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parallel-states
whisper.cpp/whisper.cpp
Georgi Gerganov a2f3b82db3
whisper : free backend instances in whisper_state
2023-11-12 14:31:51 +02:00

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#include "whisper.h"
#ifdef WHISPER_USE_COREML
#include "coreml/whisper-encoder.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#endif
#ifdef WHISPER_USE_OPENVINO
#include "openvino/whisper-openvino-encoder.h"
#endif
#include "ggml.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include <algorithm>
#include <cassert>
#define _USE_MATH_DEFINES
#include <cmath>
#include <cstdio>
#include <cstdarg>
#include <cstring>
#include <fstream>
#include <map>
#include <set>
#include <string>
#include <thread>
#include <vector>
#include <regex>
#include <random>
#include <functional>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#if defined(GGML_BIG_ENDIAN)
#include <bit>
template<typename T>
static T byteswap(T value) {
return std::byteswap(value);
}
template<>
float byteswap(float value) {
return std::bit_cast<float>(byteswap(std::bit_cast<std::uint32_t>(value)));
}
template<typename T>
static void byteswap_tensor_data(ggml_tensor * tensor) {
T * datum = reinterpret_cast<T *>(tensor->data);
for (int i = 0; i < ggml_nelements(tensor); i++) {
datum[i] = byteswap(datum[i]);
}
}
static void byteswap_tensor(ggml_tensor * tensor) {
switch (tensor->type) {
case GGML_TYPE_I16: {
byteswap_tensor_data<int16_t>(tensor);
break;
}
case GGML_TYPE_F16: {
byteswap_tensor_data<ggml_fp16_t>(tensor);
break;
}
case GGML_TYPE_I32: {
byteswap_tensor_data<int32_t>(tensor);
break;
}
case GGML_TYPE_F32: {
byteswap_tensor_data<float>(tensor);
break;
}
default: { // GML_TYPE_I8
break;
}
}
}
#define BYTESWAP_VALUE(d) d = byteswap(d)
#define BYTESWAP_FILTERS(f) \
do { \
for (auto & datum : f.data) { \
datum = byteswap(datum); \
} \
} while (0)
#define BYTESWAP_TENSOR(t) \
do { \
byteswap_tensor(t); \
} while (0)
#else
#define BYTESWAP_VALUE(d) do {} while (0)
#define BYTESWAP_FILTERS(f) do {} while (0)
#define BYTESWAP_TENSOR(t) do {} while (0)
#endif
#ifdef __GNUC__
#ifdef __MINGW32__
#define WHISPER_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define WHISPER_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define WHISPER_ATTRIBUTE_FORMAT(...)
#endif
//
// logging
//
WHISPER_ATTRIBUTE_FORMAT(2, 3)
static void whisper_log_internal (ggml_log_level level, const char* format, ...);
static void whisper_log_callback_default(ggml_log_level level, const char * text, void * user_data);
#define WHISPER_LOG_INFO(...) whisper_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define WHISPER_LOG_WARN(...) whisper_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define WHISPER_LOG_ERROR(...) whisper_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define WHISPER_ASSERT(x) \
do { \
if (!(x)) { \
WHISPER_LOG_ERROR("WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
// define this to enable verbose trace logging - useful for debugging purposes
//#define WHISPER_DEBUG
#if defined(WHISPER_DEBUG)
#define WHISPER_PRINT_DEBUG(...) \
do { \
fprintf(stderr, __VA_ARGS__); \
} while (0)
#else
#define WHISPER_PRINT_DEBUG(...)
#endif
//#define WHISPER_USE_FLASH_ATTN
//#define WHISPER_USE_FLASH_FF
#define WHISPER_MAX_DECODERS 16
#define WHISPER_MAX_NODES 4096
//
// ggml helpers
//
static void ggml_graph_compute_helper(
struct ggml_cgraph * graph,
std::vector<uint8_t> & buf,
int n_threads,
whisper_abort_callback abort_callback,
void * abort_callback_data) {
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
plan.abort_callback = abort_callback;
plan.abort_callback_data = abort_callback_data;
if (plan.work_size > 0) {
buf.resize(plan.work_size);
plan.work_data = buf.data();
}
ggml_graph_compute(graph, &plan);
}
static void ggml_graph_compute_helper(
struct ggml_backend * backend,
struct ggml_cgraph * graph,
int n_threads) {
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, n_threads);
}
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(backend)) {
ggml_backend_metal_set_n_cb(backend, n_threads);
}
#endif
ggml_backend_graph_compute(backend, graph);
}
// faster matrix multiplications for tensors that do not have dimension 0 divisible by "pad"
// the idea is to represent the original matrix multiplication:
//
// Z = X @ Y
//
// with the sum of two matrix multiplications:
//
// Z = (X_0 @ Y_0) + (X_1 @ Y_1)
//
// here X_0 and Y_0 are views of X and Y that have dimension 0 divisible by "pad"
// and X_1 and Y_1 are the remaining views. X_1 and Y_1 end up being small matrices that can be processed with more
// general-purpose kernels
//
static struct ggml_tensor * ggml_mul_mat_pad(struct ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * y, int pad = 32) {
// use padding only if dimension 0 is at least 8 times larger than the padding
// else we won't get much benefit from the optimization
const int n_pad_req = 8;
if (x->ne[0] % pad == 0 || x->ne[0] / pad < n_pad_req) {
return ggml_mul_mat(ctx, x, y);
}
struct ggml_tensor * x_0 = ggml_view_3d(ctx, x, (x->ne[0]/pad)*pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], 0);
struct ggml_tensor * x_1 = ggml_view_3d(ctx, x, x->ne[0]%pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], x_0->ne[0]*x_0->nb[0]);
struct ggml_tensor * y_0 = ggml_view_3d(ctx, y, (y->ne[0]/pad)*pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], 0);
struct ggml_tensor * y_1 = ggml_view_3d(ctx, y, y->ne[0]%pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], y_0->ne[0]*y_0->nb[0]);
return ggml_add(ctx,
ggml_mul_mat(ctx, x_0, y_0),
ggml_mul_mat(ctx, x_1, y_1));
}
// TODO: check if other platforms can benefit from this optimization
// TODO: CUDA is currently broken - seems ggml_mul_mat does not handle views correctly
#if defined(GGML_USE_METAL)
#define ggml_mul_mat ggml_mul_mat_pad
#endif
// available whisper models
enum e_model {
MODEL_UNKNOWN,
MODEL_TINY,
MODEL_BASE,
MODEL_SMALL,
MODEL_MEDIUM,
MODEL_LARGE,
};
static const std::map<e_model, std::string> g_model_name = {
{ MODEL_UNKNOWN, "unknown" },
{ MODEL_TINY, "tiny" },
{ MODEL_BASE, "base" },
{ MODEL_SMALL, "small" },
{ MODEL_MEDIUM, "medium" },
{ MODEL_LARGE, "large" },
};
static const std::map<std::string, std::pair<int, std::string>> g_lang = {
{ "en", { 0, "english", } },
{ "zh", { 1, "chinese", } },
{ "de", { 2, "german", } },
{ "es", { 3, "spanish", } },
{ "ru", { 4, "russian", } },
{ "ko", { 5, "korean", } },
{ "fr", { 6, "french", } },
{ "ja", { 7, "japanese", } },
{ "pt", { 8, "portuguese", } },
{ "tr", { 9, "turkish", } },
{ "pl", { 10, "polish", } },
{ "ca", { 11, "catalan", } },
{ "nl", { 12, "dutch", } },
{ "ar", { 13, "arabic", } },
{ "sv", { 14, "swedish", } },
{ "it", { 15, "italian", } },
{ "id", { 16, "indonesian", } },
{ "hi", { 17, "hindi", } },
{ "fi", { 18, "finnish", } },
{ "vi", { 19, "vietnamese", } },
{ "he", { 20, "hebrew", } },
{ "uk", { 21, "ukrainian", } },
{ "el", { 22, "greek", } },
{ "ms", { 23, "malay", } },
{ "cs", { 24, "czech", } },
{ "ro", { 25, "romanian", } },
{ "da", { 26, "danish", } },
{ "hu", { 27, "hungarian", } },
{ "ta", { 28, "tamil", } },
{ "no", { 29, "norwegian", } },
{ "th", { 30, "thai", } },
{ "ur", { 31, "urdu", } },
{ "hr", { 32, "croatian", } },
{ "bg", { 33, "bulgarian", } },
{ "lt", { 34, "lithuanian", } },
{ "la", { 35, "latin", } },
{ "mi", { 36, "maori", } },
{ "ml", { 37, "malayalam", } },
{ "cy", { 38, "welsh", } },
{ "sk", { 39, "slovak", } },
{ "te", { 40, "telugu", } },
{ "fa", { 41, "persian", } },
{ "lv", { 42, "latvian", } },
{ "bn", { 43, "bengali", } },
{ "sr", { 44, "serbian", } },
{ "az", { 45, "azerbaijani", } },
{ "sl", { 46, "slovenian", } },
{ "kn", { 47, "kannada", } },
{ "et", { 48, "estonian", } },
{ "mk", { 49, "macedonian", } },
{ "br", { 50, "breton", } },
{ "eu", { 51, "basque", } },
{ "is", { 52, "icelandic", } },
{ "hy", { 53, "armenian", } },
{ "ne", { 54, "nepali", } },
{ "mn", { 55, "mongolian", } },
{ "bs", { 56, "bosnian", } },
{ "kk", { 57, "kazakh", } },
{ "sq", { 58, "albanian", } },
{ "sw", { 59, "swahili", } },
{ "gl", { 60, "galician", } },
{ "mr", { 61, "marathi", } },
{ "pa", { 62, "punjabi", } },
{ "si", { 63, "sinhala", } },
{ "km", { 64, "khmer", } },
{ "sn", { 65, "shona", } },
{ "yo", { 66, "yoruba", } },
{ "so", { 67, "somali", } },
{ "af", { 68, "afrikaans", } },
{ "oc", { 69, "occitan", } },
{ "ka", { 70, "georgian", } },
{ "be", { 71, "belarusian", } },
{ "tg", { 72, "tajik", } },
{ "sd", { 73, "sindhi", } },
{ "gu", { 74, "gujarati", } },
{ "am", { 75, "amharic", } },
{ "yi", { 76, "yiddish", } },
{ "lo", { 77, "lao", } },
{ "uz", { 78, "uzbek", } },
{ "fo", { 79, "faroese", } },
{ "ht", { 80, "haitian creole", } },
{ "ps", { 81, "pashto", } },
{ "tk", { 82, "turkmen", } },
{ "nn", { 83, "nynorsk", } },
{ "mt", { 84, "maltese", } },
{ "sa", { 85, "sanskrit", } },
{ "lb", { 86, "luxembourgish", } },
{ "my", { 87, "myanmar", } },
{ "bo", { 88, "tibetan", } },
{ "tl", { 89, "tagalog", } },
{ "mg", { 90, "malagasy", } },
{ "as", { 91, "assamese", } },
{ "tt", { 92, "tatar", } },
{ "haw", { 93, "hawaiian", } },
{ "ln", { 94, "lingala", } },
{ "ha", { 95, "hausa", } },
{ "ba", { 96, "bashkir", } },
{ "jw", { 97, "javanese", } },
{ "su", { 98, "sundanese", } },
{ "yue", { 99, "cantonese", } },
};
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;
int n_vocab = 51864;
std::map<token, id> token_to_id;
std::map<id, token> id_to_token;
// reference: https://github.com/openai/whisper/blob/248b6cb124225dd263bb9bd32d060b6517e067f8/whisper/tokenizer.py#L334-L349
id token_eot = 50256;
id token_sot = 50257;
// task tokens (used only for multilingual models)
id token_translate = 50357;
id token_transcribe = 50358;
// other special tokens
id token_solm = 50359; // [TDRZ] used by tinydiarize models to indicate speaker turn
id token_prev = 50360;
id token_nosp = 50361;
id token_not = 50362; // no timestamps
id token_beg = 50363; // begin timestamps
bool is_multilingual() const {
return n_vocab >= 51865;
}
int num_languages() const {
return n_vocab - 51765 - (is_multilingual() ? 1 : 0);
}
};
struct whisper_segment {
int64_t t0;
int64_t t1;
std::string text;
std::vector<whisper_token_data> tokens;
bool speaker_turn_next;
};
// medium
// hparams: {
// 'n_mels': 80,
// 'n_vocab': 51864,
// 'n_audio_ctx': 1500,
// 'n_audio_state': 1024,
// 'n_audio_head': 16,
// 'n_audio_layer': 24,
// 'n_text_ctx': 448,
// 'n_text_state': 1024,
// 'n_text_head': 16,
// 'n_text_layer': 24
// }
//
// default hparams (Whisper tiny)
struct whisper_hparams {
int32_t n_vocab = 51864;
int32_t n_audio_ctx = 1500;
int32_t n_audio_state = 384;
int32_t n_audio_head = 6;
int32_t n_audio_layer = 4;
int32_t n_text_ctx = 448;
int32_t n_text_state = 384;
int32_t n_text_head = 6;
int32_t n_text_layer = 4;
int32_t n_mels = 80;
int32_t ftype = 1;
float eps = 1e-5f;
};
// audio encoding layer
struct whisper_layer_encoder {
// encoder.blocks.*.attn_ln
struct ggml_tensor * attn_ln_0_w;
struct ggml_tensor * attn_ln_0_b;
// encoder.blocks.*.attn.out
struct ggml_tensor * attn_ln_1_w;
struct ggml_tensor * attn_ln_1_b;
// encoder.blocks.*.attn.query
struct ggml_tensor * attn_q_w;
struct ggml_tensor * attn_q_b;
// encoder.blocks.*.attn.key
struct ggml_tensor * attn_k_w;
// encoder.blocks.*.attn.value
struct ggml_tensor * attn_v_w;
struct ggml_tensor * attn_v_b;
// encoder.blocks.*.mlp_ln
struct ggml_tensor * mlp_ln_w;
struct ggml_tensor * mlp_ln_b;
// encoder.blocks.*.mlp.0
struct ggml_tensor * mlp_0_w;
struct ggml_tensor * mlp_0_b;
// encoder.blocks.*.mlp.2
struct ggml_tensor * mlp_1_w;
struct ggml_tensor * mlp_1_b;
};
// token decoding layer
struct whisper_layer_decoder {
// decoder.blocks.*.attn_ln
struct ggml_tensor * attn_ln_0_w;
struct ggml_tensor * attn_ln_0_b;
// decoder.blocks.*.attn.out
struct ggml_tensor * attn_ln_1_w;
struct ggml_tensor * attn_ln_1_b;
// decoder.blocks.*.attn.query
struct ggml_tensor * attn_q_w;
struct ggml_tensor * attn_q_b;
// decoder.blocks.*.attn.key
struct ggml_tensor * attn_k_w;
// decoder.blocks.*.attn.value
struct ggml_tensor * attn_v_w;
struct ggml_tensor * attn_v_b;
// decoder.blocks.*.cross_attn_ln
struct ggml_tensor * cross_attn_ln_0_w;
struct ggml_tensor * cross_attn_ln_0_b;
// decoder.blocks.*.cross_attn.out
struct ggml_tensor * cross_attn_ln_1_w;
struct ggml_tensor * cross_attn_ln_1_b;
// decoder.blocks.*.cross_attn.query
struct ggml_tensor * cross_attn_q_w;
struct ggml_tensor * cross_attn_q_b;
// decoder.blocks.*.cross_attn.key
struct ggml_tensor * cross_attn_k_w;
// decoder.blocks.*.cross_attn.value
struct ggml_tensor * cross_attn_v_w;
struct ggml_tensor * cross_attn_v_b;
// decoder.blocks.*.mlp_ln
struct ggml_tensor * mlp_ln_w;
struct ggml_tensor * mlp_ln_b;
// decoder.blocks.*.mlp.0
struct ggml_tensor * mlp_0_w;
struct ggml_tensor * mlp_0_b;
// decoder.blocks.*.mlp.2
struct ggml_tensor * mlp_1_w;
struct ggml_tensor * mlp_1_b;
};
struct whisper_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx;
ggml_backend_buffer_t buffer;
int n; // number of tokens currently in the cache
};
struct whisper_model {
e_model type = MODEL_UNKNOWN;
whisper_hparams hparams;
whisper_filters filters;
// encoder.positional_embedding
struct ggml_tensor * e_pe;
// encoder.conv1
struct ggml_tensor * e_conv_1_w;
struct ggml_tensor * e_conv_1_b;
// encoder.conv2
struct ggml_tensor * e_conv_2_w;
struct ggml_tensor * e_conv_2_b;
// encoder.ln_post
struct ggml_tensor * e_ln_w;
struct ggml_tensor * e_ln_b;
// decoder.positional_embedding
struct ggml_tensor * d_pe;
// decoder.token_embedding
struct ggml_tensor * d_te;
// decoder.ln
struct ggml_tensor * d_ln_w;
struct ggml_tensor * d_ln_b;
std::vector<whisper_layer_encoder> layers_encoder;
std::vector<whisper_layer_decoder> layers_decoder;
// ggml context that contains all the meta information about the model tensors
struct ggml_context * ctx;
// the model backend data is read-only and can be shared between processors
struct ggml_backend_buffer * buffer;
// tensors
int n_loaded;
std::map<std::string, struct ggml_tensor *> tensors;
};
struct whisper_sequence {
std::vector<whisper_token_data> tokens;
// the accumulated transcription in the current iteration (used to truncate the tokens array)
int result_len;
double sum_logprobs_all; // the sum of the log probabilities of the tokens
double sum_logprobs; // the sum of the log probabilities of the tokens (first result_len tokens)
double avg_logprobs; // the average log probability of the tokens
double entropy; // the entropy of the tokens
double score; // likelihood rank score
};
// TAGS: WHISPER_DECODER_INIT
struct whisper_decoder {
// each decoder keeps its own KV-cache
whisper_kv_cache kv_self;
// the currently generated sequence of tokens
whisper_sequence sequence;
int seek_delta; // the window shift found so far based on the decoded timestamp tokens
bool failed; // has the current segment failed to decode?
bool completed; // has the decoder completed the current segment?
bool has_ts; // have we already sampled a non-beg timestamp token for the current segment?
