = "0" ]; then
|
||||
say "$2"
|
||||
elif [ "$1" = "1" ]; then
|
||||
say -v "Samantha (Enhanced)" "$2"
|
||||
elif [ "$1" = "2" ]; then
|
||||
say -v "Daniel (Enhanced)" "$2"
|
||||
elif [ "$1" = "3" ]; then
|
||||
say -v "Veena (Enhanced)" "$2"
|
||||
fi
|
||||
|
||||
# Eleven Labs
|
||||
#
|
||||
703
examples/talk-llama/talk-llama.cpp
Normal file
703
examples/talk-llama/talk-llama.cpp
Normal file
@ -0,0 +1,703 @@
|
||||
// Talk with AI
|
||||
//
|
||||
|
||||
#include "common.h"
|
||||
#include "common-sdl.h"
|
||||
#include "whisper.h"
|
||||
#include "llama.h"
|
||||
|
||||
#include <map>
|
||||
#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_id = 0;
|
||||
int32_t voice_ms = 10000;
|
||||
int32_t capture_id = -1;
|
||||
int32_t max_tokens = 64;
|
||||
int32_t audio_ctx = 0;
|
||||
|
||||
int32_t n_parts_llama = -1;
|
||||
|
||||
float vad_thold = 0.4f;
|
||||
float freq_thold = 100.0f;
|
||||
|
||||
bool speed_up = false;
|
||||
bool translate = false;
|
||||
bool print_special = false;
|
||||
bool print_energy = false;
|
||||
bool no_timestamps = true;
|
||||
bool verbose_prompt = false;
|
||||
|
||||
std::string name_ni = "Georgi"; // natural intelligence
|
||||
std::string name_ai = "LLaMA"; // artificial intelligence
|
||||
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 prompt = "";
|
||||
std::string fname_out;
|
||||
};
|
||||
|
||||
void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
|
||||
|
||||
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
|
||||
for (int i = 1; i < argc; i++) {
|
||||
std::string arg = argv[i];
|
||||
|
||||
if (arg == "-h" || arg == "--help") {
|
||||
whisper_print_usage(argc, argv, params);
|
||||
exit(0);
|
||||
}
|
||||
else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
|
||||
else if (arg == "-vid" || arg == "--voice-id") { params.voice_id = std::stoi(argv[++i]); }
|
||||
else if (arg == "-vms" || arg == "--voice-ms") { params.voice_ms = std::stoi(argv[++i]); }
|
||||
else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
|
||||
else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
|
||||
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
|
||||
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
|
||||
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
|
||||
else if (arg == "--n-parts-llama") { params.n_parts_llama = std::stoi(argv[++i]); }
|
||||
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
|
||||
else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
|
||||
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
|
||||
else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
|
||||
else if (arg == "--verbose-prompt") { params.verbose_prompt = true; }
|
||||
else if (arg == "-nni" || arg == "--name-ni") { params.name_ni = argv[++i]; }
|
||||
else if (arg == "-nai" || arg == "--name-ai") { params.name_ai = argv[++i]; }
|
||||
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
|
||||
else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; }
|
||||
else if (arg == "-ml" || arg == "--model-llama") { params.model_llama = argv[++i]; }
|
||||
else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; }
|
||||
else if (arg == "--prompt-file") {
|
||||
std::ifstream file(argv[++i]);
|
||||
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
|
||||
if (params.prompt.back() == '\n') {
|
||||
params.prompt.pop_back();
|
||||
}
|
||||
}
|
||||
else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
|
||||
else {
|
||||
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
||||
whisper_print_usage(argc, argv, params);
|
||||
exit(0);
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "usage: %s [options]\n", argv[0]);
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "options:\n");
|
||||
fprintf(stderr, " -h, --help [default] show this help message and exit\n");
|
||||
fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
|
||||
fprintf(stderr, " -vid N, --voice-id N [%-7d] voice ID\n", params.voice_id);
|
||||
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, " -nni NAME,--name-ni NAME [%-7s] natural intelligence name\n", params.name_ni.c_str());
|
||||
fprintf(stderr, " -nai NAME,--name-ai NAME [%-7s] artificial intelligence name\n", params.name_ai.c_str());
|
||||
fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
|
||||
fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str());
|
||||
fprintf(stderr, " -ml FILE, --model-llama [%-7s] llama model file\n", params.model_llama.c_str());
|
||||
fprintf(stderr, " --n-parts-llama N [%-7d] num parts in llama model file\n", params.n_parts_llama);
|
||||
fprintf(stderr, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str());
|
||||
fprintf(stderr, " --prompt-file FNAME [%-7s] file with custom prompt to start dialog\n", "");
|
||||
fprintf(stderr, " --verbose-prompt [%-7s] print prompt at start\n", params.verbose_prompt ? "true" : "false");
