gcc -pthread -O3 -mavx -mavx2 -mfma -mf16c -c ggml.c
g++ -pthread -O3 -std=c++11 -c main.cpp
-g++ -o main ggml.o main.o
+g++ -pthread -o main ggml.o main.o
./main -h
usage: ./main [options]
-h, --help show this help message and exit
-s SEED, --seed SEED RNG seed (default: -1)
-t N, --threads N number of threads to use during computation (default: 4)
- -T N, --tokens N maximum number of tokens to generate per iteration (default: 64)
-v, --verbose verbose output
--translate translate from source language to english
-ps, --print_special print special tokens
+ -nt, --no_timestamps do not print timestamps
-l LANG, --language LANG spoken language (default: en)
-m FNAME, --model FNAME model path (default: models/ggml-base.en.bin)
-f FNAME, --file FNAME input WAV file path (default: samples/jfk.wav)
bash ./download-ggml-model.sh base.en
Downloading ggml model base.en ...
-models/ggml-base.en.bin 100%[=====================================>] 141.11M 7.84MB/s in 18s
-Done! Model 'base.en' saved in 'models/ggml-base.en.bin'
-You can now use it like this:
-
- $ ./main -m models/ggml-base.en.bin -f samples/jfk.wav
-
+Model base.en already exists. Skipping download.
===============================================
Running base.en on all samples in ./samples ...
log_mel_spectrogram: n_sample = 176000, n_len = 1100
log_mel_spectrogram: recording length: 11.000000 s
-main: processing 176000 samples (11.0 sec), 4 threads, lang = english, task = transcribe ...
+main: processing 176000 samples (11.0 sec), 4 threads, lang = english, task = transcribe, timestamps = 1 ...
- And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.
+[00:00.000 --> 00:11.000] And so my fellow Americans ask not what your country can do for you. Ask what you can do for your country.
-main: load time = 71.89 ms
-main: mel time = 36.95 ms
+
+main: load time = 61.78 ms
+main: mel time = 41.74 ms
main: sample time = 2.10 ms
-main: encode time = 700.94 ms / 116.82 ms per layer
-main: decode time = 86.14 ms
-main: total time = 898.72 ms
+main: encode time = 718.60 ms / 119.77 ms per layer
+main: decode time = 83.55 ms
+main: total time = 908.15 ms
```
The command downloads the `base.en` model converted to custom `ggml` format and runs the inference on all `.wav` samples in the folder `samples`.
ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
```
+Here is another example of transcribing a [3:24 min speech](https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg) in less than a minute, using `medium.en` model:
+
+```bash
+$ ./main -m models/ggml-medium.en.bin -f samples/gb1.wav -t 8
+whisper_model_load: loading model from 'models/ggml-medium.en.bin'
+whisper_model_load: n_vocab = 51864
+whisper_model_load: n_audio_ctx = 1500
+whisper_model_load: n_audio_state = 1024
+whisper_model_load: n_audio_head = 16
+whisper_model_load: n_audio_layer = 24
+whisper_model_load: n_text_ctx = 448
+whisper_model_load: n_text_state = 1024
+whisper_model_load: n_text_head = 16
+whisper_model_load: n_text_layer = 24
+whisper_model_load: n_mels = 80
+whisper_model_load: f16 = 1
+whisper_model_load: type = 4
+whisper_model_load: mem_required = 2786.00 MB
+whisper_model_load: adding 1607 extra tokens
+whisper_model_load: ggml ctx size = 1644.97 MB
+whisper_model_load: memory size = 182.62 MB
+whisper_model_load: model size = 1462.12 MB
+log_mel_spectrogram: n_sample = 3179750, n_len = 19873
+log_mel_spectrogram: recording length: 198.734375 s
+
+main: processing 3179750 samples (198.7 sec), 8 threads, lang = english, task = transcribe, timestamps = 1 ...
+
+[00:00.000 --> 00:08.000] My fellow Americans, this day has brought terrible news and great sadness to our country.
+[00:08.000 --> 00:17.000] At 9 o'clock this morning, Mission Control in Houston lost contact with our Space Shuttle Columbia.
+[00:17.000 --> 00:24.000] A short time later, debris was seen falling from the skies above Texas.