// new token probs, logits and logprobs after the last whisper_decode (1-dimensional array: [n_vocab])
std::vector<float> probs;
std::vector<float> logits;
std::vector<float> logprobs;
std::vector<whisper_token> tokens_tmp; // used for whisper_decode calls
};
// replace std::pair by using customized pair struct (reason: std::pair is very slow)
template<typename A, typename B>
struct whisper_pair {
A first;
B second;
// Define a constructor that takes two arguments.
whisper_pair(const A& a, const B& b) : first(a), second(b) {}
// Define a constructor that takes no argument.
whisper_pair() : first(A()), second(B()) {}
};
// beam-search helpers
struct kv_buf {
std::vector<uint8_t> k;
std::vector<uint8_t> v;
};
// ggml_allocr wrapper for whisper usage
struct whisper_allocr {
ggml_allocr * alloc = nullptr;
std::vector<uint8_t> meta;
ggml_backend_buffer_t buffer;
};
static size_t whisper_allocr_size(struct whisper_allocr & allocr) {
return allocr.meta.size() + ggml_allocr_max_size(allocr.alloc);
}
// measure the memory usage of a graph and prepare the allocr's internal data buffer
static void whisper_allocr_graph_init(struct whisper_allocr & allocr, ggml_backend_t backend, std::function<struct ggml_cgraph *()> && get_graph) {
auto & alloc = allocr.alloc;
auto & meta = allocr.meta;
alloc = ggml_allocr_new_measure_from_backend(backend);
meta.resize(ggml_tensor_overhead()*WHISPER_MAX_NODES + ggml_graph_overhead());
ggml_allocr_alloc_graph(alloc, get_graph());
}
static void whisper_allocr_graph_realloc(struct whisper_allocr & allocr, ggml_backend_t backend) {
if (allocr.alloc == nullptr) {
// this can be null if we use external encoder like CoreML or OpenVINO
return;
}
auto & alloc = allocr.alloc;
auto & buffer = allocr.buffer;
size_t size = ggml_allocr_max_size(alloc);
ggml_allocr_free(alloc);
buffer = ggml_backend_alloc_buffer(backend, size);
alloc = ggml_allocr_new_from_buffer(buffer);
}
static void whisper_allocr_free(struct whisper_allocr & allocr) {
if (allocr.alloc) {
ggml_allocr_free(allocr.alloc);
ggml_backend_buffer_free(allocr.buffer);
allocr.alloc = nullptr;
}
}
struct whisper_state {
int64_t t_sample_us = 0;
int64_t t_encode_us = 0;
int64_t t_decode_us = 0;
int64_t t_prompt_us = 0;
int64_t t_mel_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_encode = 0; // number of encoder calls
int32_t n_decode = 0; // number of decoder calls with n_tokens == 1 (text-generation)
int32_t n_prompt = 0; // number of decoder calls with n_tokens > 1 (prompt encoding)
int32_t n_fail_p = 0; // number of logprob threshold failures
int32_t n_fail_h = 0; // number of entropy threshold failures
// cross-attention KV cache for the decoders
// shared between all decoders
whisper_kv_cache kv_cross;
whisper_mel mel;
whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
// buffer for swapping KV caches between decoders during beam-search
std::vector<kv_buf> kv_swap_bufs;
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
whisper_allocr alloc_conv;
whisper_allocr alloc_encode;
whisper_allocr alloc_cross;
whisper_allocr alloc_decode;
// result of the encoder
struct ggml_tensor * embd_conv = nullptr;
struct ggml_tensor * embd_enc = nullptr;
// helper for GPU offloading
std::vector<float> inp_mel;
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
std::vector<whisper_segment> result_all;
std::vector<whisper_token> prompt_past;
// work container used to avoid memory allocations
std::vector<whisper_pair<double, whisper_vocab::id>> logits_id;
mutable std::mt19937 rng; // used for sampling at t > 0.0
int lang_id = 0; // english by default
std::string path_model; // populated by whisper_init_from_file_with_params()
#ifdef WHISPER_USE_COREML
whisper_coreml_context * ctx_coreml = nullptr;
#endif
#ifdef WHISPER_USE_OPENVINO
whisper_openvino_context * ctx_openvino = nullptr;
#endif
// [EXPERIMENTAL] token-level timestamps data
int64_t t_beg = 0;
int64_t t_last = 0;
whisper_token tid_last;
std::vector<float> energy; // PCM signal energy
// [EXPERIMENTAL] speed-up techniques
int32_t exp_n_audio_ctx = 0; // 0 - use default
};
struct whisper_context {
int64_t t_load_us = 0;
int64_t t_start_us = 0;
ggml_type wtype = ggml_type::GGML_TYPE_F16; // weight type (FP32 / FP16 / QX)
ggml_type itype = ggml_type::GGML_TYPE_F16; // intermediate type (FP32 or FP16)
whisper_context_params params;
whisper_model model;
whisper_vocab vocab;
whisper_state * state = nullptr;
ggml_backend_t backend = nullptr;
std::string path_model; // populated by whisper_init_from_file_with_params()
};
struct whisper_global {
// We save the log callback globally
ggml_log_callback log_callback = whisper_log_callback_default;
void * log_callback_user_data = nullptr;
};
static whisper_global g_state;
template<typename T>
static void read_safe(whisper_model_loader * loader, T & dest) {
loader->read(loader->context, &dest, sizeof(T));
BYTESWAP_VALUE(dest);
}
static bool kv_cache_init(
const struct whisper_hparams & hparams,
struct whisper_kv_cache & cache,
ggml_backend_t backend,
ggml_type wtype,
int n_ctx) {
const int64_t n_text_state = hparams.n_text_state;
const int64_t n_text_layer = hparams.n_text_layer;
const int64_t n_mem = n_text_layer*n_ctx;
const int64_t n_elements = n_text_state*n_mem;
struct ggml_init_params params = {
/*.mem_size =*/ 2*ggml_tensor_overhead(),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
cache.ctx = ggml_init(params);
if (!cache.ctx) {
WHISPER_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
const size_t mem_bytes = ggml_nbytes(cache.k) + ggml_nbytes(cache.v);
cache.buffer = ggml_backend_alloc_buffer(backend, mem_bytes);
// allocate the tensors into the backend buffer
{
ggml_allocr * alloc = ggml_allocr_new_from_buffer(cache.buffer);
ggml_allocr_alloc(alloc, cache.k);
ggml_allocr_alloc(alloc, cache.v);
ggml_allocr_free(alloc);
}
return true;
}
// TODO: remove after batched decoding
static bool kv_cache_reinit(struct whisper_kv_cache & cache, ggml_backend_t backend) {
WHISPER_ASSERT(cache.ctx);
const int n_elements = ggml_nelements(cache.k);
WHISPER_ASSERT(n_elements == ggml_nelements(cache.v));
const ggml_type wtype = cache.k->type;
WHISPER_ASSERT(wtype == cache.v->type);
struct ggml_init_params params = {
/*.mem_size =*/ 2*ggml_tensor_overhead(),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
cache.ctx = ggml_init(params);
if (!cache.ctx) {
WHISPER_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
const size_t mem_bytes = ggml_nbytes(cache.k) + ggml_nbytes(cache.v);
cache.buffer = ggml_backend_alloc_buffer(backend, mem_bytes);
// allocate the tensors into the backend buffer
{
ggml_allocr * alloc = ggml_allocr_new_from_buffer(cache.buffer);
ggml_allocr_alloc(alloc, cache.k);
ggml_allocr_alloc(alloc, cache.v);
ggml_allocr_free(alloc);
}
return true;
}
static void kv_cache_free(struct whisper_kv_cache & cache) {
if (cache.ctx) {
ggml_free(cache.ctx);
ggml_backend_buffer_free(cache.buffer);
cache.ctx = nullptr;
}
}
static ggml_backend_t whisper_backend_init(const whisper_context_params & params) {
ggml_backend_t backend_gpu = NULL;
// initialize the backends
#ifdef GGML_USE_CUBLAS
if (params.use_gpu) {
WHISPER_LOG_INFO("%s: using CUDA backend\n", __func__);
backend_gpu = ggml_backend_cuda_init();
if (!backend_gpu) {
WHISPER_LOG_ERROR("%s: ggml_backend_cuda_init() failed\n", __func__);
}
}
#endif
#ifdef GGML_USE_METAL
if (params.use_gpu) {
WHISPER_LOG_INFO("%s: using Metal backend\n", __func__);
ggml_metal_log_set_callback(whisper_log_callback_default, nullptr);
backend_gpu = ggml_backend_metal_init();
if (!backend_gpu) {
WHISPER_LOG_ERROR("%s: ggml_backend_metal_init() failed\n", __func__);
}
}
#endif
if (backend_gpu) {
return backend_gpu;
}
return ggml_backend_cpu_init();
}
// load the model from a ggml file
//
// file format:
//
// - hparams
// - pre-computed mel filters
// - vocab
// - weights
//
// see the convert-pt-to-ggml.py script for details
//
static bool whisper_model_load(struct whisper_model_loader * loader, whisper_context & wctx) {
WHISPER_LOG_INFO("%s: loading model\n", __func__);
const int64_t t_start_us = ggml_time_us();
wctx.t_start_us = t_start_us;
auto & model = wctx.model;
auto & vocab = wctx.vocab;
// verify magic
{
uint32_t magic;
read_safe(loader, magic);
if (magic != GGML_FILE_MAGIC) {
WHISPER_LOG_ERROR("%s: invalid model data (bad magic)\n", __func__);
return false;
}
}
//load hparams
{
auto & hparams = model.hparams;
read_safe(loader, hparams.n_vocab);
read_safe(loader, hparams.n_audio_ctx);
read_safe(loader, hparams.n_audio_state);
read_safe(loader, hparams.n_audio_head);
read_safe(loader, hparams.n_audio_layer);
read_safe(loader, hparams.n_text_ctx);
read_safe(loader, hparams.n_text_state);
read_safe(loader, hparams.n_text_head);
read_safe(loader, hparams.n_text_layer);
read_safe(loader, hparams.n_mels);
read_safe(loader, hparams.ftype);
assert(hparams.n_text_state == hparams.n_audio_state);
std::string mver = "";
if (hparams.n_audio_layer == 4) {
model.type = e_model::MODEL_TINY;
}
if (hparams.n_audio_layer == 6) {
model.type = e_model::MODEL_BASE;
}
if (hparams.n_audio_layer == 12) {
model.type = e_model::MODEL_SMALL;
}
if (hparams.n_audio_layer == 24) {
model.type = e_model::MODEL_MEDIUM;
}
if (hparams.n_audio_layer == 32) {
model.type = e_model::MODEL_LARGE;
if (hparams.n_vocab == 51866) {
mver = " v3";
}
}
const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
hparams.ftype %= GGML_QNT_VERSION_FACTOR;
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
// in order to save memory and also to speed up the computation
wctx.wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
if (wctx.wtype == GGML_TYPE_COUNT) {
WHISPER_LOG_ERROR("%s: invalid model (bad ftype value %d)\n", __func__, model.hparams.ftype);
return false;
}
WHISPER_LOG_INFO("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
WHISPER_LOG_INFO("%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
WHISPER_LOG_INFO("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
WHISPER_LOG_INFO("%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
WHISPER_LOG_INFO("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
WHISPER_LOG_INFO("%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
WHISPER_LOG_INFO("%s: n_text_state = %d\n", __func__, hparams.n_text_state);
WHISPER_LOG_INFO("%s: n_text_head = %d\n", __func__, hparams.n_text_head);
WHISPER_LOG_INFO("%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
WHISPER_LOG_INFO("%s: n_mels = %d\n", __func__, hparams.n_mels);
WHISPER_LOG_INFO("%s: ftype = %d\n", __func__, model.hparams.ftype);
WHISPER_LOG_INFO("%s: qntvr = %d\n", __func__, qntvr);
WHISPER_LOG_INFO("%s: type = %d (%s%s)\n", __func__, model.type, g_model_name.at(model.type).c_str(), mver.c_str());
}
// load mel filters
{
auto & filters = wctx.model.filters;
read_safe(loader, filters.n_mel);
read_safe(loader, filters.n_fft);
filters.data.resize(filters.n_mel * filters.n_fft);
loader->read(loader->context, filters.data.data(), filters.data.size() * sizeof(float));
BYTESWAP_FILTERS(filters);
}
// load vocab
{
int32_t n_vocab = 0;
read_safe(loader, n_vocab);
//if (n_vocab != model.hparams.n_vocab) {
// WHISPER_LOG_ERROR("%s: invalid model file '%s' (bad vocab size %d != %d)\n",
// __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
// return false;
//}
std::string word;
std::vector<char> tmp;
tmp.reserve(128);
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
read_safe(loader, len);
if (len > 0) {
tmp.resize(len);
loader->read(loader->context, &tmp[0], tmp.size()); // read to buffer
word.assign(&tmp[0], tmp.size());
} else {
// seems like we have an empty-string token in multi-language models (i = 50256)
//WHISPER_LOG_WARN("%s: warning: empty-string token in vocab, i = %d\n", __func__, i);
word = "";
}
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
//printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
}
vocab.n_vocab = model.hparams.n_vocab;
if (vocab.is_multilingual()) {
vocab.token_eot++;
vocab.token_sot++;
// account for variable number of language tokens
const int dt = vocab.num_languages() - 98;
vocab.token_translate += dt;
vocab.token_transcribe += dt;
vocab.token_solm += dt;
vocab.token_prev += dt;
vocab.token_nosp += dt;
vocab.token_not += dt;
vocab.token_beg += dt;
}
if (n_vocab < model.hparams.n_vocab) {
WHISPER_LOG_INFO("%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
for (int i = n_vocab; i < model.hparams.n_vocab; i++) {
if (i > vocab.token_beg) {
word = "[_TT_" + std::to_string(i - vocab.token_beg) + "]";
} else if (i == vocab.token_eot) {
word = "[_EOT_]";
} else if (i == vocab.token_sot) {
word = "[_SOT_]";
} else if (i == vocab.token_solm) {
word = "[_SOLM_]";
} else if (i == vocab.token_prev) {
word = "[_PREV_]";
} else if (i == vocab.token_nosp) {
word = "[_NOSP_]";
} else if (i == vocab.token_not) {
word = "[_NOT_]";
} else if (i == vocab.token_beg) {
word = "[_BEG_]";
} else {
word = "[_extra_token_" + std::to_string(i) + "]";
}
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
}
}
WHISPER_LOG_INFO("%s: n_langs = %d\n", __func__, vocab.num_languages());
}
const ggml_type wtype = wctx.wtype;
const ggml_type vtype = wctx.wtype == GGML_TYPE_F32 ? GGML_TYPE_F32 : GGML_TYPE_F16; // conv type
// create the ggml context
{
const auto & hparams = model.hparams;
const int n_audio_layer = hparams.n_audio_layer;
const int n_text_layer = hparams.n_text_layer;
const size_t n_tensors = 10 /* input */ + 15 + 15*n_audio_layer + 24*n_text_layer;
struct ggml_init_params params = {
/*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ true,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
WHISPER_LOG_ERROR("%s: ggml_init() failed\n", __func__);
return false;
}
}
// prepare tensors for the weights
{
auto & ctx = model.ctx;
const auto & hparams = model.hparams;
const int n_vocab = hparams.n_vocab;
const int n_audio_ctx = hparams.n_audio_ctx;
const int n_audio_state = hparams.n_audio_state;
const int n_audio_layer = hparams.n_audio_layer;
const int n_text_ctx = hparams.n_text_ctx;
const int n_text_state = hparams.n_text_state;
const int n_text_layer = hparams.n_text_layer;
const int n_mels = hparams.n_mels;
model.layers_encoder.resize(n_audio_layer);
model.layers_decoder.resize(n_text_layer);
// encoder
{
model.e_pe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_state, n_audio_ctx);
model.e_conv_1_w = ggml_new_tensor_3d(ctx, vtype, 3, n_mels, n_audio_state);
model.e_conv_1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2*n_audio_ctx, n_audio_state);
model.e_conv_2_w = ggml_new_tensor_3d(ctx, vtype, 3, n_audio_state, n_audio_state);
model.e_conv_2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_ctx, n_audio_state);
model.e_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
model.e_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
// map by name
model.tensors["encoder.positional_embedding"] = model.e_pe;
model.tensors["encoder.conv1.weight"] = model.e_conv_1_w;
model.tensors["encoder.conv1.bias"] = model.e_conv_1_b;
model.tensors["encoder.conv2.weight"] = model.e_conv_2_w;
model.tensors["encoder.conv2.bias"] = model.e_conv_2_b;
model.tensors["encoder.ln_post.weight"] = model.e_ln_w;
model.tensors["encoder.ln_post.bias"] = model.e_ln_b;
for (int i = 0; i < n_audio_layer; ++i) {
auto & layer = model.layers_encoder[i];
layer.mlp_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.mlp_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.mlp_0_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, 4*n_audio_state);
layer.mlp_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_audio_state);
layer.mlp_1_w = ggml_new_tensor_2d(ctx, wtype, 4*n_audio_state, n_audio_state);
layer.mlp_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.attn_q_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
layer.attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.attn_k_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
layer.attn_v_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
layer.attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
layer.attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
layer.attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
// map by name
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp_ln.weight"] = layer.mlp_ln_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp_ln.bias"] = layer.mlp_ln_b;
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.0.weight"] = layer.mlp_0_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.0.bias"] = layer.mlp_0_b;
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.2.weight"] = layer.mlp_1_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.2.bias"] = layer.mlp_1_b;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn_ln.weight"] = layer.attn_ln_0_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn_ln.bias"] = layer.attn_ln_0_b;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.query.weight"] = layer.attn_q_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.query.bias"] = layer.attn_q_b;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.key.weight"] = layer.attn_k_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.value.weight"] = layer.attn_v_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.value.bias"] = layer.attn_v_b;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.out.weight"] = layer.attn_ln_1_w;
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.out.bias"] = layer.attn_ln_1_b;
}
}
// decoder
{
model.d_pe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_text_state, n_text_ctx);
model.d_te = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_vocab);
model.d_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
model.d_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
// map by name
model.tensors["decoder.positional_embedding"] = model.d_pe;
model.tensors["decoder.token_embedding.weight"] = model.d_te;
model.tensors["decoder.ln.weight"] = model.d_ln_w;
model.tensors["decoder.ln.bias"] = model.d_ln_b;
for (int i = 0; i < n_text_layer; ++i) {
auto & layer = model.layers_decoder[i];
layer.mlp_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.mlp_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.mlp_0_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, 4*n_text_state);
layer.mlp_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_text_state);
layer.mlp_1_w = ggml_new_tensor_2d(ctx, wtype, 4*n_text_state, n_text_state);
layer.mlp_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.attn_q_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.attn_k_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.attn_v_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.cross_attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.cross_attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.cross_attn_q_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.cross_attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.cross_attn_k_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.cross_attn_v_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.cross_attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
layer.cross_attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
layer.cross_attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
// map by name
