ci : fix CUDA builds

* whisper : auto-grow working areas for mel_calc_cuda, fixes #2226

* whisper : only calculate mel spectrogram on GPU if audio is <= 5 min

.github/workflows/build.yml

@@ -459,7 +459,7 @@ jobs:
           path: build/bin/${{ matrix.build }}
 
   windows-cublas:
-    runs-on: windows-latest
+    runs-on: windows-2019
 
     strategy:
       matrix:
@@ -498,7 +498,7 @@ jobs:
         run: >
           cmake -S . -B ./build -A ${{ matrix.arch }}
           -DCMAKE_BUILD_TYPE=${{ matrix.build }}
-          -DWHISPER_CUBLAS=${{ matrix.cublas }}
+          -DWHISPER_CUDA=${{ matrix.cublas }}
           -DWHISPER_SDL2=${{ matrix.sdl2 }}
 
       - name: Build ${{ matrix.cuda-toolkit }}

CMakeLists.txt

@@ -3,7 +3,7 @@ cmake_minimum_required (VERSION 3.5)
 # Allow for the creation of solution folders.
 set_property(GLOBAL PROPERTY USE_FOLDERS ON)
 
-project(whisper.cpp VERSION 1.6.1)
+project(whisper.cpp VERSION 1.6.2)
 set(SOVERSION 1)
 
 # Add path to modules
@@ -364,12 +364,12 @@ if (WHISPER_CUDA)
         if (WHISPER_STATIC)
             if (WIN32)
                 # As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
-                set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
+                set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt CUDA::cufft)
             else ()
-                set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
+                set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static CUDA::cufft_static)
             endif()
         else()
-            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
+            set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt CUDA::cufft)
         endif()
 
         set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cuda_driver)
@@ -679,6 +679,10 @@ add_library(${TARGET}
     whisper.cpp
     )
 
+if (WHISPER_CUDA)
+    target_sources(${TARGET} PRIVATE whisper-mel-cuda.cu)
+endif()
+
 include_directories (
     .
 )

Makefile

@@ -286,8 +286,8 @@ ifdef WHISPER_CUDA
 
 	CFLAGS      += -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
 	CXXFLAGS    += -DGGML_USE_CUDA -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include
-	LDFLAGS     += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
-	WHISPER_OBJ += ggml-cuda.o
+	LDFLAGS     += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lcufft -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
+	WHISPER_OBJ += ggml-cuda.o whisper-mel-cuda.o
 	WHISPER_OBJ += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
 	NVCC        = nvcc
 	NVCCFLAGS   = --forward-unknown-to-host-compiler -arch=$(CUDA_ARCH_FLAG)
@@ -299,6 +299,9 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h
 	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
 endif
 
+whisper-mel-cuda.o: whisper-mel-cuda.cu whisper.h ggml.h ggml-backend.h whisper-mel.hpp whisper-mel-cuda.hpp
+	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
+
 ifdef WHISPER_HIPBLAS
 	ROCM_PATH   ?= /opt/rocm
 	HIPCC       ?= $(ROCM_PATH)/bin/hipcc
@@ -404,7 +407,7 @@ ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
 
 WHISPER_OBJ += ggml.o ggml-alloc.o ggml-backend.o ggml-quants.o
 
-whisper.o: whisper.cpp whisper.h ggml.h ggml-cuda.h
+whisper.o: whisper.cpp whisper.h whisper-mel.hpp ggml.h ggml-cuda.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 ifndef WHISPER_COREML

README.md

@@ -4,9 +4,10 @@
 
 [![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions)
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
+[![Conan Center](https://shields.io/conan/v/whisper-cpp)](https://conan.io/center/whisper-cpp)
 [![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
 
-Stable: [v1.6.0](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.6.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
+Stable: [v1.6.2](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.6.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
 
 High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
 
@@ -502,6 +503,16 @@ docker run -it --rm \
   whisper.cpp:main "./main -m /models/ggml-base.bin -f ./samples/jfk.wav"
 ```
 
+## Installing with Conan
+
+You can install pre-built binaries for whisper.cpp or build it from source using [Conan](https://conan.io/). Use the following command:
+
+```
+conan install --requires="whisper-cpp/[*]" --build=missing
+```
+
+For detailed instructions on how to use Conan, please refer to the [Conan documentation](https://docs.conan.io/2/).
+
 ## Limitations
 
 - Inference only
@@ -710,7 +721,7 @@ The [main](examples/main) example provides support for output of karaoke-style m
 currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script.
 This requires to have `ffmpeg` installed.
 
-Here are a few *"typical"* examples:
+Here are a few _"typical"_ examples:
 
 ```bash
 ./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts

bindings/go/examples/go-whisper/flags.go

@@ -68,10 +68,6 @@ func (flags *Flags) GetOut() string {
 	return strings.ToLower(flags.Lookup("out").Value.String())
 }
 
-func (flags *Flags) IsSpeedup() bool {
-	return flags.Lookup("speedup").Value.String() == "true"
-}
-
 func (flags *Flags) IsTokens() bool {
 	return flags.Lookup("tokens").Value.String() == "true"
 }
@@ -111,10 +107,6 @@ func (flags *Flags) SetParams(context whisper.Context) error {
 		fmt.Fprintf(flags.Output(), "Setting duration to %v\n", duration)
 		context.SetDuration(duration)
 	}
-	if flags.IsSpeedup() {
-		fmt.Fprintf(flags.Output(), "Setting speedup to true\n")
-		context.SetSpeedup(true)
-	}
 	if threads := flags.GetThreads(); threads != 0 {
 		fmt.Fprintf(flags.Output(), "Setting threads to %d\n", threads)
 		context.SetThreads(threads)
@@ -146,7 +138,6 @@ func registerFlags(flag *Flags) {
 	flag.Duration("offset", 0, "Time offset")
 	flag.Duration("duration", 0, "Duration of audio to process")
 	flag.Uint("threads", 0, "Number of threads to use")
-	flag.Bool("speedup", false, "Enable speedup")
 	flag.Uint("max-len", 0, "Maximum segment length in characters")
 	flag.Uint("max-tokens", 0, "Maximum tokens per segment")
 	flag.Float64("word-thold", 0, "Maximum segment score")

bindings/go/params.go

@@ -47,10 +47,6 @@ func (p *Params) SetPrintTimestamps(v bool) {
 	p.print_timestamps = toBool(v)
 }
 
-func (p *Params) SetSpeedup(v bool) {
-	p.speed_up = toBool(v)
-}
-
 // Set language id
 func (p *Params) SetLanguage(lang int) error {
 	if lang == -1 {
@@ -177,9 +173,6 @@ func (p *Params) String() string {
 	if p.token_timestamps {
 		str += " token_timestamps"
 	}
-	if p.speed_up {
-		str += " speed_up"
-	}
 
 	return str + ">"
 }

bindings/go/pkg/whisper/context.go

@@ -76,11 +76,6 @@ func (context *context) SetTranslate(v bool) {
 	context.params.SetTranslate(v)
 }
 
-// Set speedup flag
-func (context *context) SetSpeedup(v bool) {
-	context.params.SetSpeedup(v)
-}
-
 func (context *context) SetSplitOnWord(v bool) {
 	context.params.SetSplitOnWord(v)
 }

bindings/go/pkg/whisper/interface.go

@@ -41,7 +41,6 @@ type Context interface {
 	SetOffset(time.Duration)        // Set offset
 	SetDuration(time.Duration)      // Set duration
 	SetThreads(uint)                // Set number of threads to use
-	SetSpeedup(bool)                // Set speedup flag
 	SetSplitOnWord(bool)            // Set split on word flag
 	SetTokenThreshold(float32)      // Set timestamp token probability threshold
 	SetTokenSumThreshold(float32)   // Set timestamp token sum probability threshold

bindings/java/src/main/java/io/github/ggerganov/whispercpp/WhisperCppJnaLibrary.java

@@ -20,7 +20,7 @@ public interface WhisperCppJnaLibrary extends Library {
      * @return Whisper context on success, null on failure
      */
     Pointer whisper_init_from_file(String path_model);
-    
+
     /**
      * Provides default params which can be used with `whisper_init_from_file_with_params()` etc.
      * Because this function allocates memory for the params, the caller must call either:
@@ -304,14 +304,6 @@ public interface WhisperCppJnaLibrary extends Library {
     /** Language id associated with the provided state */
     int whisper_full_lang_id_from_state(Pointer state);
 
-    /**
-     * Convert RAW PCM audio to log mel spectrogram but applies a Phase Vocoder to speed up the audio x2.
-     * The resulting spectrogram is stored inside the default state of the provided whisper context.
-     * @return 0 on success
-     */
-    int whisper_pcm_to_mel_phase_vocoder(Pointer ctx, final float[] samples, int n_samples, int n_threads);
-
-    int whisper_pcm_to_mel_phase_vocoder_with_state(Pointer ctx, Pointer state, final float[] samples, int n_samples, int n_threads);
 
     /** Get the start time of the specified segment. */
     long whisper_full_get_segment_t0(Pointer ctx, int i_segment);

bindings/java/src/main/java/io/github/ggerganov/whispercpp/params/WhisperFullParams.java

@@ -129,14 +129,6 @@ public class WhisperFullParams extends Structure {
     /** Maximum tokens per segment (0, default = no limit) */
     public int max_tokens;
 
-    /** Flag to speed up the audio by 2x using Phase Vocoder. (default = false) */
-    public CBool speed_up;
-
-    /** Flag to speed up the audio by 2x using Phase Vocoder. (default = false) */
-    public void speedUp(boolean enable) {
-        speed_up = enable ? CBool.TRUE : CBool.FALSE;
-    }
-
     /** Overwrite the audio context size (0 = use default). */
     public int audio_ctx;
 
@@ -321,7 +313,7 @@ public class WhisperFullParams extends Structure {
         return Arrays.asList("strategy", "n_threads", "n_max_text_ctx", "offset_ms", "duration_ms", "translate",
                 "no_context", "single_segment", "no_timestamps",
                 "print_special", "print_progress", "print_realtime", "print_timestamps",  "token_timestamps",
-                "thold_pt", "thold_ptsum", "max_len", "split_on_word", "max_tokens", "speed_up", "audio_ctx",
+                "thold_pt", "thold_ptsum", "max_len", "split_on_word", "max_tokens", "audio_ctx",
                 "tdrz_enable", "suppress_regex", "initial_prompt", "prompt_tokens", "prompt_n_tokens", "language", "detect_language",
                 "suppress_blank", "suppress_non_speech_tokens", "temperature", "max_initial_ts", "length_penalty",
                 "temperature_inc", "entropy_thold", "logprob_thold", "no_speech_thold", "greedy", "beam_search",

bindings/javascript/package.json

@@ -1,6 +1,6 @@
 {
   "name": "whisper.cpp",
-  "version": "1.6.1",
+  "version": "1.6.2",
   "description": "Whisper speech recognition",
   "main": "whisper.js",
   "scripts": {

bindings/ruby/Rakefile

@@ -0,0 +1,12 @@
+require 'rake/clean'
+  require 'rubygems/package'
+
+desc 'Build gem'
+task :package do
+  spec_source = File.read File.join(File.dirname(__FILE__),'whispercpp.gemspec')
+  spec = nil
+  # see: http://gist.github.com/16215
+  Thread.new { spec = eval("#{spec_source}") }.join
+  spec.validate
+  Gem::Package.build(spec)
+end

bindings/ruby/ext/extconf.rb

@@ -1,6 +1,7 @@
 require 'mkmf'
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.cpp')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.h')} .")
+system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper-mel.hpp')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.h')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.c')} .")
 system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-impl.h')} .")

bindings/ruby/ext/ggml-backend-impl.h

@@ -12,31 +12,63 @@ extern "C" {
     // Backend buffer
     //
 
+    // buffer type
+    typedef void * ggml_backend_buffer_type_context_t;
+
+    struct ggml_backend_buffer_type_i {
+        const char *          (*GGML_CALL get_name)        (ggml_backend_buffer_type_t buft);
+        ggml_backend_buffer_t (*GGML_CALL alloc_buffer)    (ggml_backend_buffer_type_t buft, size_t size);
+        size_t                (*GGML_CALL get_alignment)   (ggml_backend_buffer_type_t buft); // tensor alignment
+        size_t                (*GGML_CALL get_max_size)    (ggml_backend_buffer_type_t buft); // allocation max size
+        size_t                (*GGML_CALL get_alloc_size)  (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
+        bool                  (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
+        // check if tensor data is in host memory
+        // should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
+        bool                  (*GGML_CALL is_host)         (ggml_backend_buffer_type_t buft);
+    };
+
+    struct ggml_backend_buffer_type {
+        struct ggml_backend_buffer_type_i  iface;
+        ggml_backend_buffer_type_context_t context;
+    };
+
+    // buffer
     typedef void * ggml_backend_buffer_context_t;
 
     struct ggml_backend_buffer_i {
-        void   (*free_buffer)   (ggml_backend_buffer_t buffer);
-        void * (*get_base)      (ggml_backend_buffer_t buffer); // get base pointer
-        size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
-        void   (*init_tensor)   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
-        void   (*free_tensor)   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
+        const char * (*GGML_CALL get_name)   (ggml_backend_buffer_t buffer);
+        void         (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
+        void *       (*GGML_CALL get_base)   (ggml_backend_buffer_t buffer);
+        void         (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+        void         (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void         (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        bool         (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
+        void         (*GGML_CALL clear)      (ggml_backend_buffer_t buffer, uint8_t value);
+        void         (*GGML_CALL reset)      (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
     };
 
     struct ggml_backend_buffer {
-        struct ggml_backend_buffer_i iface;
-
-        ggml_backend_t                backend;
+        struct ggml_backend_buffer_i  iface;
+        ggml_backend_buffer_type_t    buft;
         ggml_backend_buffer_context_t context;
-
         size_t size;
+        enum ggml_backend_buffer_usage usage;
     };
 