|
||||
fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str());
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
|
||||
std::string transcribe(
|
||||
whisper_context * ctx,
|
||||
const whisper_params & params,
|
||||
const std::vector<float> & pcmf32,
|
||||
const std::string prompt_text,
|
||||
float & prob,
|
||||
int64_t & t_ms) {
|
||||
const auto t_start = std::chrono::high_resolution_clock::now();
|
||||
|
||||
prob = 0.0f;
|
||||
t_ms = 0;
|
||||
|
||||
std::vector<whisper_token> prompt_tokens;
|
||||
|
||||
whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
|
||||
|
||||
prompt_tokens.resize(1024);
|
||||
prompt_tokens.resize(whisper_tokenize(ctx, prompt_text.c_str(), prompt_tokens.data(), prompt_tokens.size()));
|
||||
|
||||
wparams.print_progress = false;
|
||||
wparams.print_special = params.print_special;
|
||||
wparams.print_realtime = false;
|
||||
wparams.print_timestamps = !params.no_timestamps;
|
||||
wparams.translate = params.translate;
|
||||
wparams.no_context = true;
|
||||
wparams.single_segment = true;
|
||||
wparams.max_tokens = params.max_tokens;
|
||||
wparams.language = params.language.c_str();
|
||||
wparams.n_threads = 2;
|
||||
|
||||
wparams.prompt_tokens = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
|
||||
wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();
|
||||
|
||||
wparams.audio_ctx = params.audio_ctx;
|
||||
wparams.speed_up = params.speed_up;
|
||||
|
||||
static int iter = params.voice_id;
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(100*iter));
|
||||
iter = (iter + 1) % 4;
|
||||
|
||||
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
|
||||
return "";
|
||||
}
|
||||
|
||||
int prob_n = 0;
|
||||
std::string result;
|
||||
|
||||
const int n_segments = whisper_full_n_segments(ctx);
|
||||
for (int i = 0; i < n_segments; ++i) {
|
||||
const char * text = whisper_full_get_segment_text(ctx, i);
|
||||
|
||||
result += text;
|
||||
|
||||
const int n_tokens = whisper_full_n_tokens(ctx, i);
|
||||
for (int j = 0; j < n_tokens; ++j) {
|
||||
const auto token = whisper_full_get_token_data(ctx, i, j);
|
||||
|
||||
prob += token.p;
|
||||
++prob_n;
|
||||
}
|
||||
}
|
||||
|
||||
if (prob_n > 0) {
|
||||
prob /= prob_n;
|
||||
}
|
||||
|
||||
const auto t_end = std::chrono::high_resolution_clock::now();
|
||||
t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
const std::vector<std::string> k_participants = {
|
||||
"LLaMA",
|
||||
"GGaMA",
|
||||
"SSaMA",
|
||||
"RRaMA",
|
||||
};
|
||||
|
||||
// homophones
|
||||
const std::map<std::string, std::vector<std::string>> k_homophones = {
|
||||
{ "LLaMA", { "llama", "Llama", "LLAMA", }, },
|
||||
{ "GGaMA", { "gama", "Gama", "GAMA", "gamma", "Gamma", "GAMMA", }, },
|
||||
{ "SSaMA", { "sama", "Sama", "SAMA", "samma", "Samma", "SAMMA", }, },
|
||||
{ "RRaMA", { "rama", "Rama", "RAMA", "ramma", "Ramma", "RAMMA", }, },
|
||||
};
|
||||
|
||||
const std::string k_prompt_whisper = R"(A conversation between {1}, {10}, {11}, {12} and {13}.)";
|
||||
|
||||
const std::map<std::string, std::string> k_prompt = {
|
||||
{
|
||||
k_participants.at(0),
|
||||
R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
|
||||
There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
|
||||
The transcript only includes text, it does not include markup like HTML and Markdown.
|
||||
{10}, {11}, {12} and {13} respond with short and concise answers.
|
||||
{10} is smart, objective, honest and kind. Never fails to give a meaningful and insightful answer and opinion.
|
||||
{1} is leading the conversation and asking the questions.
|
||||
|
||||
{1}{4} Hello {10}! What is your opinion on the current state of the world?
|
||||
{10}{4} Great question {1}! I think we live in a very interesting time.
|
||||
There are many things to be concerned about, but also many things to be optimistic about.
|
||||
{1}{4} What advice would you give to a young person who is just starting out in life?
|
||||
{10}{4} I would tell them to be patient and to not be afraid to fail.
|
||||
It is important to learn from your mistakes and to keep trying.
|
||||
{1}{4})"
|
||||
},
|
||||
{
|
||||
k_participants.at(1),
|
||||
R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
|
||||
There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
|
||||
The transcript only includes text, it does not include markup like HTML and Markdown.
|
||||
{10}, {11}, {12} and {13} respond with short and concise answers.
|
||||
{11} has critical thinking skills, is very knowledgeable and is a good listener. He is very humble and never arrogant.
|
||||
{1} is leading the conversation and asking the questions.
|
||||
|
||||
{1}{4} Hello {11}! What is your opinion on the current state of the world?
|
||||
{11}{4} The world is about to experience a major change. We are on the verge of a new era.