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp_ln.weight"] = layer.mlp_ln_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp_ln.bias"] = layer.mlp_ln_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.0.weight"] = layer.mlp_0_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.0.bias"] = layer.mlp_0_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.2.weight"] = layer.mlp_1_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.2.bias"] = layer.mlp_1_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn_ln.weight"] = layer.attn_ln_0_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn_ln.bias"] = layer.attn_ln_0_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.query.weight"] = layer.attn_q_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.query.bias"] = layer.attn_q_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.key.weight"] = layer.attn_k_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.value.weight"] = layer.attn_v_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.value.bias"] = layer.attn_v_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.out.weight"] = layer.attn_ln_1_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.out.bias"] = layer.attn_ln_1_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn_ln.weight"] = layer.cross_attn_ln_0_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn_ln.bias"] = layer.cross_attn_ln_0_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.query.weight"] = layer.cross_attn_q_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.query.bias"] = layer.cross_attn_q_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.key.weight"] = layer.cross_attn_k_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.value.weight"] = layer.cross_attn_v_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.value.bias"] = layer.cross_attn_v_b;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.out.weight"] = layer.cross_attn_ln_1_w;
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.out.bias"] = layer.cross_attn_ln_1_b;
}
}
}
wctx.backend = whisper_backend_init(wctx.params);
{
size_t size_main = 0;
for (const auto & t : model.tensors) {
size_main += ggml_nbytes(t.second) + ggml_tensor_overhead();
}
model.buffer = ggml_backend_alloc_buffer(wctx.backend, size_main);
WHISPER_LOG_INFO("%s: %8s buffer size = %8.2f MB\n", __func__, ggml_backend_name(wctx.backend), size_main / 1024.0 / 1024.0);
}
ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer);
// allocate tensors in the backend buffers
{
for (const auto & t : model.tensors) {
ggml_allocr_alloc(alloc, t.second);
}
}
// load weights
{
size_t total_size = 0;
model.n_loaded = 0;
std::vector<char> read_buf;
while (true) {
int32_t n_dims;
int32_t length;
int32_t ttype;
read_safe(loader, n_dims);
read_safe(loader, length);
read_safe(loader, ttype);
if (loader->eof(loader->context)) {
break;
}
int32_t nelements = 1;
int32_t ne[4] = { 1, 1, 1, 1 };
for (int i = 0; i < n_dims; ++i) {
read_safe(loader, ne[i]);
nelements *= ne[i];
}
std::string name;
std::vector<char> tmp(length); // create a buffer
loader->read(loader->context, &tmp[0], tmp.size()); // read to buffer
name.assign(&tmp[0], tmp.size());
if (model.tensors.find(name) == model.tensors.end()) {
WHISPER_LOG_ERROR("%s: unknown tensor '%s' in model file\n", __func__, name.data());
return false;
}
auto tensor = model.tensors[name.data()];
const bool is_conv_bias = (name == "encoder.conv1.bias" || name == "encoder.conv2.bias");
if (!is_conv_bias) {
if (ggml_nelements(tensor) != nelements) {
WHISPER_LOG_ERROR("%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
WHISPER_LOG_ERROR("%s: shape: [%d, %d, %d], expected: [%d, %d, %d]\n",
__func__, ne[0], ne[1], ne[2], (int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2]);
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1] || tensor->ne[2] != ne[2]) {
WHISPER_LOG_ERROR("%s: tensor '%s' has wrong shape in model file: got [%d, %d, %d], expected [%d, %d, %d]\n",
__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], ne[0], ne[1], ne[2]);
return false;
}
const size_t bpe = ggml_type_size(ggml_type(ttype));
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
WHISPER_LOG_ERROR("%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
return false;
}
}
ggml_backend_t backend = wctx.backend;
//printf("%s: [%5.5s] %s\n", __func__, ggml_backend_name(backend), name.c_str());
if ((ggml_backend_is_cpu(backend)
#ifdef GGML_USE_METAL
|| ggml_backend_is_metal(backend)
#endif
) && !is_conv_bias) {
// for the CPU and Metal backend, we can read directly into the tensor
loader->read(loader->context, tensor->data, ggml_nbytes(tensor));
BYTESWAP_TENSOR(tensor);
} else {
// read into a temporary buffer first, then copy to device memory
read_buf.resize(ggml_nbytes(tensor));
// we repeat the 2 bias tensors along dim 0:
// [1, 512] -> [3000, 512] (conv1.bias)
// [1, 512] -> [1500, 512] (conv2.bias)
if (is_conv_bias) {
loader->read(loader->context, read_buf.data(), read_buf.size() / tensor->ne[0]);
float * data_f32 = (float *) read_buf.data();
for (int64_t y = 0; y < tensor->ne[1]; ++y) {
const int64_t yy = tensor->ne[1] - y - 1;
const float val = data_f32[yy];
for (int64_t x = 0; x < tensor->ne[0]; ++x) {
data_f32[yy*tensor->ne[0] + x] = val;
}
}
} else {
loader->read(loader->context, read_buf.data(), read_buf.size());
}
ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
}
//printf("%48s - [%5d, %5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type) ttype), ggml_nbytes(tensor)/1024.0/1024.0);
total_size += ggml_nbytes(tensor);
model.n_loaded++;
}
WHISPER_LOG_INFO("%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
if (model.n_loaded == 0) {
WHISPER_LOG_WARN("%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
} else if (model.n_loaded != (int) model.tensors.size()) {
WHISPER_LOG_ERROR("%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
return false;
}
}
ggml_allocr_free(alloc);
wctx.t_load_us = ggml_time_us() - t_start_us;
return true;
}
static bool whisper_encode_external(const whisper_state & wstate) {
GGML_UNUSED(wstate);
#ifndef WHISPER_USE_COREML
const bool use_coreml = false;
#else
const bool use_coreml = wstate.ctx_coreml != nullptr;
#endif
#ifndef WHISPER_USE_OPENVINO
const bool use_openvino = false;
#else
const bool use_openvino = wstate.ctx_openvino != nullptr;
#endif
return use_coreml || use_openvino;
}
static struct ggml_cgraph * whisper_build_graph_conv(
whisper_context & wctx,
whisper_state & wstate,
const int mel_offset) {
const auto & model = wctx.model;
const auto & mel_inp = wstate.mel;
const auto & hparams = model.hparams;
const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
const int n_state = hparams.n_audio_state; GGML_UNUSED(n_state);
const int n_mels = hparams.n_mels;
struct ggml_init_params params = {
/*.mem_size =*/ wstate.alloc_conv.meta.size(),
/*.mem_buffer =*/ wstate.alloc_conv.meta.data(),
/*.no_alloc =*/ true,
};
struct ggml_context * ctx0 = ggml_init(params);
ggml_cgraph * gf = ggml_new_graph(ctx0);
ggml_allocr * alloc = wstate.alloc_conv.alloc;
struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
ggml_allocr_alloc(alloc, mel);
assert(mel->type == GGML_TYPE_F32);
if (!ggml_allocr_is_measure(alloc)) {
assert(mel_inp.n_mel == 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));
}
struct ggml_tensor * cur = nullptr;
if (!whisper_encode_external(wstate)) {
// convolution + gelu
{
cur = ggml_conv_1d_ph(ctx0, model.e_conv_1_w, mel, 1, 1);
cur = ggml_add(ctx0, cur, model.e_conv_1_b);
//cur = ggml_add(ctx0,
// ggml_repeat(ctx0,
// model.e_conv_1_b,
// cur),
// cur);
cur = ggml_gelu(ctx0, cur);
cur = ggml_conv_1d_ph(ctx0, model.e_conv_2_w, cur, 2, 1);
cur = ggml_add(ctx0, cur, model.e_conv_2_b);
//cur = ggml_add(ctx0,
// ggml_repeat(ctx0,
// model.e_conv_2_b,
// cur),
// cur);
cur = ggml_gelu(ctx0, cur);
}
ggml_set_name(cur, "embd_conv");
wstate.embd_conv = cur;
} else {
#ifdef WHISPER_USE_COREML
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx);
ggml_allocr_alloc(alloc, cur);
if (!ggml_allocr_is_measure(alloc)) {
whisper_coreml_encode(wstate.ctx_coreml, mel->ne[0], mel->ne[1], (float *) mel->data, (float *) cur->data);
}
#endif
#ifdef WHISPER_USE_OPENVINO
cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx);
ggml_allocr_alloc(alloc, cur);
if (!ggml_allocr_is_measure(alloc)) {
whisper_openvino_encode(wstate.ctx_openvino, mel, cur);
}
#endif
ggml_set_name(cur, "embd_enc");
wstate.embd_enc = cur;
}
ggml_build_forward_expand(gf, cur);
ggml_free(ctx0);
return gf;
}
static struct ggml_cgraph * whisper_build_graph_encoder(
whisper_context & wctx,
whisper_state & wstate) {
const auto & model = wctx.model;
const auto & hparams = model.hparams;
const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
const int n_state = hparams.n_audio_state;
const int n_head = hparams.n_audio_head;
const int n_layer = hparams.n_audio_layer;
struct ggml_init_params params = {
/*.mem_size =*/ wstate.alloc_encode.meta.size(),
/*.mem_buffer =*/ wstate.alloc_encode.meta.data(),
/*.no_alloc =*/ true,
};
struct ggml_context * ctx0 = ggml_init(params);
ggml_cgraph * gf = ggml_new_graph_custom(ctx0, WHISPER_MAX_NODES, false);
ggml_allocr * alloc = wstate.alloc_encode.alloc;
//struct ggml_tensor * cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_ctx, n_state);
//ggml_allocr_alloc(alloc, cur);
//if (!ggml_allocr_is_measure(alloc)) {
// ggml_backend_tensor_copy(wstate.embd_conv, cur);
//}
struct ggml_tensor * cur = ggml_view_tensor(ctx0, wstate.embd_conv);
struct ggml_tensor * KQscale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, KQscale);
if (!ggml_allocr_is_measure(alloc)) {
const float val = 1.0f/sqrtf(float(n_state)/n_head);
ggml_backend_tensor_set(KQscale, &val, 0, sizeof(float));
}
// ===================================================================
// NOTE: experimenting with partial evaluation of the encoder (ignore)
//static int iter = -1;
//const int n_iter = 1500/n_ctx;
//iter = (iter + 1) % n_iter;
//if (iter == 0) {
// memset(model.memory_cross_k->data, 0, ggml_nbytes(model.memory_cross_k));
// memset(model.memory_cross_v->data, 0, ggml_nbytes(model.memory_cross_v));
//}
static int iter = 0;
const size_t e_pe_stride = model.e_pe->ne[0]*ggml_element_size(model.e_pe);
const size_t e_pe_offset = model.e_pe->ne[0]*ggml_element_size(model.e_pe)*n_ctx*iter;
struct ggml_tensor * e_pe = ggml_view_2d(ctx0, model.e_pe, model.e_pe->ne[0], n_ctx, e_pe_stride, e_pe_offset);
cur = ggml_add(ctx0, e_pe, ggml_cont(ctx0, ggml_transpose(ctx0, cur)));
// ===================================================================
// original:
//cur = ggml_add(ctx0, model.e_pe, ggml_transpose(ctx0, cur));
struct ggml_tensor * inpL = cur;
for (int il = 0; il < n_layer; ++il) {
const auto & layer = model.layers_encoder[il];
// norm
{
cur = ggml_norm(ctx0, inpL, hparams.eps);
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctx0,
ggml_mul(ctx0, cur, layer.attn_ln_0_w),
layer.attn_ln_0_b);
}
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
layer.attn_q_w,
cur);
Qcur = ggml_add(ctx0, Qcur, layer.attn_q_b);
//Qcur = ggml_scale(ctx0, Qcur, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
// note: no bias for Key
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0,
layer.attn_k_w,
cur);
//Kcur = ggml_scale(ctx0, Kcur, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0,
layer.attn_v_w,
cur);
Vcur = ggml_add(ctx0, Vcur, layer.attn_v_b);
// ------
#ifdef WHISPER_USE_FLASH_ATTN
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, wctx.itype, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Kcur,
ggml_new_tensor_3d(ctx0, wctx.itype, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
struct ggml_tensor * V =
ggml_cpy(ctx0,
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
Vcur,
n_state/n_head, n_head, n_ctx),
1, 2, 0, 3),
ggml_new_tensor_3d(ctx0, wctx.itype, n_ctx, n_state/n_head, n_head));
struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, false);
#else
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Qcur,
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_cpy(ctx0,
Kcur,
ggml_new_tensor_3d(ctx0, wctx.itype, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQscale);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_scaled);
struct ggml_tensor * V =
ggml_cpy(ctx0,
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
Vcur,
n_state/n_head, n_head, n_ctx),
1, 2, 0, 3),
ggml_new_tensor_3d(ctx0, wctx.itype, n_ctx, n_state/n_head, n_head)
);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
#endif
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx));
}
// projection
{
cur = ggml_mul_mat(ctx0,
layer.attn_ln_1_w,
cur);
cur = ggml_add(ctx0, cur, layer.attn_ln_1_b);
}
// add the input
cur = ggml_add(ctx0, cur, inpL);
struct ggml_tensor * inpFF = cur;
// feed-forward network
{
// norm
{
cur = ggml_norm(ctx0, inpFF, hparams.eps);
// cur = mlp_ln_w*cur + mlp_ln_b
cur = ggml_add(ctx0,
ggml_mul(ctx0, cur, layer.mlp_ln_w),
layer.mlp_ln_b);
}
#ifdef WHISPER_USE_FLASH_FF
cur = ggml_flash_ff(ctx0,
ggml_cpy(ctx0, cur, ggml_new_tensor_2d(ctx0, wstate.itype, n_state, n_ctx)),
layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
#else
// fully connected
cur = ggml_mul_mat(ctx0,
layer.mlp_0_w,
cur);
cur = ggml_add(ctx0, cur, layer.mlp_0_b);
// GELU activation
cur = ggml_gelu(ctx0, cur);
// projection
cur = ggml_mul_mat(ctx0,
layer.mlp_1_w,
cur);
cur = ggml_add(ctx0, cur, layer.mlp_1_b);
#endif
}
inpL = ggml_add(ctx0, cur, inpFF);
}
cur = inpL;
// norm
{
cur = ggml_norm(ctx0, cur, hparams.eps);
// cur = ln_f_g*cur + ln_f_b
cur = ggml_add(ctx0,
ggml_mul(ctx0, cur, model.e_ln_w),
model.e_ln_b);
}
ggml_build_forward_expand(gf, cur);
wstate.embd_enc = cur;
//ggml_graph_print(gf);
////////////////////////////////////////////////////////////////////////////
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
// ggml_used_mem(ctx0)/1024.0/1024.0,
// wstate.get_buf_max_mem(0)/1024.0/1024.0,
// wstate.get_buf_max_mem(1)/1024.0/1024.0,
// wstate.get_buf_max_mem(2)/1024.0/1024.0,
// wstate.get_buf_max_mem(3)/1024.0/1024.0);
ggml_free(ctx0);
return gf;
}
// pre-compute cross-attention memory
static struct ggml_cgraph * whisper_build_graph_cross(
whisper_context & wctx,
whisper_state & wstate) {
const auto & model = wctx.model;
const auto & hparams = model.hparams;
const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
const int n_state = hparams.n_audio_state;
const int n_head = hparams.n_audio_head;
struct ggml_init_params params = {
/*.mem_size =*/ wstate.alloc_cross.meta.size(),
/*.mem_buffer =*/ wstate.alloc_cross.meta.data(),
/*.no_alloc =*/ true,
};
struct ggml_context * ctx0 = ggml_init(params);
ggml_cgraph * gf = ggml_new_graph(ctx0);
ggml_allocr * alloc = wstate.alloc_cross.alloc;
//struct ggml_tensor * cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx);
//ggml_allocr_alloc(alloc, cur);
//if (!ggml_allocr_is_measure(alloc)) {
// ggml_backend_tensor_copy(wstate.embd_enc, cur);
//}
struct ggml_tensor * cur = ggml_view_tensor(ctx0, wstate.embd_enc);
struct ggml_tensor * Kscale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, Kscale);
if (!ggml_allocr_is_measure(alloc)) {
const float val = pow(float(n_state) / n_head, -0.25);
ggml_backend_tensor_set(Kscale, &val, 0, sizeof(float));
}
for (int il = 0; il < model.hparams.n_text_layer; ++il) {
auto & layer = model.layers_decoder[il];
struct ggml_tensor* Kcross = ggml_mul_mat(ctx0,
layer.cross_attn_k_w,
cur);
Kcross = ggml_scale(ctx0, Kcross, Kscale);
struct ggml_tensor* Vcross = ggml_mul_mat(ctx0,
layer.cross_attn_v_w,
cur);
Vcross = ggml_add(ctx0,
Vcross,
layer.cross_attn_v_b);
Vcross = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcross, n_state, n_ctx));
struct ggml_tensor * k = ggml_view_1d(ctx0, wstate.kv_cross.k,
n_state*n_ctx,
(ggml_element_size(wstate.kv_cross.k)*n_state)*(il*n_ctx));
struct ggml_tensor * v = ggml_view_2d(ctx0, wstate.kv_cross.v, n_ctx, n_state,
( n_ctx)*ggml_element_size(wstate.kv_cross.v),
(il*n_ctx)*ggml_element_size(wstate.kv_cross.v)*n_state);
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcross, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcross, v));
}
//ggml_graph_print(gf);
ggml_free(ctx0);
return gf;
}
// evaluate the encoder with the given state
//
// given audio recording (more specifically, its log mel spectrogram), runs forward pass of the encoder
// part of the transformer model and returns the encoded features
//
// - wctx: the model
// - wstate: the state of the encoder
// - n_threads: number of threads to use
// - mel_offset: offset in the mel spectrogram (i.e. audio offset)
//
static bool whisper_encode_internal(
whisper_context & wctx,
whisper_state & wstate,
const int mel_offset,
const int n_threads,
whisper_abort_callback abort_callback,
void * abort_callback_data) {
const int64_t t_start_us = ggml_time_us();
// conv
{
auto & alloc = wstate.alloc_conv.alloc;
ggml_allocr_reset(alloc);
ggml_cgraph * gf = whisper_build_graph_conv(wctx, wstate, mel_offset);
ggml_allocr_alloc_graph(alloc, gf);
if (!whisper_encode_external(wstate)) {
ggml_graph_compute_helper(wstate.backend, gf, n_threads);
}
}
// encoder
if (!whisper_encode_external(wstate)) {
auto & alloc = wstate.alloc_encode.alloc;
ggml_allocr_reset(alloc);
ggml_cgraph * gf = whisper_build_graph_encoder(wctx, wstate);
ggml_allocr_alloc_graph(alloc, gf);
ggml_graph_compute_helper(wstate.backend, gf, n_threads);
}
// cross
{
auto & alloc = wstate.alloc_cross.alloc;
ggml_allocr_reset(alloc);
ggml_cgraph * gf = whisper_build_graph_cross(wctx, wstate);
ggml_allocr_alloc_graph(alloc, gf);
ggml_graph_compute_helper(wstate.backend, gf, n_threads);
}
wstate.t_encode_us += ggml_time_us() - t_start_us;
wstate.n_encode++;
return !(abort_callback && abort_callback(abort_callback_data));
}
static struct ggml_cgraph * whisper_build_graph_decoder(
whisper_context & wctx,
whisper_state & wstate,
whisper_decoder & decoder,
const whisper_token * tokens,
int n_tokens,
int n_past) {
const auto & model = wctx.model;
const auto & hparams = model.hparams;
auto & kv_self = decoder.kv_self;
WHISPER_ASSERT(!!kv_self.ctx);
const int n_ctx = hparams.n_text_ctx;
const int n_state = hparams.n_text_state;
const int n_head = hparams.n_text_head;
const int n_layer = hparams.n_text_layer;
const int N = n_tokens;
const int M = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
//WHISPER_PRINT_DEBUG("%s: n_past = %d, N = %d, M = %d, n_ctx = %d\n", __func__, n_past, N, M, n_ctx);
struct ggml_init_params params = {
/*.mem_size =*/ wstate.alloc_decode.meta.size(),
/*.mem_buffer =*/ wstate.alloc_decode.meta.data(),
/*.no_alloc =*/ true,
};
struct ggml_context * ctx0 = ggml_init(params);
ggml_cgraph * gf = ggml_new_graph_custom(ctx0, WHISPER_MAX_NODES, false);
ggml_allocr * alloc = wstate.alloc_decode.alloc;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_allocr_alloc(alloc, embd);
if (!ggml_allocr_is_measure(alloc)) {
ggml_backend_tensor_set(embd, tokens, 0, N*ggml_element_size(embd));
}
struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_allocr_alloc(alloc, position);
if (!ggml_allocr_is_measure(alloc)) {
for (int i = 0; i < N; ++i) {
const int32_t val = n_past + i;
ggml_backend_tensor_set(position, &val, i*sizeof(int32_t), sizeof(int32_t));
}
}
struct ggml_tensor * KQscale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, KQscale);
if (!ggml_allocr_is_measure(alloc)) {
const float val = pow(float(n_state)/n_head, -0.25);
ggml_backend_tensor_set(KQscale, &val, 0, sizeof(float));
}
// token encoding + position encoding
struct ggml_tensor * cur =
ggml_add(ctx0,
ggml_get_rows(ctx0, model.d_te, embd),
ggml_get_rows(ctx0, model.d_pe, position));
struct ggml_tensor * inpL = cur;
for (int il = 0; il < n_layer; ++il) {
const auto & layer = model.layers_decoder[il];