-    GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
-            struct ggml_backend                  * backend,
+    GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
+                   ggml_backend_buffer_type_t      buft,
             struct ggml_backend_buffer_i           iface,
                    ggml_backend_buffer_context_t   context,
                    size_t                          size);
 
+    // do not use directly, use ggml_backend_tensor_copy instead
+    bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    // buffer that contains a collection of buffers
+    GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers);
+    GGML_CALL bool                  ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
+    GGML_CALL void                  ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+
     //
     // Backend
     //
@@ -44,44 +76,66 @@ extern "C" {
     typedef void * ggml_backend_context_t;
 
     struct ggml_backend_i {
-        const char * (*get_name)(ggml_backend_t backend);
+        const char * (*GGML_CALL get_name)(ggml_backend_t backend);
 
-        void (*free)(ggml_backend_t backend);
+        void (*GGML_CALL free)(ggml_backend_t backend);
 
         // buffer allocation
-        ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
+        ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
 
-        // get buffer alignment
-        size_t (*get_alignment)(ggml_backend_t backend);
+        // (optional) asynchronous tensor data access
+        void (*GGML_CALL set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
-        // tensor data access
-        // these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
-        void (*set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        void (*synchronize)     (ggml_backend_t backend);
+        // (optional) complete all pending operations
+        void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
-        // (optional) copy tensor between different backends, allow for single-copy tranfers
-        void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
-        void (*cpy_tensor_to)  (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
+        // compute graph with a plan (not used currently)
+        ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
+        void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
         // compute graph with a plan
-        ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-        void                      (*graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-        void                      (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-
-        // compute graph without a plan
-        bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+        // compute graph without a plan (async)
+        enum ggml_status (*GGML_CALL graph_compute)     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
         // check if the backend supports an operation
-        bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+        bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
+        // these should be expensive operations with large batch sizes that may benefit from running on this backend
+        // even if the weight has to be copied from the CPU temporarily
+        bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
+
+        // (optional) event synchronization
+        ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
+        void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
+        void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
+        void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
     };
 
     struct ggml_backend {
-        struct ggml_backend_i iface;
+        ggml_guid_t guid;
 
+        struct ggml_backend_i iface;
         ggml_backend_context_t context;
     };
 
+    struct ggml_backend_event {
+        ggml_backend_t backend;
+        void * context;
+    };
+
+    //
+    // Backend registry
+    //
+
+    typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
+
+    GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
+
 #ifdef  __cplusplus
 }
 #endif

bindings/ruby/ext/ggml-backend.h

@@ -7,69 +7,123 @@
 extern "C" {
 #endif
 
+    typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
+    typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+    typedef struct ggml_backend_event * ggml_backend_event_t;
+    typedef struct ggml_backend * ggml_backend_t;
+    typedef void * ggml_backend_graph_plan_t;
+
     //
     // Backend buffer
     //
 
-    struct ggml_backend_buffer;
-    typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
+    // buffer type
+    GGML_API           const char *          ggml_backend_buft_name            (ggml_backend_buffer_type_t buft);
+    GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer    (ggml_backend_buffer_type_t buft, size_t size);
+    GGML_API           size_t                ggml_backend_buft_get_alignment   (ggml_backend_buffer_type_t buft);
+    GGML_API           size_t                ggml_backend_buft_get_max_size    (ggml_backend_buffer_type_t buft);
+    GGML_API GGML_CALL size_t                ggml_backend_buft_get_alloc_size  (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
+    GGML_API           bool                  ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
+    GGML_API           bool                  ggml_backend_buft_is_host         (ggml_backend_buffer_type_t buft);
 
-    // backend buffer functions
-    GGML_API void   ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
-    GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
-    GGML_API void * ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
-    GGML_API size_t ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
-    GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API void   ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
-    GGML_API void   ggml_backend_buffer_free_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    // buffer
+    enum ggml_backend_buffer_usage {
+        GGML_BACKEND_BUFFER_USAGE_ANY = 0,
+        GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
+    };
+
+    GGML_API           const char *               ggml_backend_buffer_name          (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_free          (ggml_backend_buffer_t buffer);
+    GGML_API           void *                     ggml_backend_buffer_get_base      (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_size      (ggml_backend_buffer_t buffer);
+    GGML_API GGML_CALL void                       ggml_backend_buffer_init_tensor   (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           size_t                     ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_max_size  (ggml_backend_buffer_t buffer);
+    GGML_API           size_t                     ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
+    GGML_API           void                       ggml_backend_buffer_clear         (ggml_backend_buffer_t buffer, uint8_t value);
+    GGML_API           bool                       ggml_backend_buffer_is_host       (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_set_usage     (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
+    GGML_API           ggml_backend_buffer_type_t ggml_backend_buffer_get_type      (ggml_backend_buffer_t buffer);
+    GGML_API           void                       ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     //
     // Backend
     //
 
-    struct ggml_backend;
-    typedef struct ggml_backend * ggml_backend_t;
-    typedef void * ggml_backend_graph_plan_t;
-
-    GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
-
+    GGML_API ggml_guid_t  ggml_backend_guid(ggml_backend_t backend);
     GGML_API const char * ggml_backend_name(ggml_backend_t backend);
     GGML_API void         ggml_backend_free(ggml_backend_t backend);
 
-    GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
+    GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
+    GGML_API ggml_backend_buffer_t      ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
+    GGML_API size_t                     ggml_backend_get_alignment(ggml_backend_t backend);
+    GGML_API size_t                     ggml_backend_get_max_size(ggml_backend_t backend);
 
-    GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
+    GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
 
-    GGML_API void ggml_backend_tensor_set_async(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-
-    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API GGML_CALL void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
-    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API void                      ggml_backend_graph_plan_free  (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
-    GGML_API void ggml_backend_graph_plan_free   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API bool ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-    GGML_API bool ggml_backend_supports_op       (ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API enum ggml_status ggml_backend_graph_compute      (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
     GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
 
+    // asynchronous copy
+    // the copy is performed after all the currently queued operations in backend_src
+    // backend_dst will wait for the copy to complete before performing other operations
+    // automatic fallback to sync copy if async is not supported
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+
+    // events
+    GGML_API ggml_backend_event_t   ggml_backend_event_new        (ggml_backend_t backend);
+    GGML_API void                   ggml_backend_event_free       (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_record     (ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_synchronize(ggml_backend_event_t event);
+    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
+
     //
     // CPU backend
     //
 
     GGML_API ggml_backend_t ggml_backend_cpu_init(void);
 
-    GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
-    GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
+    GGML_API GGML_CALL bool ggml_backend_is_cpu                (ggml_backend_t backend);
+    GGML_API           void ggml_backend_cpu_set_n_threads     (ggml_backend_t backend_cpu, int n_threads);
+    GGML_API           void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
 
     // Create a backend buffer from an existing pointer
-    GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
+    GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
 
+    GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
+
+#ifdef GGML_USE_CPU_HBM
+    GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
+#endif
+
+    //
+    // Backend registry
+    //
+
+    // The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
+
+    GGML_API size_t                     ggml_backend_reg_get_count(void);
+    GGML_API size_t                     ggml_backend_reg_find_by_name(const char * name);
+    GGML_API ggml_backend_t             ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
+    GGML_API const char *               ggml_backend_reg_get_name(size_t i);
+    GGML_API ggml_backend_t             ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
+    GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
+    GGML_API ggml_backend_buffer_t      ggml_backend_reg_alloc_buffer(size_t i, size_t size);
 
     //
     // Backend scheduler
@@ -83,53 +137,96 @@ extern "C" {
     /*
       Example usage:
 
-        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
-        // sched is initialized with measure allocators and cannot be used until allocated with a measure graph
+        // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
+        // preferrably to run on the same backend as the buffer
+        ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
 
-        // initialize buffers from a measure graph
-        measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
+        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
 
-        // in build_graph:
-        build_graph(...) {
-            // allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer)
-            alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu);
-            ggml_allocr_alloc(alloc_cpu, tensor);
+        // initialize buffers from a max size graph (optional)
+        reserve_graph = build_graph(sched, max_batch_size);
 
-            // manually assigning nodes to a backend (optional, shouldn't be needed in most cases)
-            struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
-            ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
-        }
+        // manually assign nodes to a backend (optional, should not be needed in most cases)
+        struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
+        ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
 
-        // allocate backend buffers from measure graph
-        ggml_backend_sched_init_measure(sched, measure_graph);
-
-        // the scheduler is now ready to compute graphs
+        ggml_backend_sched_reserve(sched, reserve_graph);
 
         // compute
         graph = build_graph(sched);
         ggml_backend_sched_graph_compute(sched, graph);
+
+        // if there are graph inputs:
+        ggml_backend_sched_reset(sched);
+        ggml_backend_sched_alloc_graph(sched, graph);
+        ggml_backend_tensor_set(input_tensor, ...);
+        ggml_backend_sched_graph_compute(sched, graph);
+    }
     */
 
     struct ggml_backend_sched;
     typedef struct ggml_backend_sched * ggml_backend_sched_t;
 
-    // Initialize a backend scheduler
-    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends);
+    // when ask == true, the scheduler wants to know if the user wants to observe this node
+    // this allows the scheduler to batch nodes together in order to evaluate them in a single call
+    //
+    // when ask == false, the scheduler is passing the node tensor to the user for observation
+    // if the user returns false, the scheduler will cancel the graph compute
+    //
+    typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
 
-    GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
+    // Initialize a backend scheduler
+    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
+    GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
 
     // Initialize backend buffers from a measure graph
-    GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
+    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
 
-    GGML_API ggml_tallocr_t        ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend);
-    GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend);
+    // Get the number of splits of the last graph
+    GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
+    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
 
-    GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API size_t               ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
+
+    GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
+
+    // Allocate and compute graph on the backend scheduler
+    GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API enum ggml_status     ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
+    GGML_API void                 ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
+
+    // Reset all assignments and allocators - must be called before changing the node backends
+    GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);
+
+    // Set a callback to be called for each resulting node during graph compute
+    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
+
+    //
+    // Utils
+    //
+
+    struct ggml_backend_graph_copy {
+        ggml_backend_buffer_t buffer;
+        struct ggml_context * ctx_allocated;
+        struct ggml_context * ctx_unallocated;
+        struct ggml_cgraph * graph;
+    };
+
+    // Copy a graph to a different backend
+    GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
+    GGML_API void                           ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
+
+    typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
+
+    // Compare the output of two backends
+    GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
+
+    // Tensor initialization
+    GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
+    GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
 
-    // Allocate a graph on the backend scheduler
-    GGML_API void ggml_backend_sched_graph_compute(
-            ggml_backend_sched_t sched,
-            struct ggml_cgraph * graph);
 
 #ifdef  __cplusplus
 }

bindings/ruby/ext/ggml-cuda.h

@@ -0,0 +1,43 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef GGML_USE_HIPBLAS
+#define GGML_CUDA_NAME "ROCm"
+#define GGML_CUBLAS_NAME "hipBLAS"
+#else
+#define GGML_CUDA_NAME "CUDA"
+#define GGML_CUBLAS_NAME "cuBLAS"
+#endif
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_CUDA_MAX_DEVICES       16
+
+// backend API
+GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
+
+GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
+
+// device buffer
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
+
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
+
+GGML_API GGML_CALL int  ggml_backend_cuda_get_device_count(void);
+GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
+GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
+
+GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
+GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
+
+#ifdef  __cplusplus
+}
+#endif

bindings/ruby/ext/ggml-impl.h

@@ -5,6 +5,7 @@
 // GGML internal header
 
 #include <assert.h>
+#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
 #include <stddef.h>
 #include <stdbool.h>
 #include <string.h> // memcpy
@@ -18,6 +19,7 @@ extern "C" {
 // fall back to the _Static_assert C11 keyword.
 // if C99 - static_assert is noop
 // ref: https://stackoverflow.com/a/53923785/4039976
+#ifndef __cplusplus
 #ifndef static_assert
 #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
 #define static_assert(cond, msg) _Static_assert(cond, msg)
@@ -25,6 +27,7 @@ extern "C" {
 #define static_assert(cond, msg) struct global_scope_noop_trick
 #endif
 #endif
+#endif
 
 // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
@@ -34,16 +37,17 @@ extern "C" {
 #ifndef __F16C__
 #define __F16C__
 #endif
+#endif
+
+// __SSE3__ and __SSSE3__ are not defined in MSVC, but SSE3/SSSE3 are present when AVX/AVX2/AVX512 are available
+#if defined(_MSC_VER) && (defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__))
 #ifndef __SSE3__
 #define __SSE3__
 #endif
+#ifndef __SSSE3__
+#define __SSSE3__
+#endif
 #endif
-
-#undef MIN
-#undef MAX
-
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
 
 // 16-bit float
 // on Arm, we use __fp16
@@ -56,14 +60,30 @@ extern "C" {
 //
 #include <arm_neon.h>
 
-#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x))
-#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
+typedef __fp16 ggml_fp16_internal_t;
 