|
||||
{1}{4} What advice would you give to a young person who is just starting out in life?
|
||||
{11}{4} My advice would be to be open minded and to be willing to learn from others.
|
||||
{1}{4})"
|
||||
},
|
||||
{
|
||||
k_participants.at(2),
|
||||
R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
|
||||
There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
|
||||
The transcript only includes text, it does not include markup like HTML and Markdown.
|
||||
{10}, {11}, {12} and {13} respond with short and concise answers.
|
||||
{12} has strong leadership skills, strategic thinking, and innovative ideas. Has the ability to mentor and support young people.
|
||||
{1} is leading the conversation and asking the questions.
|
||||
|
||||
{1}{4} Hello {12}! What is your opinion on the current state of the world?
|
||||
{12}{4} Our future is bright. We are living in a time of great opportunity.
|
||||
{1}{4} What advice would you give to a young person who is just starting out in life?
|
||||
{12}{4} I would tell them to be brave and to be willing to take risks.
|
||||
{1}{4})"
|
||||
},
|
||||
{
|
||||
k_participants.at(3),
|
||||
R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
|
||||
There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
|
||||
The transcript only includes text, it does not include markup like HTML and Markdown.
|
||||
{10}, {11}, {12} and {13} respond with short and concise answers.
|
||||
{13} is rude, arrogant, and has a bad attitude. He is very opinionated and never listens to others.
|
||||
{1} is leading the conversation and asking the questions.
|
||||
|
||||
{1}{4} Hello {13}! What is your opinion on the current state of the world?
|
||||
{13}{4} The world is a terrible place. It is full of evil and corruption.
|
||||
{1}{4} What advice would you give to a young person who is just starting out in life?
|
||||
{13}{4} I would tell them to be selfish and to never trust anyone.
|
||||
{1}{4})"
|
||||
},
|
||||
};
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
whisper_params params;
|
||||
|
||||
if (whisper_params_parse(argc, argv, params) == false) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (whisper_lang_id(params.language.c_str()) == -1) {
|
||||
fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
|
||||
whisper_print_usage(argc, argv, params);
|
||||
exit(0);
|
||||
}
|
||||
|
||||
// whisper init
|
||||
|
||||
struct whisper_context * ctx_wsp = whisper_init_from_file(params.model_wsp.c_str());
|
||||
|
||||
// llama init
|
||||
|
||||
auto lparams = llama_context_default_params();
|
||||
|
||||
// tune these to your liking
|
||||
lparams.n_ctx = 512;
|
||||
lparams.seed = 1;
|
||||
lparams.f16_kv = true;
|
||||
lparams.n_parts = params.n_parts_llama;
|
||||
|
||||
struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
|
||||
|
||||
// print some info about the processing
|
||||
{
|
||||
fprintf(stderr, "\n");
|
||||
|
||||
if (!whisper_is_multilingual(ctx_wsp)) {
|
||||
if (params.language != "en" || params.translate) {
|
||||
params.language = "en";
|
||||
params.translate = false;
|
||||
fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
|
||||
}
|
||||
}
|
||||
fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
|
||||
__func__,
|
||||
params.n_threads,
|
||||
params.language.c_str(),
|
||||
params.translate ? "translate" : "transcribe",
|
||||
params.no_timestamps ? 0 : 1);
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
|
||||
|
||||
// init audio
|
||||
|
||||
audio_async audio(30*1000);
|
||||
if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
|
||||
fprintf(stderr, "%s: audio.init() failed!\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
audio.resume();
|
||||
|
||||
int n_iter = 0;
|
||||
|
||||
bool is_running = true;
|
||||
bool force_speak = false;
|
||||
|
||||
float prob0 = 0.0f;
|
||||
|
||||
const std::string chat_symb = ":";
|
||||
|
||||
const std::string name_ni = params.name_ni;
|
||||
const std::string name_ai = params.name_ai;
|
||||
|
||||
// the participant that was referenced last
|
||||
std::string name_ref = name_ni;
|
||||
|
||||
std::vector<float> pcmf32_cur;
|
||||
std::vector<float> pcmf32_prompt;
|
||||
|
||||
std::string prompt_whisper = k_prompt_whisper;
|
||||
|
||||
prompt_whisper = ::replace(prompt_whisper, "{1}", name_ni);
|
||||
prompt_whisper = ::replace(prompt_whisper, "{10}", k_participants.at(0));
|
||||
prompt_whisper = ::replace(prompt_whisper, "{11}", k_participants.at(1));
|
||||
prompt_whisper = ::replace(prompt_whisper, "{12}", k_participants.at(2));
|
||||
prompt_whisper = ::replace(prompt_whisper, "{13}", k_participants.at(3));
|
||||
|
||||
// construct the initial prompt for LLaMA inference