// norm
{
cur = ggml_norm(ctx0, inpL, hparams.eps);
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctx0,
ggml_mul(ctx0,
cur,
layer.attn_ln_0_w),
layer.attn_ln_0_b);
}
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
layer.attn_q_w,
cur);
Qcur = ggml_add(ctx0,
Qcur,
layer.attn_q_b);
Qcur = ggml_scale(ctx0, Qcur, KQscale);
// note: no bias for Key
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0,
layer.attn_k_w,
cur);
Kcur = ggml_scale(ctx0, Kcur, KQscale);
// store key and value to memory
{
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0,
layer.attn_v_w,
cur);
Vcur = ggml_add(ctx0,
Vcur,
layer.attn_v_b);
Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_state, N));
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_state, (ggml_element_size(kv_self.k)*n_state)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_state,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_state + n_past*ggml_element_size(kv_self.v));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
}
// ------
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0, Qcur, n_state/n_head, n_head, N),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k,
n_state/n_head, n_past + N, n_head,
ggml_element_size(kv_self.k)*n_state,
ggml_element_size(kv_self.k)*n_state/n_head,
ggml_element_size(kv_self.k)*n_state*n_ctx*il);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
//struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ, n_past);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_past + N, n_state/n_head, n_head,
n_ctx*ggml_element_size(kv_self.v),
n_ctx*ggml_element_size(kv_self.v)*n_state/n_head,
il*n_ctx*ggml_element_size(kv_self.v)*n_state);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, N));
}
// projection
{
cur = ggml_mul_mat(ctx0,
layer.attn_ln_1_w,
cur);
cur = ggml_add(ctx0,
cur,
layer.attn_ln_1_b);
}
// add the input
struct ggml_tensor * inpCA = ggml_add(ctx0, cur, inpL);
// norm
{
cur = ggml_norm(ctx0, inpCA, hparams.eps); // note: we use inpCA here
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctx0,
ggml_mul(ctx0,
cur,
layer.cross_attn_ln_0_w),
layer.cross_attn_ln_0_b);
}
// cross-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
layer.cross_attn_q_w,
cur);
Qcur = ggml_add(ctx0,
Qcur,
layer.cross_attn_q_b);
Qcur = ggml_scale(ctx0, Qcur, KQscale);
// Kcross is already scaled
struct ggml_tensor * Kcross =
ggml_view_3d(ctx0, wstate.kv_cross.k,
n_state/n_head, M, n_head,
ggml_element_size(wstate.kv_cross.k)*n_state,
ggml_element_size(wstate.kv_cross.k)*n_state/n_head,
ggml_element_size(wstate.kv_cross.k)*n_state*M*il);
//struct ggml_tensor * Vcross =
// ggml_reshape_3d(ctx0,
// ggml_view_1d(ctx0, wstate.kv_cross.v, M*n_state, il*M*ggml_element_size(wstate.kv_cross.v)*n_state),
// n_state/n_head, n_head, M);
//struct ggml_tensor * V_trans =
// ggml_cpy(ctx0,
// ggml_permute(ctx0, Vcross, 1, 2, 0, 3),
// ggml_new_tensor_3d(ctx0, Vcross->type, M, n_state/n_head, n_head));
struct ggml_tensor * V =
ggml_view_3d(ctx0, wstate.kv_cross.v,
M, n_state/n_head, n_head,
M*ggml_element_size(wstate.kv_cross.v),
M*ggml_element_size(wstate.kv_cross.v)*n_state/n_head,
il*M*ggml_element_size(wstate.kv_cross.v)*n_state);
// ------
struct ggml_tensor * Q =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0, Qcur, n_state/n_head, n_head, N),
0, 2, 1, 3);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, Kcross, Q);
//struct ggml_tensor * KQ_scaled =
// ggml_scale(ctx0,
// KQ,
// ggml_new_f32(ctx0, 1.0f/sqrt(float(n_state)/n_head))
// );
// no masking for cross-attention
//struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_state, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, N));
}
// projection
{
cur = ggml_mul_mat(ctx0,
layer.cross_attn_ln_1_w,
cur);
cur = ggml_add(ctx0,
cur,
layer.cross_attn_ln_1_b);
}
// add the input
cur = ggml_add(ctx0, cur, inpCA);
struct ggml_tensor * inpFF = cur;
// feed-forward network
{
// norm
{
cur = ggml_norm(ctx0, inpFF, hparams.eps);
// cur = mlp_ln_w*cur + mlp_ln_b
cur = ggml_add(ctx0,
ggml_mul(ctx0,
cur,
layer.mlp_ln_w),
layer.mlp_ln_b);
}
// fully connected
cur = ggml_mul_mat(ctx0,
layer.mlp_0_w,
cur);
cur = ggml_add(ctx0,
cur,
layer.mlp_0_b);
// GELU activation
cur = ggml_gelu(ctx0, cur);
// projection
cur = ggml_mul_mat(ctx0,
layer.mlp_1_w,
cur);
cur = ggml_add(ctx0,
cur,
layer.mlp_1_b);
}
inpL = ggml_add(ctx0, cur, inpFF);
}
cur = inpL;
// norm
{
cur = ggml_norm(ctx0, cur, hparams.eps);
cur = ggml_add(ctx0,
ggml_mul(ctx0,
cur,
model.d_ln_w),
model.d_ln_b);
}
// compute logits only for the last token
// comment this line to compute logits for all N tokens
// might be useful in the future
cur = ggml_view_2d(ctx0, cur, cur->ne[0], 1, cur->nb[1], (cur->ne[1] - 1)*cur->nb[1]);
struct ggml_tensor * logits = ggml_mul_mat(ctx0, model.d_te, cur);
ggml_build_forward_expand(gf, logits);
ggml_free(ctx0);
return gf;
}
// evaluate the decoder
//
// given text prompt + audio features -> computes the logits for the next token
//
// - model: the model
// - n_threads: number of threads to use
// - tokens: text prompt
// - n_tokens: number of tokens in the prompt
// - n_past: number of past tokens to prefix the prompt with
//
static bool whisper_decode_internal(
whisper_context & wctx,
whisper_state & wstate,
whisper_decoder & decoder,
const whisper_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads,
whisper_abort_callback abort_callback,
void * abort_callback_data) {
const int64_t t_start_us = ggml_time_us();
const auto & model = wctx.model;
const auto & hparams = model.hparams;
const int n_vocab = hparams.n_vocab;
auto & logits_out = wstate.logits;
struct ggml_tensor * logits;
// decoder
{
auto & alloc = wstate.alloc_decode.alloc;
ggml_allocr_reset(alloc);
ggml_cgraph * gf = whisper_build_graph_decoder(wctx, wstate, decoder, tokens, n_tokens, n_past);
ggml_allocr_alloc_graph(alloc, gf);
logits = gf->nodes[gf->n_nodes - 1];
ggml_graph_compute_helper(wstate.backend, gf, n_threads);
}
// extract logits for all N tokens
//logits_out.resize(n_tokens*n_vocab);
//memcpy(logits_out.data(), ggml_get_data(logits), sizeof(float)*n_tokens*n_vocab);
//ggml_backend_tensor_get(logits, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), sizeof(float)*n_vocab);
// extract logits only for the last token
logits_out.resize(n_vocab);
//memcpy(logits_out.data(), ggml_get_data(logits), sizeof(float)*n_vocab);
ggml_backend_tensor_get(logits, logits_out.data(), 0, sizeof(float)*n_vocab);
if (n_tokens > 1) {
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
// ggml_used_mem(ctx0)/1024.0/1024.0,
// wstate.get_buf_max_mem(0)/1024.0/1024.0,
// wstate.get_buf_max_mem(1)/1024.0/1024.0,
// wstate.get_buf_max_mem(2)/1024.0/1024.0,
// wstate.get_buf_max_mem(3)/1024.0/1024.0);
}
if (n_tokens == 1) {
wstate.t_decode_us += ggml_time_us() - t_start_us;
wstate.n_decode++;
} else {
wstate.t_prompt_us += ggml_time_us() - t_start_us;
wstate.n_prompt++;
}
return !(abort_callback && abort_callback(abort_callback_data));
}
// 500 -> 00:05.000
// 6000 -> 01:00.000
static std::string to_timestamp(int64_t t, bool comma = false) {
int64_t msec = t * 10;
int64_t hr = msec / (1000 * 60 * 60);
msec = msec - hr * (1000 * 60 * 60);
int64_t min = msec / (1000 * 60);
msec = msec - min * (1000 * 60);
int64_t sec = msec / 1000;
msec = msec - sec * 1000;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
return std::string(buf);
}
#define SIN_COS_N_COUNT WHISPER_N_FFT
static float sin_vals[SIN_COS_N_COUNT];
static 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);
}
is_filled = true;
}
// naive Discrete Fourier Transform
// input is real-valued
// output is complex-valued
static void dft(const std::vector<float> & in, std::vector<float> & out) {
int N = in.size();
out.resize(N*2);
const int sin_cos_step = SIN_COS_N_COUNT / N;
for (int k = 0; k < N; k++) {
float re = 0;
float im = 0;
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)
}
out[k*2 + 0] = re;
out[k*2 + 1] = im;
}
}
// Cooley-Tukey FFT
// poor man's implementation - use something better
// input is real-valued
// output is complex-valued
static void fft(const std::vector<float> & in, std::vector<float> & out) {
out.resize(in.size()*2);
int N = in.size();
if (N == 1) {
out[0] = in[0];
out[1] = 0;
return;
}
if (N%2 == 1) {
dft(in, out);
return;
}
std::vector<float> even;
std::vector<float> odd;
even.reserve(N/2);
odd.reserve(N/2);
for (int i = 0; i < N; i++) {
if (i % 2 == 0) {
even.push_back(in[i]);
} else {
odd.push_back(in[i]);
}
}
std::vector<float> even_fft;
std::vector<float> odd_fft;
fft(even, even_fft);
fft(odd, odd_fft);
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_odd = odd_fft[2*k + 0];
float im_odd = odd_fft[2*k + 1];
out[2*k + 0] = even_fft[2*k + 0] + re*re_odd - im*im_odd;
out[2*k + 1] = even_fft[2*k + 1] + re*im_odd + im*re_odd;
out[2*(k + N/2) + 0] = even_fft[2*k + 0] - re*re_odd + im*im_odd;
out[2*(k + N/2) + 1] = even_fft[2*k + 1] - re*im_odd - im*re_odd;
}
}
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)));
}
return true;
}
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) {
std::vector<float> fft_in(frame_size, 0.0);
std::vector<float> fft_out(2 * frame_step);
// make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist
int n_fft = 1 + (frame_size / 2);
int i = ith;
// calculate FFT only when fft_in are not all zero
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)
for (int j = 0; j < std::min(frame_size, n_samples - offset); j++) {
fft_in[j] = hann[j] * samples[offset + j];
}
// fill the rest with zeros
if (n_samples - offset < frame_size) {
std::fill(fft_in.begin() + (n_samples - offset), fft_in.end(), 0.0);
}
// FFT
fft(fft_in, fft_out);
// Calculate modulus^2 of complex numbers
// Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting.
for (int j = 0; j < frame_size; j++) {
fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
}
// mel spectrogram
for (int j = 0; j < mel.n_mel; j++) {
double sum = 0.0;
// unroll loop (suggested by GH user @lunixbochs)
int k = 0;
for (k = 0; k < n_fft - 3; k += 4) {
sum +=
fft_out[k + 0] * filters.data[j * n_fft + k + 0] +
fft_out[k + 1] * filters.data[j * n_fft + k + 1] +
fft_out[k + 2] * filters.data[j * n_fft + k + 2] +
fft_out[k + 3] * filters.data[j * n_fft + k + 3];
}
// handle n_fft remainder
for (; k < n_fft; k++) {
sum += fft_out[k] * filters.data[j * n_fft + k];
}
sum = log10(std::max(sum, 1e-10));
mel.data[j * mel.n_len + i] = sum;
}
}
// Otherwise fft_out are all zero
double sum = log10(1e-10);
for (; i < mel.n_len; i += n_threads) {
for (int j = 0; j < mel.n_mel; j++) {
mel.data[j * mel.n_len + i] = sum;
}
}
}
// 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();
// 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);
// Calculate the length of padding
int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
int64_t stage_2_pad = frame_size / 2;
// 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);
// 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);
{
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));
}
// main thread
log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, 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;
}
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;
}
// split text into tokens
//
// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
//
// Regex (Python):
// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
//
// Regex (C++):
// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
//
static std::vector<whisper_vocab::id> tokenize(const whisper_vocab & vocab, const std::string & text) {
std::vector<std::string> words;
// first split the text into words
{
std::string str = text;
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
std::regex re(pat);
std::smatch m;
while (std::regex_search(str, m, re)) {
for (auto x : m) {
words.push_back(x);
}
str = m.suffix();
}
}
// find the longest tokens that form the words:
std::vector<whisper_vocab::id> tokens;
for (const auto & word : words) {
if (word.empty()) continue;
int i = 0;
int n = word.size();
while (i < n) {
int j = n;
bool found = false;
while (j > i) {
auto sub = word.substr(i, j-i);
auto it = vocab.token_to_id.find(sub);
if (it != vocab.token_to_id.end()) {
tokens.push_back(it->second);
i = j;
found = true;
break;
}
--j;
}
if (!found) {
WHISPER_LOG_ERROR("unknown token\n");
++i;
}
}
}
return tokens;
}
//
// interface implementation
//
#ifdef WHISPER_USE_COREML
// replace .bin with -encoder.mlmodelc
static std::string whisper_get_coreml_path_encoder(std::string path_bin) {
auto pos = path_bin.rfind('.');
if (pos != std::string::npos) {
path_bin = path_bin.substr(0, pos);
}
// match "-qx_x"
pos = path_bin.rfind('-');
if (pos != std::string::npos) {
auto sub = path_bin.substr(pos);
if (sub.size() == 5 && sub[1] == 'q' && sub[3] == '_') {
path_bin = path_bin.substr(0, pos);
}
}
path_bin += "-encoder.mlmodelc";
return path_bin;
}
#endif
#ifdef WHISPER_USE_OPENVINO
// replace .bin with-encoder-openvino.xml
static std::string whisper_openvino_get_path_encoder(std::string path_bin) {
auto pos = path_bin.rfind('.');
if (pos != std::string::npos) {
path_bin = path_bin.substr(0, pos);
}
path_bin += "-encoder-openvino.xml";
return path_bin;
}
static std::string whisper_openvino_get_path_cache(std::string path_bin) {
auto pos = path_bin.rfind('.');
if (pos != std::string::npos) {
path_bin = path_bin.substr(0, pos);
}
path_bin += "-encoder-openvino-cache";
return 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 (!kv_cache_init(ctx->model.hparams, state->decoders[0].kv_self, ctx->backend, ctx->itype, ctx->model.hparams.n_text_ctx)) {
WHISPER_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
delete state;
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(state->decoders[0].kv_self.k) + ggml_nbytes(state->decoders[0].kv_self.v);
WHISPER_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
if (!kv_cache_init(ctx->model.hparams, state->kv_cross, ctx->backend, ctx->itype, ctx->model.hparams.n_audio_ctx)) {
WHISPER_LOG_ERROR("%s: kv_cache_init() failed for cross-attention cache\n", __func__);
delete state;
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(state->kv_cross.k) + ggml_nbytes(state->kv_cross.v);
WHISPER_LOG_INFO("%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
#ifdef WHISPER_USE_COREML
const auto path_coreml = whisper_get_coreml_path_encoder(ctx->path_model);
WHISPER_LOG_INFO("%s: loading Core ML model from '%s'\n", __func__, path_coreml.c_str());
WHISPER_LOG_INFO("%s: first run on a device may take a while ...\n", __func__);
state->ctx_coreml = whisper_coreml_init(path_coreml.c_str());
if (!state->ctx_coreml) {
WHISPER_LOG_ERROR("%s: failed to load Core ML model from '%s'\n", __func__, path_coreml.c_str());
#ifndef WHISPER_COREML_ALLOW_FALLBACK
delete state;
return nullptr;
#endif
} else {
WHISPER_LOG_INFO("%s: Core ML model loaded\n", __func__);
}
#endif
state->logits.reserve(ctx->vocab.n_vocab * ctx->model.hparams.n_text_ctx);
state->logits_id.reserve(ctx->model.hparams.n_vocab);
// TAGS: WHISPER_DECODER_INIT
state->decoders[0].sequence.tokens.reserve(ctx->model.hparams.n_text_ctx);
state->decoders[0].probs.reserve (ctx->vocab.n_vocab);
state->decoders[0].logits.reserve (ctx->vocab.n_vocab);
state->decoders[0].logprobs.reserve(ctx->vocab.n_vocab);
// conv allocator
{
whisper_allocr_graph_init(state->alloc_conv, ctx->backend,
[&]() {
return whisper_build_graph_conv(*ctx, *state, 0);
});
WHISPER_LOG_INFO("%s: compute buffer (conv) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_conv) / 1024.0 / 1024.0);
}
// encoder allocator
if (!whisper_encode_external(*state)) {
whisper_allocr_graph_init(state->alloc_encode, ctx->backend,
[&]() {
return whisper_build_graph_encoder(*ctx, *state);
});
WHISPER_LOG_INFO("%s: compute buffer (encode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_encode) / 1024.0 / 1024.0);
}
// cross allocator
{
whisper_allocr_graph_init(state->alloc_cross, ctx->backend,
[&]() {
return whisper_build_graph_cross(*ctx, *state);
});
WHISPER_LOG_INFO("%s: compute buffer (cross) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_cross) / 1024.0 / 1024.0);
}
// decoder allocator
{
whisper_allocr_graph_init(state->alloc_decode, ctx->backend,
[&]() {
const auto & hparams = ctx->model.hparams;
// TODO: make sure this is the worst-case scenario
const int n_tokens = hparams.n_text_ctx;
const int n_past = 0;
return whisper_build_graph_decoder(*ctx, *state, state->decoders[0], nullptr, n_tokens, n_past);
});
WHISPER_LOG_INFO("%s: compute buffer (decode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_decode) / 1024.0 / 1024.0);
}
whisper_allocr_graph_realloc(state->alloc_conv, ctx->backend);
whisper_allocr_graph_realloc(state->alloc_encode, ctx->backend);
whisper_allocr_graph_realloc(state->alloc_cross, ctx->backend);
whisper_allocr_graph_realloc(state->alloc_decode, ctx->backend);
state->rng = std::mt19937(0);
return state;
}
int whisper_ctx_init_openvino_encoder(
struct whisper_context * ctx,
const char * model_path,
const char * device,
const char * cache_dir) {
#ifndef WHISPER_USE_OPENVINO
(void)(ctx);
(void)(model_path);
(void)(device);
(void)(cache_dir);
return 1;
#else
if (!model_path && ctx->path_model.empty()) {
WHISPER_LOG_ERROR("%s: model_path is nullptr, and ctx has no model_path set.\n", __func__);
return 1;
}
std::string path_encoder;
if (!model_path) {
//if model_path is not set, attempt to find it in the same directory as ggml-<model>.bin model
path_encoder = whisper_openvino_get_path_encoder(ctx->path_model);
} else {
path_encoder = model_path;
}
std::string path_cache;
if (!cache_dir) {
//if cache_dir is not set, set it as a dir residing next to ggml-<model>.bin
path_cache = whisper_openvino_get_path_cache(ctx->path_model);
} else {
path_cache = cache_dir;
}
WHISPER_LOG_INFO("%s: loading OpenVINO model from '%s'\n", __func__, path_encoder.c_str());
WHISPER_LOG_INFO("%s: first run on a device may take a while ...\n", __func__);
ctx->state->ctx_openvino = whisper_openvino_init(path_encoder.c_str(), device, path_cache.c_str());
if (!ctx->state->ctx_openvino) {
WHISPER_LOG_ERROR("%s: failed to init OpenVINO encoder from '%s'\n", __func__, path_encoder.c_str());
return 1;
} else {
WHISPER_LOG_INFO("%s: OpenVINO model loaded\n", __func__);
}
return 0;
#endif
}
struct whisper_context_params whisper_context_default_params() {
struct whisper_context_params result = {
/*.use_gpu =*/ true,
};
return result;
}
struct whisper_context * whisper_init_from_file_with_params_no_state(const char * path_model, struct whisper_context_params params) {
WHISPER_LOG_INFO("%s: loading model from '%s'\n", __func__, path_model);
auto fin = std::ifstream(path_model, std::ios::binary);
if (!fin) {
WHISPER_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_model);
return nullptr;
}
whisper_model_loader loader = {};
loader.context = &fin;
loader.read = [](void * ctx, void * output, size_t read_size) {
std::ifstream * fin = (std::ifstream*)ctx;
fin->read((char *)output, read_size);
return read_size;
};
loader.eof = [](void * ctx) {
std::ifstream * fin = (std::ifstream*)ctx;
return fin->eof();
};
loader.close = [](void * ctx) {
std::ifstream * fin = (std::ifstream*)ctx;
fin->close();
};
auto ctx = whisper_init_with_params_no_state(&loader, params);
if (ctx) {
ctx->path_model = path_model;
}
return ctx;
}
struct whisper_context * whisper_init_from_buffer_with_params_no_state(void * buffer, size_t buffer_size, struct whisper_context_params params) {
