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whisper : various updates and improvements

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experiments/blocking
Georgi Gerganov 2 years ago
parent 787efb4d2e
commit 0116c03fb7
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2 changed files with 214 additions and 102 deletions
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14
examples/whisper/README.md
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@ -9,13 +9,13 @@ Checkout https://github.com/ggerganov/whisper.cpp
## Memory usage
| Model | Mem |
| --- | --- |
| tiny.en | ~460 MB |
| base.en | ~620 MB |
| small.en | ~1.3 GB |
| medium.en | ~2.8 GB |
| large | ~4.9 GB |
| Model | Disk | Mem |
| --- | --- | --- |
| tiny | 75 MB | ~240 MB |
| base | 142 MB | ~380 MB |
| small | 466 MB | ~970 MB |
| medium | 1.5 GB | ~2.5 GB |
| large | 2.9 GB | ~4.6 GB |
## ggml format

302
examples/whisper/main.cpp
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@ -158,11 +158,11 @@ const std::map<e_model, size_t> MEM_REQ_ENCODE_LAYER = {
};
const std::map<e_model, size_t> MEM_REQ_DECODE = {
{ MODEL_TINY, 190ull*MB },
{ MODEL_BASE, 190ull*MB },
{ MODEL_SMALL, 190ull*MB },
{ MODEL_MEDIUM, 200ull*MB },
{ MODEL_LARGE, 200ull*MB },
{ MODEL_TINY, 94ull*MB },
{ MODEL_BASE, 96ull*MB },
{ MODEL_SMALL, 98ull*MB },
{ MODEL_MEDIUM, 100ull*MB },
{ MODEL_LARGE, 102ull*MB },
};
const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
@ -173,6 +173,11 @@ const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
{ MODEL_LARGE, 110ull*MB },
};
// the memory buffers used to store the model in memory and perform the inference computations
std::vector<uint8_t> g_buf_model;
std::vector<uint8_t> g_buf_compute;
std::vector<uint8_t> g_buf_compute_layer;
const int SAMPLE_RATE = 16000;
const int N_FFT = 400;
const int N_MEL = 80;
@ -206,6 +211,7 @@ struct whisper_vocab {
id token_sot = 50257;
id token_prev = 50360;
id token_solm = 50361; // ??
id token_not = 50362; // no timestamps
id token_beg = 50363;
// available tasks
@ -217,17 +223,20 @@ struct whisper_vocab {
}
};
struct whisper_result {
whisper_vocab::id id;
int64_t t;
};
// command-line parameters
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());
// sampling parameter - used for the greedy strategy
int32_t max_tokens_per_iter = 64;
bool verbose = false;
bool translate = false;
bool print_special_tokens = false;
bool no_timestamps = false;
std::string language = "en";
std::string model = "models/ggml-base.en.bin";
@ -244,8 +253,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
params.seed = std::stoi(argv[++i]);
} else if (arg == "-t" || arg == "--threads") {
params.n_threads = std::stoi(argv[++i]);
} else if (arg == "-T" || arg == "--tokens") {
params.max_tokens_per_iter = std::stoi(argv[++i]);
} else if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
} else if (arg == "--translate") {
@ -259,6 +266,8 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
}
} else if (arg == "-ps" || arg == "--print_special") {
params.print_special_tokens = true;
} else if (arg == "-nt" || arg == "--no_timestamps") {
params.no_timestamps = true;
} else if (arg == "-m" || arg == "--model") {
params.model = argv[++i];
} else if (arg == "-f" || arg == "--file") {
@ -284,10 +293,10 @@ void whisper_print_usage(int argc, char ** argv, const whisper_params & params)
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, " -T N, --tokens N maximum number of tokens to generate per iteration (default: %d)\n", params.max_tokens_per_iter);
fprintf(stderr, " -v, --verbose verbose output\n");
fprintf(stderr, " --translate translate from source language to english\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());
@ -538,13 +547,15 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
printf("%s: f16 = %d\n", __func__, hparams.f16);
printf("%s: type = %d\n", __func__, model.type);
g_buf_model.resize(MEM_REQ_MODEL.at(model.type));
g_buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