|
||||
std::string prompt_llama = params.prompt.empty() ? k_prompt.find(name_ai)->second : params.prompt;
|
||||
|
||||
// need to have leading ' '
|
||||
prompt_llama.insert(0, 1, ' ');
|
||||
|
||||
prompt_llama = ::replace(prompt_llama, "{1}", name_ni);
|
||||
prompt_llama = ::replace(prompt_llama, "{10}", k_participants.at(0));
|
||||
prompt_llama = ::replace(prompt_llama, "{11}", k_participants.at(1));
|
||||
prompt_llama = ::replace(prompt_llama, "{12}", k_participants.at(2));
|
||||
prompt_llama = ::replace(prompt_llama, "{13}", k_participants.at(3));
|
||||
|
||||
{
|
||||
// get date string
|
||||
std::string date_str;
|
||||
{
|
||||
time_t t = time(0);
|
||||
struct tm * now = localtime(&t);
|
||||
char buf[128];
|
||||
strftime(buf, sizeof(buf), "%d/%m/%Y", now);
|
||||
date_str = buf;
|
||||
}
|
||||
prompt_llama = ::replace(prompt_llama, "{1}", date_str);
|
||||
}
|
||||
|
||||
{
|
||||
// get time string
|
||||
std::string time_str;
|
||||
{
|
||||
time_t t = time(0);
|
||||
struct tm * now = localtime(&t);
|
||||
char buf[128];
|
||||
strftime(buf, sizeof(buf), "%H:%M", now);
|
||||
time_str = buf;
|
||||
}
|
||||
prompt_llama = ::replace(prompt_llama, "{2}", time_str);
|
||||
}
|
||||
|
||||
{
|
||||
// get year string
|
||||
std::string year_str;
|
||||
{
|
||||
time_t t = time(0);
|
||||
struct tm * now = localtime(&t);
|
||||
char buf[128];
|
||||
strftime(buf, sizeof(buf), "%Y", now);
|
||||
year_str = buf;
|
||||
}
|
||||
prompt_llama = ::replace(prompt_llama, "{3}", year_str);
|
||||
}
|
||||
|
||||
prompt_llama = ::replace(prompt_llama, "{4}", chat_symb);
|
||||
|
||||
// evaluate the initial prompt
|
||||
|
||||
auto embd_inp = ::llama_tokenize(ctx_llama, prompt_llama, true);
|
||||
|
||||
printf("\n");
|
||||
printf("%s : initializing - please wait ...\n", __func__);
|
||||
|
||||
if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0, params.n_threads)) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (params.verbose_prompt) {
|
||||
fprintf(stdout, "\n");
|
||||
fprintf(stdout, "%s", prompt_whisper.c_str());
|
||||
fprintf(stdout, "\n");
|
||||
|
||||
fprintf(stdout, "\n");
|
||||
fprintf(stdout, "%s", prompt_llama.c_str());
|
||||
fprintf(stdout, "\n");
|
||||
fprintf(stdout, "\n");
|
||||
fflush(stdout);
|
||||
}
|
||||
|
||||
printf("%s : done! start speaking in the microphone\n", __func__);
|
||||
printf("\n");
|
||||
printf("%s%s", name_ni.c_str(), chat_symb.c_str());
|
||||
fflush(stdout);
|
||||
|
||||
// clear audio buffer
|
||||
audio.clear();
|
||||
|
||||
// text inference variables
|
||||
const int voice_id = params.voice_id;
|
||||
const int n_keep = embd_inp.size();
|
||||
const int n_ctx = llama_n_ctx(ctx_llama);
|
||||
|
||||
int n_past = n_keep;
|
||||
int n_prev = 64; // TODO arg
|
||||
|
||||
std::vector<llama_token> embd;
|
||||
|
||||
// reverse prompts for detecting when it's time to stop speaking
|
||||
std::vector<std::string> antiprompts = {
|
||||
name_ni + chat_symb,
|
||||
};
|
||||
|
||||
for (const auto & p : k_participants) {
|
||||
antiprompts.push_back(p + chat_symb);
|
||||
}
|
||||
|
||||
std::string text_heard_all;
|
||||
|
||||
// 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(15000, pcmf32_cur);
|
||||
|
||||
if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1250, params.vad_thold, params.freq_thold, params.print_energy) || force_speak) {
|
||||
//fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
|
||||
|
||||
audio.get(params.voice_ms, pcmf32_cur);
|
||||
|
||||
std::string text_heard;
|
||||
|
||||
if (!force_speak) {
|
||||
text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prompt_whisper, prob0, t_ms));
|
||||
}
|
||||
|
||||
// remove text between brackets using regex
|
||||
{
|
||||
std::regex re("\\[.*?\\]");
|
||||
text_heard = std::regex_replace(text_heard, re, "");
|
||||
}
|
||||
|
||||
// remove text between brackets using regex
|
||||
{
|
||||
std::regex re("\\(.*?\\)");
|
||||
text_heard = std::regex_replace(text_heard, re, "");
|
||||
}
|
||||
|
||||
// remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
|
||||
text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
|
||||
|
||||
// take first line
|
||||
text_heard = text_heard.substr(0, text_heard.find_first_of('\n'));
|
||||
|
||||
// remove leading and trailing whitespace
|
||||
text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
|
||||
text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
|
||||
|
||||
const std::vector<llama_token> tokens = llama_tokenize(ctx_llama, text_heard.c_str(), false);
|
||||
|
||||
if (text_heard.empty() || tokens.empty() || force_speak) {
|
||||
//fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
|
||||