struct buf_context {
uint8_t* buffer;
size_t size;
size_t current_offset;
};
buf_context ctx = { reinterpret_cast<uint8_t*>(buffer), buffer_size, 0 };
WHISPER_LOG_INFO("%s: loading model from buffer\n", __func__);
whisper_model_loader loader = {};
loader.context = &ctx;
loader.read = [](void * ctx, void * output, size_t read_size) {
buf_context * buf = reinterpret_cast<buf_context *>(ctx);
size_t size_to_copy = buf->current_offset + read_size < buf->size ? read_size : buf->size - buf->current_offset;
memcpy(output, buf->buffer + buf->current_offset, size_to_copy);
buf->current_offset += size_to_copy;
return size_to_copy;
};
loader.eof = [](void * ctx) {
buf_context * buf = reinterpret_cast<buf_context *>(ctx);
return buf->current_offset >= buf->size;
};
loader.close = [](void * /*ctx*/) { };
return whisper_init_with_params_no_state(&loader, params);
}
struct whisper_context * whisper_init_with_params_no_state(struct whisper_model_loader * loader, struct whisper_context_params params) {
ggml_time_init();
whisper_context * ctx = new whisper_context;
ctx->params = params;
if (!whisper_model_load(loader, *ctx)) {
loader->close(loader->context);
WHISPER_LOG_ERROR("%s: failed to load model\n", __func__);
delete ctx;
return nullptr;
}
loader->close(loader->context);
return ctx;
}
struct whisper_context * whisper_init_from_file_with_params(const char * path_model, struct whisper_context_params params) {
whisper_context * ctx = whisper_init_from_file_with_params_no_state(path_model, params);
if (!ctx) {
return nullptr;
}
ctx->state = whisper_init_state(ctx);
if (!ctx->state) {
whisper_free(ctx);
return nullptr;
}
return ctx;
}
struct whisper_context * whisper_init_from_buffer_with_params(void * buffer, size_t buffer_size, struct whisper_context_params params) {
whisper_context * ctx = whisper_init_from_buffer_with_params_no_state(buffer, buffer_size, params);
if (!ctx) {
return nullptr;
}
ctx->state = whisper_init_state(ctx);
if (!ctx->state) {
whisper_free(ctx);
return nullptr;
}
return ctx;
}
struct whisper_context * whisper_init_with_params(struct whisper_model_loader * loader, struct whisper_context_params params) {
whisper_context * ctx = whisper_init_with_params_no_state(loader, params);
if (!ctx) {
return nullptr;
}
ctx->state = whisper_init_state(ctx);
if (!ctx->state) {
whisper_free(ctx);
return nullptr;
}
return ctx;
}
struct whisper_context * whisper_init_from_file(const char * path_model) {
return whisper_init_from_file_with_params(path_model, whisper_context_default_params());
}
struct whisper_context * whisper_init_from_buffer(void * buffer, size_t buffer_size) {
return whisper_init_from_buffer_with_params(buffer, buffer_size, whisper_context_default_params());
}
struct whisper_context * whisper_init(struct whisper_model_loader * loader) {
return whisper_init_with_params(loader, whisper_context_default_params());
}
struct whisper_context * whisper_init_from_file_no_state(const char * path_model) {
return whisper_init_from_file_with_params_no_state(path_model, whisper_context_default_params());
}
struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size) {
return whisper_init_from_buffer_with_params_no_state(buffer, buffer_size, whisper_context_default_params());
}
struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loader) {
return whisper_init_with_params_no_state(loader, whisper_context_default_params());
}
void whisper_free_state(struct whisper_state * state)
{
if (state) {
kv_cache_free(state->kv_cross);
for (int i = 0; i < WHISPER_MAX_DECODERS; ++i) {
kv_cache_free(state->decoders[i].kv_self);
}
#ifdef WHISPER_USE_COREML
if (state->ctx_coreml != nullptr) {
whisper_coreml_free(state->ctx_coreml);
state->ctx_coreml = nullptr;
}
#endif
#ifdef WHISPER_USE_OPENVINO
if (state->ctx_openvino != nullptr) {
whisper_openvino_free(state->ctx_openvino);
state->ctx_openvino = nullptr;
}
#endif
whisper_allocr_free(state->alloc_conv);
whisper_allocr_free(state->alloc_encode);
whisper_allocr_free(state->alloc_cross);
whisper_allocr_free(state->alloc_decode);
ggml_backend_free(state->backend);
delete state;
}
}
void whisper_free(struct whisper_context * ctx) {
if (ctx) {
if (ctx->model.ctx) {
ggml_free(ctx->model.ctx);
}
if (ctx->model.buffer) {
ggml_backend_buffer_free(ctx->model.buffer);
}
whisper_free_state(ctx->state);
ggml_backend_free(ctx->backend);
delete ctx;
}
}
void whisper_free_context_params(struct whisper_context_params * params) {
if (params) {
delete params;
}
}
void whisper_free_params(struct whisper_full_params * params) {
if (params) {
delete 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;
}
return 0;
}
int whisper_pcm_to_mel(struct whisper_context * ctx, const float * samples, int n_samples, int n_threads) {
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,
const float * data,
int n_len,
int n_mel) {
if (n_mel != ctx->model.filters.n_mel) {
WHISPER_LOG_ERROR("%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, ctx->model.filters.n_mel);
return -1;
}
state->mel.n_len = n_len;
state->mel.n_len_org = n_len;
state->mel.n_mel = n_mel;
state->mel.data.resize(n_len*n_mel);
memcpy(state->mel.data.data(), data, n_len*n_mel*sizeof(float));
return 0;
}
int whisper_set_mel(
struct whisper_context * ctx,
const float * data,
int n_len,
int n_mel) {
return whisper_set_mel_with_state(ctx, ctx->state, data, n_len, n_mel);
}
int whisper_encode_with_state(struct whisper_context * ctx, struct whisper_state * state, int offset, int n_threads) {
if (!whisper_encode_internal(*ctx, *state, offset, n_threads, nullptr, nullptr)) {
WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
return -1;
}
return 0;
}
int whisper_encode(struct whisper_context * ctx, int offset, int n_threads) {
if (!whisper_encode_internal(*ctx, *ctx->state, offset, n_threads, nullptr, nullptr)) {
WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
return -1;
}
return 0;
}
int whisper_decode_with_state(struct whisper_context * ctx, struct whisper_state * state, const whisper_token * tokens, int n_tokens, int n_past, int n_threads) {
const int selected_decoder_id = 0;
if (!whisper_decode_internal(*ctx, *state, state->decoders[selected_decoder_id], tokens, n_tokens, n_past, n_threads, nullptr, nullptr)) {
WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
return 1;
}
return 0;
}
int whisper_decode(struct whisper_context * ctx, const whisper_token * tokens, int n_tokens, int n_past, int n_threads) {
// TODO: add selected_decoder_id to state
const int selected_decoder_id = 0;
if (ctx->state == nullptr) {
WHISPER_LOG_ERROR("%s: ERROR state was not loaded.\n", __func__);
return false;
}
if (!whisper_decode_internal(*ctx, *ctx->state, ctx->state->decoders[selected_decoder_id], tokens, n_tokens, n_past, n_threads, nullptr, nullptr)) {
WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
return 1;
}
return 0;
}
int whisper_tokenize(struct whisper_context * ctx, const char * text, whisper_token * tokens, int n_max_tokens) {
const auto res = tokenize(ctx->vocab, text);
if (n_max_tokens < (int) res.size()) {
WHISPER_LOG_ERROR("%s: too many resulting tokens: %d (max %d)\n", __func__, (int) res.size(), n_max_tokens);
return -1;
}
for (int i = 0; i < (int) res.size(); i++) {
tokens[i] = res[i];
}
return res.size();
}
int whisper_lang_max_id() {
auto max_id = 0;
for (const auto & kv : g_lang) {
max_id = std::max(max_id, kv.second.first);
}
return max_id;
}
int whisper_lang_id(const char * lang) {
if (!g_lang.count(lang)) {
for (const auto & kv : g_lang) {
if (kv.second.second == lang) {
return kv.second.first;
}
}
WHISPER_LOG_ERROR("%s: unknown language '%s'\n", __func__, lang);
return -1;
}
return g_lang.at(lang).first;
}
const char * whisper_lang_str(int id) {
for (const auto & kv : g_lang) {
if (kv.second.first == id) {
return kv.first.c_str();
}
}
WHISPER_LOG_ERROR("%s: unknown language id %d\n", __func__, id);
return nullptr;
}
int whisper_lang_auto_detect_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
int offset_ms,
int n_threads,
float * lang_probs) {
const int seek = offset_ms/10;
if (seek < 0) {
WHISPER_LOG_ERROR("%s: offset %dms is before the start of the audio\n", __func__, offset_ms);
return -1;
}
if (seek >= state->mel.n_len_org) {
WHISPER_LOG_ERROR("%s: offset %dms is past the end of the audio (%dms)\n", __func__, offset_ms, state->mel.n_len_org*10);
return -2;
}
// run the encoder
if (whisper_encode_with_state(ctx, state, seek, n_threads) != 0) {
WHISPER_LOG_ERROR("%s: failed to encode\n", __func__);
return -6;
}
const std::vector<whisper_token> prompt = { whisper_token_sot(ctx) };
if (whisper_decode_with_state(ctx, state, prompt.data(), prompt.size(), 0, n_threads) != 0) {
WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
return -7;
}
auto & logits_id = state->logits_id;
logits_id.clear();
for (const auto & kv : g_lang) {
const auto token_lang = whisper_token_lang(ctx, kv.second.first);
logits_id.emplace_back(state->logits[token_lang], kv.second.first);
}
// sort descending
{
using pair_type = std::remove_reference<decltype(logits_id)>::type::value_type;
std::sort(logits_id.begin(), logits_id.end(), [](const pair_type & a, const pair_type & b) {
return a.first > b.first;
});
}
// softmax
{
const auto max = logits_id[0].first;
double sum = 0.0f;
for (auto & kv : logits_id) {
kv.first = exp(kv.first - max);
sum += kv.first;
}
for (auto & kv : logits_id) {
kv.first /= sum;
}
}
{
for (const auto & prob : logits_id) {
if (lang_probs) {
lang_probs[prob.second] = prob.first;
}
//printf("%s: lang %2d (%3s): %f\n", __func__, prob.second, whisper_lang_str(prob.second), prob.first);
}
}
return logits_id[0].second;
}
int whisper_lang_auto_detect(
struct whisper_context * ctx,
int offset_ms,
int n_threads,
float * lang_probs) {
return whisper_lang_auto_detect_with_state(ctx, ctx->state, offset_ms, n_threads, lang_probs);
}
int whisper_model_n_vocab(struct whisper_context * ctx) {
return ctx->model.hparams.n_vocab;
}
int whisper_model_n_audio_ctx(struct whisper_context * ctx) {
return ctx->model.hparams.n_audio_ctx;
}
int whisper_model_n_audio_state(struct whisper_context * ctx) {
return ctx->model.hparams.n_audio_state;
}
int whisper_model_n_audio_head(struct whisper_context * ctx) {
return ctx->model.hparams.n_audio_head;
}
int whisper_model_n_audio_layer(struct whisper_context * ctx) {
return ctx->model.hparams.n_audio_layer;
}
int whisper_model_n_text_ctx(struct whisper_context * ctx) {
return ctx->model.hparams.n_text_ctx;
}
int whisper_model_n_text_state(struct whisper_context * ctx) {
return ctx->model.hparams.n_text_state;
}
int whisper_model_n_text_head(struct whisper_context * ctx) {
return ctx->model.hparams.n_text_head;
}
int whisper_model_n_text_layer(struct whisper_context * ctx) {
return ctx->model.hparams.n_text_layer;
}
int whisper_model_n_mels(struct whisper_context * ctx) {
return ctx->model.hparams.n_mels;
}
int whisper_model_ftype(struct whisper_context * ctx) {
return ctx->model.hparams.ftype;
}
int whisper_model_type(struct whisper_context * ctx) {
return ctx->model.type;
}
const char *whisper_model_type_readable(struct whisper_context * ctx) {
switch (ctx->model.type) {
case e_model::MODEL_TINY:
return "tiny";
case e_model::MODEL_BASE:
return "base";
case e_model::MODEL_SMALL:
return "small";
case e_model::MODEL_MEDIUM:
return "medium";
case e_model::MODEL_LARGE:
return "large";
default:
return "unknown";
}
}
int whisper_n_len_from_state(struct whisper_state * state) {
return state->mel.n_len_org;
}
int whisper_n_len(struct whisper_context * ctx) {
return ctx->state->mel.n_len_org;
}
int whisper_n_vocab(struct whisper_context * ctx) {
return ctx->vocab.n_vocab;
}
int whisper_n_text_ctx(struct whisper_context * ctx) {
return ctx->model.hparams.n_text_ctx;
}
int whisper_n_audio_ctx(struct whisper_context * ctx) {
return ctx->model.hparams.n_audio_ctx;
}
int whisper_is_multilingual(struct whisper_context * ctx) {
return ctx->vocab.is_multilingual() ? 1 : 0;
}
float * whisper_get_logits(struct whisper_context * ctx) {
return ctx->state->logits.data();
}
float * whisper_get_logits_from_state(struct whisper_state * state) {
return state->logits.data();
}
const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token) {
return ctx->vocab.id_to_token.at(token).c_str();
}
whisper_token whisper_token_eot(struct whisper_context * ctx) {
return ctx->vocab.token_eot;
}
whisper_token whisper_token_sot(struct whisper_context * ctx) {
return ctx->vocab.token_sot;
}
whisper_token whisper_token_solm(struct whisper_context * ctx) {
return ctx->vocab.token_solm;
}
whisper_token whisper_token_prev(struct whisper_context * ctx) {
return ctx->vocab.token_prev;
}
whisper_token whisper_token_nosp(struct whisper_context * ctx) {
return ctx->vocab.token_nosp;
}
whisper_token whisper_token_not(struct whisper_context * ctx) {
return ctx->vocab.token_not;
}
whisper_token whisper_token_beg(struct whisper_context * ctx) {
return ctx->vocab.token_beg;
}
whisper_token whisper_token_lang(struct whisper_context * ctx, int lang_id) {
return whisper_token_sot(ctx) + 1 + lang_id;
}
whisper_token whisper_token_translate(struct whisper_context * ctx) {
return ctx->vocab.token_translate;
}
whisper_token whisper_token_transcribe(struct whisper_context * ctx) {
return ctx->vocab.token_transcribe;
}
void whisper_print_timings(struct whisper_context * ctx) {
const int64_t t_end_us = ggml_time_us();
WHISPER_LOG_INFO("\n");
WHISPER_LOG_INFO("%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
if (ctx->state != nullptr) {
const int32_t n_sample = std::max(1, ctx->state->n_sample);
const int32_t n_encode = std::max(1, ctx->state->n_encode);
const int32_t n_decode = std::max(1, ctx->state->n_decode);
const int32_t n_prompt = std::max(1, ctx->state->n_prompt);
WHISPER_LOG_INFO("%s: fallbacks = %3d p / %3d h\n", __func__, ctx->state->n_fail_p, ctx->state->n_fail_h);
WHISPER_LOG_INFO("%s: mel time = %8.2f ms\n", __func__, ctx->state->t_mel_us / 1000.0f);
WHISPER_LOG_INFO("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_sample_us, n_sample, 1e-3f * ctx->state->t_sample_us / n_sample);
WHISPER_LOG_INFO("%s: encode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_encode_us, n_encode, 1e-3f * ctx->state->t_encode_us / n_encode);
WHISPER_LOG_INFO("%s: decode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_decode_us, n_decode, 1e-3f * ctx->state->t_decode_us / n_decode);
WHISPER_LOG_INFO("%s: prompt time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_prompt_us, n_prompt, 1e-3f * ctx->state->t_prompt_us / n_prompt);
}
WHISPER_LOG_INFO("%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
}
void whisper_reset_timings(struct whisper_context * ctx) {
ctx->t_start_us = ggml_time_us();
if (ctx->state != nullptr) {
ctx->state->t_mel_us = 0;
ctx->state->t_sample_us = 0;
ctx->state->t_encode_us = 0;
ctx->state->t_decode_us = 0;
ctx->state->t_prompt_us = 0;
ctx->state->n_sample = 0;
ctx->state->n_encode = 0;
ctx->state->n_decode = 0;
ctx->state->n_prompt = 0;
}
}
static int whisper_has_coreml(void) {
#ifdef WHISPER_USE_COREML
return 1;
#else
return 0;
#endif
}
static int whisper_has_openvino(void) {
#ifdef WHISPER_USE_OPENVINO
return 1;
#else
return 0;
#endif
}
const char * whisper_print_system_info(void) {
static std::string s;
s = "";
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
s += "METAL = " + std::to_string(ggml_cpu_has_metal()) + " | ";
s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | ";
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
s += "CUDA = " + std::to_string(ggml_cpu_has_cublas()) + " | ";
s += "COREML = " + std::to_string(whisper_has_coreml()) + " | ";
s += "OPENVINO = " + std::to_string(whisper_has_openvino()) + " | ";
return s.c_str();
}
////////////////////////////////////////////////////////////////////////////
struct whisper_context_params * whisper_context_default_params_by_ref() {
struct whisper_context_params params = whisper_context_default_params();
struct whisper_context_params* result = new whisper_context_params();
*result = params;
return result;
}
struct whisper_full_params * whisper_full_default_params_by_ref(enum whisper_sampling_strategy strategy) {
struct whisper_full_params params = whisper_full_default_params(strategy);
struct whisper_full_params* result = new whisper_full_params();
*result = params;
return result;
}
struct whisper_full_params whisper_full_default_params(enum whisper_sampling_strategy strategy) {
struct whisper_full_params result = {
/*.strategy =*/ strategy,
/*.n_threads =*/ std::min(4, (int32_t) std::thread::hardware_concurrency()),
/*.n_max_text_ctx =*/ 16384,
/*.offset_ms =*/ 0,
/*.duration_ms =*/ 0,
/*.translate =*/ false,
/*.no_context =*/ true,
/*.single_segment =*/ false,
/*.print_special =*/ false,
/*.print_progress =*/ true,
/*.print_realtime =*/ false,
/*.print_timestamps =*/ true,
/*.token_timestamps =*/ false,
/*.thold_pt =*/ 0.01f,
/*.thold_ptsum =*/ 0.01f,
/*.max_len =*/ 0,
/*.split_on_word =*/ false,
/*.max_tokens =*/ 0,
/*.speed_up =*/ false,
/*.debug_mode =*/ false,
/*.audio_ctx =*/ 0,
/*.tdrz_enable =*/ false,
/*.initial_prompt =*/ nullptr,
/*.prompt_tokens =*/ nullptr,
/*.prompt_n_tokens =*/ 0,
/*.language =*/ "en",
/*.detect_language =*/ false,
/*.suppress_blank =*/ true,
/*.suppress_non_speech_tokens =*/ false,
/*.temperature =*/ 0.0f,
/*.max_initial_ts =*/ 1.0f,
/*.length_penalty =*/ -1.0f,
/*.temperature_inc =*/ 0.4f,
/*.entropy_thold =*/ 2.4f,
/*.logprob_thold =*/ -1.0f,
/*.no_speech_thold =*/ 0.6f,
/*.greedy =*/ {
/*.best_of =*/ -1,
},
/*.beam_search =*/ {
/*.beam_size =*/ -1,
/*.patience =*/ -1.0f,
},
/*.new_segment_callback =*/ nullptr,
/*.new_segment_callback_user_data =*/ nullptr,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.encoder_begin_callback =*/ nullptr,
/*.encoder_begin_callback_user_data =*/ nullptr,
/*.abort_callback =*/ nullptr,
/*.abort_callback_user_data =*/ nullptr,
/*.logits_filter_callback =*/ nullptr,
/*.logits_filter_callback_user_data =*/ nullptr,
};
switch (strategy) {
case WHISPER_SAMPLING_GREEDY:
{
result.greedy = {
/*.best_of =*/ 2, // TODO: increase to 5 when we speed-up batch decoding
};
} break;
case WHISPER_SAMPLING_BEAM_SEARCH:
{
result.beam_search = {
/*.beam_size =*/ 2, // TODO: increase to 5 when we speed-up batch decoding
/*.patience =*/ -1.0f,
};
} break;
}
return result;
}
// forward declarations
static std::vector<float> get_signal_energy(const float * signal, int n_samples, int n_samples_per_half_window);
static void whisper_exp_compute_token_level_timestamps(
struct whisper_context & ctx,
struct whisper_state & state,
int i_segment,
float thold_pt,
float thold_ptsum);
static inline bool should_split_on_word(const char * txt, bool split_on_word) {
if (!split_on_word) return true;
return txt[0] == ' ';
}
// wrap the last segment to max_len characters
// returns the number of new segments
static int whisper_wrap_segment(struct whisper_context & ctx, struct whisper_state & state, int max_len, bool split_on_word) {
auto segment = state.result_all.back();
int res = 1;
int acc = 0;
std::string text;
for (int i = 0; i < (int) segment.tokens.size(); i++) {
const auto & token = segment.tokens[i];
if (token.id >= whisper_token_eot(&ctx)) {
continue;
}
const auto txt = whisper_token_to_str(&ctx, token.id);
const int cur = strlen(txt);
if (acc + cur > max_len && i > 0 && should_split_on_word(txt, split_on_word)) {
state.result_all.back().text = std::move(text);
state.result_all.back().t1 = token.t0;
state.result_all.back().tokens.resize(i);
state.result_all.back().speaker_turn_next = false;