g_buf_compute_layer.resize(std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
// this is the total memory required to run the inference
const size_t mem_required =
MEM_REQ_MODEL.at(model.type) +
MEM_REQ_ENCODE.at(model.type) +
MEM_REQ_ENCODE_LAYER.at(model.type) +
MEM_REQ_DECODE.at(model.type) +
MEM_REQ_DECODE_LAYER.at(model.type);
g_buf_model.size() +
g_buf_compute.size() +
g_buf_compute_layer.size();
printf("%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
}
@ -591,6 +602,7 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
vocab.token_sot++;
vocab.token_prev++;
vocab.token_solm++;
vocab.token_not++;
vocab.token_beg++;
}
@ -605,6 +617,8 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
word = "[_SOT_]";
} else if (i == vocab.token_prev) {
word = "[_PREV_]";
} else if (i == vocab.token_not) {
word = "[_NOT_]";
} else if (i == vocab.token_beg) {
word = "[_BEG_]";
} else {
@ -745,8 +759,8 @@ bool whisper_model_load(const std::string & fname, whisper_model & model, whispe
// create the ggml context
{
struct ggml_init_params params = {
.mem_size = ctx_size,
.mem_buffer = NULL,
.mem_size = g_buf_model.size(),
.mem_buffer = g_buf_model.data(),
};
model.ctx = ggml_init(params);
@ -1082,17 +1096,10 @@ bool whisper_encode(
const int n_mels = hparams.n_mels;
assert(mel_inp.n_mel == n_mels);
struct ggml_init_params params;
{
static size_t buf_size = MEM_REQ_ENCODE.at(model.type);
static void * buf = malloc(buf_size);
params = {
.mem_size = buf_size,
.mem_buffer = buf,
};
}
struct ggml_init_params params = {
.mem_size = g_buf_compute.size(),
.mem_buffer = g_buf_compute.data(),
};
struct ggml_context * ctx0 = ggml_init(params);
@ -1144,16 +1151,10 @@ bool whisper_encode(
// create separate context for each layer to reduce memory usage
struct ggml_init_params paramsL;
{
static size_t buf_size = MEM_REQ_ENCODE_LAYER.at(model.type);
static void * buf = malloc(buf_size);
paramsL = {
.mem_size = buf_size,
.mem_buffer = buf,
};
}
struct ggml_init_params paramsL = {
.mem_size = g_buf_compute_layer.size(),
.mem_buffer = g_buf_compute_layer.data(),
};
struct ggml_context * ctxL = ggml_init(paramsL);
@ -1485,17 +1486,10 @@ bool whisper_decode(
const int N = prompt.size();
const int M = hparams.n_audio_ctx;
struct ggml_init_params params;
{
static size_t buf_size = MEM_REQ_DECODE.at(model.type);
static void * buf = malloc(buf_size);
params = {
.mem_size = buf_size,
.mem_buffer = buf,
struct ggml_init_params params = {
.mem_size = g_buf_compute.size(),
.mem_buffer = g_buf_compute.data(),
};
}
struct ggml_context * ctx0 = ggml_init(params);
@ -1518,17 +1512,10 @@ bool whisper_decode(
for (int il = 0; il < n_layer; ++il) {
const auto & layer = model.layers_decoder[il];
struct ggml_init_params paramsL;
{
static size_t buf_size = MEM_REQ_DECODE_LAYER.at(model.type);
static void * buf = malloc(buf_size);
paramsL = {
.mem_size = buf_size,
.mem_buffer = buf,
};
}
struct ggml_init_params paramsL = {
.mem_size = g_buf_compute_layer.size(),
.mem_buffer = g_buf_compute_layer.data(),
};
struct ggml_context * ctxL = ggml_init(paramsL);
struct ggml_cgraph gf = { .n_threads = n_threads };
@ -1842,19 +1829,17 @@ bool whisper_decode(
// TODO: temperature
whisper_vocab::id whisper_sample_best(
const whisper_vocab & vocab,
const float * probs,
double temp,
int offset = 0) {
const float * probs, bool need_timestamp) {
int n_logits = vocab.id_to_token.size();
std::vector<std::pair<double, whisper_vocab::id>> probs_id;
probs_id.reserve(n_logits);
for (int i = offset; i < n_logits; i++) {
for (int i = 0; i < n_logits; i++) {
probs_id.push_back(std::make_pair(probs[i], i));
}
const int top_k = 10;
const int top_k = 4;
// find the top K tokens
std::partial_sort(
@ -1871,14 +1856,59 @@ whisper_vocab::id whisper_sample_best(
// printf("%d: '%s' %f, %d\n", i, vocab.id_to_token.at(probs_id[i].second).c_str(), probs_id[i].first, probs_id[i].second);
//}
if (need_timestamp) {
// at the end of the 30-second audio segment, we start giving preference to time tokens
