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whisper : sync with whisper.cpp

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experiments/blocking
Georgi Gerganov 2 years ago
parent 8e3c634b27
commit e2f39f4b52
No known key found for this signature in database
GPG Key ID: 449E073F9DC10735
4 changed files with 317 additions and 130 deletions
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268
examples/whisper/main.cpp
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@ -5,6 +5,7 @@
#define DR_WAV_IMPLEMENTATION
#include "dr_wav.h"
#include <fstream>
#include <cstdio>
#include <string>
#include <thread>
@ -28,15 +29,20 @@ std::string to_timestamp(int64_t t) {
struct whisper_params {
int32_t seed = -1; // RNG seed, not used currently
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t offset_ms = 0;
bool verbose = false;
bool translate = false;
bool output_txt = false;
bool output_vtt = false;
bool output_srt = false;
bool print_special_tokens = false;
bool no_timestamps = false;
std::string language = "en";
std::string model = "models/ggml-base.en.bin";
std::string fname_inp = "samples/jfk.wav";
std::vector<std::string> fname_inp = {};
};
void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
@ -45,10 +51,17 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
if (arg[0] != '-') {
params.fname_inp.push_back(arg);
continue;
}
if (arg == "-s" || arg == "--seed") {
params.seed = std::stoi(argv[++i]);
} else if (arg == "-t" || arg == "--threads") {
params.n_threads = std::stoi(argv[++i]);
} else if (arg == "-o" || arg == "--offset") {
params.offset_ms = std::stoi(argv[++i]);
} else if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
} else if (arg == "--translate") {
@ -60,6 +73,12 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
whisper_print_usage(argc, argv, params);
exit(0);
}
} else if (arg == "-otxt" || arg == "--output-txt") {
params.output_txt = true;
} else if (arg == "-ovtt" || arg == "--output-vtt") {
params.output_vtt = true;
} else if (arg == "-osrt" || arg == "--output-srt") {
params.output_srt = true;
} else if (arg == "-ps" || arg == "--print_special") {
params.print_special_tokens = true;
} else if (arg == "-nt" || arg == "--no_timestamps") {
@ -67,7 +86,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
} else if (arg == "-m" || arg == "--model") {
params.model = argv[++i];
} else if (arg == "-f" || arg == "--file") {
params.fname_inp = argv[++i];
params.fname_inp.push_back(argv[++i]);
} else if (arg == "-h" || arg == "--help") {
whisper_print_usage(argc, argv, params);
exit(0);
@ -83,19 +102,23 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
fprintf(stderr, "\n");
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -o N, --offset N offset in milliseconds (default: %d)\n", params.offset_ms);
fprintf(stderr, " -v, --verbose verbose output\n");
fprintf(stderr, " --translate translate from source language to english\n");
fprintf(stderr, " -otxt, --output-txt output result in a text file\n");
fprintf(stderr, " -ovtt, --output-vtt output result in a vtt file\n");
fprintf(stderr, " -osrt, --output-srt output result in a srt file\n");
fprintf(stderr, " -ps, --print_special print special tokens\n");
fprintf(stderr, " -nt, --no_timestamps do not print timestamps\n");
fprintf(stderr, " -l LANG, --language LANG spoken language (default: %s)\n", params.language.c_str());
fprintf(stderr, " -m FNAME, --model FNAME model path (default: %s)\n", params.model.c_str());
fprintf(stderr, " -f FNAME, --file FNAME input WAV file path (default: %s)\n", params.fname_inp.c_str());
fprintf(stderr, " -f FNAME, --file FNAME input WAV file path\n");
fprintf(stderr, "\n");
}
@ -110,106 +133,189 @@ int main(int argc, char ** argv) {
params.seed = time(NULL);
}
if (params.fname_inp.empty()) {
fprintf(stderr, "error: no input files specified\n");
whisper_print_usage(argc, argv, params);
return 2;
}
// whisper init
struct whisper_context * ctx = whisper_init(params.model.c_str());
// WAV input
std::vector<float> pcmf32;
{
drwav wav;
if (!drwav_init_file(&wav, params.fname_inp.c_str(), NULL)) {
fprintf(stderr, "%s: failed to open WAV file '%s' - check your input\n", argv[0], params.fname_inp.c_str());
whisper_print_usage(argc, argv, {});