state.result_all.push_back({});
state.result_all.back().t0 = token.t0;
state.result_all.back().t1 = segment.t1;
// add tokens [i, end] to the new segment
state.result_all.back().tokens.insert(
state.result_all.back().tokens.end(),
segment.tokens.begin() + i,
segment.tokens.end());
state.result_all.back().speaker_turn_next = segment.speaker_turn_next;
acc = 0;
text = "";
segment = state.result_all.back();
i = -1;
res++;
} else {
acc += cur;
text += txt;
}
}
state.result_all.back().text = std::move(text);
return res;
}
static const std::vector<std::string> non_speech_tokens = {
"\"", "#", "(", ")", "*", "+", "/", ":", ";", "<", "=", ">", "@", "[", "\\", "]", "^",
"_", "`", "{", "|", "}", "~", "「", "」", "『", "』", "<<", ">>", "<<<", ">>>", "--",
"---", "-(", "-[", "('", "(\"", "((", "))", "(((", ")))", "[[", "]]", "{{", "}}", "♪♪",
"♪♪♪","♩", "♪", "♫", "♬", "♭", "♮", "♯"
};
// process the logits for the selected decoder
// - applies logit filters
// - computes logprobs and probs
static void whisper_process_logits(
struct whisper_context & ctx,
struct whisper_state & state,
const struct whisper_full_params params,
struct whisper_decoder & decoder,
float temperature) {
const auto & vocab = ctx.vocab;
const auto & tokens_cur = decoder.sequence.tokens;
const bool is_initial = tokens_cur.size() == 0;
const int n_logits = vocab.id_to_token.size();
WHISPER_ASSERT(n_logits == ctx.vocab.n_vocab);
// extract the logits for the last token
// we will be mutating, and therefore we don't want to use the ctx.logits buffer directly
auto & probs = decoder.probs;
auto & logits = decoder.logits;
auto & logprobs = decoder.logprobs;
{
logits.resize(n_logits);
memcpy(logits.data(), state.logits.data() + (state.logits.size() - n_logits), n_logits*sizeof(float));
if (temperature > 0.0f) {
for (int i = 0; i < n_logits; i++) {
logits[i] /= temperature;
}
}
// will be populated a bit later
probs.resize(n_logits);
logprobs.resize(n_logits);
}
// apply logit filters here
// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L480-L493
{
// suppress blank
// https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L388-L390
if (params.suppress_blank) {
if (is_initial) {
logits[vocab.token_eot] = -INFINITY;
logits[vocab.token_to_id.at(" ")] = -INFINITY;
}
}
// suppress <|notimestamps|> token
// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L410-L412
logits[vocab.token_not] = -INFINITY;
// suppress sot and nosp tokens
logits[vocab.token_sot] = -INFINITY;
logits[vocab.token_nosp] = -INFINITY; // TODO: ignore this token for now
// [TDRZ] when tinydiarize is disabled, suppress solm token
if (params.tdrz_enable == false) {
logits[vocab.token_solm] = -INFINITY;
}
// suppress task tokens
logits[vocab.token_translate] = -INFINITY;
logits[vocab.token_transcribe] = -INFINITY;
logits[vocab.token_prev] = -INFINITY;
if (params.logits_filter_callback) {
params.logits_filter_callback(&ctx, &state, tokens_cur.data(), tokens_cur.size(), logits.data(), params.logits_filter_callback_user_data);
}
// suppress non-speech tokens
// ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/tokenizer.py#L224-L253
if (params.suppress_non_speech_tokens) {
for (const std::string & token : non_speech_tokens) {
const std::string suppress_tokens[] = {token, " " + token};
for (const std::string & suppress_token : suppress_tokens) {
if (vocab.token_to_id.find(suppress_token) != vocab.token_to_id.end()) {
logits[vocab.token_to_id.at(suppress_token)] = -INFINITY;
}
}
}
// allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
if (vocab.token_to_id.find(" -") != vocab.token_to_id.end()) {
logits[vocab.token_to_id.at(" -")] = -INFINITY;
}
if (vocab.token_to_id.find(" '") != vocab.token_to_id.end()) {
logits[vocab.token_to_id.at(" '")] = -INFINITY;
}
}
// timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
// https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L414-L424
{
const bool last_was_timestamp = tokens_cur.size() > 0 && tokens_cur.back().id >= vocab.token_beg;
const bool penultimate_was_timestamp = tokens_cur.size() < 2 || tokens_cur[tokens_cur.size() - 2].id >= vocab.token_beg;
//WHISPER_LOG_INFO("last_was_timestamp=%d penultimate_was_timestamp=%d\n", last_was_timestamp, penultimate_was_timestamp);
if (last_was_timestamp) {
if (penultimate_was_timestamp) {
for (int i = vocab.token_beg; i < n_logits; ++i) {
logits[i] = -INFINITY;
}
} else {
for (int i = 0; i < vocab.token_eot; ++i) {
logits[i] = -INFINITY;
}
}
}
}
// the initial timestamp cannot be larger than max_initial_ts
// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L426-L429
if (is_initial && params.max_initial_ts > 0.0f) {
const float precision = float(WHISPER_CHUNK_SIZE)/ctx.model.hparams.n_audio_ctx;
const int tid0 = std::round(params.max_initial_ts/precision);
for (int i = vocab.token_beg + tid0 + 1; i < n_logits; ++i) {
logits[i] = -INFINITY;
}
}
// condition timestamp tokens to be increasing
// ref: https://github.com/openai/whisper/pull/831#issuecomment-1385910556
if (decoder.has_ts) {
const int tid0 = decoder.seek_delta/2;
for (int i = vocab.token_beg; i < vocab.token_beg + tid0; ++i) {
logits[i] = -INFINITY;
}
}
// populate the logprobs array (log_softmax)
{
const float logit_max = *std::max_element(logits.begin(), logits.end());
float logsumexp = 0.0f;
for (int i = 0; i < n_logits; ++i) {
if (logits[i] > -INFINITY) {
logsumexp += expf(logits[i] - logit_max);
}
}
logsumexp = logf(logsumexp) + logit_max;
for (int i = 0; i < n_logits; ++i) {
if (logits[i] > -INFINITY) {
logprobs[i] = logits[i] - logsumexp;
} else {
logprobs[i] = -INFINITY;
}
}
}
// if sum of probability over timestamps is above any other token, sample timestamp
// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L431-L437
{
// logsumexp over timestamps
float timestamp_logprob = -INFINITY;
{
float logsumexp = 0.0f;
const float logprob_max = *std::max_element(logprobs.begin() + vocab.token_beg, logprobs.end());
for (int i = vocab.token_beg; i < n_logits; ++i) {
if (logprobs[i] > -INFINITY) {
logsumexp += expf(logprobs[i] - logprob_max);
}
}
if (logsumexp > 0.0f) {
timestamp_logprob = logf(logsumexp) + logprob_max;
}
}
const float max_text_token_logprob = *std::max_element(logprobs.begin(), logprobs.begin() + vocab.token_beg);
//WHISPER_LOG_INFO("timestamp_logprob=%f max_text_token_logprob=%f\n", timestamp_logprob, max_text_token_logprob);
if (timestamp_logprob > max_text_token_logprob) {
for (int i = 0; i < vocab.token_beg; ++i) {
logits[i] = -INFINITY;
logprobs[i] = -INFINITY;
}
}
}
}
// compute probs
{
for (int i = 0; i < n_logits; ++i) {
if (logits[i] == -INFINITY) {
probs[i] = 0.0f;
} else {
probs[i] = expf(logprobs[i]);
}
}
}
#if 0
// print first 100 logits - token string : logit
for (int i = 0; i < 100; i++) {
const auto token = vocab.id_to_token.at(i);
const auto prob = probs[i];
const auto logit = logits[i];
const auto logprob = logprobs[i];
printf("%s : prob=%9.5f logit=%9.5f logprob=%9.5f\n", token.c_str(), prob, logit, logprob);
}
// "And", "and", " And", " and"
printf("logits[\"and\"] = %f\n", logits[vocab.token_to_id.at("and")]);
printf("logits[\"And\"] = %f\n", logits[vocab.token_to_id.at("And")]);
printf("logits[\" and\"] = %f\n", logits[vocab.token_to_id.at(" and")]);
printf("logits[\" And\"] = %f\n", logits[vocab.token_to_id.at(" And")]);
printf("logits[\" so\"] = %f\n", logits[vocab.token_to_id.at(" so")]);
printf("logprobs[\"and\"] = %f\n", logprobs[vocab.token_to_id.at("and")]);
printf("logprobs[\"And\"] = %f\n", logprobs[vocab.token_to_id.at("And")]);
printf("logprobs[\" and\"] = %f\n", logprobs[vocab.token_to_id.at(" and")]);
printf("logprobs[\" And\"] = %f\n", logprobs[vocab.token_to_id.at(" And")]);
printf("logprobs[\" so\"] = %f\n", logprobs[vocab.token_to_id.at(" so")]);
printf("probs[\"and\"] = %f\n", probs[vocab.token_to_id.at("and")]);
printf("probs[\"And\"] = %f\n", probs[vocab.token_to_id.at("And")]);
printf("probs[\" and\"] = %f\n", probs[vocab.token_to_id.at(" and")]);
printf("probs[\" And\"] = %f\n", probs[vocab.token_to_id.at(" And")]);
printf("probs[\" so\"] = %f\n", probs[vocab.token_to_id.at(" so")]);
#endif
}
static whisper_token_data whisper_sample_token(
whisper_context & ctx,
whisper_state & state,
const whisper_decoder & decoder,
bool best) {
whisper_token_data result = {
0, 0, 0.0f, 0.0f, 0.0f, 0.0f, -1, -1, 0.0f,
};
const auto & vocab = ctx.vocab;
const auto & probs = decoder.probs;
const auto & logprobs = decoder.logprobs;
const int n_logits = vocab.n_vocab;
{
double sum_ts = 0.0;
double max_ts = 0.0;
for (int i = vocab.token_beg; i < n_logits; i++) {
if (probs[i] == -INFINITY) {
continue;
}
sum_ts += probs[i];
if (max_ts < probs[i]) {
max_ts = probs[i];
result.tid = i;
}
}
result.pt = max_ts/(sum_ts + 1e-10);
result.ptsum = sum_ts;
}
if (best) {
for (int i = 0; i < n_logits; ++i) {
if (result.p < probs[i]) {
result.id = i;
result.p = probs[i];
result.plog = logprobs[i];
}
}
} else {
std::discrete_distribution<> dist(probs.begin(), probs.end());
result.id = dist(state.rng);
result.p = probs[result.id];
result.plog = logprobs[result.id];
}
if (result.id >= vocab.token_beg) {
result.tid = result.id;
result.pt = result.p;
}
state.n_sample++;
return result;
}
static std::vector<whisper_token_data> whisper_sample_token_topk(
whisper_context & ctx,
whisper_state & state,
const whisper_decoder & decoder,
int k) {
const auto & vocab = ctx.vocab;
const auto & probs = decoder.probs;
const auto & logits = decoder.logits;
const auto & logprobs = decoder.logprobs;
const int n_logits = vocab.n_vocab;
auto & logits_id = state.logits_id;
logits_id.resize(n_logits);
for (int i = 0; i < n_logits; ++i) {
logits_id[i].first = logits[i];
logits_id[i].second = i;
}
{
using pair_type = std::remove_reference<decltype(logits_id)>::type::value_type;
std::partial_sort(
logits_id.begin(),
logits_id.begin() + k, logits_id.end(),
[](const pair_type & a, const pair_type & b) {
return a.first > b.first;
});
}
std::vector<whisper_token_data> result;
result.reserve(k);
whisper_token tid = vocab.token_beg;
float pt = 0.0;
float ptsum = 0.0;
{
double sum_ts = 0.0;
double max_ts = 0.0;
for (int i = vocab.token_beg; i < n_logits; i++) {
if (probs[i] == -INFINITY) {
continue;
}
sum_ts += probs[i];
if (max_ts < probs[i]) {
max_ts = probs[i];
tid = i;
}
}
pt = max_ts/(sum_ts + 1e-10);
ptsum = sum_ts;
}
for (int i = 0; i < k; ++i) {
const auto id = logits_id[i].second;
result.push_back({ id, tid, probs[id], logprobs[id], pt, ptsum, -1, -1, 0.0f, });
if (result[i].id >= vocab.token_beg) {
result[i].tid = result[i].id;
result[i].pt = result[i].p;
}
}
state.n_sample++;
return result;
}
// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L178-L192
static void whisper_sequence_score(
const struct whisper_full_params & params,
whisper_sequence & sequence) {
if (sequence.result_len == 0) {
return;
}
double result = 0.0f;
for (int i = 0; i < sequence.result_len; ++i) {
result += sequence.tokens[i].plog;
}
sequence.sum_logprobs = result;
sequence.avg_logprobs = result/sequence.result_len;
double penalty = sequence.result_len;
if (params.length_penalty > 0.0f) {
penalty = pow((5.0 + penalty)/6.0, params.length_penalty);
}
sequence.score = result/penalty;
// compute the entropy of the sequence of the last 32 tokens
{
const int n = 32;
int cnt = 0;
double entropy = 0.0f;
std::map<whisper_token, int> token_counts;
for (int i = std::max(0, sequence.result_len - n); i < sequence.result_len; ++i) {
token_counts[sequence.tokens[i].id]++;
cnt++;
}
for (const auto & kv : token_counts) {
const auto p = kv.second/(double)cnt;
entropy -= p*log(p);
//WHISPER_PRINT_DEBUG("entropy: %d %f %f, count %d\n", kv.first, p, log(p), kv.second);
}
sequence.entropy = entropy;
}
}
static bool whisper_kv_swap_fast(
std::vector<int> & view,
whisper_decoder src[],
std::vector<kv_buf> & kv_swap_bufs,
const int & n_decoders) {
WHISPER_PRINT_DEBUG("%s: n_decoders %d\n", __func__, n_decoders);
// (decoder->buffer->decoder or decoder->buffer + decoder->decoder)
std::set<int> two_copy; // decoder indices require two copies to safely modify KV caches
// (buffer->decoder or decoder->decoder)
std::set<int> one_copy; // decoder indices require one copy to safely modify KV caches
// (decoder<->decoder)
std::set<int> p_swap_set; // decoder indices able to swap KV-cache pointers
std::vector<whisper_pair<int, int>> p_swap_vec;
p_swap_vec.reserve(n_decoders);
// see https://github.com/ggerganov/whisper.cpp/wiki
for (int i = 0; i < n_decoders; i++) {
// zero-copy (no modification)
if (i == view[i] || view[i] < 0) {
continue;
}
bool is_one_copy = true;
// since we modify data sequentially, we only consider decoder indices after current index
for (int j = i + 1; j < n_decoders; j++) {
if (i == view[j]) {
// detect symmetric diagram
if (j == view[i]) {
p_swap_set.insert(i);
p_swap_set.insert(j);
p_swap_vec.emplace_back(i, j);
} else {
two_copy.insert(i);
is_one_copy = false;
}
break;
}
}
if (is_one_copy) {
one_copy.insert(i);
}
}
kv_swap_bufs.resize(n_decoders);
for (int i = 0; i < n_decoders; i++) {
kv_swap_bufs[i].k.resize(ggml_nbytes(src[i].kv_self.k));
kv_swap_bufs[i].v.resize(ggml_nbytes(src[i].kv_self.v));
}
for (auto & i : two_copy) {
// make a copy of KV caches
WHISPER_PRINT_DEBUG("%s: store KV cache into swap: idx %d\n", __func__, i);
//memcpy(kv_swap_bufs[i].k.data(), src[i].kv_self.k->data, kv_swap_bufs[i].k.size());
//memcpy(kv_swap_bufs[i].v.data(), src[i].kv_self.v->data, kv_swap_bufs[i].v.size());
ggml_backend_tensor_get(src[i].kv_self.k, kv_swap_bufs[i].k.data(), 0, kv_swap_bufs[i].k.size());
ggml_backend_tensor_get(src[i].kv_self.v, kv_swap_bufs[i].v.data(), 0, kv_swap_bufs[i].v.size());
}
// since two-copy decoder KV caches are protected by kv_swap_bufs, modify them first
for (auto & i : two_copy) {
// skip the decoder indices that require pointer swapping
if (p_swap_set.find(i) != p_swap_set.end()) {
continue;
}
if (two_copy.find(view[i]) != two_copy.end()) {
// modify KV caches of decoder using data from kv_swap_bufs
WHISPER_PRINT_DEBUG("%s: two-copy decoder using swap buffers: swap[%d] -> %d\n", __func__, view[i], i);
//memcpy(src[i].kv_self.k->data, kv_swap_bufs[view[i]].k.data(), kv_swap_bufs[view[i]].k.size());
//memcpy(src[i].kv_self.v->data, kv_swap_bufs[view[i]].v.data(), kv_swap_bufs[view[i]].v.size());
ggml_backend_tensor_set(src[i].kv_self.k, kv_swap_bufs[view[i]].k.data(), 0, kv_swap_bufs[view[i]].k.size());
ggml_backend_tensor_set(src[i].kv_self.v, kv_swap_bufs[view[i]].v.data(), 0, kv_swap_bufs[view[i]].v.size());
} else {
// modify KV caches of decoder using data from correspond decoder KV caches directly
WHISPER_PRINT_DEBUG("%s: two-copy decoder without swap buffers: %d -> %d\n", __func__, view[i], i);
//memcpy(src[i].kv_self.k->data, src[view[i]].kv_self.k->data, ggml_nbytes(src[view[i]].kv_self.k));
//memcpy(src[i].kv_self.v->data, src[view[i]].kv_self.v->data, ggml_nbytes(src[view[i]].kv_self.v));
ggml_backend_tensor_copy(src[view[i]].kv_self.k, src[i].kv_self.k);
ggml_backend_tensor_copy(src[view[i]].kv_self.v, src[i].kv_self.v);
}
}
// then modify one-copy decoder KV caches
for (auto & i : one_copy) {
// skip the decoder indices that require pointer swapping
if (p_swap_set.find(i) != p_swap_set.end()) {
continue;
}
if (two_copy.find(view[i]) != two_copy.end()) {
// modify KV caches of decoder using data from kv_swap_bufs
WHISPER_PRINT_DEBUG("%s: one-copy decoder using swap buffers: swap[%d] -> %d\n", __func__, view[i], i);
//memcpy(src[i].kv_self.k->data, kv_swap_bufs[view[i]].k.data(), kv_swap_bufs[view[i]].k.size());
//memcpy(src[i].kv_self.v->data, kv_swap_bufs[view[i]].v.data(), kv_swap_bufs[view[i]].v.size());
ggml_backend_tensor_set(src[i].kv_self.k, kv_swap_bufs[view[i]].k.data(), 0, kv_swap_bufs[view[i]].k.size());
ggml_backend_tensor_set(src[i].kv_self.v, kv_swap_bufs[view[i]].v.data(), 0, kv_swap_bufs[view[i]].v.size());
} else {
// modify KV caches of decoder using data from correspond decoder KV caches directly
WHISPER_PRINT_DEBUG("%s: one-copy decoder without swap buffers: %d -> %d\n", __func__, view[i], i);
//memcpy(src[i].kv_self.k->data, src[view[i]].kv_self.k->data, ggml_nbytes(src[view[i]].kv_self.k));
//memcpy(src[i].kv_self.v->data, src[view[i]].kv_self.v->data, ggml_nbytes(src[view[i]].kv_self.v));
ggml_backend_tensor_copy(src[view[i]].kv_self.k, src[i].kv_self.k);
ggml_backend_tensor_copy(src[view[i]].kv_self.v, src[i].kv_self.v);
}
}
// swap the pointers
for (auto & i : p_swap_vec) {
WHISPER_PRINT_DEBUG("%s: swap pointers: %d <-> %d\n", __func__, i.first, i.second);
std::swap(src[i.first].kv_self, src[i.second].kv_self);
}
return true;
}
int whisper_full_with_state(
struct whisper_context * ctx,
struct whisper_state * state,
struct whisper_full_params params,
const float * samples,
int n_samples) {
// clear old results
auto & result_all = state->result_all;
result_all.clear();
if (n_samples > 0) {
// compute log mel spectrogram
if (params.speed_up) {
// TODO: Replace PV with more advanced algorithm
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;
}
}
}
// auto-detect language if not specified
if (params.language == nullptr || strlen(params.language) == 0 || strcmp(params.language, "auto") == 0 || params.detect_language) {
std::vector<float> probs(whisper_lang_max_id() + 1, 0.0f);
const auto lang_id = whisper_lang_auto_detect_with_state(ctx, state, 0, params.n_threads, probs.data());
if (lang_id < 0) {
WHISPER_LOG_ERROR("%s: failed to auto-detect language\n", __func__);
return -3;
}
state->lang_id = lang_id;
params.language = whisper_lang_str(lang_id);
WHISPER_LOG_INFO("%s: auto-detected language: %s (p = %f)\n", __func__, params.language, probs[whisper_lang_id(params.language)]);
if (params.detect_language) {
return 0;
}
}
if (params.token_timestamps) {
state->t_beg = 0;
state->t_last = 0;
state->tid_last = 0;
if (n_samples > 0) {
state->energy = get_signal_energy(samples, n_samples, 32);
}
}
const int seek_start = params.offset_ms/10;
const int seek_end = params.duration_ms == 0 ? whisper_n_len_from_state(state) : seek_start + params.duration_ms/10;
// 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)) {
return 0;
}
// a set of temperatures to use
// [ t0, t0 + delta, t0 + 2*delta, ..., < 1.0f + 1e-6f ]
std::vector<float> temperatures;
if (params.temperature_inc > 0.0f) {
for (float t = params.temperature; t < 1.0f + 1e-6f; t += params.temperature_inc) {
temperatures.push_back(t);
}
} else {
temperatures.push_back(params.temperature);
}
// initialize the decoders
int n_decoders = 1;
switch (params.strategy) {
case WHISPER_SAMPLING_GREEDY:
{
n_decoders = params.greedy.best_of;
} break;
case WHISPER_SAMPLING_BEAM_SEARCH:
{
n_decoders = std::max(params.greedy.best_of, params.beam_search.beam_size);
} break;
};
n_decoders = std::max(1, n_decoders);
// TAGS: WHISPER_DECODER_INIT
for (int j = 1; j < n_decoders; j++) {
auto & decoder = state->decoders[j];
if (decoder.kv_self.ctx == nullptr) {
decoder.kv_self = state->decoders[0].kv_self;
if (!kv_cache_reinit(decoder.kv_self, ctx->backend)) {
WHISPER_LOG_ERROR("%s: kv_cache_reinit() failed for self-attention, decoder %d\n", __func__, j);
return -4;
}
WHISPER_PRINT_DEBUG("%s: initialized self-attention kv cache, decoder %d\n", __func__, j);
decoder.sequence.tokens.reserve(state->decoders[0].sequence.tokens.capacity());
decoder.probs.resize (ctx->vocab.n_vocab);
decoder.logits.resize (ctx->vocab.n_vocab);
decoder.logprobs.resize(ctx->vocab.n_vocab);
}
}
// the accumulated text context so far
auto & prompt_past = state->prompt_past;