for (int i = 0; i < top_k; i++) {
if (probs_id[i].second > vocab.token_beg + 1300 && probs_id[i].first > probs_id[0].first*0.1) {
return probs_id[i].second;
}
}
}
int res = 0;
while (probs_id[res].second == vocab.token_solm && res < (int) probs_id.size() - 1) {
while ((probs_id[res].second == vocab.token_sot ||
probs_id[res].second == vocab.token_solm ||
probs_id[res].second == vocab.token_not) &&
res < (int) probs_id.size() - 1) {
res++;
}
return probs_id[res].second;
}
// samples only from the timestamps tokens
whisper_vocab::id whisper_sample_timestamp(
const whisper_vocab & vocab,
const float * probs) {
int n_logits = vocab.id_to_token.size();
std::vector<std::pair<double, whisper_vocab::id>> probs_id;
probs_id.reserve(n_logits);
for (int i = vocab.token_beg + 1; i < n_logits; i++) {
probs_id.push_back(std::make_pair(probs[i], i));
}
const int top_k = 10;
// find the top K tokens
std::partial_sort(
probs_id.begin(),
probs_id.begin() + top_k, probs_id.end(),
[](const std::pair<double, whisper_vocab::id> & a, const std::pair<double, whisper_vocab::id> & b) {
return a.first > b.first;
});
probs_id.resize(top_k);
//printf("\n");
//for (int i = 0; i < (int) probs_id.size(); i++) {
// printf("%d: '%s' %f, %d\n", i, vocab.id_to_token.at(probs_id[i].second).c_str(), probs_id[i].first, probs_id[i].second);
//}
return probs_id[0].second;
}
// Cooley-Tukey FFT
// poor man's implmentation - use something better
// input is real-valued
@ -2032,6 +2062,20 @@ bool log_mel_spectrogram(
return true;
}
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t) {
int64_t sec = t/100;
int64_t msec = t - sec*100;
int64_t min = sec/60;
sec = sec - min*60;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
return std::string(buf);
}
int main(int argc, char ** argv) {
const int64_t t_main_start_us = ggml_time_us();
@ -2051,7 +2095,7 @@ int main(int argc, char ** argv) {
int64_t t_load_us = 0;
int64_t t_mel_us = 0;
int64_t t_sample_us = 0;
int64_t t_sample_us = 0;
int64_t t_encode_us = 0;
int64_t t_decode_us = 0;
@ -2128,10 +2172,12 @@ int main(int argc, char ** argv) {
printf("%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 ...\n",
printf("%s: processing %d samples (%.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
__func__, int(pcmf32.size()), float(pcmf32.size())/SAMPLE_RATE, params.n_threads,
g_lang.at(params.language).second.c_str(),
params.translate ? "translate" : "transcribe");
params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1);
printf("\n");
}
// the accumulated text context so far
@ -2148,6 +2194,9 @@ int main(int argc, char ** argv) {
}
}
// the generated text including timestamps
std::vector<whisper_result> result_all;
// main loop
int seek = 0;
while (true) {
@ -2165,7 +2214,7 @@ int main(int argc, char ** argv) {
return 1;
}
t_encode_us = ggml_time_us() - t_start_us;
t_encode_us += ggml_time_us() - t_start_us;
}
std::vector<float> probs;
@ -2192,11 +2241,17 @@ int main(int argc, char ** argv) {
int seek_delta = 100*CHUNK_SIZE;
whisper_vocab::id last_id = 0;
// print the prompt
//printf("\n\n");
//for (int i = 0; i < prompt.size(); i++) {
// printf("%s: prompt[%d] = %s\n", __func__, i, vocab.id_to_token[prompt[i]].c_str());
//}
//printf("\n\n");
// the accumulated transcription in the current interation
int result_len = 0;
std::vector<whisper_result> result_cur;
printf("\n");
for (int i = 0; i < model.hparams.n_text_ctx/2; ++i) {
// decode
if (prompt.size() > 0) {
@ -2216,63 +2271,120 @@ int main(int argc, char ** argv) {
// very basic greedy sampling strategy:
//
// - always take the most probable token
// - if we have accumulated more than 'params.max_tokens_per_iter' -> pick most probable timestamp token
// and advance the sliding window by that amount
// - in the meantime, if we encounter 2 consecutive timestamp tokens, we advance the sliding window too
//
// more sophisticated sampling strategies could be implemented here, but we keep it simple
// feel free to experiment!