+[00:24.000 --> 00:29.000] The Columbia's lost. There are no survivors.
+[00:29.000 --> 00:32.000] On board was a crew of seven.
+[00:32.000 --> 00:43.000] Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain David Brown, Commander William McCool,
+[00:43.000 --> 00:52.000] Dr. Kultner Aschavla, and Elon Ramon, a Colonel in the Israeli Air Force.
+[00:52.000 --> 00:58.000] These men and women assumed great risk in the service to all humanity.
+[00:58.000 --> 01:06.000] In an age when space flight has come to seem almost routine, it is easy to overlook the dangers of travel by rocket
+[01:06.000 --> 01:12.000] and the difficulties of navigating the fierce outer atmosphere of the Earth.
+[01:12.000 --> 01:22.000] These astronauts knew the dangers, and they faced them willingly, knowing they had a high and noble purpose in life.
+[01:22.000 --> 01:30.000] Because of their courage, endearing, and idealism, we will miss them all the more.
+[01:30.000 --> 01:40.000] All Americans today are thinking as well of the families of these men and women who have been given this sudden shock and grief.
+[01:40.000 --> 01:45.000] You're not alone. Our entire nation agrees with you.
+[01:45.000 --> 01:52.000] And those you love will always have the respect and gratitude of this country.
+[01:52.000 --> 01:56.000] The cause in which they died will continue.
+[01:56.000 --> 02:07.000] Mankind is led into the darkness beyond our world by the inspiration of discovery and the longing to understand.
+[02:07.000 --> 02:11.000] Our journey into space will go on.
+[02:11.000 --> 02:16.000] In the skies today, we saw destruction and tragedy.
+[02:16.000 --> 02:22.000] Yet farther than we can see, there is comfort and hope.
+[02:22.000 --> 02:31.000] In the words of the prophet Isaiah, "Lift your eyes and look to the heavens who created all these.
+[02:31.000 --> 02:39.000] He who brings out the starry hosts one by one and calls them each by name."
+[02:39.000 --> 02:46.000] Because of his great power and mighty strength, not one of them is missing.
+[02:46.000 --> 02:55.000] The same creator who names the stars also knows the names of the seven souls we mourn today.
+[02:55.000 --> 03:05.000] The crew of the shuttle Columbia did not return safely to Earth, yet we can pray that all are safely home.
+[03:05.000 --> 03:14.000] May God bless the grieving families and may God continue to bless America.
+[03:14.000 --> 03:24.000] [Music]
+
+
+main: load time = 438.55 ms
+main: mel time = 440.22 ms
+main: sample time = 32.23 ms
+main: encode time = 42329.63 ms / 1763.73 ms per layer
+main: decode time = 15190.00 ms
+main: total time = 58444.63 ms
+```
+
## Limitations
- Very basic greedy sampling scheme - always pick up the top token
-- No timestamps
- Inference only
- Runs on the CPU
- Only mono-channel 16-bit WAV is supported
id token_sot = 50257;
id token_prev = 50360;
id token_solm = 50361; // ??
+ id token_not = 50362; // no timestamps
id token_beg = 50363;
// available tasks
}
};
+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";
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") {
}
} 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") {
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());
vocab.token_sot++;
vocab.token_prev++;
vocab.token_solm++;
+ vocab.token_not++;
vocab.token_beg++;
}
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 {
// TODO: temperature
whisper_vocab::id whisper_sample_best(
const whisper_vocab & vocab,
- const float * probs,
- double temp,
- int offset = 0) {
+ 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 = offset; i < n_logits; i++) {
+ for (int i = 0; i < n_logits; i++) {
probs_id.push_back(std::make_pair(probs[i], i));
}
//}
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
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();
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;
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
}
}
+ // the generated text including timestamps
+ std::vector<whisper_result> result_all;
+
// main loop
int seek = 0;
while (true) {
return 1;
}
- t_encode_us = ggml_time_us() - t_start_us;
+ t_encode_us += ggml_time_us() - t_start_us;
}
std::vector<float> probs;
int seek_delta = 100*CHUNK_SIZE;
whisper_vocab::id last_id = 0;
+ //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) {
// 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));
+ 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) });
- // 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();
overview / pkg / ggml / sources / whisper.cpp / commitdiff