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ggml : sync latest whisper.cpp

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4bit
Georgi Gerganov 2 years ago
parent a0f2f68cdb
commit c40a5b51a0
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GPG Key ID: 449E073F9DC10735
3 changed files with 1459 additions and 736 deletions
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31
examples/whisper/main.cpp
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@ -59,8 +59,12 @@ struct whisper_params {
int32_t duration_ms = 0;
int32_t max_context = -1;
int32_t max_len = 0;
int32_t best_of = 5;
int32_t beam_size = -1;
float word_thold = 0.01f;
float entropy_thold = 2.4f;
float logprob_thold = -1.0f;
bool speed_up = false;
bool translate = false;
@ -104,7 +108,11 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-d" || arg == "--duration") { params.duration_ms = std::stoi(argv[++i]); }
else if (arg == "-mc" || arg == "--max-context") { params.max_context = std::stoi(argv[++i]); }
else if (arg == "-ml" || arg == "--max-len") { params.max_len = std::stoi(argv[++i]); }
else if (arg == "-bo" || arg == "--best-of") { params.best_of = std::stoi(argv[++i]); }
else if (arg == "-bs" || arg == "--beam-size") { params.beam_size = std::stoi(argv[++i]); }
else if (arg == "-wt" || arg == "--word-thold") { params.word_thold = std::stof(argv[++i]); }
else if (arg == "-et" || arg == "--entropy-thold") { params.entropy_thold = std::stof(argv[++i]); }
else if (arg == "-lpt" || arg == "--logprob-thold") { params.logprob_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-di" || arg == "--diarize") { params.diarize = true; }
@ -144,7 +152,11 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -d N, --duration N [%-7d] duration of audio to process in milliseconds\n", params.duration_ms);
fprintf(stderr, " -mc N, --max-context N [%-7d] maximum number of text context tokens to store\n", params.max_context);
fprintf(stderr, " -ml N, --max-len N [%-7d] maximum segment length in characters\n", params.max_len);
fprintf(stderr, " -bo N, --best-of N [%-7d] number of best candidates to keep\n", params.best_of);
fprintf(stderr, " -bs N, --beam-size N [%-7d] beam size for beam search\n", params.beam_size);
fprintf(stderr, " -wt N, --word-thold N [%-7.2f] word timestamp probability threshold\n", params.word_thold);
fprintf(stderr, " -et N, --entropy-thold N [%-7.2f] entropy threshold for decoder fail\n", params.entropy_thold);
fprintf(stderr, " -lpt N, --logprob-thold N [%-7.2f] log probability threshold for decoder fail\n", params.logprob_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -di, --diarize [%-7s] stereo audio diarization\n", params.diarize ? "true" : "false");
@ -235,7 +247,7 @@ void whisper_print_segment_callback(struct whisper_context * ctx, int n_new, voi
const char * text = whisper_full_get_token_text(ctx, i, j);
const float p = whisper_full_get_token_p (ctx, i, j);
const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
const int col = std::max(0, std::min((int) k_colors.size() - 1, (int) (std::pow(p, 3)*float(k_colors.size()))));
printf("%s%s%s%s", speaker.c_str(), k_colors[col].c_str(), text, "\033[0m");
}
@ -331,20 +343,19 @@ bool output_csv(struct whisper_context * ctx, const char * fname) {
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 (text[0] == ' ')
if (text[0] == ' ') {
text = text + sizeof(char); //whisper_full_get_segment_text() returns a string with leading space, point to the next character.
}
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
//need to multiply times returned from whisper_full_get_segment_t{0,1}() by 10 to get milliseconds.
fout << 10 * t0 << ", "
<< 10 * t1 << ", \""
<< text << "\"\n";
fout << 10 * t0 << ", " << 10 * t1 << ", \"" << text << "\"\n";
}
return true;
}
// karaoke video generation
// outputs a bash script that uses ffmpeg to generate a video with the subtitles
// TODO: font parameter adjustments
@ -620,6 +631,8 @@ int main(int argc, char ** argv) {
{
whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
wparams.strategy = params.beam_size > 1 ? WHISPER_SAMPLING_BEAM_SEARCH : WHISPER_SAMPLING_GREEDY;
wparams.print_realtime = false;
wparams.print_progress = params.print_progress;
wparams.print_timestamps = !params.no_timestamps;
@ -633,10 +646,16 @@ int main(int argc, char ** argv) {
wparams.token_timestamps = params.output_wts || params.max_len > 0;
wparams.thold_pt = params.word_thold;
wparams.entropy_thold = params.entropy_thold;
wparams.logprob_thold = params.logprob_thold;
wparams.max_len = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
wparams.speed_up = params.speed_up;
wparams.greedy.best_of = params.best_of;
wparams.beam_search.beam_size = params.beam_size;
wparams.temperature_inc = -1;
wparams.prompt_tokens = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();