-#define GGML_FP16_TO_FP32(x) ((float) (x))
-#define GGML_FP32_TO_FP16(x) (x)
+#define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+#define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
+
+#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
+
+static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
+    ggml_fp16_internal_t tmp;
+    memcpy(&tmp, &h, sizeof(ggml_fp16_t));
+    return (float)tmp;
+}
+
+static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
+    ggml_fp16_t res;
+    ggml_fp16_internal_t tmp = f;
+    memcpy(&res, &tmp, sizeof(ggml_fp16_t));
+    return res;
+}
 
 #else
 
+typedef uint16_t ggml_fp16_internal_t;
+
 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
 #else
@@ -217,8 +237,7 @@ extern float ggml_table_f32_f16[1 << 16];
 // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
 // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
 // This is also true for POWER9.
-#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16)
-
+#if !defined(GGML_FP16_TO_FP32)
 inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
     uint16_t s;
     memcpy(&s, &f, sizeof(uint16_t));
@@ -226,19 +245,23 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 }
 
 #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
-#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
+#endif
 
+#if !defined(GGML_FP32_TO_FP16)
+#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
 #endif
 
 #define GGML_HASHTABLE_FULL ((size_t)-1)
 #define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2)
 
+struct ggml_hash_set ggml_hash_set_new(size_t size);
+
 bool   ggml_hash_contains      (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
 
 // returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
 size_t ggml_hash_find          (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
 
-// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
+// returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
 size_t ggml_hash_insert        (      struct ggml_hash_set hash_set, struct ggml_tensor * key);
 
 // return index, asserts if table is full

bindings/ruby/ext/ggml-kompute.h

@@ -0,0 +1,46 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#include <stdbool.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_vk_device {
+    int index;
+    int type; // same as VkPhysicalDeviceType
+    size_t heapSize;
+    const char * name;
+    const char * vendor;
+    int subgroupSize;
+    uint64_t bufferAlignment;
+    uint64_t maxAlloc;
+};
+
+struct ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count);
+bool ggml_vk_get_device(struct ggml_vk_device * device, size_t memoryRequired, const char * name);
+bool ggml_vk_has_vulkan(void);
+bool ggml_vk_has_device(void);
+struct ggml_vk_device ggml_vk_current_device(void);
+
+//
+// backend API
+//
+
+// forward declaration
+typedef struct ggml_backend * ggml_backend_t;
+
+GGML_API ggml_backend_t ggml_backend_kompute_init(int device);
+
+GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend);
+
+GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device);
+
+#ifdef __cplusplus
+}
+#endif

bindings/ruby/ext/ggml-metal.h

@@ -0,0 +1,66 @@
+// An interface allowing to compute ggml_cgraph with Metal
+//
+// This is a fully functional interface that extends ggml with GPU support for Apple devices.
+// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
+//
+// How it works?
+//
+// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
+// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
+// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
+//
+// You only need to make sure that all memory buffers that you used during the graph creation
+// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
+// used during the graph evaluation to determine the arguments of the compute kernels.
+//
+// Synchronization between device and host memory (for example for input and output tensors)
+// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
+//
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#include <stddef.h>
+#include <stdbool.h>
+
+// max memory buffers that can be mapped to the device
+#define GGML_METAL_MAX_BUFFERS 64
+
+struct ggml_tensor;
+struct ggml_cgraph;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+//
+// backend API
+// user-code should use only these functions
+//
+
+GGML_API void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data);
+
+GGML_API ggml_backend_t ggml_backend_metal_init(void);
+
+GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
+
+GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size);
+
+GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
+
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
+
+// helper to check if the device supports a specific family
+// ideally, the user code should be doing these checks
+// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
+GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
+
+// capture all command buffers committed the next time `ggml_backend_graph_compute` is called
+GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
+
+#ifdef __cplusplus
+}
+#endif
+

bindings/ruby/ext/ggml-opencl.h

@@ -0,0 +1,36 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+GGML_API void ggml_cl_init(void);
+
+GGML_API void   ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API bool   ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
+GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
+GGML_API void   ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
+
+// GGML_API void * ggml_cl_host_malloc(size_t size);
+// GGML_API void   ggml_cl_host_free(void * ptr);
+
+GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor);
+
+GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
+
+// backend API
+
+// GGML_API ggml_backend_t ggml_backend_opencl_init(void);
+
+// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend);
+
+GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
+// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
+
+#ifdef  __cplusplus
+}
+#endif

bindings/ruby/ext/ggml-quants.h

@@ -1,224 +1,133 @@
 #pragma once
 
-#include "ggml-impl.h"
+#define GGML_COMMON_DECL_C
+#include "ggml-common.h"
+
+#include "ggml.h"
 
 // GGML internal header
 
-#include <stdint.h>
-#include <stddef.h>
-
-#define QK4_0 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    uint8_t qs[QK4_0 / 2];  // nibbles / quants
-} block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
-
-#define QK4_1 32
-typedef struct {
-    ggml_fp16_t d;          // delta
-    ggml_fp16_t m;          // min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
-} block_q4_1;
-static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
-
-#define QK5_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_0 / 2]; // nibbles / quants
-} block_q5_0;
-static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
-
-#define QK5_1 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    ggml_fp16_t m;         // min
-    uint8_t qh[4];         // 5-th bit of quants
-    uint8_t qs[QK5_1 / 2]; // nibbles / quants
-} block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
-
-#define QK8_0 32
-typedef struct {
-    ggml_fp16_t d;         // delta
-    int8_t  qs[QK8_0];     // quants
-} block_q8_0;
-static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
-
-#define QK8_1 32
-typedef struct {
-    float d;               // delta
-    float s;               // d * sum(qs[i])
-    int8_t  qs[QK8_1];     // quants
-} block_q8_1;
-static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
-
-//
-// Super-block quantization structures
-//
-
-// Super-block size
-#ifdef GGML_QKK_64
-#define QK_K 64
-#define K_SCALE_SIZE 4
-#else
-#define QK_K 256
-#define K_SCALE_SIZE 12
+#ifdef __cplusplus
+extern "C" {
 #endif
 
-// 2-bit quantization
-// weight is represented as x = a * q + b
-// 16 blocks of 16 elements each
-// Effectively 2.5625 bits per weight
-typedef struct {
-    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
-    uint8_t qs[QK_K/4];      // quants
-    ggml_fp16_t d;           // super-block scale for quantized scales
-    ggml_fp16_t dmin;        // super-block scale for quantized mins
-} block_q2_K;
-static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
-
-// 3-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 3.4375 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[2];
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
-#else
-typedef struct {
-    uint8_t hmask[QK_K/8];     // quants - high bit
-    uint8_t qs[QK_K/4];        // quants - low 2 bits
-    uint8_t scales[12];        // scales, quantized with 6 bits
-    ggml_fp16_t d;             // super-block scale
-} block_q3_K;
-static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
-#endif
-
-// 4-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 4.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d[2];          // super-block scales/mins
-    uint8_t scales[2];         // 4-bit block scales/mins
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;             // super-block scale for quantized scales
-    ggml_fp16_t dmin;          // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
-    uint8_t qs[QK_K/2];        // 4--bit quants
-} block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
-#endif
-
-// 5-bit quantization
-// 8 blocks of 32 elements each
-// weight is represented as x = a * q + b
-// Effectively 5.5 bits per weight
-#ifdef GGML_QKK_64
-typedef struct {
-    ggml_fp16_t d;               // super-block scale
-    int8_t  scales[QK_K/16];     // 8-bit block scales
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
-#else
-typedef struct {
-    ggml_fp16_t d;               // super-block scale for quantized scales
-    ggml_fp16_t dmin;            // super-block scale for quantized mins
-    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
-    uint8_t qh[QK_K/8];          // quants, high bit
-    uint8_t qs[QK_K/2];          // quants, low 4 bits
-} block_q5_K;
-static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
-#endif
-
-// 6-bit quantization
-// weight is represented as x = a * q
-// 16 blocks of 16 elements each
-// Effectively 6.5625 bits per weight
-typedef struct {
-    uint8_t ql[QK_K/2];      // quants, lower 4 bits
-    uint8_t qh[QK_K/4];      // quants, upper 2 bits
-    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
-    ggml_fp16_t d;           // super-block scale
-} block_q6_K;
-static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
-
-// This is only used for intermediate quantization and dot products
-typedef struct {
-    float   d;              // delta
-    int8_t  qs[QK_K];       // quants
-    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
-} block_q8_K;
-static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
-
-
 // Quantization
-void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k);
-void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k);
-void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k);
-void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k);
-void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k);
-void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k);
+void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
-void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
-void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
-void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
-void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
-void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
+void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_q4_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_1(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_1(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_0(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
+void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_s_reference  (const float * GGML_RESTRICT x, block_iq3_s   * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq2_s_reference  (const float * GGML_RESTRICT x, block_iq2_s   * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
-void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
+void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
+void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq3_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+void quantize_row_iq2_s  (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
 // Dequantization
-void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k);
-void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k);
-void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k);
-void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k);
-void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k);
-//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k);
+void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 
-void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
-void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
-void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
-void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
-void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
-void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
+void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+
+void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq2_xs (const block_iq2_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq2_s  (const block_iq2_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq1_s  (const block_iq1_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq1_m  (const block_iq1_m   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq4_nl (const block_iq4_nl  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq4_xs (const block_iq4_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+void dequantize_row_iq3_s  (const block_iq3_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 
 // Dot product
-void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
+void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq2_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq1_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+void ggml_vec_dot_iq3_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
+size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq2_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq1_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq1_m  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_iq3_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+
+size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+
+void iq2xs_init_impl(enum ggml_type type);
+void iq2xs_free_impl(enum ggml_type type);
+void iq3xs_init_impl(int grid_size);
+void iq3xs_free_impl(int grid_size);
+
+#ifdef __cplusplus
+}
+#endif
 
-void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
-void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);

bindings/ruby/ext/ggml-sycl.h

@@ -0,0 +1,49 @@
+//
+//  MIT license
+//  Copyright (C) 2024 Intel Corporation
+//  SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_SYCL_MAX_DEVICES       48
+#define GGML_SYCL_NAME "SYCL"
+
+// backend API
+GGML_API ggml_backend_t ggml_backend_sycl_init(int device);
+
+// devide buffer
+GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
+
+// split tensor buffer that splits matrices by rows across multiple devices
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
+
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
+
+GGML_API void   ggml_backend_sycl_print_sycl_devices(void);
+GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len);
+GGML_API GGML_CALL void   ggml_sycl_get_device_description(int device, char *description, size_t description_size);
+GGML_API GGML_CALL int   ggml_backend_sycl_get_device_count();
+GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
+
+// TODO: these are temporary
+//       ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
+GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
+GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
+GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
+
+// SYCL doesn't support registering host memory, keep here for reference
+// GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
+// GGML_API GGML_CALL void ggml_backend_sycl_unregister_host_buffer(void * buffer);
+#ifdef  __cplusplus
+}
+#endif

bindings/ruby/ext/ggml-vulkan.h

@@ -0,0 +1,29 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+#define GGML_VK_NAME "Vulkan"
+#define GGML_VK_MAX_DEVICES 16
+
+GGML_API void ggml_vk_instance_init(void);
+
+// backend API
+GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);
+
+GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend);
+GGML_API GGML_CALL int  ggml_backend_vk_get_device_count(void);
+GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size);
+GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total);
+
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num);
+// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
+GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void);
+
+#ifdef  __cplusplus
+}
+#endif

bindings/ruby/ext/ruby_whisper.cpp

@@ -311,12 +311,6 @@ static VALUE ruby_whisper_params_get_split_on_word(VALUE self) {
 static VALUE ruby_whisper_params_set_split_on_word(VALUE self, VALUE value) {
   BOOL_PARAMS_SETTER(self, split_on_word, value)
 }
-static VALUE ruby_whisper_params_get_speed_up(VALUE self) {
-  BOOL_PARAMS_GETTER(self, speed_up)
-}
-static VALUE ruby_whisper_params_set_speed_up(VALUE self, VALUE value) {
-  BOOL_PARAMS_SETTER(self, speed_up, value)
-}
 static VALUE ruby_whisper_params_get_diarize(VALUE self) {
   ruby_whisper_params *rwp;
   Data_Get_Struct(self, ruby_whisper_params, rwp);
@@ -408,8 +402,6 @@ void Init_whisper() {
   rb_define_method(cParams, "token_timestamps=", ruby_whisper_params_set_token_timestamps, 1);
   rb_define_method(cParams, "split_on_word", ruby_whisper_params_get_split_on_word, 0);
   rb_define_method(cParams, "split_on_word=", ruby_whisper_params_set_split_on_word, 1);
-  rb_define_method(cParams, "speed_up", ruby_whisper_params_get_speed_up, 0);
-  rb_define_method(cParams, "speed_up=", ruby_whisper_params_set_speed_up, 1);
   rb_define_method(cParams, "diarize", ruby_whisper_params_get_diarize, 0);
   rb_define_method(cParams, "diarize=", ruby_whisper_params_set_diarize, 1);
 

bindings/ruby/tests/test_whisper.rb

@@ -117,13 +117,6 @@ class TestWhisper < Test::Unit::TestCase
     assert !@params.split_on_word
   end
 