if (params.no_context) {
prompt_past.clear();
}
// prepare prompt
{
std::vector<whisper_token> prompt_tokens;
// initial prompt
if (!params.prompt_tokens && params.initial_prompt) {
prompt_tokens.resize(1024);
prompt_tokens.resize(whisper_tokenize(ctx, params.initial_prompt, prompt_tokens.data(), prompt_tokens.size()));
params.prompt_tokens = prompt_tokens.data();
params.prompt_n_tokens = prompt_tokens.size();
}
// prepend the prompt tokens to the prompt_past
if (params.prompt_tokens && params.prompt_n_tokens > 0) {
// parse tokens from the pointer
for (int i = 0; i < params.prompt_n_tokens; i++) {
prompt_past.push_back(params.prompt_tokens[i]);
}
std::rotate(prompt_past.begin(), prompt_past.end() - params.prompt_n_tokens, prompt_past.end());
}
}
// overwrite audio_ctx, max allowed is hparams.n_audio_ctx
if (params.audio_ctx > whisper_n_audio_ctx(ctx)) {
WHISPER_LOG_ERROR("%s: audio_ctx is larger than the maximum allowed (%d > %d)\n", __func__, params.audio_ctx, whisper_n_audio_ctx(ctx));
return -5;
}
state->exp_n_audio_ctx = params.audio_ctx;
// these tokens determine the task that will be performed
std::vector<whisper_token> prompt_init = { whisper_token_sot(ctx) };
if (whisper_is_multilingual(ctx)) {
const int lang_id = whisper_lang_id(params.language);
state->lang_id = lang_id;
prompt_init.push_back(whisper_token_lang(ctx, lang_id));
if (params.translate) {
prompt_init.push_back(whisper_token_translate(ctx));
} else {
prompt_init.push_back(whisper_token_transcribe(ctx));
}
}
{
const bool is_distil = ctx->model.hparams.n_text_layer == 2;
// distilled models require the "no_timestamps" token
// TODO: add input parameter (#1229)
if (is_distil) {
WHISPER_LOG_WARN("%s: using distilled model - forcing no_timestamps\n", __func__);
prompt_init.push_back(whisper_token_not(ctx));
}
}
int seek = seek_start;
std::vector<whisper_token> prompt;
prompt.reserve(whisper_n_text_ctx(ctx));
struct beam_candidate {
int decoder_idx;
int seek_delta;
bool has_ts;
whisper_sequence sequence;
};
std::vector<beam_candidate> beam_candidates;
// main loop
while (true) {
if (params.progress_callback) {
const int progress_cur = (100*(seek - seek_start))/(seek_end - seek_start);
params.progress_callback(
ctx, ctx->state, progress_cur, params.progress_callback_user_data);
}
// of only 1 second left, then stop
if (seek + 100 >= seek_end) {
break;
}
if (params.encoder_begin_callback) {
if (params.encoder_begin_callback(ctx, state, params.encoder_begin_callback_user_data) == false) {
WHISPER_LOG_ERROR("%s: encoder_begin_callback returned false - aborting\n", __func__);
break;
}
}
// encode audio features starting at offset seek
if (!whisper_encode_internal(*ctx, *state, seek, params.n_threads, params.abort_callback, params.abort_callback_user_data)) {
WHISPER_LOG_ERROR("%s: failed to encode\n", __func__);
return -6;
}
// if there is a very short audio segment left to process, we remove any past prompt since it tends
// to confuse the decoder and often make it repeat or hallucinate stuff
if (seek > seek_start && seek + 500 >= seek_end) {
prompt_past.clear();
}
int best_decoder_id = 0;
for (int it = 0; it < (int) temperatures.size(); ++it) {
const float t_cur = temperatures[it];
int n_decoders_cur = 1;
switch (params.strategy) {
case whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY:
{
if (t_cur > 0.0f) {
n_decoders_cur = params.greedy.best_of;
}
} break;
case whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH:
{
if (t_cur > 0.0f) {
n_decoders_cur = params.greedy.best_of;
} else {
n_decoders_cur = params.beam_search.beam_size;
}
} break;
};
n_decoders_cur = std::max(1, n_decoders_cur);
WHISPER_PRINT_DEBUG("\n%s: decoding with %d decoders, temperature = %.2f\n", __func__, n_decoders_cur, t_cur);
// TAGS: WHISPER_DECODER_INIT
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
decoder.kv_self.n = 0;
decoder.sequence.tokens.clear();
decoder.sequence.result_len = 0;
decoder.sequence.sum_logprobs_all = 0.0;
decoder.sequence.sum_logprobs = -INFINITY;
decoder.sequence.avg_logprobs = -INFINITY;
decoder.sequence.entropy = 0.0;
decoder.sequence.score = -INFINITY;
decoder.seek_delta = 100*WHISPER_CHUNK_SIZE;
decoder.failed = false;
decoder.completed = false;
decoder.has_ts = false;
}
// init prompt and kv cache for the current iteration
// run whisper_decoder() only for decoder 0 and copy the results for the other decoders
{
prompt.clear();
// if we have already generated some text, use it as a prompt to condition the next generation
if (!prompt_past.empty() && t_cur < 0.5f && params.n_max_text_ctx > 0) {
int n_take = std::min(std::min(params.n_max_text_ctx, whisper_n_text_ctx(ctx)/2), int(prompt_past.size()));
prompt = { whisper_token_prev(ctx) };
prompt.insert(prompt.begin() + 1, prompt_past.end() - n_take, prompt_past.end());
}
// init new transcription with sot, language (opt) and task tokens
prompt.insert(prompt.end(), prompt_init.begin(), prompt_init.end());
// print the prompt
WHISPER_PRINT_DEBUG("\n\n");
for (int i = 0; i < (int) prompt.size(); i++) {
WHISPER_PRINT_DEBUG("%s: prompt[%d] = %s\n", __func__, i, ctx->vocab.id_to_token.at(prompt[i]).c_str());
}
WHISPER_PRINT_DEBUG("\n\n");
if (!whisper_decode_internal(*ctx, *state, state->decoders[0], prompt.data(), prompt.size(), 0, params.n_threads, params.abort_callback, params.abort_callback_user_data)) {
WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
return -7;
}
{
const int64_t t_start_sample_us = ggml_time_us();
whisper_process_logits(*ctx, *state, params, state->decoders[0], t_cur);
state->decoders[0].kv_self.n += prompt.size();
for (int j = 1; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
// TODO: fix CUDA
//memcpy(decoder.kv_self.k->data, state->decoders[0].kv_self.k->data, ggml_nbytes(decoder.kv_self.k));
//memcpy(decoder.kv_self.v->data, state->decoders[0].kv_self.v->data, ggml_nbytes(decoder.kv_self.v));
ggml_backend_tensor_copy(state->decoders[0].kv_self.k, decoder.kv_self.k);
ggml_backend_tensor_copy(state->decoders[0].kv_self.v, decoder.kv_self.v);
decoder.kv_self.n += prompt.size();
memcpy(decoder.probs.data(), state->decoders[0].probs.data(), decoder.probs.size()*sizeof(decoder.probs[0]));
memcpy(decoder.logits.data(), state->decoders[0].logits.data(), decoder.logits.size()*sizeof(decoder.logits[0]));
memcpy(decoder.logprobs.data(), state->decoders[0].logprobs.data(), decoder.logprobs.size()*sizeof(decoder.logprobs[0]));
}
state->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
for (int i = 0, n_max = whisper_n_text_ctx(ctx)/2 - 4; i < n_max; ++i) {
const int64_t t_start_sample_us = ggml_time_us();
if (params.strategy == whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH) {
beam_candidates.clear();
}
// generate new sequence candidates for each decoder
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
switch (params.strategy) {
case whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY:
{
if (t_cur < 1e-6f) {
decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, *state, decoder, true));
} else {
decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, *state, decoder, false));
}
decoder.sequence.sum_logprobs_all += decoder.sequence.tokens.back().plog;
} break;
case whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH:
{
const auto tokens_new = whisper_sample_token_topk(*ctx, *state, decoder, params.beam_search.beam_size);
for (const auto & token : tokens_new) {
beam_candidates.push_back({ j, decoder.seek_delta, decoder.has_ts, decoder.sequence });
beam_candidates.back().sequence.tokens.push_back(token);
beam_candidates.back().sequence.sum_logprobs_all += token.plog;
//WHISPER_PRINT_DEBUG("%s: beam candidate: %s (%f, %f)\n", __func__, ctx->vocab.id_to_token.at(token.id).c_str(), token.plog, beam_candidates.back().sequence.sum_logprobs_all);
}
} break;
};
}
// for beam-search, choose the top candidates and update the KV caches
if (params.strategy == whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH) {
std::sort(
beam_candidates.begin(),
beam_candidates.end(),
[](const beam_candidate & a, const beam_candidate & b) {
return a.sequence.sum_logprobs_all > b.sequence.sum_logprobs_all;
});
uint32_t cur_c = 0;
std::vector<int> decoder_idx(n_decoders_cur, -1);
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
auto & cur = beam_candidates[cur_c++];
while (beam_candidates.size() > cur_c && beam_candidates[cur_c].sequence.sum_logprobs_all == cur.sequence.sum_logprobs_all && i > 0) {
++cur_c;
}
decoder.sequence = cur.sequence;
decoder.seek_delta = cur.seek_delta;
decoder.has_ts = cur.has_ts;
decoder_idx[j] = cur.decoder_idx;
WHISPER_PRINT_DEBUG("%s: beam search: decoder %d: from decoder %d: token = %10s, plog = %8.5f, sum_logprobs = %8.5f\n",
__func__, j, cur.decoder_idx, ctx->vocab.id_to_token.at(decoder.sequence.tokens.back().id).c_str(), decoder.sequence.tokens.back().plog, decoder.sequence.sum_logprobs_all);
}
// update KV caches
whisper_kv_swap_fast(decoder_idx, state->decoders, state->kv_swap_bufs, n_decoders_cur);
}
// update the decoder state
// - check if the sequence is completed
// - check if the sequence is failed
// - update sliding window based on timestamp tokens
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
auto & has_ts = decoder.has_ts;
auto & failed = decoder.failed;
auto & completed = decoder.completed;
auto & seek_delta = decoder.seek_delta;
auto & result_len = decoder.sequence.result_len;
{
const auto & token = decoder.sequence.tokens.back();
// timestamp token - update sliding window
if (token.id > whisper_token_beg(ctx)) {
const int seek_delta_new = 2*(token.id - whisper_token_beg(ctx));
// do not allow to go back in time
if (has_ts && seek_delta > seek_delta_new && result_len < i) {
failed = true; // TODO: maybe this is not a failure ?
continue;
}
seek_delta = seek_delta_new;
result_len = i + 1;
has_ts = true;
}
#ifdef WHISPER_DEBUG
{
const auto tt = token.pt > 0.10 ? ctx->vocab.id_to_token.at(token.tid) : "[?]";
WHISPER_PRINT_DEBUG("%s: id = %3d, decoder = %d, token = %6d, p = %6.3f, ts = %10s, %6.3f, result_len = %4d '%s'\n",
__func__, i, j, token.id, token.p, tt.c_str(), token.pt, result_len, ctx->vocab.id_to_token.at(token.id).c_str());
}
#endif
// end of segment
if (token.id == whisper_token_eot(ctx) || // end of text token
(params.max_tokens > 0 && i >= params.max_tokens) || // max tokens per segment reached
(has_ts && seek + seek_delta + 100 >= seek_end) // end of audio reached
) {
if (result_len == 0) {
if (seek + seek_delta + 100 >= seek_end) {
result_len = i + 1;
} else {
failed = true;
continue;
}
}
if (params.single_segment) {
result_len = i + 1;
seek_delta = 100*WHISPER_CHUNK_SIZE;
}
completed = true;
continue;
}
// TESTS: if no tensors are loaded, it means we are running tests
if (ctx->model.n_loaded == 0) {
seek_delta = 100*WHISPER_CHUNK_SIZE;
completed = true;
continue;
}
}
// sometimes, the decoding can get stuck in a repetition loop
// this is an attempt to mitigate such cases - we flag the decoding as failed and use a fallback strategy
if (i == n_max - 1 && (result_len == 0 || seek_delta < 100*WHISPER_CHUNK_SIZE/2)) {
failed = true;
continue;
}
}
// check if all decoders have finished (i.e. completed or failed)
{
bool completed_all = true;
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
completed_all = false;
}
if (completed_all) {
break;
}
}
state->t_sample_us += ggml_time_us() - t_start_sample_us;
// obtain logits for the next token
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
if (decoder.failed || decoder.completed) {
continue;
}
decoder.tokens_tmp.resize(1);
decoder.tokens_tmp[0] = decoder.sequence.tokens.back().id;
//WHISPER_PRINT_DEBUG("%s: decoder %d: token %d, kv_self.n %d, seek_delta %d\n", __func__, j, decoder.tokens_tmp[0], decoder.kv_self.n, decoder.seek_delta);
if (!whisper_decode_internal(*ctx, *state, decoder, decoder.tokens_tmp.data(), decoder.tokens_tmp.size(), decoder.kv_self.n, params.n_threads, params.abort_callback, params.abort_callback_user_data)) {
WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
return -8;
}
{
const int64_t t_start_sample_us = ggml_time_us();
whisper_process_logits(*ctx, *state, params, decoder, t_cur);
++decoder.kv_self.n;
state->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
}
// rank the resulting sequences and select the best one
{
double best_score = -INFINITY;
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = state->decoders[j];
if (decoder.failed) {
continue;
}
decoder.sequence.tokens.resize(decoder.sequence.result_len);
whisper_sequence_score(params, decoder.sequence);
WHISPER_PRINT_DEBUG("%s: decoder %2d: score = %8.5f, result_len = %3d, avg_logprobs = %8.5f, entropy = %8.5f\n",
__func__, j, decoder.sequence.score, decoder.sequence.result_len, decoder.sequence.avg_logprobs, decoder.sequence.entropy);
if (decoder.sequence.result_len > 32 && decoder.sequence.entropy < params.entropy_thold) {
WHISPER_PRINT_DEBUG("%s: decoder %2d: failed due to entropy %8.5f < %8.5f\n",
__func__, j, decoder.sequence.entropy, params.entropy_thold);
decoder.failed = true;
state->n_fail_h++;
continue;
}
if (best_score < decoder.sequence.score) {
best_score = decoder.sequence.score;
best_decoder_id = j;
}
}
WHISPER_PRINT_DEBUG("%s: best decoder = %d\n", __func__, best_decoder_id);
}
// was the decoding successful for the current temperature?
// do fallback only if:
// - we are not at the last temperature
// - we are not at the end of the audio (3 sec)
if (it != (int) temperatures.size() - 1 &&
seek_end - seek > 10*WHISPER_CHUNK_SIZE) {
bool success = true;
const auto & decoder = state->decoders[best_decoder_id];
if (decoder.failed || decoder.sequence.avg_logprobs < params.logprob_thold) {
success = false;
state->n_fail_p++;
}
if (success) {
//for (auto & token : ctx->decoders[best_decoder_id].sequence.tokens) {
// WHISPER_PRINT_DEBUG("%s: token = %d, p = %6.3f, pt = %6.3f, ts = %s, str = %s\n", __func__, token.id, token.p, token.pt, ctx->vocab.id_to_token.at(token.tid).c_str(), ctx->vocab.id_to_token.at(token.id).c_str());
//}
break;
}
}
WHISPER_PRINT_DEBUG("\n%s: failed to decode with temperature = %.2f\n", __func__, t_cur);
}
// output results through a user-provided callback
{
const auto & best_decoder = state->decoders[best_decoder_id];
const auto seek_delta = best_decoder.seek_delta;
const auto result_len = best_decoder.sequence.result_len;
const auto & tokens_cur = best_decoder.sequence.tokens;
//WHISPER_PRINT_DEBUG("prompt_init.size() = %d, prompt.size() = %d, result_len = %d, seek_delta = %d\n", prompt_init.size(), prompt.size(), result_len, seek_delta);
// update prompt_past
prompt_past.clear();
if (prompt.front() == whisper_token_prev(ctx)) {
prompt_past.insert(prompt_past.end(), prompt.begin() + 1, prompt.end() - prompt_init.size());
}
for (int i = 0; i < result_len; ++i) {
prompt_past.push_back(tokens_cur[i].id);
}
if (!tokens_cur.empty() && ctx->model.n_loaded > 0) {
int i0 = 0;
auto t0 = seek + 2*(tokens_cur.front().tid - whisper_token_beg(ctx));
std::string text;
bool speaker_turn_next = false;
for (int i = 0; i < (int) tokens_cur.size(); i++) {
//printf("%s: %18s %6.3f %18s %6.3f\n", __func__,
// ctx->vocab.id_to_token[tokens_cur[i].id].c_str(), tokens_cur[i].p,
// ctx->vocab.id_to_token[tokens_cur[i].tid].c_str(), tokens_cur[i].pt);
if (params.print_special || tokens_cur[i].id < whisper_token_eot(ctx)) {
text += whisper_token_to_str(ctx, tokens_cur[i].id);
}
// [TDRZ] record if speaker turn was predicted after current segment
if (params.tdrz_enable && tokens_cur[i].id == whisper_token_solm(ctx)) {
speaker_turn_next = true;
}
if (tokens_cur[i].id > whisper_token_beg(ctx) && !params.single_segment) {
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;
if (params.print_realtime) {
if (params.print_timestamps) {
printf("[%s --> %s] %s\n", to_timestamp(tt0).c_str(), to_timestamp(tt1).c_str(), text.c_str());
} else {
printf("%s", text.c_str());
fflush(stdout);
}
}
//printf("tt0 = %d, tt1 = %d, text = %s, token = %s, token_id = %d, tid = %d\n", tt0, tt1, text.c_str(), ctx->vocab.id_to_token[tokens_cur[i].id].c_str(), tokens_cur[i].id, tokens_cur[i].tid);
result_all.push_back({ tt0, tt1, text, {}, speaker_turn_next });
for (int j = i0; j <= i; j++) {
result_all.back().tokens.push_back(tokens_cur[j]);
}
int n_new = 1;
if (params.token_timestamps) {
whisper_exp_compute_token_level_timestamps(
*ctx, *state, result_all.size() - 1, params.thold_pt, params.thold_ptsum);
if (params.max_len > 0) {
n_new = whisper_wrap_segment(*ctx, *state, params.max_len, params.split_on_word);
}
}
if (params.new_segment_callback) {
params.new_segment_callback(ctx, state, n_new, params.new_segment_callback_user_data);
}
}
text = "";
while (i < (int) tokens_cur.size() && tokens_cur[i].id > whisper_token_beg(ctx)) {
i++;
}
i--;
t0 = t1;
i0 = i + 1;
speaker_turn_next = false;
}
}
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;
if (params.print_realtime) {
if (params.print_timestamps) {
printf("[%s --> %s] %s\n", to_timestamp(tt0).c_str(), to_timestamp(tt1).c_str(), text.c_str());
} else {
printf("%s", text.c_str());
fflush(stdout);
}
}
result_all.push_back({ tt0, tt1, text, {} , speaker_turn_next });
for (int j = i0; j < (int) tokens_cur.size(); j++) {
result_all.back().tokens.push_back(tokens_cur[j]);
}
int n_new = 1;
if (params.token_timestamps) {
whisper_exp_compute_token_level_timestamps(
*ctx, *state, result_all.size() - 1, params.thold_pt, params.thold_ptsum);
if (params.max_len > 0) {
n_new = whisper_wrap_segment(*ctx, *state, params.max_len, params.split_on_word);
}
}
if (params.new_segment_callback) {
params.new_segment_callback(ctx, state, n_new, params.new_segment_callback_user_data);
}
}
}
// update audio window
seek += seek_delta;
WHISPER_PRINT_DEBUG("seek = %d, seek_delta = %d\n", seek, seek_delta);
}
}
return 0;
}
int whisper_full(
struct whisper_context * ctx,
struct whisper_full_params params,
const float * samples,
int n_samples) {
return whisper_full_with_state(ctx, ctx->state, params, samples, n_samples);
}
int whisper_full_parallel(
struct whisper_context * ctx,
struct whisper_full_params params,
const float * samples,
int n_samples,
int n_processors) {
if (n_processors == 1) {
return whisper_full(ctx, params, samples, n_samples);
}
int ret = 0;
// prepare separate states for each thread
std::vector<whisper_state*> states;
const int offset_samples = (WHISPER_SAMPLE_RATE*params.offset_ms)/1000;
const int n_samples_per_processor = (n_samples - offset_samples)/n_processors;
// the calling thread will process the first chunk
// while the other threads will process the remaining chunks
std::vector<std::thread> workers(n_processors - 1);
for (int i = 0; i < n_processors - 1; ++i) {
// create a new state for each thread
states.push_back(whisper_init_state(ctx));
const int start_samples = offset_samples + (i + 1)*n_samples_per_processor;
const int n_samples_cur = (i == n_processors - 2) ? n_samples - start_samples : n_samples_per_processor;
auto params_cur = params;
params_cur.offset_ms = 0;
params_cur.print_progress = false;
params_cur.print_realtime = false;
params_cur.new_segment_callback = nullptr;
params_cur.new_segment_callback_user_data = nullptr;
params_cur.progress_callback = nullptr;
params_cur.progress_callback_user_data = nullptr;
workers[i] = std::thread(whisper_full_with_state, ctx, states[i], std::move(params_cur), samples + start_samples, n_samples_cur);
}
{
auto params_cur = params;
// We need to disable the print real-time for this one as well, otherwise it will show only for the first chunk.