return 2;
}
for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
const auto fname_inp = params.fname_inp[f];
// WAV input
std::vector<float> pcmf32;
{
drwav wav;
if (!drwav_init_file(&wav, fname_inp.c_str(), NULL)) {
fprintf(stderr, "%s: failed to open WAV file '%s' - check your input\n", argv[0], fname_inp.c_str());
whisper_print_usage(argc, argv, {});
return 3;
}
if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], params.fname_inp.c_str());
return 3;
}
if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], fname_inp.c_str());
return 4;
}
if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
fprintf(stderr, "%s: WAV file '%s' must be 16 kHz\n", argv[0], params.fname_inp.c_str());
return 4;
}
if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
fprintf(stderr, "%s: WAV file '%s' must be 16 kHz\n", argv[0], fname_inp.c_str());
return 5;
}
if (wav.bitsPerSample != 16) {
fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], params.fname_inp.c_str());
return 5;
}
if (wav.bitsPerSample != 16) {
fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], fname_inp.c_str());
return 6;
}
int n = wav.totalPCMFrameCount;
int n = wav.totalPCMFrameCount;
std::vector<int16_t> pcm16;
pcm16.resize(n*wav.channels);
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
drwav_uninit(&wav);
std::vector<int16_t> pcm16;
pcm16.resize(n*wav.channels);
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
drwav_uninit(&wav);
// convert to mono, float
pcmf32.resize(n);
if (wav.channels == 1) {
for (int i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[i])/32768.0f;
}
} else {
for (int i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
// convert to mono, float
pcmf32.resize(n);
if (wav.channels == 1) {
for (int i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[i])/32768.0f;
}
} else {
for (int i = 0; i < n; i++) {
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
}
}
}
}
// print some info about the processing
{
printf("\n");
if (!whisper_is_multilingual(ctx)) {
if (params.language != "en" || params.translate) {
params.language = "en";
params.translate = false;
printf("%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
// print some info about the processing
{
fprintf(stderr, "\n");
if (!whisper_is_multilingual(ctx)) {
if (params.language != "en" || params.translate) {
params.language = "en";
params.translate = false;
fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
}
}
}
printf("%s: processing %d samples (%.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
__func__, int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE, params.n_threads,
params.language.c_str(),
params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1);
printf("\n");
}
fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
__func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE, params.n_threads,
params.language.c_str(),
params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1);
// run the inference
{
whisper_full_params wparams = whisper_full_default_params(WHISPER_DECODE_GREEDY);
wparams.print_realtime = true;
wparams.print_progress = false;
wparams.print_timestamps = !params.no_timestamps;
wparams.print_special_tokens = params.print_special_tokens;
wparams.translate = params.translate;
wparams.language = params.language.c_str();
wparams.n_threads = params.n_threads;
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
fprintf(stderr, "%s: failed to process audio\n", argv[0]);
return 6;
fprintf(stderr, "\n");
}
// print result;
if (!wparams.print_realtime) {
// run the inference
{
whisper_full_params wparams = whisper_full_default_params(WHISPER_DECODE_GREEDY);
wparams.print_realtime = true;
wparams.print_progress = false;
wparams.print_timestamps = !params.no_timestamps;
wparams.print_special_tokens = params.print_special_tokens;
wparams.translate = params.translate;
wparams.language = params.language.c_str();
wparams.n_threads = params.n_threads;
wparams.offset_ms = params.offset_ms;
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
fprintf(stderr, "%s: failed to process audio\n", argv[0]);
return 7;
}
// print result
if (!wparams.print_realtime) {
printf("\n");
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
if (params.no_timestamps) {