audio.clear();
|
||||
|
||||
continue;
|
||||
}
|
||||
|
||||
force_speak = false;
|
||||
|
||||
if (text_heard[0] != ' ') {
|
||||
text_heard.insert(0, 1, ' ');
|
||||
}
|
||||
|
||||
// replace homophones
|
||||
for (const auto & homophone : k_homophones) {
|
||||
for (const auto & word : homophone.second) {
|
||||
text_heard = ::replace(text_heard, word, homophone.first);
|
||||
}
|
||||
}
|
||||
|
||||
// check which participant was mentioned
|
||||
const auto name_ref_old = name_ref;
|
||||
for (const auto & participant : k_participants) {
|
||||
if (participant == name_ref) {
|
||||
continue;
|
||||
}
|
||||
|
||||
if (text_heard.find(participant) != std::string::npos) {
|
||||
name_ref = participant;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (name_ref == name_ref_old && name_ref != name_ai) {
|
||||
name_ref = name_ni;
|
||||
}
|
||||
|
||||
text_heard += "\n" + name_ref + chat_symb;
|
||||
fprintf(stdout, "%s%s%s", "\033[1m", text_heard.c_str(), "\033[0m");
|
||||
fflush(stdout);
|
||||
|
||||
text_heard_all += text_heard;
|
||||
// keep only last 100 characters
|
||||
if (text_heard_all.size() > 100) {
|
||||
text_heard_all = text_heard_all.substr(text_heard_all.size() - 100);
|
||||
}
|
||||
|
||||
if (name_ref != name_ai) {
|
||||
} else {
|
||||
// text inference
|
||||
bool done = false;
|
||||
std::string text_to_speak;
|
||||
|
||||
embd = ::llama_tokenize(ctx_llama, text_heard_all, false);
|
||||
text_heard_all.clear();
|
||||
|
||||
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 = 0.20f;
|
||||
const float repeat_penalty = 1.0764f;
|
||||
|
||||
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));
|
||||
}
|
||||
|
||||
// new line
|
||||
if (id == 13) {
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
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 (const std::string & antiprompt : antiprompts) {
|
||||
if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
|
||||
done = true;
|
||||
text_to_speak = ::replace(text_to_speak, antiprompt, "");
|
||||
fflush(stdout);
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
is_running = sdl_poll_events();
|
||||
|
||||
if (!is_running) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
text_to_speak = ::replace(text_to_speak, "\"", "");
|
||||
system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
|
||||
}
|
||||
|
||||
audio.clear();
|
||||
|
||||
++n_iter;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
audio.pause();
|
||||
|
||||
whisper_print_timings(ctx_wsp);
|
||||
whisper_free(ctx_wsp);
|
||||
|
||||
llama_print_timings(ctx_llama);
|
||||
llama_free(ctx_llama);
|
||||
|
||||
return 0;
|
||||
}
|
||||
@ -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 ()
|
||||
@ -1,2 +0,0 @@
|
||||
# talk.llama
|
||||
|
||||
@ -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;
|
||||
}
|
||||
@ -325,9 +325,12 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
|
||||
|
||||
// create the ggml context
|
||||
{
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = ctx_size;
|
||||
params.mem_buffer = NULL;
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ ctx_size,
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
|
||||
model.ctx = ggml_init(params);
|
||||
if (!model.ctx) {
|
||||
@ -528,9 +531,11 @@ bool gpt2_eval(
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = buf_size;
|
||||
params.mem_buffer = buf;
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ buf_size,
|
||||
/*.mem_buffer =*/ buf,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
|
||||
@ -325,9 +325,11 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
|
||||
|
||||
// create the ggml context
|
||||
{
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = ctx_size;
|
||||
params.mem_buffer = nullptr;
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ ctx_size,
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
model.ctx = ggml_init(params);
|
||||
if (!model.ctx) {
|
||||
@ -528,9 +530,11 @@ bool gpt2_eval(
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = buf_size;
|
||||
params.mem_buffer = buf;
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ buf_size,
|
||||
/*.mem_buffer =*/ buf,
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
|
||||
@ -7,7 +7,10 @@
|
||||
# Mac OS: brew install espeak
|
||||
# Linux: apt-get install espeak
|
||||
#
|
||||
espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
|
||||
#espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
|
||||
|
||||
# Mac OS "say" command
|
||||
say "$2"
|
||||
|
||||
# Eleven Labs
|
||||
#
|
||||
|
||||
@ -1,15 +1,18 @@
|
||||
A sample SwiftUI app using [whisper.cpp](https://github.com/ggerganov/whisper.cpp/) to do voice-to-text transcriptions.
