};
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 = {
{ 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;
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());
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);
}
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 {
// 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);
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);
// 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);
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);
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 };
// 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(
// 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
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;
+ // 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) {
// 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();
overview / pkg / ggml / sources / ggml / commitdiff