printf("%s", text);
fflush(stdout);
} else {
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
printf("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
}
}
}
printf("\n");
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
// output to text file
if (params.output_txt) {
const auto fname_txt = fname_inp + ".txt";
std::ofstream fout_txt(fname_txt);
if (!fout_txt.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_txt.c_str());
return 8;
}
fprintf(stderr, "%s: saving output to '%s.txt'\n", __func__, fname_inp.c_str());
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
fout_txt << text;
}
}
// output to VTT file
if (params.output_vtt) {
const auto fname_vtt = fname_inp + ".vtt";
std::ofstream fout_vtt(fname_vtt);
if (!fout_vtt.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_vtt.c_str());
return 9;
}
fprintf(stderr, "%s: saving output to '%s.vtt'\n", __func__, fname_inp.c_str());
fout_vtt << "WEBVTT\n\n";
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
fout_vtt << to_timestamp(t0) << " --> " << to_timestamp(t1) << "\n";
fout_vtt << text << "\n\n";
}
}
// output to SRT file
if (params.output_srt) {
const auto fname_srt = fname_inp + ".srt";
std::ofstream fout_srt(fname_srt);
if (!fout_srt.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_srt.c_str());
return 10;
}
fprintf(stderr, "%s: saving output to '%s.srt'\n", __func__, fname_inp.c_str());
if (params.no_timestamps) {
printf ("%s", text);
fflush(stdout);
} else {
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
printf ("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
fout_srt << i + 1 << "\n";
fout_srt << to_timestamp(t0) << " --> " << to_timestamp(t1) << "\n";
fout_srt << text << "\n\n";
}
}
}

88
examples/whisper/whisper.cpp
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@ -405,6 +405,8 @@ struct whisper_context {
std::vector<whisper_result> result_cur;
std::vector<whisper_segment> result_all;
std::vector<whisper_token> prompt_past;
};
// load the model from a ggml file
@ -419,7 +421,7 @@ struct whisper_context {
// see the convert-pt-to-ggml.py script for details
//
bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
fprintf(stderr, "%s: loading model from '%s'\n", __func__, fname.c_str());
auto & model = wctx.model;
auto & vocab = wctx.vocab;
@ -478,18 +480,18 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
model.type = e_model::MODEL_LARGE;
}
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
printf("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
printf("%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
printf("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
printf("%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
printf("%s: n_text_state = %d\n", __func__, hparams.n_text_state);
printf("%s: n_text_head = %d\n", __func__, hparams.n_text_head);
printf("%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
printf("%s: n_mels = %d\n", __func__, hparams.n_mels);
printf("%s: f16 = %d\n", __func__, hparams.f16);
printf("%s: type = %d\n", __func__, model.type);
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
fprintf(stderr, "%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
fprintf(stderr, "%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
fprintf(stderr, "%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
fprintf(stderr, "%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
fprintf(stderr, "%s: n_text_state = %d\n", __func__, hparams.n_text_state);
fprintf(stderr, "%s: n_text_head = %d\n", __func__, hparams.n_text_head);
fprintf(stderr, "%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
fprintf(stderr, "%s: n_mels = %d\n", __func__, hparams.n_mels);
fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
fprintf(stderr, "%s: type = %d\n", __func__, model.type);
wctx.buf_model.resize(MEM_REQ_MODEL.at(model.type));
wctx.buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
@ -501,7 +503,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
wctx.buf_compute.size() +
wctx.buf_compute_layer.size();
printf("%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
fprintf(stderr, "%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
}
// load mel filters
@ -551,7 +553,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