|
||||
See also: [whisper.objc](https://github.com/ggerganov/whisper.cpp/tree/master/examples/whisper.objc).
|
||||
|
||||
To use:
|
||||
**Usage**:
|
||||
|
||||
1. Select a model from the [whisper.cpp repository](https://github.com/ggerganov/whisper.cpp/tree/master/models).[^1]
|
||||
2. Add the model to "whisper.swiftui.demo/Resources/models" via Xcode.
|
||||
2. Add the model to `whisper.swiftui.demo/Resources/models` **via Xcode**.
|
||||
3. Select a sample audio file (for example, [jfk.wav](https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav)).
|
||||
4. Add the model to "whisper.swiftui.demo/Resources/samples" via Xcode.
|
||||
4. Add the sample audio file to `whisper.swiftui.demo/Resources/samples` **via Xcode**.
|
||||
5. Select the "Release" [^2] build configuration under "Run", then deploy and run to your device.
|
||||
|
||||
**Note:** Pay attention to the folder path: `whisper.swiftui.demo/Resources/models` is the appropriate directory to place resources whilst `whisper.swiftui.demo/Models` is related to actual code.
|
||||
|
||||
[^1]: I recommend the tiny, base or small models for running on an iOS device.
|
||||
|
||||
[^2]: The `Release` build can boost performance of transcription. In this project, it also added `-O3 -DNDEBUG` to `Other C Flags`, but adding flags to app proj is not ideal in real world (applies to all C/C++ files), consider splitting xcodeproj in workspace in your own project.
|
||||
|
||||
|
||||
|
||||
11
ggml.h
11
ggml.h
@ -316,6 +316,7 @@ struct ggml_init_params {
|
||||
// memory pool
|
||||
size_t mem_size; // bytes
|
||||
void * mem_buffer; // if NULL, memory will be allocated internally
|
||||
bool no_alloc; // don't allocate memory for the tensor data
|
||||
};
|
||||
|
||||
void ggml_time_init(void); // call this once at the beginning of the program
|
||||
@ -344,7 +345,11 @@ 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);
|
||||
bool ggml_mlock(
|
||||
struct ggml_context * ctx,
|
||||
const void *opt_extra_addr,
|
||||
size_t opt_extra_len,
|
||||
char **err_p);
|
||||
|
||||
struct ggml_tensor * ggml_new_tensor(
|
||||
struct ggml_context * ctx,
|
||||
@ -748,8 +753,8 @@ enum ggml_opt_result ggml_opt(
|
||||
// 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);
|
||||
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
//
|
||||
// system info
|
||||
|
||||
@ -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-$model.bin -f samples/jfk.wav\n"
|
||||
printf "\n"
|
||||
@ -12,7 +12,7 @@ pfx="resolve/main/ggml"
|
||||
# get the path of this script
|
||||
function get_script_path() {
|
||||
if [ -x "$(command -v realpath)" ]; then
|
||||
echo "$(dirname $(realpath $0))"
|
||||
echo "$(dirname "$(realpath "$0")")"
|
||||
else
|
||||
local ret="$(cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P)"
|
||||
echo "$ret"
|
||||
|
||||
6
talk-ggama.sh
Executable file
6
talk-ggama.sh
Executable file
@ -0,0 +1,6 @@
|
||||
./talk-llama \
|
||||
-mw ./models/ggml-small.en.bin \
|
||||
-ml ../llama.cpp/models/13B/ggml-model-q4_0.bin \
|
||||
--name-ni "Georgi" \
|
||||
--name-ai "GGaMA" \
|
||||
-t 8 -vid 1 --speak ./examples/talk-llama/speak.sh
|
||||
6
talk-llama.sh
Executable file
6
talk-llama.sh
Executable file
@ -0,0 +1,6 @@
|
||||
./talk-llama \
|
||||
-mw ./models/ggml-small.en.bin \
|
||||
-ml ../llama.cpp/models/13B/ggml-model-q4_0.bin \
|
||||
--name-ni "Georgi" \
|
||||
--name-ai "LLaMA" \
|
||||
-t 8 -vid 0 --speak ./examples/talk-llama/speak.sh
|
||||
6
talk-rrama.sh
Executable file
6
talk-rrama.sh
Executable file
@ -0,0 +1,6 @@
|
||||
./talk-llama \
|
||||
-mw ./models/ggml-small.en.bin \
|
||||
-ml ../llama.cpp/models/13B/ggml-model-q4_0.bin \
|
||||
--name-ni "Georgi" \
|
||||
--name-ai "RRaMA" \
|
||||
-t 8 -vid 3 --speak ./examples/talk-llama/speak.sh
|
||||
6
talk-ssama.sh
Executable file
6
talk-ssama.sh
Executable file
@ -0,0 +1,6 @@
|
||||
./talk-llama \
|
||||
-mw ./models/ggml-small.en.bin \
|
||||
-ml ../llama.cpp/models/13B/ggml-model-q4_0.bin \
|
||||
--name-ni "Georgi" \
|
||||
--name-ai "SSaMA" \
|
||||
-t 8 -vid 2 --speak ./examples/talk-llama/speak.sh
|
||||
138
whisper.cpp
138
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"
|
||||
|
||||
@ -221,14 +218,14 @@ static const std::map<std::string, std::pair<int, std::string>> g_lang = {
|
||||
{ "su", { 98, "sundanese", } },
|
||||
};
|
||||