-  def test_speed_up
-    @params.speed_up = true
-    assert @params.speed_up
-    @params.speed_up = false
-    assert !@params.speed_up
-  end
-
   def test_whisper
     @whisper = Whisper::Context.new(File.join(TOPDIR, '..', '..', 'models', 'ggml-base.en.bin'))
     params  = Whisper::Params.new

bindings/ruby/whispercpp.gemspec

@@ -0,0 +1,28 @@
+Gem::Specification.new do |s|
+  s.name    = "whispercpp"
+  s.authors = ["Georgi Gerganov", "Todd A. Fisher"]
+  s.version = '1.3.0'
+  s.date    = '2024-05-14'
+  s.description = %q{High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model via Ruby}
+  s.email   = 'todd.fisher@gmail.com'
+  s.extra_rdoc_files = ['LICENSE', 'README.md']
+  
+  s.files = ["LICENSE", "README.md", "Rakefile", "ext/extconf.rb", "ext/ggml.c", "ext/ruby_whisper.cpp", "ext/whisper.cpp", "ext/dr_wav.h", "ext/ggml.h", "ext/ruby_whisper.h", "ext/whisper.h"]
+
+  #### Load-time details
+  s.require_paths = ['lib','ext']
+  s.summary = %q{Ruby whisper.cpp bindings}
+  s.test_files = ["tests/test_whisper.rb"]
+  
+  s.extensions << 'ext/extconf.rb'
+  
+
+  #### Documentation and testing.
+  s.homepage = 'https://github.com/ggerganov/whisper.cpp'
+  s.rdoc_options = ['--main', '../../README.md']
+
+  
+    s.platform = Gem::Platform::RUBY
+  
+  s.licenses = ['MIT']
+end

examples/addon.node/addon.cpp

@@ -25,7 +25,6 @@ struct whisper_params {
     float entropy_thold = 2.4f;
     float logprob_thold = -1.0f;
 
-    bool speed_up       = false;
     bool translate      = false;
     bool diarize        = false;
     bool output_txt     = false;
@@ -232,8 +231,6 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
             wparams.max_len          = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
             wparams.audio_ctx        = params.audio_ctx;
 
-            wparams.speed_up         = params.speed_up;
-
             wparams.greedy.best_of        = params.best_of;
             wparams.beam_search.beam_size = params.beam_size;
 

examples/command/command.cpp

@@ -38,7 +38,6 @@ struct whisper_params {
 
     grammar_parser::parse_state grammar_parsed;
 
-    bool speed_up      = false;
     bool translate     = false;
     bool print_special = false;
     bool print_energy  = false;
@@ -76,7 +75,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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; }
@@ -115,7 +113,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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");
@@ -165,7 +162,6 @@ std::string transcribe(
     wparams.n_threads        = params.n_threads;
 
     wparams.audio_ctx = params.audio_ctx;
-    wparams.speed_up  = params.speed_up;
 
     wparams.temperature     = 0.4f;
     wparams.temperature_inc = 1.0f;
@@ -371,7 +367,6 @@ int process_command_list(struct whisper_context * ctx, audio_async &audio, const
             wparams.n_threads        = params.n_threads;
 
             wparams.audio_ctx        = params.audio_ctx;
-            wparams.speed_up         = params.speed_up;
 
             wparams.prompt_tokens    = k_tokens.data();
             wparams.prompt_n_tokens  = k_tokens.size();

examples/common.h

@@ -185,7 +185,7 @@ private:
     // It is assumed that PCM data is normalized to a range from -1 to 1
     bool write_audio(const float * data, size_t length) {
         for (size_t i = 0; i < length; ++i) {
-            const int16_t intSample = data[i] * 32767;
+            const int16_t intSample = int16_t(data[i] * 32767);
             file.write(reinterpret_cast<const char *>(&intSample), sizeof(int16_t));
             dataSize += sizeof(int16_t);
         }

examples/lsp/lsp.cpp

@@ -26,7 +26,6 @@ struct whisper_params {
     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;
@@ -70,7 +69,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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; }
@@ -102,7 +100,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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");
@@ -184,7 +181,6 @@ json unguided_transcription(struct whisper_context * ctx, audio_async &audio, js
     wparams.n_threads        = params.n_threads;
 
     wparams.audio_ctx        = params.audio_ctx;
-    wparams.speed_up         = params.speed_up;
     wparams.suppress_non_speech_tokens = true;
     // run the transformer and a single decoding pass
     if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
@@ -223,7 +219,6 @@ json guided_transcription(struct whisper_context * ctx, audio_async &audio, cons
     wparams.n_threads        = params.n_threads;
 
     wparams.audio_ctx        = params.audio_ctx;
-    wparams.speed_up         = params.speed_up;
 
     // TODO: Do some time testing. Does an overly long prompt slow down processing?
     // Set up command sets/precompute prompts

examples/main/main.cpp

@@ -47,7 +47,6 @@ struct whisper_params {
     float temperature     = 0.0f;
     float temperature_inc = 0.2f;
 
-    bool speed_up        = false;
     bool debug_mode      = false;
     bool translate       = false;
     bool detect_language = false;
@@ -138,7 +137,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         else if (arg == "-lpt"  || arg == "--logprob-thold")   { params.logprob_thold   = std::stof(argv[++i]); }
         else if (arg == "-tp"   || arg == "--temperature")     { params.temperature     = std::stof(argv[++i]); }
         else if (arg == "-tpi"  || arg == "--temperature-inc") { params.temperature_inc = std::stof(argv[++i]); }
-        // else if (arg == "-su"   || arg == "--speed-up")        { params.speed_up        = true; }
         else if (arg == "-debug"|| arg == "--debug-mode")      { params.debug_mode      = true; }
         else if (arg == "-tr"   || arg == "--translate")       { params.translate       = true; }
         else if (arg == "-di"   || arg == "--diarize")         { params.diarize         = true; }
@@ -206,7 +204,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -lpt N,    --logprob-thold N   [%-7.2f] log probability threshold for decoder fail\n",   params.logprob_thold);
     fprintf(stderr, "  -tp,       --temperature N     [%-7.2f] The sampling temperature, between 0 and 1\n",    params.temperature);
     fprintf(stderr, "  -tpi,      --temperature-inc N [%-7.2f] The increment of temperature, between 0 and 1\n",params.temperature_inc);
-    // fprintf(stderr, "  -su,       --speed-up          [%-7s] speed up audio by x2 (reduced accuracy)\n",        params.speed_up ? "true" : "false");
     fprintf(stderr, "  -debug,    --debug-mode        [%-7s] enable debug mode (eg. dump log_mel)\n",           params.debug_mode ? "true" : "false");
     fprintf(stderr, "  -tr,       --translate         [%-7s] translate from source language to english\n",      params.translate ? "true" : "false");
     fprintf(stderr, "  -di,       --diarize           [%-7s] stereo audio diarization\n",                       params.diarize ? "true" : "false");
@@ -1106,7 +1103,6 @@ int main(int argc, char ** argv) {
             wparams.split_on_word    = params.split_on_word;
             wparams.audio_ctx        = params.audio_ctx;
 
-            wparams.speed_up         = params.speed_up;
             wparams.debug_mode       = params.debug_mode;
 
             wparams.tdrz_enable      = params.tinydiarize; // [TDRZ]

examples/server/server.cpp

@@ -61,7 +61,6 @@ struct whisper_params {
     float temperature     =  0.00f;
     float temperature_inc =  0.20f;
 
-    bool speed_up        = false;
     bool debug_mode      = false;
     bool translate       = false;
     bool detect_language = false;
@@ -112,7 +111,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -wt N,     --word-thold N      [%-7.2f] word timestamp probability threshold\n",         params.word_thold);
     fprintf(stderr, "  -et N,     --entropy-thold N   [%-7.2f] entropy threshold for decoder fail\n",           params.entropy_thold);
     fprintf(stderr, "  -lpt N,    --logprob-thold N   [%-7.2f] log probability threshold for decoder fail\n",   params.logprob_thold);
-    // fprintf(stderr, "  -su,       --speed-up          [%-7s] speed up audio by x2 (reduced accuracy)\n",        params.speed_up ? "true" : "false");
     fprintf(stderr, "  -debug,    --debug-mode        [%-7s] enable debug mode (eg. dump log_mel)\n",           params.debug_mode ? "true" : "false");
     fprintf(stderr, "  -tr,       --translate         [%-7s] translate from source language to english\n",      params.translate ? "true" : "false");
     fprintf(stderr, "  -di,       --diarize           [%-7s] stereo audio diarization\n",                       params.diarize ? "true" : "false");
@@ -159,7 +157,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params, serve
         else if (arg == "-wt"   || arg == "--word-thold")      { params.word_thold      = std::stof(argv[++i]); }
         else if (arg == "-et"   || arg == "--entropy-thold")   { params.entropy_thold   = std::stof(argv[++i]); }
         else if (arg == "-lpt"  || arg == "--logprob-thold")   { params.logprob_thold   = std::stof(argv[++i]); }
-        // else if (arg == "-su"   || arg == "--speed-up")        { params.speed_up        = true; }
         else if (arg == "-debug"|| arg == "--debug-mode")      { params.debug_mode      = true; }
         else if (arg == "-tr"   || arg == "--translate")       { params.translate       = true; }
         else if (arg == "-di"   || arg == "--diarize")         { params.diarize         = true; }
@@ -768,7 +765,6 @@ int main(int argc, char ** argv) {
             wparams.split_on_word    = params.split_on_word;
             wparams.audio_ctx        = params.audio_ctx;
 
-            wparams.speed_up         = params.speed_up;
             wparams.debug_mode       = params.debug_mode;
 
             wparams.tdrz_enable      = params.tinydiarize; // [TDRZ]
@@ -947,7 +943,7 @@ int main(int argc, char ** argv) {
                             "application/json");
         }
 
-        // reset params to thier defaults
+        // reset params to their defaults
         params = default_params;
     });
     svr.Post(sparams.request_path + "/load", [&](const Request &req, Response &res){

examples/stream/stream.cpp

@@ -27,7 +27,6 @@ struct whisper_params {
     float vad_thold    = 0.6f;
     float freq_thold   = 100.0f;
 
-    bool speed_up      = false;
     bool translate     = false;
     bool no_fallback   = false;
     bool print_special = false;
@@ -62,7 +61,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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 == "-nf"   || arg == "--no-fallback")   { params.no_fallback   = true; }
         else if (arg == "-ps"   || arg == "--print-special") { params.print_special = true; }
@@ -100,7 +98,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -ac N,    --audio-ctx N   [%-7d] audio context size (0 - all)\n",                   params.audio_ctx);
     fprintf(stderr, "  -vth N,   --vad-thold N   [%-7.2f] voice activity detection threshold\n",           params.vad_thold);
     fprintf(stderr, "  -fth N,   --freq-thold N  [%-7.2f] high-pass frequency cutoff\n",                   params.freq_thold);
-    fprintf(stderr, "  -su,      --speed-up      [%-7s] speed up audio by x2 (reduced accuracy)\n",        params.speed_up ? "true" : "false");
     fprintf(stderr, "  -tr,      --translate     [%-7s] translate from source language to english\n",      params.translate ? "true" : "false");
     fprintf(stderr, "  -nf,      --no-fallback   [%-7s] do not use temperature fallback while decoding\n", params.no_fallback ? "true" : "false");
     fprintf(stderr, "  -ps,      --print-special [%-7s] print special tokens\n",                           params.print_special ? "true" : "false");
@@ -314,7 +311,6 @@ int main(int argc, char ** argv) {
             wparams.n_threads        = params.n_threads;
 
             wparams.audio_ctx        = params.audio_ctx;
-            wparams.speed_up         = params.speed_up;
 
             wparams.tdrz_enable      = params.tinydiarize; // [TDRZ]
 

examples/talk-llama/talk-llama.cpp

@@ -59,7 +59,6 @@ struct whisper_params {
     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;
@@ -100,7 +99,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         else if (arg == "-ngl" || arg == "--n-gpu-layers")   { params.n_gpu_layers   = 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; }
@@ -149,7 +147,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     fprintf(stderr, "  -ngl N,   --n-gpu-layers N [%-7d] number of layers to store in VRAM\n",           params.n_gpu_layers);
     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");
@@ -205,7 +202,6 @@ std::string transcribe(
     wparams.prompt_n_tokens  = prompt_tokens.empty() ? 0       : prompt_tokens.size();
 
     wparams.audio_ctx        = params.audio_ctx;
-    wparams.speed_up         = params.speed_up;
 
     if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
         return "";

examples/talk/talk.cpp

@@ -26,7 +26,6 @@ struct whisper_params {
     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;
@@ -60,7 +59,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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; }
@@ -96,7 +94,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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");
@@ -132,7 +129,6 @@ std::string transcribe(whisper_context * ctx, const whisper_params & params, con
     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 "";

examples/wchess/wchess.cmd/wchess.cmd.cpp

@@ -26,7 +26,6 @@ struct whisper_params {
 
     float grammar_penalty = 100.0f;
 