}
if (n_vocab < model.hparams.n_vocab) {
printf("%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
fprintf(stderr, "%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) + "]";
@ -696,7 +698,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
@ -943,11 +945,12 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v) +
ggml_nbytes(model.memory_cross_k) + ggml_nbytes(model.memory_cross_v);
printf("%s: memory size = %8.2f MB \n", __func__, memory_size/1024.0/1024.0);
fprintf(stderr, "%s: memory size = %8.2f MB \n", __func__, memory_size/1024.0/1024.0);
}
// load weights
{
int n_loaded = 0;
size_t total_size = 0;
while (true) {
@ -1002,9 +1005,17 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
//printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
total_size += ggml_nbytes(tensor);
n_loaded++;
}
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
fprintf(stderr, "%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
if (n_loaded == 0) {
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
} else if (n_loaded != (int) model.tensors.size()) {
fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), n_loaded);
return false;
}
}
fin.close();
@ -1020,8 +1031,6 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
// - model: the model
// - n_threads: number of threads to use
// - mel_offset: offset in the mel spectrogram (i.e. audio offset)
// - mel_inp: input mel spectrogram
// - features: output encoded features
//
bool whisper_encode(
whisper_context & wctx,
@ -1405,10 +1414,9 @@ bool whisper_encode(
//
// - model: the model
// - n_threads: number of threads to use
// - n_past: prompt length
// - prompt: text prompt
// - logits_out: output logits
// - probs_out: output probabilities
// - tokens: text prompt
// - n_tokens: number of tokens in the prompt
// - n_past: number of past tokens to prefix the prompt with
//
bool whisper_decode(
whisper_context & wctx,
@ -1773,8 +1781,6 @@ bool whisper_decode(
}
// the most basic sampling scheme - select the top token
// TODO: beam search
// TODO: temperature
whisper_vocab::id whisper_sample_best(
const whisper_vocab & vocab,
const float * probs, bool need_timestamp) {
@ -2236,13 +2242,13 @@ whisper_token whisper_token_transcribe() {
void whisper_print_timings(struct whisper_context * ctx) {
const int64_t t_end_us = ggml_time_us();
printf("\n\n");
printf("%s: load time = %8.2f ms\n", __func__, ctx->t_load_us/1000.0f);
printf("%s: mel time = %8.2f ms\n", __func__, ctx->t_mel_us/1000.0f);
printf("%s: sample time = %8.2f ms\n", __func__, ctx->t_sample_us/1000.0f);
printf("%s: encode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_encode_us/1000.0f, ctx->t_encode_us/1000.0f/ctx->model.hparams.n_audio_layer);
printf("%s: decode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_decode_us/1000.0f, ctx->t_decode_us/1000.0f/ctx->model.hparams.n_text_layer);
printf("%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
fprintf(stderr, "\n");
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us/1000.0f);
fprintf(stderr, "%s: mel time = %8.2f ms\n", __func__, ctx->t_mel_us/1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, ctx->t_sample_us/1000.0f);
fprintf(stderr, "%s: encode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_encode_us/1000.0f, ctx->t_encode_us/1000.0f/ctx->model.hparams.n_audio_layer);
fprintf(stderr, "%s: decode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_decode_us/1000.0f, ctx->t_decode_us/1000.0f/ctx->model.hparams.n_text_layer);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
}
////////////////////////////////////////////////////////////////////////////
@ -2256,8 +2262,10 @@ struct whisper_full_params whisper_full_default_params(enum whisper_decode_strat
result = (struct whisper_full_params) {
.strategy = WHISPER_DECODE_GREEDY,
.n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()),
.offset_ms = 0,
.translate = false,
.no_context = false,
.print_special_tokens = false,
.print_progress = true,
.print_realtime = false,
@ -2275,8 +2283,10 @@ struct whisper_full_params whisper_full_default_params(enum whisper_decode_strat
result = (struct whisper_full_params) {
.strategy = WHISPER_DECODE_GREEDY,