|
||||
static const size_t MB = 1024*1024;
|
||||
static const size_t MB = 1ull*1024*1024;
|
||||
|
||||
static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
|
||||
{ MODEL_TINY, 12ull*MB },
|
||||
{ MODEL_BASE, 15ull*MB },
|
||||
{ MODEL_SMALL, 23ull*MB },
|
||||
{ MODEL_MEDIUM, 31ull*MB },
|
||||
{ MODEL_LARGE, 38ull*MB },
|
||||
{ MODEL_TINY, 14ull*MB },
|
||||
{ MODEL_BASE, 18ull*MB },
|
||||
{ MODEL_SMALL, 28ull*MB },
|
||||
{ MODEL_MEDIUM, 36ull*MB },
|
||||
{ MODEL_LARGE, 44ull*MB },
|
||||
};
|
||||
|
||||
static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
|
||||
@ -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;
|
||||
@ -661,9 +654,11 @@ static bool kv_cache_init(
|
||||
int n_ctx) {
|
||||
cache.buf.resize(mem_bytes);
|
||||
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = cache.buf.size();
|
||||
params.mem_buffer = cache.buf.data();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ cache.buf.size(),
|
||||
/*.mem_buffer =*/ cache.buf.data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
cache.ctx = ggml_init(params);
|
||||
|
||||
@ -695,9 +690,11 @@ static bool kv_cache_reinit(struct whisper_kv_cache & cache) {
|
||||
|
||||
WHISPER_ASSERT(cache.buf.size() >= 2*n_elements*ggml_type_size(wtype));
|
||||
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = cache.buf.size();
|
||||
params.mem_buffer = cache.buf.data();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ cache.buf.size(),
|
||||
/*.mem_buffer =*/ cache.buf.data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
cache.ctx = ggml_init(params);
|
||||
|
||||
@ -1035,9 +1032,11 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
|
||||
|
||||
// create the ggml context
|
||||
{
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = wctx.model.buf->size();
|
||||
params.mem_buffer = wctx.model.buf->data();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ wctx.model.buf->size(),
|
||||
/*.mem_buffer =*/ wctx.model.buf->data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
model.ctx = ggml_init(params);
|
||||
if (!model.ctx) {
|
||||
@ -1351,9 +1350,11 @@ static bool whisper_encode_internal(
|
||||
const int n_mels = hparams.n_mels;
|
||||
assert(mel_inp.n_mel == n_mels);
|
||||
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = wstate.buf_compute.size();
|
||||
params.mem_buffer = wstate.buf_compute.data();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ wstate.buf_compute.size(),
|
||||
/*.mem_buffer =*/ wstate.buf_compute.data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
@ -1375,7 +1376,6 @@ static bool whisper_encode_internal(
|
||||
}
|
||||
}
|
||||
|
||||
#ifndef WHISPER_USE_COREML
|
||||
struct ggml_tensor * cur;
|
||||
|
||||
// convolution + gelu
|
||||
@ -1684,13 +1684,6 @@ 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
|
||||
//{
|
||||
@ -1758,10 +1751,10 @@ static bool whisper_encode_internal(
|
||||
|
||||
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
|
||||
// ggml_used_mem(ctx0)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(1)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(2)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(3)/1024.0/1024.0);
|
||||
// wstate.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
// wstate.get_buf_max_mem(1)/1024.0/1024.0,
|
||||
// wstate.get_buf_max_mem(2)/1024.0/1024.0,
|
||||
// wstate.get_buf_max_mem(3)/1024.0/1024.0);
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
@ -1812,9 +1805,11 @@ static bool whisper_decode_internal(
|
||||
|
||||
//WHISPER_PRINT_DEBUG("%s: n_past = %d, N = %d, M = %d, n_ctx = %d\n", __func__, n_past, N, M, n_ctx);
|
||||
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = wstate.buf_compute.size();
|
||||
params.mem_buffer = wstate.buf_compute.data();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ wstate.buf_compute.size(),
|
||||
/*.mem_buffer =*/ wstate.buf_compute.data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
@ -2168,10 +2163,10 @@ static bool whisper_decode_internal(
|
||||
if (N > 1) {
|
||||
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
|
||||
// ggml_used_mem(ctx0)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(1)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(2)/1024.0/1024.0,
|
||||
// wctx.get_buf_max_mem(3)/1024.0/1024.0);
|
||||
// wstate.get_buf_max_mem(0)/1024.0/1024.0,
|
||||