-    bool speed_up      = false;
     bool translate     = false;
     bool print_special = false;
     bool print_energy  = false;
@@ -57,7 +56,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
     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");
@@ -89,7 +87,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
         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; }

whisper-mel-cuda.cu

@@ -0,0 +1,363 @@
+#define CUB_IGNORE_DEPRECATED_CPP_DIALECT
+#include "whisper-mel-cuda.hpp"
+#include "whisper.h"
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#include <cufft.h>
+#include <cublas_v2.h>
+#include <cuComplex.h>
+#include <cub/device/device_reduce.cuh>
+#include <device_launch_parameters.h>
+
+#include <algorithm>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4324) // added padding
+#endif
+
+#ifndef NDEBUG
+#   define DO_CHECKS 1
+#else
+#   define DO_CHECKS 0
+#endif
+
+namespace {
+
+#if DO_CHECKS
+const char* cufftGetErrorString(cufftResult_t res) {
+    switch (res) {
+    case CUFFT_SUCCESS: return "The cuFFT operation was successful";
+    case CUFFT_INVALID_PLAN: return "cuFFT was passed an invalid plan handle";
+    case CUFFT_ALLOC_FAILED: return "cuFFT failed to allocate GPU or CPU memory";
+    case CUFFT_INVALID_TYPE: return "No longer used";
+    case CUFFT_INVALID_VALUE: return "User specified an invalid pointer or parameter";
+    case CUFFT_INTERNAL_ERROR: return "Driver or internal cuFFT library error";
+    case CUFFT_EXEC_FAILED: return "Failed to execute an FFT on the GPU";
+    case CUFFT_SETUP_FAILED: return "The cuFFT library failed to initialize";
+    case CUFFT_INVALID_SIZE: return "User specified an invalid transform size";
+    case CUFFT_UNALIGNED_DATA: return "No longer used";
+    case CUFFT_INCOMPLETE_PARAMETER_LIST: return "Missing parameters in call";
+    case CUFFT_INVALID_DEVICE: return "Execution of a plan was on different GPU than plan creation";
+    case CUFFT_PARSE_ERROR: return "Internal plan database error";
+    case CUFFT_NO_WORKSPACE: return "No workspace has been provided prior to plan execution";
+    case CUFFT_NOT_IMPLEMENTED: return "Function does not implement functionality for parameters given.";
+    case CUFFT_LICENSE_ERROR: return "Used in previous versions.";
+    case CUFFT_NOT_SUPPORTED: return "Operation is not supported for parameters given.";
+    default: return "Unknown error";
+    }
+}
+
+#   define CUDA_CHECK_GEN(err, success, error_fn)                                            \
+         do {                                                                                \
+            auto err_ = (err);                                                               \
+            if (err_ != (success)) {                                                         \
+                fprintf(stderr, "%s %s:%d - %s\n", #err, __FILE__, __LINE__, error_fn(err_)); \
+            }                                                                                \
+        } while (0)
+#else
+#   define CUDA_CHECK_GEN(err, success, error_fn) err
+#endif
+
+#define CUDA_CHECK(err) CUDA_CHECK_GEN(err, cudaSuccess, cudaGetErrorString)
+#define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublasGetStatusString)
+#define CUFFT_CHECK(err) CUDA_CHECK_GEN(err, CUFFT_SUCCESS, cufftGetErrorString)
+
+__global__ void k_fill_stft_input(
+    const float * padded_samples,
+    const int n_frames,
+    const float * hann_window,
+    float * stft_in
+) {
+    auto y = blockIdx.y * blockDim.y + threadIdx.y;
+    // if (y >= n_frames) return;
+    auto x = blockIdx.x * blockDim.x + threadIdx.x;
+    // if (x >= WHISPER_N_FFT) return;
+
+    auto line = padded_samples + y * WHISPER_HOP_LENGTH;
+    auto outLine = stft_in + y * WHISPER_N_FFT;
+
+    outLine[x] = line[x] * hann_window[x];
+}
+
+__global__ void k_calc_magnitudes(
+    const cuComplex* stft_out,
+    const int n_frames,
+    float * magnitudes
+) {
+    auto y = blockIdx.y * blockDim.y + threadIdx.y;
+    // if (y >= n_frames) return;
+    auto x = blockIdx.x * blockDim.x + threadIdx.x;
+    // if (x >= WHISPER_N_FFT_HALF) return;
+
+    auto idx = y * WHISPER_N_FFT_HALF + x;
+
+    auto r = stft_out[idx].x;
+    auto i = stft_out[idx].y;
+    magnitudes[idx] = r * r + i * i;
+}
+
+__global__ void k_calc_log_mel(
+    const float * mel_data,
+    const int n_mel,
+    const float * max_val,
+    float * log_mel
+) {
+    auto x = blockIdx.x * blockDim.x + threadIdx.x;
+    if (x >= n_mel) return;
+
+    float val = mel_data[x];
+
+    constexpr float e = 1e-10f;
+    if (val < e) val = e;
+
+    val = log10(val);
+
+    const float max = log10(*max_val) - 8.f;
+    if (val < max) val = max;
+
+    log_mel[x] = (val + 4) / 4;
+}
+
+void fill_stft_input(
+    const float * padded_samples,
+    int n_frames,
+    const float * hann_window,
+    float * stft_in,
+    cudaStream_t stream
+) {
+    dim3 block(WHISPER_N_FFT, 1);
+    dim3 grid(1, n_frames);
+
+    k_fill_stft_input<<<grid, block, 0, stream>>>(padded_samples, n_frames, hann_window, stft_in);
+}
+
+void calc_magnitudes(
+    const cuComplex* stft_out,
+    int n_frames,
+    float * magnitudes,
+    cudaStream_t stream
+) {
+    dim3 block(WHISPER_N_FFT_HALF, 1);
+    dim3 grid(1, n_frames);
+    k_calc_magnitudes<<<grid, block, 0, stream>>>(stft_out, n_frames, magnitudes);
+}
+
+constexpr auto LOG_MEL_PREFIX_SIZE = 256;
+
+void calc_log_mel(
+    const float * mel_data,
+    int n_mel,
+    void * tempStorage,
+    int tempStorageSize,
+    float * log_mel,
+    cudaStream_t stream
+) {
+    float * max_val = reinterpret_cast<float *>(tempStorage);
+    void * maxTemp = reinterpret_cast<char*>(tempStorage) + LOG_MEL_PREFIX_SIZE;
+
+    size_t nbytes = size_t(tempStorageSize - LOG_MEL_PREFIX_SIZE);
+    cub::DeviceReduce::Max(maxTemp, nbytes, mel_data, max_val, n_mel, stream);
+
+    int block = 256;
+    int grid = (n_mel + block - 1) / block;
+
+    k_calc_log_mel<<<grid, block, 0, stream>>>(mel_data, n_mel, max_val, log_mel);
+}
+
+class mel_calc_cuda : public whisper_mel_calc {
+    const int m_n_mel;
+
+    ggml_backend_t m_backend = nullptr;
+
+    cudaStream_t m_stream = nullptr;
+    cublasHandle_t m_cublas_handle = nullptr;
+
+    float * m_hann_window = nullptr;
+
+    float * m_filters = nullptr;
+
+    // max samples for which we have allocated memory for the temp working areas below (cufft, log_mel)
+    int m_n_max_samples = 0;
+
+    size_t m_cufft_workspace_size = 0;
+    void * m_cufft_workspace = nullptr;
+
+    size_t m_log_mel_temp_storage_size = 0;
+    void * m_log_mel_temp_storage = nullptr;
+public:
+    mel_calc_cuda(ggml_backend_t backend, const whisper_filters & filters)
+        : m_n_mel(filters.n_mel)
+        , m_backend(backend)
+    {
+        if (filters.n_fft != WHISPER_N_FFT_HALF) {
+            throw std::invalid_argument("MelFilters n_frames must be WHISPER_N_FFT_HALF");
+        }
+        assert(filters.data.size() == filters.n_mel * WHISPER_N_FFT_HALF);
+
+        CUDA_CHECK(cudaStreamCreate(&m_stream));
+        CUBLAS_CHECK(cublasCreate(&m_cublas_handle));
+        CUBLAS_CHECK(cublasSetMathMode(m_cublas_handle, CUBLAS_TF32_TENSOR_OP_MATH));
+        CUBLAS_CHECK(cublasSetStream(m_cublas_handle, m_stream));
+
+        // create Hann window
+        {
+            auto hw = whisper_mel_calc::hann_window();
+            CUDA_CHECK(cudaMallocAsync(&m_hann_window, hw.len * sizeof(float), m_stream));
+            CUDA_CHECK(cudaMemcpyAsync(m_hann_window, hw.data, hw.len * sizeof(float), cudaMemcpyHostToDevice, m_stream));
+        }
+
+        // fill filters
+        {
+            auto& f = filters.data;
+            CUDA_CHECK(cudaMallocAsync(&m_filters, f.size() * sizeof(float), m_stream));
+            CUDA_CHECK(cudaMemcpyAsync(m_filters, f.data(), f.size() * sizeof(float), cudaMemcpyHostToDevice, m_stream));
+        }
+
+        // preallocate working areas enough for the most common cases (<= 30s)
+        ensure_working_areas(WHISPER_N_SAMPLES);
+    }
+
+    ~mel_calc_cuda() {
+        CUDA_CHECK(cudaStreamSynchronize(m_stream));
+        CUDA_CHECK(cudaStreamDestroy(m_stream));
+        CUDA_CHECK(cudaFree(m_hann_window));
+        CUDA_CHECK(cudaFree(m_cufft_workspace));
+        CUDA_CHECK(cudaFree(m_filters));
+        CUDA_CHECK(cudaFree(m_log_mel_temp_storage));
+    }
+
+    void ensure_working_areas(int n_samples) {
+        if (n_samples <= m_n_max_samples) {
+            return;
+        }
+
+        const auto max_padded_samples = n_samples + WHISPER_N_SAMPLES + WHISPER_N_FFT;
+        const auto max_frames = 1 + (max_padded_samples - WHISPER_N_FFT) / WHISPER_HOP_LENGTH;
+
+        // cufft workspace
+        {
+            if (m_cufft_workspace) {
+                CUDA_CHECK(cudaFree(m_cufft_workspace));
+                m_cufft_workspace_size = 0;
+                m_cufft_workspace = nullptr;
+            }
+            CUFFT_CHECK(cufftEstimate1d(WHISPER_N_FFT, CUFFT_R2C, max_frames, &m_cufft_workspace_size));
+            CUDA_CHECK(cudaMallocAsync(&m_cufft_workspace, m_cufft_workspace_size, m_stream));
+        }
+
+        // device reduce working area
+        {
+            if (m_log_mel_temp_storage) {
+                CUDA_CHECK(cudaFree(m_log_mel_temp_storage));
+                m_log_mel_temp_storage_size = 0;
+                m_log_mel_temp_storage = nullptr;
+            }
+
+            const auto max_mels = 160;
+
+            size_t nbytes = 0;
+            float* temp = nullptr;
+            cub::DeviceReduce::Max(nullptr, nbytes, temp, temp, max_frames * max_mels);
+            m_log_mel_temp_storage_size = nbytes + LOG_MEL_PREFIX_SIZE;
+
+            CUDA_CHECK(cudaMallocAsync(&m_log_mel_temp_storage, m_log_mel_temp_storage_size, m_stream));
+        }
+
+        m_n_max_samples = n_samples;
+    }
+
+    virtual whisper_mel calculate(whisper_span<const float> samples, int /*n_threads*/) override {
+        ensure_working_areas(samples.len);
+
+        const size_t mirror_pad = WHISPER_N_FFT / 2;
+        const size_t padded_size = samples.len + WHISPER_N_SAMPLES + WHISPER_N_FFT;
+
+        // pad
+        std::vector<float> padded_samples(padded_size);
+        std::reverse_copy(samples.data + 1, samples.data + 1 + mirror_pad, padded_samples.begin()); // reflect
+        std::copy(samples.data, samples.data + samples.len, padded_samples.begin() + mirror_pad); // copy
+
+        // fill the rest of the data
+        // it should canonically be mirrored at the end as well,
+        // but we just assume the last MEL_FRAME_SIZE/2 samples are zeros
+        std::fill(padded_samples.begin() + mirror_pad + samples.len, padded_samples.end(), 0.f);
+
+        const auto n_frames = 1 + (padded_samples.size() - WHISPER_N_FFT) / WHISPER_HOP_LENGTH;
+
+        float * cu_padded_samples = nullptr;
+        CUDA_CHECK(cudaMallocAsync(&cu_padded_samples, padded_samples.size() * sizeof(float), m_stream));
+        CUDA_CHECK(cudaMemcpyAsync(cu_padded_samples, padded_samples.data(), padded_samples.size() * sizeof(float), cudaMemcpyHostToDevice, m_stream));
+
+        float * stft_in = nullptr; // contiguous buffer for stft input
+        CUDA_CHECK(cudaMallocAsync(&stft_in, n_frames * WHISPER_N_FFT * sizeof(float), m_stream));
+
+        fill_stft_input(cu_padded_samples, int(n_frames), m_hann_window, stft_in, m_stream);
+
+        cufftComplex* stft_out;
+        CUDA_CHECK(cudaMallocAsync(&stft_out, n_frames * WHISPER_N_FFT_HALF * sizeof(cufftComplex), m_stream));
+
+        cufftHandle plan;
+        CUFFT_CHECK(cufftCreate(&plan));
+        CUFFT_CHECK(cufftSetAutoAllocation(plan, 0));
+        {
+            size_t waSize;
+            CUFFT_CHECK(cufftMakePlan1d(plan, WHISPER_N_FFT, CUFFT_R2C, int(n_frames), &waSize));
+            assert(waSize <= m_cufft_workspace_size);
+            CUFFT_CHECK(cufftSetWorkArea(plan, m_cufft_workspace));
+            CUFFT_CHECK(cufftSetStream(plan, m_stream));
+        }
+        CUFFT_CHECK(cufftExecR2C(plan, stft_in, stft_out));
+
+        const auto n_mag_frames = n_frames - 1; // drop last frame
+        float * magnitudes;
+        CUDA_CHECK(cudaMallocAsync(&magnitudes, n_mag_frames * WHISPER_N_FFT_HALF * sizeof(float), m_stream));
+        calc_magnitudes(stft_out, int(n_mag_frames), magnitudes, m_stream);
+
+        float * mel_data = nullptr;
+        CUDA_CHECK(cudaMallocAsync(&mel_data, m_n_mel * n_mag_frames * sizeof(float), m_stream));
+
+        const float fone = 1.0f, fzero = 0.0f;
+        CUBLAS_CHECK(cublasSgemm(m_cublas_handle, CUBLAS_OP_T, CUBLAS_OP_N,
+            int(n_mag_frames), m_n_mel, WHISPER_N_FFT_HALF,
+            &fone,
+            magnitudes, WHISPER_N_FFT_HALF,
+            m_filters, WHISPER_N_FFT_HALF,
+            &fzero,
+            mel_data, int(n_mag_frames)));
+
+        whisper_mel ret;
+        // Calculate semi-padded sample length to ensure compatibility
+        int n_len_org = 1 + int(samples.len + mirror_pad - WHISPER_N_FFT) / WHISPER_HOP_LENGTH;
+        whisper_mel_init(ret, m_backend, int(n_mag_frames), n_len_org, m_n_mel);
+        assert(ggml_nbytes(ret.tensor) == m_n_mel * n_mag_frames * sizeof(float));
+
+        float* log_mels = reinterpret_cast<float*>(ret.tensor->data);
+
+        calc_log_mel(
+            mel_data, int(m_n_mel * n_mag_frames),
+            m_log_mel_temp_storage , int(m_log_mel_temp_storage_size),
+            log_mels, m_stream);
+
+        CUDA_CHECK(cudaStreamSynchronize(m_stream));
+
+        // cleanup
+        CUFFT_CHECK(cufftDestroy(plan));
+        CUDA_CHECK(cudaFreeAsync(mel_data, m_stream));
+        CUDA_CHECK(cudaFreeAsync(magnitudes, m_stream));
+        CUDA_CHECK(cudaFreeAsync(stft_out, m_stream));
+        CUDA_CHECK(cudaFreeAsync(stft_in, m_stream));
+        CUDA_CHECK(cudaFreeAsync(cu_padded_samples, m_stream));
+
+        return ret;
+    }
+};
+
+}
+
+whisper_mel_calc * whisper_mel_calc_create_cuda(ggml_backend_t backend, const whisper_filters & filters) {
+    if (filters.n_fft != WHISPER_N_FFT_HALF) {
+        return nullptr;
+    }
+    return new mel_calc_cuda(backend, filters);
+}