1662
examples/whisper/whisper.cpp
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60
examples/whisper/whisper.h
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@ -74,6 +74,7 @@ extern "C" {
whisper_token tid; // forced timestamp token id
float p; // probability of the token
float plog; // log probability of the token
float pt; // probability of the timestamp token
float ptsum; // sum of probabilities of all timestamp tokens
@ -136,6 +137,7 @@ extern "C" {
// 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
// TODO: add support for multiple decoders
WHISPER_API int whisper_decode(
struct whisper_context * ctx,
const whisper_token * tokens,
@ -143,14 +145,6 @@ 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_data whisper_sample_best(struct whisper_context * ctx);
WHISPER_API whisper_token_data whisper_sample_timestamp(struct whisper_context * ctx, bool is_initial);
// Convert the provided text into tokens.
// The tokens pointer must be large enough to hold the resulting tokens.
// Returns the number of tokens on success, no more than n_max_tokens
@ -192,8 +186,11 @@ extern "C" {
WHISPER_API int whisper_n_audio_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 logits obtained from the last call to whisper_decode()
// The logits for the last token are stored in the last row
// Rows: n_tokens
// Cols: n_vocab
WHISPER_API float * whisper_get_logits(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);
@ -222,8 +219,8 @@ extern "C" {
// Available sampling strategies
enum whisper_sampling_strategy {
WHISPER_SAMPLING_GREEDY, // Always select the most probable token
WHISPER_SAMPLING_BEAM_SEARCH, // TODO: not implemented yet!
WHISPER_SAMPLING_GREEDY, // similar to OpenAI's GreefyDecoder
WHISPER_SAMPLING_BEAM_SEARCH, // similar to OpenAI's BeamSearchDecoder
};
// Text segment callback
@ -243,17 +240,17 @@ extern "C" {
enum whisper_sampling_strategy strategy;
int n_threads;
int n_max_text_ctx;
int n_max_text_ctx; // max tokens to use from past text as prompt for the decoder
int offset_ms; // start offset in ms
int duration_ms; // audio duration to process in ms
bool translate;
bool no_context;
bool no_context; // do not use initial prompt for the decoder (if any)
bool single_segment; // force single segment output (useful for streaming)
bool print_special;
bool print_progress;
bool print_realtime;
bool print_timestamps;
bool print_special; // print special tokens (e.g. <SOT>, <EOT>, <BEG>, etc.)
bool print_progress; // print progress information
bool print_realtime; // print results from within whisper.cpp (avoid it, use callback instead)
bool print_timestamps; // print timestamps for each text segment when printing realtime
// [EXPERIMENTAL] token-level timestamps
bool token_timestamps; // enable token-level timestamps
@ -263,10 +260,11 @@ extern "C" {
int max_tokens; // max tokens per segment (0 = no limit)
// [EXPERIMENTAL] speed-up techniques
// note: these can significantly reduce the quality of the output
bool speed_up; // speed-up the audio by 2x using Phase Vocoder
int audio_ctx; // overwrite the audio context size (0 = use default)
// tokens to provide the whisper model as initial prompt
// tokens to provide to the whisper decoder as initial prompt
// these are prepended to any existing text context from a previous call
const whisper_token * prompt_tokens;
int prompt_n_tokens;
@ -274,19 +272,35 @@ extern "C" {
// for auto-detection, set to nullptr, "" or "auto"
const char * language;
// common decoding parameters:
bool suppress_blank; // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/decoding.py#L89
float temperature; // initial decoding temperature, ref: https://ai.stackexchange.com/a/32478
float max_initial_ts; // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/decoding.py#L97
float length_penalty; // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L267
// fallback parameters
// ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L274-L278
float temperature_inc;
float entropy_thold; // similar to OpenAI's "compression_ratio_threshold"
float logprob_thold;
float no_speech_thold; // TODO: not implemented
struct {
int n_past;
int best_of; // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L264
} greedy;
struct {
int n_past;
int beam_width;
int n_best;
int beam_size; // ref: https://github.com/openai/whisper/blob/f82bc59f5ea234d4b97fb2860842ed38519f7e65/whisper/transcribe.py#L265
float patience; // TODO: not implemented, ref: https://arxiv.org/pdf/2204.05424.pdf
} beam_search;
// called for every newly generated text segment
whisper_new_segment_callback new_segment_callback;
void * new_segment_callback_user_data;
// called each time before the encoder starts
whisper_encoder_begin_callback encoder_begin_callback;
void * encoder_begin_callback_user_data;
};

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