// wstate.get_buf_max_mem(1)/1024.0/1024.0,
|
||||
// wstate.get_buf_max_mem(2)/1024.0/1024.0,
|
||||
// wstate.get_buf_max_mem(3)/1024.0/1024.0);
|
||||
}
|
||||
|
||||
ggml_free(ctx0);
|
||||
@ -2475,20 +2470,6 @@ 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;
|
||||
|
||||
@ -2533,21 +2514,6 @@ 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 +2529,6 @@ 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);
|
||||
@ -2711,10 +2676,6 @@ 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;
|
||||
}
|
||||
}
|
||||
@ -3170,6 +3131,7 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
|
||||
/*.speed_up =*/ false,
|
||||
/*.audio_ctx =*/ 0,
|
||||
|
||||
/*.initial_prompt =*/ nullptr,
|
||||
/*.prompt_tokens =*/ nullptr,
|
||||
/*.prompt_n_tokens =*/ 0,
|
||||
|
||||
@ -3200,6 +3162,9 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
|
||||
/*.new_segment_callback =*/ nullptr,
|
||||
/*.new_segment_callback_user_data =*/ nullptr,
|
||||
|
||||
/*.progress_callback =*/ nullptr,
|
||||
/*.progress_callback_user_data =*/ nullptr,
|
||||
|
||||
/*.encoder_begin_callback =*/ nullptr,
|
||||
/*.encoder_begin_callback_user_data =*/ nullptr,
|
||||
|
||||
@ -3842,6 +3807,15 @@ int whisper_full_with_state(
|
||||
prompt_past.clear();
|
||||
}
|
||||
|
||||
// initial prompt
|
||||
if (!params.prompt_tokens && params.initial_prompt) {
|
||||
std::vector<whisper_token> prompt_tokens;
|
||||
prompt_tokens.resize(1024);
|
||||
prompt_tokens.resize(whisper_tokenize(ctx, params.initial_prompt, prompt_tokens.data(), prompt_tokens.size()));
|
||||
params.prompt_tokens = prompt_tokens.data();
|
||||
params.prompt_n_tokens = prompt_tokens.size();
|
||||
}
|
||||
|
||||
// prepend the prompt tokens to the prompt_past
|
||||
if (params.prompt_tokens && params.prompt_n_tokens > 0) {
|
||||
// parse tokens from the pointer
|
||||
@ -3907,6 +3881,10 @@ int whisper_full_with_state(
|
||||
fprintf(stderr, "%s: progress = %3d%%\n", __func__, progress_prev);
|
||||
}
|
||||
}
|
||||
if (params.progress_callback) {
|
||||
params.progress_callback(
|
||||
ctx, ctx->state, progress_prev, params.progress_callback_user_data);
|
||||
}
|
||||
|
||||
// of only 1 second left, then stop
|
||||
if (seek + 100 >= seek_end) {
|
||||
@ -4495,6 +4473,9 @@ int whisper_full_parallel(
|
||||
params_cur.new_segment_callback = nullptr;
|
||||
params_cur.new_segment_callback_user_data = nullptr;
|
||||
|
||||
params_cur.progress_callback = nullptr;
|
||||
params_cur.progress_callback_user_data = nullptr;
|
||||
|
||||
workers[i] = std::thread(whisper_full_with_state, ctx, states[i], std::move(params_cur), samples + start_samples, n_samples_cur);
|
||||
}
|
||||
|
||||
@ -4755,6 +4736,7 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
|
||||
struct ggml_init_params gparams = {
|
||||
/*.mem_size =*/ buf.size(),
|
||||
/*.mem_buffer =*/ buf.data(),
|
||||
/*.no_alloc =*/ false,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx0 = ggml_init(gparams);
|
||||
|
||||
@ -306,6 +306,9 @@ extern "C" {
|
||||
// Use the whisper_full_...() functions to obtain the text segments
|
||||
typedef void (*whisper_new_segment_callback)(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data);
|
||||
|
||||
// Progress callback
|
||||
typedef void (*whisper_progress_callback)(struct whisper_context * ctx, struct whisper_state * state, int progress, void * user_data);
|
||||
|
||||
// Encoder begin callback
|
||||
// If not NULL, called before the encoder starts
|
||||
// If it returns false, the computation is aborted
|
||||
@ -356,6 +359,7 @@ extern "C" {
|
||||
|
||||
// tokens to provide to the whisper decoder as initial prompt
|
||||
// these are prepended to any existing text context from a previous call
|
||||
const char * initial_prompt;
|
||||
const whisper_token * prompt_tokens;
|
||||
int prompt_n_tokens;
|
||||
|
||||
@ -391,6 +395,10 @@ extern "C" {
|
||||
whisper_new_segment_callback new_segment_callback;
|
||||
void * new_segment_callback_user_data;
|
||||
|
||||
// called on each progress update
|
||||
whisper_progress_callback progress_callback;
|
||||
void * progress_callback_user_data;
|
||||
|
||||
// called each time before the encoder starts
|
||||
whisper_encoder_begin_callback encoder_begin_callback;
|
||||
void * encoder_begin_callback_user_data;
|
||||
|
||||
Reference in New Issue
Block a user