.n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()),
.offset_ms = 0,
.translate = false,
.no_context = false,
.print_special_tokens = false,
.print_progress = true,
.print_realtime = false,
@ -2295,6 +2305,7 @@ struct whisper_full_params whisper_full_default_params(enum whisper_decode_strat
return result;
}
int whisper_full(
struct whisper_context * ctx,
struct whisper_full_params params,
@ -2307,7 +2318,10 @@ int whisper_full(
}
// the accumulated text context so far
std::vector<whisper_token> prompt_past = { };
auto & prompt_past = ctx->prompt_past;
if (params.no_context) {
prompt_past.clear();
}
// these tokens determine the task that will be performed
std::vector<whisper_token> prompt_init = { whisper_token_sot(ctx) };
@ -2329,13 +2343,13 @@ int whisper_full(
int progress_step = 5;
// main loop
int seek = 0;
int seek = params.offset_ms/10;
while (true) {
int progress_cur = (100*seek)/whisper_n_len(ctx);
while (progress_cur >= progress_prev + progress_step) {
progress_prev += progress_step;
if (params.print_progress) {
printf("%s: progress = %3d%%\n", __func__, progress_prev);
fprintf(stderr, "%s: progress = %3d%%\n", __func__, progress_prev);
}
}
@ -2463,7 +2477,7 @@ int whisper_full(
result_all.push_back({ t0, t1, text });
}
text = "";
while (result_cur[i].id > whisper_token_beg(ctx) && i < (int) result_cur.size()) {
while (i < (int) result_cur.size() && result_cur[i].id > whisper_token_beg(ctx)) {
i++;
}
i--;

87
examples/whisper/whisper.h
Unescape View File

@ -31,33 +31,81 @@ extern "C" {
// C interface
//
// TODO: documentation will come soon
//
// Basic usage:
//
// #include "whisper.h"
//
// ...
//
// struct whisper_context * ctx = whisper_init("/path/to/ggml-base.en.bin");
//
// if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
// fprintf(stderr, "failed to process audio\n");
// return 7;
// }
//
// const int n_segments = whisper_full_n_segments(ctx);
// for (int i = 0; i < n_segments; ++i) {
// const char * text = whisper_full_get_segment_text(ctx, i);
// printf("%s", text);
// }
//
// whisper_free(ctx);
//
// ...
//
// This is a demonstration of the most straightforward usage of the library.
// "pcmf32" contains the RAW audio data in 32-bit floating point format.
//
// The interface also allows for more fine-grained control over the computation, but it requires a deeper
// understanding of how the model works.
//
struct whisper_context;
typedef int whisper_token;
// Allocates all memory needed for the model and loads the model from the given file.
// Returns NULL on failure.
WHISPER_API struct whisper_context * whisper_init(const char * path_model);
// Frees all memory allocated by the model.
WHISPER_API void whisper_free(struct whisper_context * ctx);
// Convert RAW PCM audio to log mel spectrogram.
// The resulting spectrogram is stored inside the provided whisper context.
// Returns 0 on success
WHISPER_API int whisper_pcm_to_mel(
struct whisper_context * ctx,
const float * samples,
int n_samples,
int n_threads);
// This can be used to set a custom log mel spectrogram inside the provided whisper context.
// Use this instead of whisper_pcm_to_mel() if you want to provide your own log mel spectrogram.
// n_mel must be 80
// Returns 0 on success
WHISPER_API int whisper_set_mel(
struct whisper_context * ctx,
const float * data,
int n_len,
int n_mel);
// Run the Whisper encoder on the log mel spectrogram stored inside the provided whisper context.
// Make sure to call whisper_pcm_to_mel() or whisper_set_mel() first.
// offset can be used to specify the offset of the first frame in the spectrogram.
// Returns 0 on success
WHISPER_API int whisper_encode(
struct whisper_context * ctx,
int offset,
int n_threads);
// Run the Whisper decoder to obtain the logits and probabilities for the next token.
// Make sure to call whisper_encode() first.
// tokens + n_tokens is the provided context for the decoder.
// n_past is the number of tokens to use from previous decoder calls.
// Returns 0 on success
WHISPER_API int whisper_decode(
struct whisper_context * ctx,
const whisper_token * tokens,
@ -65,20 +113,29 @@ extern "C" {
int n_past,
int n_threads);
// Token sampling methods.
// These are provided for convenience and can be used after each call to whisper_decode().