params_cur.print_realtime = false;
// Run the first transformation using default state but only for the first chunk.
ret = whisper_full_with_state(ctx, ctx->state, std::move(params_cur), samples, offset_samples + n_samples_per_processor);
}
for (int i = 0; i < n_processors - 1; ++i) {
workers[i].join();
}
const int64_t offset_t = (int64_t) params.offset_ms/10.0;
// combine results into result_state->result_all from all other states
for (int i = 0; i < n_processors - 1; ++i) {
auto& results_i = states[i]->result_all;
for (auto& result : results_i) {
// correct the segment timestamp taking into account the offset
result.t0 += 100 * ((i + 1) * n_samples_per_processor) / WHISPER_SAMPLE_RATE + offset_t;
result.t1 += 100 * ((i + 1) * n_samples_per_processor) / WHISPER_SAMPLE_RATE + offset_t;
// make sure that segments are not overlapping
if (!ctx->state->result_all.empty()) {
result.t0 = std::max(result.t0, ctx->state->result_all.back().t1);
}
ctx->state->result_all.push_back(std::move(result));
// call the new_segment_callback for each segment
if (params.new_segment_callback) {
params.new_segment_callback(ctx, ctx->state, 1, params.new_segment_callback_user_data);
}
}
ctx->state->t_mel_us += states[i]->t_mel_us;
ctx->state->t_sample_us += states[i]->t_sample_us;
ctx->state->t_encode_us += states[i]->t_encode_us;
ctx->state->t_decode_us += states[i]->t_decode_us;
ctx->state->t_prompt_us += states[i]->t_prompt_us;
ctx->state->n_sample += states[i]->n_sample;
ctx->state->n_encode += states[i]->n_encode;
ctx->state->n_decode += states[i]->n_decode;
ctx->state->n_prompt += states[i]->n_prompt;
whisper_free_state(states[i]);
}
// average the timings
ctx->state->t_mel_us /= n_processors;
ctx->state->t_sample_us /= n_processors;
ctx->state->t_encode_us /= n_processors;
ctx->state->t_decode_us /= n_processors;
// print information about the audio boundaries
WHISPER_LOG_WARN("\n");
WHISPER_LOG_WARN("%s: the audio has been split into %d chunks at the following times:\n", __func__, n_processors);
for (int i = 0; i < n_processors - 1; ++i) {
WHISPER_LOG_WARN("%s: split %d - %s\n", __func__, (i + 1), to_timestamp(100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t).c_str());
}
WHISPER_LOG_WARN("%s: the transcription quality may be degraded near these boundaries\n", __func__);
return ret;
}
int whisper_full_n_segments_from_state(struct whisper_state * state) {
return state->result_all.size();
}
int whisper_full_n_segments(struct whisper_context * ctx) {
return ctx->state->result_all.size();
}
int whisper_full_lang_id_from_state(struct whisper_state * state) {
return state->lang_id;
}
int whisper_full_lang_id(struct whisper_context * ctx) {
return ctx->state->lang_id;
}
int64_t whisper_full_get_segment_t0_from_state(struct whisper_state * state, int i_segment) {
return state->result_all[i_segment].t0;
}
int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment) {
return ctx->state->result_all[i_segment].t0;
}
int64_t whisper_full_get_segment_t1_from_state(struct whisper_state * state, int i_segment) {
return state->result_all[i_segment].t1;
}
int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment) {
return ctx->state->result_all[i_segment].t1;
}
bool whisper_full_get_segment_speaker_turn_next_from_state(struct whisper_state * state, int i_segment) {
return state->result_all[i_segment].speaker_turn_next;
}
bool whisper_full_get_segment_speaker_turn_next(struct whisper_context * ctx, int i_segment) {
return ctx->state->result_all[i_segment].speaker_turn_next;
}
const char * whisper_full_get_segment_text_from_state(struct whisper_state * state, int i_segment) {
return state->result_all[i_segment].text.c_str();
}
const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment) {
return ctx->state->result_all[i_segment].text.c_str();
}
int whisper_full_n_tokens_from_state(struct whisper_state * state, int i_segment) {
return state->result_all[i_segment].tokens.size();
}
int whisper_full_n_tokens(struct whisper_context * ctx, int i_segment) {
return ctx->state->result_all[i_segment].tokens.size();
}
const char * whisper_full_get_token_text_from_state(struct whisper_context * ctx, struct whisper_state * state, int i_segment, int i_token) {
return ctx->vocab.id_to_token[state->result_all[i_segment].tokens[i_token].id].c_str();
}
const char* whisper_full_get_token_text(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->vocab.id_to_token[ctx->state->result_all[i_segment].tokens[i_token].id].c_str();
}
whisper_token whisper_full_get_token_id_from_state(struct whisper_state * state, int i_segment, int i_token) {
return state->result_all[i_segment].tokens[i_token].id;
}
whisper_token whisper_full_get_token_id(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->state->result_all[i_segment].tokens[i_token].id;
}
struct whisper_token_data whisper_full_get_token_data_from_state(struct whisper_state * state, int i_segment, int i_token) {
return state->result_all[i_segment].tokens[i_token];
}
struct whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->state->result_all[i_segment].tokens[i_token];
}
float whisper_full_get_token_p_from_state(struct whisper_state * state, int i_segment, int i_token) {
return state->result_all[i_segment].tokens[i_token].p;
}
float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->state->result_all[i_segment].tokens[i_token].p;
}
// =================================================================================================
//
// Temporary interface needed for exposing ggml interface
// Will be removed in the future when ggml becomes a separate library
//
WHISPER_API int whisper_bench_memcpy(int n_threads) {
fputs(whisper_bench_memcpy_str(n_threads), stderr);
return 0;
}
WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
static std::string s;
s = "";
char strbuf[256];
ggml_time_init();
size_t n = 20;
size_t arr = n_threads > 0 ? 1024llu : n_threads; // trick to avoid compiler optimizations
// 1GB MB array
const size_t size = arr*1024llu*1024llu;
// single-thread
{
char * src = (char *) malloc(size);
char * dst = (char *) malloc(size);
for (size_t i = 0; i < size; i++) src[i] = i;
memcpy(dst, src, size); // heat-up
double tsum = 0.0;
double sum = 0.0;
for (size_t i = 0; i < n; i++) {
const int64_t t0 = ggml_time_us();
memcpy(dst, src, size);
const int64_t t1 = ggml_time_us();
tsum += (t1 - t0)*1e-6;
src[rand() % size] = rand() % 256;
}
snprintf(strbuf, sizeof(strbuf), "memcpy: %.2f GB/s (1 thread)\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
s += strbuf;
// needed to prevent the compiler from optimizing the memcpy away
{
for (size_t i = 0; i < size; i++) sum += dst[i];
snprintf(strbuf, sizeof(strbuf), "sum: %f\n", sum);
s += strbuf;
}
free(src);
free(dst);
}
return s.c_str();
}
WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
fputs(whisper_bench_ggml_mul_mat_str(n_threads), stderr);
return 0;
}
WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
static std::string s;
s = "";
char strbuf[256];
ggml_time_init();
const int n_max = 128;
const std::vector<size_t> sizes = {
64, 128, 256, 512, 1024, 2048, 4096,
};
const size_t N_max = sizes.back();
// a: N*N*sizeof(float)
// b: N*N*sizeof(float)
// c: N*N*sizeof(float)
// when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
std::vector<uint8_t> buf(3llu*N_max*N_max*sizeof(float) + 3*ggml_tensor_overhead() + ggml_graph_overhead());
std::vector<uint8_t> work;
// put a bunch of random data in the buffer
for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
for (int j = 0; j < (int) sizes.size(); j++) {
int n_q4_0 = 0;
int n_q4_1 = 0;
int n_q5_0 = 0;
int n_q5_1 = 0;
int n_q8_0 = 0;
int n_fp16 = 0;
int n_fp32 = 0;
// GFLOPS/s
double s_q4_0 = 0.0;
double s_q4_1 = 0.0;
double s_q5_0 = 0.0;
double s_q5_1 = 0.0;
double s_q8_0 = 0.0;
double s_fp16 = 0.0;
double s_fp32 = 0.0;
const size_t N = sizes[j];
for (int k = 0; k < 7; ++k) {
const ggml_type wtype =
k == 0 ? GGML_TYPE_Q4_0 :
k == 1 ? GGML_TYPE_Q4_1 :
k == 2 ? GGML_TYPE_Q5_0 :
k == 3 ? GGML_TYPE_Q5_1 :
k == 4 ? GGML_TYPE_Q8_0 :
k == 5 ? GGML_TYPE_F16 : GGML_TYPE_F32;
double & s = k == 0 ? s_q4_0 : k == 1 ? s_q4_1 : k == 2 ? s_q5_0 : k == 3 ? s_q5_1 : k == 4 ? s_q8_0 : k == 5 ? s_fp16 : /*k == 6*/ s_fp32;
int & n = k == 0 ? n_q4_0 : k == 1 ? n_q4_1 : k == 2 ? n_q5_0 : k == 3 ? n_q5_1 : k == 4 ? n_q8_0 : k == 5 ? n_fp16 : /*k == 6*/ n_fp32;
struct ggml_init_params gparams = {
/*.mem_size =*/ buf.size(),
/*.mem_buffer =*/ buf.data(),
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(gparams);
struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype, N, N);
struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);
struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
ggml_build_forward_expand(gf, c);
double tsum = 0.0;
// heat-up
ggml_graph_compute_helper(gf, work, n_threads, nullptr, nullptr);
for (int i = 0; i < n_max; ++i) {
const int64_t t0 = ggml_time_us();
ggml_graph_compute_helper(gf, work, n_threads, nullptr, nullptr);
const int64_t t1 = ggml_time_us();
tsum += (t1 - t0)*1e-6;
n++;
if (tsum > 1.0 && n >= 3) {
break;
}
}
ggml_free(ctx0);
s = ((2.0*N*N*N*n)/tsum)*1e-9;
}
// Q4_0 | Q4_1
snprintf(strbuf, sizeof(strbuf), "%4zu x %4zu: Q4_0 %7.1f GFLOPS (%3d runs) | Q4_1 %7.1f GFLOPS (%3d runs)\n",
N, N, s_q4_0, n_q4_0, s_q4_1, n_q4_1);
s += strbuf;
// Q5_0 | Q5_1 | Q8_0
snprintf(strbuf, sizeof(strbuf), "%4zu x %4zu: Q5_0 %7.1f GFLOPS (%3d runs) | Q5_1 %7.1f GFLOPS (%3d runs) | Q8_0 %7.1f GFLOPS (%3d runs)\n",
N, N, s_q5_0, n_q5_0, s_q5_1, n_q5_1, s_q8_0, n_q8_0);
s += strbuf;
// F16 | F32
snprintf(strbuf, sizeof(strbuf), "%4zu x %4zu: F16 %7.1f GFLOPS (%3d runs) | F32 %7.1f GFLOPS (%3d runs)\n",
N, N, s_fp16, n_fp16, s_fp32, n_fp32);
s += strbuf;
}
return s.c_str();
}
// =================================================================================================
// =================================================================================================
//
// Experimental stuff below
//
// Not sure if these should be part of the library at all, because the quality of the results is not
// guaranteed. Might get removed at some point unless a robust algorithm implementation is found
//
// =================================================================================================
//
// token-level timestamps
//
static int timestamp_to_sample(int64_t t, int n_samples) {
return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
}
static int64_t sample_to_timestamp(int i_sample) {
return (100ll*i_sample)/WHISPER_SAMPLE_RATE;
}
// a cost-function / heuristic that is high for text that takes longer to pronounce
// obviously, can be improved
static float voice_length(const std::string & text) {
float res = 0.0f;
for (char c : text) {
if (c == ' ') {
res += 0.01f;
} else if (c == ',') {
res += 2.00f;
} else if (c == '.') {
res += 3.00f;
} else if (c == '!') {
res += 3.00f;
} else if (c == '?') {
res += 3.00f;
} else if (c >= '0' && c <= '9') {
res += 3.00f;
} else {
res += 1.00f;
}
}
return res;
}
// average the fabs of the signal
static std::vector<float> get_signal_energy(const float * signal, int n_samples, int n_samples_per_half_window) {
const int hw = n_samples_per_half_window;
std::vector<float> result(n_samples);
for (int i = 0; i < n_samples; i++) {
float sum = 0;
for (int j = -hw; j <= hw; j++) {
if (i + j >= 0 && i + j < n_samples) {
sum += fabs(signal[i + j]);
}
}
result[i] = sum/(2*hw + 1);
}
return result;
}
static void whisper_exp_compute_token_level_timestamps(
struct whisper_context & ctx,
struct whisper_state & state,
int i_segment,
float thold_pt,
float thold_ptsum) {
auto & segment = state.result_all[i_segment];
auto & tokens = segment.tokens;
const int n_samples = state.energy.size();
if (n_samples == 0) {
WHISPER_LOG_ERROR("%s: no signal data available\n", __func__);
return;
}
const int64_t t0 = segment.t0;
const int64_t t1 = segment.t1;
const int n = tokens.size();
if (n == 0) {
return;
}
if (n == 1) {
tokens[0].t0 = t0;
tokens[0].t1 = t1;
return;
}
auto & t_beg = state.t_beg;
auto & t_last = state.t_last;
auto & tid_last = state.tid_last;
for (int j = 0; j < n; ++j) {
auto & token = tokens[j];
if (j == 0) {
if (token.id == whisper_token_beg(&ctx)) {
tokens[j ].t0 = t0;
tokens[j ].t1 = t0;
tokens[j + 1].t0 = t0;
t_beg = t0;
t_last = t0;
tid_last = whisper_token_beg(&ctx);
} else {
tokens[j ].t0 = t_last;
}
}
const int64_t tt = t_beg + 2*(token.tid - whisper_token_beg(&ctx));
tokens[j].id = token.id;
tokens[j].tid = token.tid;
tokens[j].p = token.p;
tokens[j].pt = token.pt;
tokens[j].ptsum = token.ptsum;
tokens[j].vlen = voice_length(whisper_token_to_str(&ctx, token.id));
if (token.pt > thold_pt && token.ptsum > thold_ptsum && token.tid > tid_last && tt <= t1) {
if (j > 0) {
tokens[j - 1].t1 = tt;
}
tokens[j].t0 = tt;
tid_last = token.tid;
}
}
tokens[n - 2].t1 = t1;
tokens[n - 1].t0 = t1;
tokens[n - 1].t1 = t1;
t_last = t1;
// find intervals of tokens with unknown timestamps
// fill the timestamps by proportionally splitting the interval based on the token voice lengths
{
int p0 = 0;
int p1 = 0;
while (true) {
while (p1 < n && tokens[p1].t1 < 0) {
p1++;
}
if (p1 >= n) {
p1--;
}
//printf("p0=%d p1=%d t0=%lld t1=%lld\n", p0, p1, tokens[p0].t0, tokens[p1].t1);
if (p1 > p0) {
double psum = 0.0;
for (int j = p0; j <= p1; j++) {
psum += tokens[j].vlen;
}
//printf("analyzing %d - %d, psum = %f\n", p0, p1, psum);
const double dt = tokens[p1].t1 - tokens[p0].t0;
// split the time proportionally to the voice length
for (int j = p0 + 1; j <= p1; j++) {
const double ct = tokens[j - 1].t0 + dt*tokens[j - 1].vlen/psum;
tokens[j - 1].t1 = ct;
tokens[j ].t0 = ct;
}
}
p1++;
p0 = p1;
if (p1 >= n) {
break;
}
}
}
// fix up (just in case)
for (int j = 0; j < n - 1; j++) {
if (tokens[j].t1 < 0) {
tokens[j + 1].t0 = tokens[j].t1;
}
if (j > 0) {
if (tokens[j - 1].t1 > tokens[j].t0) {
tokens[j].t0 = tokens[j - 1].t1;
tokens[j].t1 = std::max(tokens[j].t0, tokens[j].t1);
}
}
}
// VAD
// expand or contract tokens based on voice activity
{
const int hw = WHISPER_SAMPLE_RATE/8;
for (int j = 0; j < n; j++) {
if (tokens[j].id >= whisper_token_eot(&ctx)) {
continue;
}
int s0 = timestamp_to_sample(tokens[j].t0, n_samples);
int s1 = timestamp_to_sample(tokens[j].t1, n_samples);
const int ss0 = std::max(s0 - hw, 0);
const int ss1 = std::min(s1 + hw, n_samples);
const int ns = ss1 - ss0;
float sum = 0.0f;
for (int k = ss0; k < ss1; k++) {
sum += state.energy[k];
}
const float thold = 0.5*sum/ns;
{
int k = s0;
if (state.energy[k] > thold && j > 0) {
while (k > 0 && state.energy[k] > thold) {
k--;
}
tokens[j].t0 = sample_to_timestamp(k);
if (tokens[j].t0 < tokens[j - 1].t1) {
tokens[j].t0 = tokens[j - 1].t1;
} else {
s0 = k;
}
} else {
while (state.energy[k] < thold && k < s1) {
k++;
}
s0 = k;
tokens[j].t0 = sample_to_timestamp(k);
}
}
{
int k = s1;
if (state.energy[k] > thold) {
while (k < n_samples - 1 && state.energy[k] > thold) {
k++;
}
tokens[j].t1 = sample_to_timestamp(k);
if (j < ns - 1 && tokens[j].t1 > tokens[j + 1].t0) {
tokens[j].t1 = tokens[j + 1].t0;
} else {
s1 = k;
}
} else {
while (state.energy[k] < thold && k > s0) {
k--;
}
s1 = k;
tokens[j].t1 = sample_to_timestamp(k);
}
}
}
}
// fixed token expand (optional)
//{
// const int t_expand = 0;
// for (int j = 0; j < n; j++) {
// if (j > 0) {
// tokens[j].t0 = std::max(0, (int) (tokens[j].t0 - t_expand));
// }
// if (j < n - 1) {
// tokens[j].t1 = tokens[j].t1 + t_expand;
// }
// }
//}
// debug info
//for (int j = 0; j < n; ++j) {
// const auto & token = tokens[j];
// const auto tt = token.pt > thold_pt && token.ptsum > 0.01 ? whisper_token_to_str(&ctx, token.tid) : "[?]";
// printf("%s: %10s %6.3f %6.3f %6.3f %6.3f %5d %5d '%s'\n", __func__,
// tt, token.p, token.pt, token.ptsum, token.vlen, (int) token.t0, (int) token.t1, whisper_token_to_str(&ctx, token.id));
// if (tokens[j].id >= whisper_token_eot(&ctx)) {
// continue;
// }
//}
}
void whisper_log_set(ggml_log_callback log_callback, void * user_data) {
g_state.log_callback = log_callback ? log_callback : whisper_log_callback_default;
g_state.log_callback_user_data = user_data;
}
static void whisper_log_internal_v(ggml_log_level level, const char * format, va_list args) {
va_list args_copy;
va_copy(args_copy, args);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
g_state.log_callback(level, buffer, g_state.log_callback_user_data);
} else {
char* buffer2 = new char[len+1];
vsnprintf(buffer2, len+1, format, args_copy);
buffer2[len] = 0;
g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
delete[] buffer2;
}
va_end(args_copy);
}
static void whisper_log_internal(ggml_log_level level, const char * format, ...) {
va_list args;
va_start(args, format);
whisper_log_internal_v(level, format, args);
va_end(args);
}
static void whisper_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
fputs(text, stderr);
fflush(stderr);
}
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