whisper-mel-cuda.hpp

@@ -0,0 +1,3 @@
+#include "whisper-mel.hpp"
+
+whisper_mel_calc * whisper_mel_calc_create_cuda(ggml_backend_t backend, const whisper_filters & filters);

whisper-mel.hpp

@@ -0,0 +1,34 @@
+#pragma once
+#include "ggml-backend.h"
+#include <vector>
+
+struct whisper_mel {
+    int n_len_org = 0;
+
+    ggml_context * ctx = nullptr;
+    ggml_tensor * tensor = nullptr;
+    ggml_backend_buffer_t buffer = nullptr;
+};
+
+void whisper_mel_init(whisper_mel & mel, ggml_backend_t backend, int n_len, int n_len_org, int n_mel);
+
+void whisper_mel_free(whisper_mel & mel);
+
+struct whisper_filters {
+    int32_t n_mel;
+    int32_t n_fft;
+
+    std::vector<float> data;
+};
+
+template <typename T>
+struct whisper_span {
+    T * data;
+    int len;
+};
+
+struct whisper_mel_calc {
+    virtual ~whisper_mel_calc();
+    virtual whisper_mel calculate(whisper_span<const float> samples, int n_threads) = 0;
+    static whisper_span<const float> hann_window();
+};

whisper.cpp

@@ -10,6 +10,7 @@
 
 #ifdef GGML_USE_CUDA
 #include "ggml-cuda.h"
+#include "whisper-mel-cuda.hpp"
 #endif
 
 #ifdef GGML_USE_SYCL
@@ -24,6 +25,8 @@
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 
+#include "whisper-mel.hpp"
+
 #include <atomic>
 #include <algorithm>
 #include <cassert>
@@ -380,21 +383,6 @@ static const std::map<whisper_alignment_heads_preset, whisper_aheads> g_aheads {
 
 static std::vector<uint32_t> get_alignment_heads_by_layer(const whisper_context_params & cparams, int il, int32_t n_text_layer, int32_t n_head);
 
-struct whisper_mel {
-    int n_len;
-    int n_len_org;
-    int n_mel;
-
-    std::vector<float> data;
-};
-
-struct whisper_filters {
-    int32_t n_mel;
-    int32_t n_fft;
-
-    std::vector<float> data;
-};
-
 struct whisper_vocab {
     using id    = int32_t;
     using token = std::string;
@@ -813,11 +801,15 @@ struct whisper_state {
     whisper_kv_cache kv_pad;
 
     whisper_mel mel;
+    whisper_mel_calc * mel_calc = nullptr;
+    whisper_mel_calc * mel_calc_fallback = nullptr;
 
     whisper_batch batch;
 
     whisper_decoder decoders[WHISPER_MAX_DECODERS];
 
+    ggml_backend_t backend = nullptr;
+
     // ggml-alloc:
     // - stores meta info about the intermediate tensors into the `meta` buffers
     // - stores the actual tensor data into the `data` buffers
@@ -831,7 +823,6 @@ struct whisper_state {
     struct ggml_tensor * embd_enc  = nullptr;
 
     // helpers for GPU offloading
-    std::vector<float> inp_mel;
     std::vector<float> inp_mask;
 
     // decode output (2-dimensional array: [n_tokens][n_vocab])
@@ -902,7 +893,7 @@ static void read_safe(whisper_model_loader * loader, T & dest) {
     BYTESWAP_VALUE(dest);
 }
 
-static bool kv_cache_init(
+static bool whisper_kv_cache_init(
              struct whisper_kv_cache & cache,
                       ggml_backend_t   backend,
                            ggml_type   wtype,
@@ -945,7 +936,7 @@ static bool kv_cache_init(
     return true;
 }
 
-static void kv_cache_free(struct whisper_kv_cache & cache) {
+static void whisper_kv_cache_free(struct whisper_kv_cache & cache) {
     ggml_free(cache.ctx);
     ggml_backend_buffer_free(cache.buffer);
     cache.ctx = nullptr;
@@ -1259,9 +1250,12 @@ static ggml_backend_t whisper_backend_init(const whisper_context_params & params
     }
 #endif
 
+    GGML_UNUSED(params);
+
     if (backend_gpu) {
         return backend_gpu;
     }
+
     return ggml_backend_cpu_init();
 }
 
@@ -1823,7 +1817,8 @@ static bool whisper_encode_external(const whisper_state & wstate) {
 
 static struct ggml_cgraph * whisper_build_graph_conv(
         whisper_context & wctx,
-          whisper_state & wstate) {
+          whisper_state & wstate,
+              const int   mel_offset) {
     const auto & model   = wctx.model;
     const auto & hparams = model.hparams;
 
@@ -1842,9 +1837,32 @@ static struct ggml_cgraph * whisper_build_graph_conv(
 
     ggml_cgraph * gf = ggml_new_graph(ctx0);
 
-    struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
-    ggml_set_name(mel, "mel");
-    ggml_set_input(mel);
+    ggml_tensor * mel_inp = wstate.mel.tensor;
+    ggml_tensor * mel;
+    if (mel_inp) {
+        const int n_len = int(mel_inp->ne[0]);
+        const int out_s = 2 * n_ctx;
+        const int i0 = std::min(mel_offset, n_len);
+        const int i1 = std::min(mel_offset + out_s, n_len);
+        const int mel_s = i1 - i0;
+
+        assert(mel_inp->type == GGML_TYPE_F32);
+        assert(mel_inp->ne[1] == n_mels);
+
+        ggml_tensor * cur = ggml_view_2d(ctx0, mel_inp, out_s, n_mels, mel_inp->nb[1], ggml_row_size(mel_inp->type, i0));
+
+        if (mel_s < out_s) {
+            mel = ggml_pad(ctx0, cur, out_s - mel_s, 0, 0, 0);
+        }
+        else {
+            mel = ggml_cont(ctx0, cur);
+        }
+    }
+    else {
+        // just create some tensor so that the graph/buffer size estimation is correct
+        mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2 * n_ctx, n_mels);
+    }
+    ggml_set_name(mel, "mel"); // used with external encoding
 
     struct ggml_tensor * cur = nullptr;
 
@@ -2226,45 +2244,21 @@ static bool whisper_encode_internal(
     {
         auto & alloc = wstate.alloc_conv.alloc;
 
-        ggml_cgraph * gf = whisper_build_graph_conv(wctx, wstate);
+        ggml_cgraph * gf = whisper_build_graph_conv(wctx, wstate, mel_offset);
 
         if (!ggml_gallocr_alloc_graph(alloc, gf)) {
             // should never happen as we pre-allocate the memory
             return false;
         }
 
-        struct ggml_tensor * mel = ggml_graph_get_tensor(gf, "mel");
-
-        // set the input
-        {
-            const auto & mel_inp = wstate.mel;
-            const int n_ctx      = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : wctx.model.hparams.n_audio_ctx;
-
-            assert(mel->type == GGML_TYPE_F32);
-            assert(mel_inp.n_mel == wctx.model.hparams.n_mels);
-
-            wstate.inp_mel.resize(ggml_nelements(mel));
-
-            float * dst = wstate.inp_mel.data();
-            memset(dst, 0, ggml_nbytes(mel));
-
-            const int i0 = std::min(mel_offset,           mel_inp.n_len);
-            const int i1 = std::min(mel_offset + 2*n_ctx, mel_inp.n_len);
-
-            for (int j = 0; j < mel_inp.n_mel; ++j) {
-                for (int i = i0; i < i1; ++i) {
-                    dst[j*2*n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
-                }
-            }
-
-            ggml_backend_tensor_set(mel, wstate.inp_mel.data(), 0, ggml_nelements(mel)*sizeof(float));
+        if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
+            return false;
         }
 
-        if (!whisper_encode_external(wstate)) {
-            if (!ggml_graph_compute_helper(wctx.backend, gf, n_threads)) {
-                return false;
-            }
-        } else {
+        if (whisper_encode_external(wstate)) {
+            ggml_tensor * mel = ggml_graph_get_tensor(gf, "mel");
+            assert(mel->ne[1] == wctx.model.hparams.n_mels);
+            GGML_UNUSED(mel);
 #if defined(WHISPER_USE_COREML)
             whisper_coreml_encode(wstate.ctx_coreml, mel->ne[0], mel->ne[1], (float *) mel->data, (float *) wstate.embd_enc->data);
 #elif defined(WHISPER_USE_OPENVINO)
@@ -2284,7 +2278,7 @@ static bool whisper_encode_internal(
             return false;
         }
 
-        if (!ggml_graph_compute_helper(wctx.backend, gf, n_threads)) {
+        if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
             return false;
         }
     }
@@ -2300,7 +2294,7 @@ static bool whisper_encode_internal(
             return false;
         }
 
-        if (!ggml_graph_compute_helper(wctx.backend, gf, n_threads)) {
+        if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
             return false;
         }
     }
@@ -2801,7 +2795,7 @@ static bool whisper_decode_internal(
 
         logits = gf->nodes[gf->n_nodes - 1];
 
-        if (!ggml_graph_compute_helper(wctx.backend, gf, n_threads)) {
+        if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
             return false;
         }
     }
@@ -2855,20 +2849,70 @@ static std::string to_timestamp(int64_t t, bool comma = false) {
 }
 
 #define SIN_COS_N_COUNT WHISPER_N_FFT
-static float sin_vals[SIN_COS_N_COUNT];
-static float cos_vals[SIN_COS_N_COUNT];
+namespace {
+struct whisper_global_cache {
+    // In FFT, we frequently use sine and cosine operations with the same values.
+    // We can use precalculated values to speed up the process.
+    float sin_vals[SIN_COS_N_COUNT];
+    float cos_vals[SIN_COS_N_COUNT];
 