// You can also implement your own sampling method using the whisper_get_probs() function.
// whisper_sample_best() returns the token with the highest probability
// whisper_sample_timestamp() returns the most probable timestamp token
WHISPER_API whisper_token whisper_sample_best(struct whisper_context * ctx, bool need_timestamp);
WHISPER_API whisper_token whisper_sample_timestamp(struct whisper_context * ctx);
// return the id of the specified language, returns -1 if not found
// Return the id of the specified language, returns -1 if not found
WHISPER_API int whisper_lang_id(const char * lang);
WHISPER_API int whisper_n_len (struct whisper_context * ctx); // mel length
WHISPER_API int whisper_n_vocab (struct whisper_context * ctx);
WHISPER_API int whisper_n_text_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_is_multilingual(struct whisper_context * ctx);
WHISPER_API float * whisper_get_probs (struct whisper_context * ctx);
WHISPER_API int whisper_n_len (struct whisper_context * ctx); // mel length
WHISPER_API int whisper_n_vocab (struct whisper_context * ctx);
WHISPER_API int whisper_n_text_ctx (struct whisper_context * ctx);
WHISPER_API int whisper_is_multilingual(struct whisper_context * ctx);
// The probabilities for the next token
WHISPER_API float * whisper_get_probs(struct whisper_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
WHISPER_API const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token);
// Special tokens
WHISPER_API whisper_token whisper_token_eot (struct whisper_context * ctx);
WHISPER_API whisper_token whisper_token_sot (struct whisper_context * ctx);
WHISPER_API whisper_token whisper_token_prev(struct whisper_context * ctx);
@ -86,24 +143,29 @@ extern "C" {
WHISPER_API whisper_token whisper_token_not (struct whisper_context * ctx);
WHISPER_API whisper_token whisper_token_beg (struct whisper_context * ctx);
// Task tokens
WHISPER_API whisper_token whisper_token_translate ();
WHISPER_API whisper_token whisper_token_transcribe();
// Performance information
WHISPER_API void whisper_print_timings(struct whisper_context * ctx);
////////////////////////////////////////////////////////////////////////////
// Available decoding strategies
enum whisper_decode_strategy {
WHISPER_DECODE_GREEDY,
WHISPER_DECODE_BEAM_SEARCH,
WHISPER_DECODE_GREEDY, // Always select the most probable token
WHISPER_DECODE_BEAM_SEARCH, // TODO: not implemented yet!
};
struct whisper_full_params {
enum whisper_decode_strategy strategy;
int n_threads;
int offset_ms;
bool translate;
bool no_context;
bool print_special_tokens;
bool print_progress;
bool print_realtime;
@ -126,18 +188,23 @@ extern "C" {
WHISPER_API struct whisper_full_params whisper_full_default_params(enum whisper_decode_strategy strategy);
// full whisper run - encode + decode
// Run the entire model: PCM -> log mel spectrogram -> encoder -> decoder -> text
// Uses the specified decoding strategy to obtain the text.
WHISPER_API int whisper_full(
struct whisper_context * ctx,
struct whisper_full_params params,
const float * samples,
int n_samples);
// Number of generated text segments.
// A segment can be a few words, a sentence, or even a paragraph.
WHISPER_API int whisper_full_n_segments(struct whisper_context * ctx);
// Get the start and end time of the specified segment.
WHISPER_API int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment);
WHISPER_API int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment);
// Get the text of the specified segment.
WHISPER_API const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment);
#ifdef __cplusplus

4
src/ggml.c
Unescape View File

@ -181,9 +181,9 @@ int64_t ggml_cycles_per_ms(void) {
//
#if defined(__cpp_lib_hardware_interference_size)
const size_t CACHE_LINE_SIZE = hardware_destructive_interference_size;
#define CACHE_LINE_SIZE hardware_destructive_interference_size
#else
const size_t CACHE_LINE_SIZE = 64;
#define CACHE_LINE_SIZE 64
#endif
const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);

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