//
{
// sample next token
const float temp = 1.0; // TODO
const int n_vocab = model.hparams.n_vocab;
whisper_vocab::id id = 0;
whisper_vocab::id id = 0;
whisper_vocab::id tid = vocab.token_beg;
{
const int64_t t_start_sample_us = ggml_time_us();
id = whisper_sample_best(vocab, probs.data() + (probs.size() - n_vocab), temp, i > params.max_tokens_per_iter ? vocab.token_beg : 0);
id = whisper_sample_best(vocab, probs.data() + (probs.size() - n_vocab), result_len == 0);
if (i > 0) {
tid = whisper_sample_timestamp(vocab, probs.data() + (probs.size() - n_vocab));
}
t_sample_us += ggml_time_us() - t_start_sample_us;
}
// end of text token
if (id == vocab.token_eot) {
break;
}
// 2 consecutive time tokens
if (id > vocab.token_beg && last_id > vocab.token_beg) {
// update sliding window
if (id > vocab.token_beg) {
seek_delta = 2*(id - vocab.token_beg);
done = true;
result_len = i + 1;
}
last_id = id;
// add it to the context
prompt.push_back(id);
prompt_past.push_back(id);
}
result_cur.push_back({ id, seek + 2*(tid - vocab.token_beg) });
//printf("%s: %s\n", __func__, vocab.id_to_token[id].c_str());
// display text
for (auto id : prompt) {
if (params.print_special_tokens == false && id >= vocab.token_eot) {
continue;
// end of text token
if (id == vocab.token_eot) {
break;
}
printf("%s", vocab.id_to_token[id].c_str());
}
fflush(stdout);
if (done) {
break;
}
}
result_cur.resize(result_len);
result_all.insert(result_all.end(), result_cur.begin(), result_cur.end());
for (const auto & r : result_cur) {
prompt_past.push_back(r.id);
}
// print the text from this iteration
if (result_cur.size() > 0) {
auto t0 = result_cur.front().t;
std::string text = "";
for (int i = 0; i < result_cur.size(); i++) {
if (params.print_special_tokens == false && result_cur[i].id >= vocab.token_eot) {
} else {
text += vocab.id_to_token[result_cur[i].id];
}
if (result_cur[i].id > vocab.token_beg) {
const auto t1 = result_cur[i].t;
if (!text.empty()) {
if (params.no_timestamps) {
printf ("%s", text.c_str());
fflush(stdout);
} else {
printf ("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text.c_str());
}
}
text = "";
while (result_cur[i].id > vocab.token_beg && i < result_cur.size()) {
i++;
}
i--;
t0 = result_cur[i].t;
}
}
if (!text.empty()) {
printf ("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(seek + seek_delta).c_str(), text.c_str());
}
}
seek += seek_delta;
}
// WIP: attempt for per-token timestamps
//if (!params.no_timestamps && result_all.size() > 0) {
// const int64_t dt = 500; // 5 second intervals
// int i0 = 0;
// int64_t t0 = result_all[0].t;
// int64_t t1 = t0;
// printf("\n\n");
// for (int i = 0; i < result_all.size(); ++i) {
// printf("'%s' -> %lld\n", vocab.id_to_token[result_all[i].id].c_str(), result_all[i].t);
// if (result_all[i].t - t0 > dt) {
// t1 = result_all[i - 1].t;
// printf("[%s --> %s] ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
// for (int j = i0; j < i; ++j) {
// printf("%s", vocab.id_to_token.at(result_all[j].id).c_str());
// }
// printf("\n");
// i0 = i;
// t0 = result_all[i].t;
// }
// }
//}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();

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