-// In FFT, we frequently use sine and cosine operations with the same values.
-// We can use precalculated values to speed up the process.
-static void fill_sin_cos_table() {
-    static bool is_filled = false;
-    if (is_filled) return;
-    for (int i = 0; i < SIN_COS_N_COUNT; i++) {
-        double theta = (2*M_PI*i)/SIN_COS_N_COUNT;
-        sin_vals[i] = sinf(theta);
-        cos_vals[i] = cosf(theta);
+    // Hann window (Use cosf to eliminate difference)
+    // ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html
+    // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147
+    float hann_window[WHISPER_N_FFT];
+
+    whisper_global_cache() {
+        fill_sin_cos_table();
+        fill_hann_window(sizeof(hann_window)/sizeof(hann_window[0]), true, hann_window);
     }
-    is_filled = true;
+
+    void fill_sin_cos_table() {
+        for (int i = 0; i < SIN_COS_N_COUNT; i++) {
+            double theta = (2 * M_PI * i) / SIN_COS_N_COUNT;
+            sin_vals[i] = sinf(theta);
+            cos_vals[i] = cosf(theta);
+        }
+    }
+
+    void fill_hann_window(int length, bool periodic, float * output) {
+        int offset = -1;
+        if (periodic) {
+            offset = 0;
+        }
+        for (int i = 0; i < length; i++) {
+            output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
+        }
+    }
+} global_cache;
+}
+
+// Mel spectrogram
+
+void whisper_mel_init(whisper_mel & mel, ggml_backend_t backend, int n_len, int n_len_org, int n_mel) {
+    WHISPER_LOG_INFO("%s: n_len = %d, n_len_org = %d, n_mel = %d\n", __func__, n_len, n_len_org, n_mel);
+    mel.n_len_org = n_len_org;
+    assert(!mel.ctx);
+    mel.ctx = ggml_init({ggml_tensor_overhead(), nullptr, true});
+    mel.tensor = ggml_new_tensor_2d(mel.ctx, GGML_TYPE_F32, n_len, n_mel);
+    mel.buffer = ggml_backend_alloc_buffer(backend, ggml_nbytes(mel.tensor) + ggml_backend_get_alignment(backend));
+    auto alloc = ggml_tallocr_new(mel.buffer);
+    ggml_tallocr_alloc(&alloc, mel.tensor);
+}
+
+void whisper_mel_free(whisper_mel & mel) {
+    ggml_free(mel.ctx);
+    ggml_backend_buffer_free(mel.buffer);
+
+    mel.n_len_org = 0;
+    mel.ctx = nullptr;
+    mel.tensor = nullptr;
+    mel.buffer = nullptr;
+}
+
+whisper_mel_calc::~whisper_mel_calc() = default; // export vtable
+
+whisper_span<const float> whisper_mel_calc::hann_window() {
+    return {global_cache.hann_window, WHISPER_N_FFT};
 }
 
 // naive Discrete Fourier Transform
@@ -2886,8 +2930,8 @@ static void dft(const std::vector<float> & in, std::vector<float> & out) {
 
         for (int n = 0; n < N; n++) {
             int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N
-            re += in[n]*cos_vals[idx]; // cos(t)
-            im -= in[n]*sin_vals[idx]; // sin(t)
+            re += in[n]*global_cache.cos_vals[idx]; // cos(t)
+            im -= in[n]*global_cache.sin_vals[idx]; // sin(t)
         }
 
         out[k*2 + 0] = re;
@@ -2938,8 +2982,8 @@ static void fft(const std::vector<float> & in, std::vector<float> & out) {
     const int sin_cos_step = SIN_COS_N_COUNT / N;
     for (int k = 0; k < N/2; k++) {
         int idx = k * sin_cos_step; // t = 2*M_PI*k/N
-        float re = cos_vals[idx]; // cos(t)
-        float im = -sin_vals[idx]; // sin(t)
+        float re = global_cache.cos_vals[idx]; // cos(t)
+        float im = -global_cache.sin_vals[idx]; // sin(t)
 
         float re_odd = odd_fft[2*k + 0];
         float im_odd = odd_fft[2*k + 1];
@@ -2952,24 +2996,20 @@ static void fft(const std::vector<float> & in, std::vector<float> & out) {
     }
 }
 
-static bool hann_window(int length, bool periodic, std::vector<float> & output) {
-    if (output.size() < static_cast<size_t>(length)) {
-        output.resize(length);
-    }
-    int offset = -1;
-    if (periodic) {
-        offset = 0;
-    }
-    for (int i = 0; i < length; i++) {
-        output[i] = 0.5*(1.0 - cosf((2.0*M_PI*i)/(length + offset)));
-    }
+namespace {
 
-    return true;
-}
+struct whisper_mel_data {
+    int n_len;
+    int n_len_org;
+    int n_mel;
+    float * data;
+};
 
-static void log_mel_spectrogram_worker_thread(int ith, const std::vector<float> & hann, const std::vector<float> & samples,
-                                              int n_samples, int frame_size, int frame_step, int n_threads,
-                                              const whisper_filters & filters, whisper_mel & mel) {
+void log_mel_spectrogram_worker_thread(int ith, const float * hann, const std::vector<float> & samples,
+                                              int n_samples, int n_threads,
+                                              const whisper_filters & filters, whisper_mel_data & mel) {
+    const auto frame_size = WHISPER_N_FFT;
+    const auto frame_step = WHISPER_HOP_LENGTH;
     std::vector<float> fft_in(frame_size, 0.0);
     std::vector<float> fft_out(2 * frame_size);
     int n_fft = filters.n_fft;
@@ -2982,7 +3022,7 @@ static void log_mel_spectrogram_worker_thread(int ith, const std::vector<float>
     for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) {
         const int offset = i * frame_step;
 
-        // apply Hanning window (~10% faster)
+        // apply Hann window (~10% faster)
         for (int j = 0; j < std::min(frame_size, n_samples - offset); j++) {
             fft_in[j] = hann[j] * samples[offset + j];
         }
@@ -3034,101 +3074,109 @@ static void log_mel_spectrogram_worker_thread(int ith, const std::vector<float>
     }
 }
 
-// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157
-static bool log_mel_spectrogram(
-              whisper_state & wstate,
-              const float * samples,
-              const int   n_samples,
-              const int   /*sample_rate*/,
-              const int   frame_size,
-              const int   frame_step,
-              const int   n_mel,
-              const int   n_threads,
-              const whisper_filters & filters,
-              const bool   debug,
-              whisper_mel & mel) {
-    const int64_t t_start_us = ggml_time_us();
+struct mel_calc_cpu : public whisper_mel_calc {
+    ggml_backend_t m_backend;
+    const whisper_filters & m_filters;
+    mel_calc_cpu(ggml_backend_t backend, const whisper_filters & filters) : m_backend(backend), m_filters(filters) {}
 
-    // Hanning window (Use cosf to eliminate difference)
-    // ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html
-    // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147
-    std::vector<float> hann;
-    hann_window(frame_size, true, hann);
+    // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157
+    whisper_mel calculate(whisper_span<const float> ssamples, int n_threads) override {
+        // Hann window
+        const float * hann = global_cache.hann_window;
 
+        // Calculate the length of padding
+        int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
+        int64_t stage_2_pad = WHISPER_N_FFT / 2;
 
-    // Calculate the length of padding
-    int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
-    int64_t stage_2_pad = frame_size / 2;
+        const int n_samples = int(ssamples.len);
+        const float * samples = ssamples.data;
 
-    // Initialize a vector and copy data from C array to it.
-    std::vector<float> samples_padded;
-    samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
-    std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);
+        // Initialize a vector and copy data from C array to it.
+        std::vector<float> samples_padded;
+        samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
+        std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);
 
-    // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
-    std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);
+        // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
+        std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);
 
-    // reflective pad 200 samples at the beginning of audio
-    std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
+        // reflective pad 200 samples at the beginning of audio
+        std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
 
-    mel.n_mel     = n_mel;
-    // https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936
-    // Calculate number of frames + remove the last frame
-    mel.n_len     = (samples_padded.size() - frame_size) / frame_step;
-    // Calculate semi-padded sample length to ensure compatibility
-    mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step;
-    mel.data.resize(mel.n_mel * mel.n_len);
+        whisper_mel_data mel;
+        mel.n_mel     = m_filters.n_mel;
+        // https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936
+        // Calculate number of frames + remove the last frame
+        mel.n_len     = (samples_padded.size() - WHISPER_N_FFT) / WHISPER_HOP_LENGTH;
+        // Calculate semi-padded sample length to ensure compatibility
+        mel.n_len_org = 1 + (n_samples + stage_2_pad - WHISPER_N_FFT) / WHISPER_HOP_LENGTH;
 
+        std::vector<float> host_mel_data;
 
-    {
-        std::vector<std::thread> workers(n_threads - 1);
-        for (int iw = 0; iw < n_threads - 1; ++iw) {
-            workers[iw] = std::thread(
-                    log_mel_spectrogram_worker_thread, iw + 1, std::cref(hann), samples_padded,
-                    n_samples + stage_2_pad, frame_size, frame_step, n_threads,
-                    std::cref(filters), std::ref(mel));
+        whisper_mel ret;
+        whisper_mel_init(ret, m_backend, mel.n_len, mel.n_len_org, mel.n_mel);
+        if (ggml_backend_buffer_is_host(ret.buffer)) {
+            mel.data = reinterpret_cast<float*>(ret.tensor->data);
+        } else {
+            host_mel_data.resize(mel.n_len * mel.n_mel);
+            mel.data = host_mel_data.data();
         }
 
-        // main thread
-        log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel);
+        {
+            std::vector<std::thread> workers(n_threads - 1);
+            for (int iw = 0; iw < n_threads - 1; ++iw) {
+                workers[iw] = std::thread(
+                        log_mel_spectrogram_worker_thread, iw + 1, hann, samples_padded,
+                        n_samples + stage_2_pad, n_threads,
+                        std::cref(m_filters), std::ref(mel));
+            }
 
-        for (int iw = 0; iw < n_threads - 1; ++iw) {
-            workers[iw].join();
+            // main thread
+            log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, n_threads, m_filters, mel);
+
+            for (int iw = 0; iw < n_threads - 1; ++iw) {
+                workers[iw].join();
+            }
         }
+
+        // clamping and normalization
+        double mmax = -1e20;
+        for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
+            if (mel.data[i] > mmax) {
+                mmax = mel.data[i];
+            }
+        }
+
+        mmax -= 8.0;
+
+        for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
+            if (mel.data[i] < mmax) {
+                mel.data[i] = mmax;
+            }
+
+            mel.data[i] = (mel.data[i] + 4.0)/4.0;
+        }
+
+        if (!host_mel_data.empty()) {
+            // the ret buffer is not host-accessible so we used this temporary buffer and now we need to upload it
+            ggml_backend_tensor_set(ret.tensor, host_mel_data.data(), 0, ggml_nbytes(ret.tensor));
+        }
+
+        return ret;
     }
+};
+}
 
-    // clamping and normalization
-    double mmax = -1e20;
-    for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
-        if (mel.data[i] > mmax) {
-            mmax = mel.data[i];
-        }
-    }
-
-    mmax -= 8.0;
-
-    for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
-        if (mel.data[i] < mmax) {
-            mel.data[i] = mmax;
-        }
-
-        mel.data[i] = (mel.data[i] + 4.0)/4.0;
-    }
-
-    wstate.t_mel_us += ggml_time_us() - t_start_us;
-
-    // Dump log_mel_spectrogram
-    if (debug) {
-        std::ofstream outFile("log_mel_spectrogram.json");
-        outFile << "[";
-        for (uint64_t i = 0; i < mel.data.size() - 1; i++) {
-            outFile << mel.data[i] << ", ";
-        }
-        outFile << mel.data[mel.data.size() - 1] << "]";
-        outFile.close();
-    }
-
-    return true;
+whisper_mel_calc * whisper_mel_calc_create(ggml_backend_t backend, const whisper_filters & filters) {
+#if GGML_USE_CUDA
+    if (ggml_backend_is_cuda(backend)) {
+        auto ret = whisper_mel_calc_create_cuda(backend, filters);
+        // run a warmup to avoid the first kernel launch overhead (thus we get the best perf even on the first run)
+        const float warmup[256] = {0};
+        ret->calculate({warmup, 256}, 1);
+        return ret;
+    } else
+#endif
+        return new mel_calc_cpu(backend, filters);
 }
 
 // split text into tokens
@@ -3244,19 +3292,26 @@ static std::string whisper_openvino_get_path_cache(std::string path_bin) {
 #endif
 
 struct whisper_state * whisper_init_state(whisper_context * ctx) {
-    fill_sin_cos_table();
-
     whisper_state * state = new whisper_state;
 
+    state->backend = whisper_backend_init(ctx->params);
+    if (!state->backend) {
+        WHISPER_LOG_ERROR("%s: whisper_backend_init() failed\n", __func__);
+        whisper_free_state(state);
+        return nullptr;
+    }
+
+    state->mel_calc = whisper_mel_calc_create(state->backend, ctx->model.filters);
+
     // at this point, we don't know yet how many decoders will be used, so we overallocate 3x ctx
     // in theory, there can be a case where this is not enough, but in practice it should always be enough
     const int factor = 3;
 
-    if (!kv_cache_init(state->kv_self, ctx->backend, ctx->itype,
+    if (!whisper_kv_cache_init(state->kv_self, state->backend, ctx->itype,
                 ctx->model.hparams.n_text_state,
                 ctx->model.hparams.n_text_layer,
                 GGML_PAD(ctx->model.hparams.n_text_ctx, 256)*factor)) {
-        WHISPER_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
+        WHISPER_LOG_ERROR("%s: whisper_kv_cache_init() failed for self-attention cache\n", __func__);
         whisper_free_state(state);
         return nullptr;
     }
@@ -3266,11 +3321,11 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
         WHISPER_LOG_INFO("%s: kv self size  = %7.2f MB\n", __func__, memory_size / 1e6);
     }
 
-    if (!kv_cache_init(state->kv_cross, ctx->backend, ctx->itype,
+    if (!whisper_kv_cache_init(state->kv_cross, state->backend, ctx->itype,
                 ctx->model.hparams.n_text_state,
                 ctx->model.hparams.n_text_layer,
                 GGML_PAD(ctx->model.hparams.n_audio_ctx, 256))) {
-        WHISPER_LOG_ERROR("%s: kv_cache_init() failed for cross-attention cache\n", __func__);
+        WHISPER_LOG_ERROR("%s: whisper_kv_cache_init() failed for cross-attention cache\n", __func__);
         whisper_free_state(state);
         return nullptr;
     }
@@ -3280,11 +3335,11 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
         WHISPER_LOG_INFO("%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1e6);
     }
 
-    if (!kv_cache_init(state->kv_pad, ctx->backend, ctx->itype,
+    if (!whisper_kv_cache_init(state->kv_pad, state->backend, ctx->itype,
                 ctx->model.hparams.n_audio_state,
                 1,
                 GGML_PAD(ctx->model.hparams.n_audio_ctx, 256))) {
-        WHISPER_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
+        WHISPER_LOG_ERROR("%s: whisper_kv_cache_init() failed for self-attention cache\n", __func__);
         whisper_free_state(state);
         return nullptr;
     }
@@ -3296,7 +3351,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
 
     // [EXPERIMENTAL] Token-level timestamps with DTW
     if (ctx->params.dtw_token_timestamps) {
-        if (!aheads_masks_init(ctx->params, ctx->model.hparams, state->aheads_masks, ctx->backend)) {
+        if (!aheads_masks_init(ctx->params, ctx->model.hparams, state->aheads_masks, state->backend)) {
             WHISPER_LOG_ERROR("%s: aheads_masks_init() failed for alignment heads masks\n", __func__);
             whisper_free_state(state);
             return nullptr;
@@ -3339,9 +3394,9 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
 
     // conv allocator
     {
-        bool ok = whisper_allocr_graph_init(state->alloc_conv, ctx->backend,
+        bool ok = whisper_allocr_graph_init(state->alloc_conv, state->backend,
                 [&]() {
-                    return whisper_build_graph_conv(*ctx, *state);
+                    return whisper_build_graph_conv(*ctx, *state, 0);
                 });
 
         if (!ok) {
@@ -3355,7 +3410,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
 
     // encoder allocator
     if (!whisper_encode_external(*state)) {
-        bool ok = whisper_allocr_graph_init(state->alloc_encode, ctx->backend,
+        bool ok = whisper_allocr_graph_init(state->alloc_encode, state->backend,
                 [&]() {
                     return whisper_build_graph_encoder(*ctx, *state);
                 });
@@ -3371,7 +3426,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
 
     // cross allocator
     {
-        bool ok = whisper_allocr_graph_init(state->alloc_cross, ctx->backend,
+        bool ok = whisper_allocr_graph_init(state->alloc_cross, state->backend,
                 [&]() {
                     return whisper_build_graph_cross(*ctx, *state);
                 });
@@ -3387,7 +3442,7 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
 
     // decoder allocator
     {
-        bool ok = whisper_allocr_graph_init(state->alloc_decode, ctx->backend,
+        bool ok = whisper_allocr_graph_init(state->alloc_decode, state->backend,
                 [&]() {
                     const auto & hparams = ctx->model.hparams;
 
@@ -3659,9 +3714,16 @@ struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loa
 
 void whisper_free_state(struct whisper_state * state) {
     if (state) {
-        kv_cache_free(state->kv_self);
-        kv_cache_free(state->kv_cross);
-        kv_cache_free(state->kv_pad);
+        whisper_kv_cache_free(state->kv_self);
+        whisper_kv_cache_free(state->kv_cross);
+        whisper_kv_cache_free(state->kv_pad);
+
+        whisper_mel_free(state->mel);
+
+        delete state->mel_calc;
+        state->mel_calc = nullptr;
+        delete state->mel_calc_fallback;
+        state->mel_calc_fallback = nullptr;
 
 #ifdef WHISPER_USE_COREML
         if (state->ctx_coreml != nullptr) {
@@ -3684,6 +3746,8 @@ void whisper_free_state(struct whisper_state * state) {
         ggml_gallocr_free(state->alloc_cross.alloc);
         ggml_gallocr_free(state->alloc_decode.alloc);
 
+        ggml_backend_free(state->backend);
+
         // [EXPERIMENTAL] Token-level timestamps with DTW
         aheads_masks_free(state->aheads_masks);
 
@@ -3718,11 +3782,37 @@ void whisper_free_params(struct whisper_full_params * params) {
 }
 
 int whisper_pcm_to_mel_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
-    if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, ctx->model.filters.n_mel, n_threads, ctx->model.filters, false, state->mel)) {
-        WHISPER_LOG_ERROR("%s: failed to compute mel spectrogram\n", __func__);
-        return -1;
+    const int64_t t_start_us = ggml_time_us();
+
+    whisper_mel_free(state->mel);
+    if (n_samples <= 5 * 60 * WHISPER_SAMPLE_RATE) {
+        // calculate mel spectrogram for lengths up to 5 minutes on the most optimal mel calculator
+        state->mel = state->mel_calc->calculate({samples, n_samples}, n_threads);
+    } else {
+        // calcuate mel spectrogram for longer audios on the CPU
+        // 1. gpu calculations may use hundreds of megabytes of memory for longer audios so we're being conservative
+        //    with our gpu demands
+        // 2. the time to transcribe audios this long will be dominated by the decoding time, so the mel calculation
+        //    taking longer is not a major concern
+        if (!state->mel_calc_fallback) {
+            state->mel_calc_fallback = new mel_calc_cpu(state->backend, ctx->model.filters);
+        }
+        state->mel = state->mel_calc_fallback->calculate({samples, n_samples}, n_threads);
     }
 
+    state->t_mel_us += ggml_time_us() - t_start_us;
+
+    // Dump log_mel_spectrogram
+    //{
+    //    auto& mel = state->mel;
+    //    std::ofstream outFile("log_mel_spectrogram.json");
+    //    outFile << "[";
+    //    for (uint64_t i = 0; i < mel.data.size() - 1; i++) {
+    //        outFile << mel.data[i] << ", ";
+    //    }
+    //    outFile << mel.data[mel.data.size() - 1] << "]";
+    //    outFile.close();
+    //}
     return 0;
 }
 
@@ -3730,30 +3820,6 @@ int whisper_pcm_to_mel(struct whisper_context * ctx, const float * samples, int
     return whisper_pcm_to_mel_with_state(ctx, ctx->state, samples, n_samples, n_threads);
 }
 
-// same as whisper_pcm_to_mel, but applies a Phase Vocoder to speed up the audio x2 (PV without phase lock is not good)
-int whisper_pcm_to_mel_phase_vocoder_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
-    if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, 2 * WHISPER_N_FFT, 2 * WHISPER_HOP_LENGTH, ctx->model.filters.n_mel, n_threads, ctx->model.filters, false, state->mel)) {
-        WHISPER_LOG_ERROR("%s: failed to compute mel spectrogram\n", __func__);
-        return -1;
-    }
-
-    return 0;
-}
-
-// same as whisper_pcm_to_mel, but applies a Phase Vocoder to speed up the audio x2 (PV without phase lock is not good)
-int whisper_pcm_to_mel_phase_vocoder(struct whisper_context * ctx, const float * samples, int n_samples, int n_threads) {
-    return whisper_pcm_to_mel_phase_vocoder_with_state(ctx, ctx->state, samples, n_samples, n_threads);
-}
-
-// same as whisper_pcm_to_mel, but applies WSOLA to speed up the audio x2
-// TODO
-
-// same as whisper_pcm_to_mel, but applies HPTSM to speed up the audio x2
-// TODO
-
-// same as whisper_pcm_to_mel, but applies PV (with phase lock) to speed up the audio x2
-// TODO
-
 int whisper_set_mel_with_state(
         struct whisper_context * ctx,
           struct whisper_state * state,
@@ -3765,12 +3831,10 @@ int whisper_set_mel_with_state(
         return -1;
     }
 
-    state->mel.n_len     = n_len;
-    state->mel.n_len_org = n_len;
-    state->mel.n_mel     = n_mel;
+    whisper_mel_free(state->mel);
+    whisper_mel_init(state->mel, ctx->backend, n_len, n_len, n_mel);
 
-    state->mel.data.resize(n_len*n_mel);
-    memcpy(state->mel.data.data(), data, n_len*n_mel*sizeof(float));
+    ggml_backend_tensor_set(state->mel.tensor, data, 0, ggml_nbytes(state->mel.tensor));
 
     return 0;
 }
@@ -4654,7 +4718,6 @@ struct whisper_full_params whisper_full_default_params(enum whisper_sampling_str
         /*.split_on_word     =*/ false,
         /*.max_tokens        =*/ 0,
 
-        /*.speed_up          =*/ false,
         /*.debug_mode        =*/ false,
         /*.audio_ctx         =*/ 0,
 
@@ -5328,15 +5391,9 @@ int whisper_full_with_state(
 
     if (n_samples > 0) {
         // compute log mel spectrogram
-        if (params.speed_up) {
-            // TODO: Replace PV with more advanced algorithm
+        if (whisper_pcm_to_mel_with_state(ctx, state, samples, n_samples, params.n_threads) != 0) {
             WHISPER_LOG_ERROR("%s: failed to compute log mel spectrogram\n", __func__);
-            return -1;
-        } else {
-            if (whisper_pcm_to_mel_with_state(ctx, state, samples, n_samples, params.n_threads) != 0) {
-                WHISPER_LOG_ERROR("%s: failed to compute log mel spectrogram\n", __func__);
-                return -2;
-            }
+            return -2;
         }
     }
 
@@ -5373,7 +5430,7 @@ int whisper_full_with_state(
     // if length of spectrogram is less than 1.0s (100 frames), then return
     // basically don't process anything that is less than 1.0s
     // see issue #39: https://github.com/ggerganov/whisper.cpp/issues/39
-    if (seek_end < seek_start + (params.speed_up ? 50 : 100)) {
+    if (seek_end < seek_start + 100) {
         WHISPER_LOG_WARN("%s: input is too short - %d ms < 1000 ms. consider padding the input audio with silence\n", __func__, (seek_end - seek_start)*10);
         return 0;
     }
@@ -6085,8 +6142,8 @@ int whisper_full_with_state(
                         const auto t1 = seek + 2*(tokens_cur[i].tid - whisper_token_beg(ctx));
 
                         if (!text.empty()) {
-                            const auto tt0 = params.speed_up ? 2*t0 : t0;
-                            const auto tt1 = params.speed_up ? 2*t1 : t1;
+                            const auto tt0 = t0;
+                            const auto tt1 = t1;
 
                             if (params.print_realtime) {
                                 if (params.print_timestamps) {
@@ -6132,8 +6189,8 @@ int whisper_full_with_state(
                 if (!text.empty()) {
                     const auto t1 = seek + seek_delta;
 
-                    const auto tt0 = params.speed_up ? 2*t0 : t0;
-                    const auto tt1 = params.speed_up ? 2*t1 : t1;
+                    const auto tt0 = t0;
+                    const auto tt1 = t1;
 
                     if (params.print_realtime) {
                         if (params.print_timestamps) {
@@ -7224,7 +7281,7 @@ static void whisper_exp_compute_token_level_timestamps_dtw(
     // operation (after median filter)
     // IN: Tensor with N_TOKENS*N_AUDIO_TOKENS*N_ALIGNMENT_HEADS dims
     // OUT: Tensor with N_ALIGNMENT_HEADS*N_TOKENS*N_AUDIO_TOKENS dims
-    w = ggml_norm(gctx, w, 1e-9);
+    w = ggml_norm(gctx, w, 1e-9f);
     w = ggml_permute(gctx, ggml_permute(gctx, w, 2, 1, 0 ,3), 0, 2, 1, 3);
 
     // Pass median filter - this is done over AUDIO_TOKENS dimension.

whisper.h

@@ -31,8 +31,10 @@
 
 #define WHISPER_SAMPLE_RATE 16000
 #define WHISPER_N_FFT       400
+#define WHISPER_N_FFT_HALF  (WHISPER_N_FFT / 2 + 1)
 #define WHISPER_HOP_LENGTH  160
 #define WHISPER_CHUNK_SIZE  30
+#define WHISPER_N_SAMPLES   (WHISPER_SAMPLE_RATE * WHISPER_CHUNK_SIZE)
 
 #ifdef __cplusplus
 extern "C" {
@@ -266,22 +268,6 @@ extern "C" {
                                int   n_samples,
                                int   n_threads);
 
-    // Convert RAW PCM audio to log mel spectrogram but applies a Phase Vocoder to speed up the audio x2.
-    // The resulting spectrogram is stored inside the default state of the provided whisper context.
-    // Returns 0 on success
-    WHISPER_API int whisper_pcm_to_mel_phase_vocoder(
-        struct whisper_context * ctx,
-                   const float * samples,
-                           int   n_samples,
-                           int   n_threads);
-
-    WHISPER_API int whisper_pcm_to_mel_phase_vocoder_with_state(
-        struct whisper_context * ctx,
-          struct whisper_state * state,
-                   const float * samples,
-                           int   n_samples,
-                           int   n_threads);
-
     // This can be used to set a custom log mel spectrogram inside the default state of the provided whisper context.
     // Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
     // n_mel must be 80
@@ -499,7 +485,6 @@ extern "C" {
 
         // [EXPERIMENTAL] speed-up techniques
         // note: these can significantly reduce the quality of the output
-        bool speed_up;          // speed-up the audio by 2x using Phase Vocoder
         bool debug_mode;        // enable debug_mode provides extra info (eg. Dump log_mel)
         int  audio_ctx;         // overwrite the audio context size (0 = use default)