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whisper : expose API to let user control log output (#1060)

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* expose api to let user control log output

Add
  whisper_set_log_callback()
that lets user set a callback for log messages.

Change all the
  fprintf(stderr, ...)
to call via the above.

* whisper : add <cstdarg>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Evan Martin 2023-07-25 08:58:25 -07:00 committed by GitHub
parent 925915ae37
commit fabf79fc67
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 118 additions and 92 deletions
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@ -14,6 +14,7 @@
#define _USE_MATH_DEFINES
#include <cmath>
#include <cstdio>
#include <cstdarg>
#include <cstring>
#include <fstream>
#include <map>
@ -92,7 +93,7 @@ static void byteswap_tensor(ggml_tensor * tensor) {
#define WHISPER_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
log("WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
@ -723,6 +724,21 @@ struct whisper_context {
std::string path_model; // populated by whisper_init_from_file()
};
static void whisper_default_log(const char * text) {
fprintf(stderr, "%s", text);
}
static whisper_log_callback whisper_log = whisper_default_log;
static void log(const char * fmt, ...) {
if (!whisper_log) return;
char buf[1024];
va_list args;
va_start(args, fmt);
vsnprintf(buf, sizeof(buf), fmt, args);
whisper_log(buf);
}
template<typename T>
static void read_safe(whisper_model_loader * loader, T & dest) {
loader->read(loader->context, &dest, sizeof(T));
@ -746,7 +762,7 @@ static bool kv_cache_init(
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
log("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
@ -782,7 +798,7 @@ static bool kv_cache_reinit(struct whisper_kv_cache & cache) {
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
log("%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
@ -811,7 +827,7 @@ static void kv_cache_free(struct whisper_kv_cache & cache) {
// see the convert-pt-to-ggml.py script for details
//
static bool whisper_model_load(struct whisper_model_loader * loader, whisper_context & wctx) {
fprintf(stderr, "%s: loading model\n", __func__);
log("%s: loading model\n", __func__);
const int64_t t_start_us = ggml_time_us();
@ -825,7 +841,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
uint32_t magic;
read_safe(loader, magic);
if (magic != GGML_FILE_MAGIC) {
fprintf(stderr, "%s: invalid model data (bad magic)\n", __func__);
log("%s: invalid model data (bad magic)\n", __func__);
return false;
}
}
@ -876,25 +892,25 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
// in order to save memory and also to speed up the computation
wctx.wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
if (wctx.wtype == GGML_TYPE_COUNT) {
fprintf(stderr, "%s: invalid model (bad ftype value %d)\n", __func__, model.hparams.ftype);
log("%s: invalid model (bad ftype value %d)\n", __func__, model.hparams.ftype);
return false;
}
const size_t scale = model.hparams.ftype ? 1 : 2;
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: ftype = %d\n", __func__, model.hparams.ftype);
fprintf(stderr, "%s: qntvr = %d\n", __func__, qntvr);
fprintf(stderr, "%s: type = %d\n", __func__, model.type);
log("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
log("%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
log("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
log("%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
log("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
log("%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
log("%s: n_text_state = %d\n", __func__, hparams.n_text_state);
log("%s: n_text_head = %d\n", __func__, hparams.n_text_head);
log("%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
log("%s: n_mels = %d\n", __func__, hparams.n_mels);
log("%s: ftype = %d\n", __func__, model.hparams.ftype);
log("%s: qntvr = %d\n", __func__, qntvr);
log("%s: type = %d\n", __func__, model.type);
// print memory requirements
{
@ -912,7 +928,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
const size_t mem_required_decoder =
scale*MEM_REQ_KV_SELF.at(model.type);
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per decoder)\n", __func__,
log("%s: mem required = %7.2f MB (+ %7.2f MB per decoder)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_decoder / 1024.0 / 1024.0);
}
@ -944,7 +960,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
read_safe(loader, n_vocab);
//if (n_vocab != model.hparams.n_vocab) {
// fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
// log("%s: invalid model file '%s' (bad vocab size %d != %d)\n",
// __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
// return false;
//}
@ -964,7 +980,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
word.assign(&tmp[0], tmp.size());
} else {
// seems like we have an empty-string token in multi-language models (i = 50256)
//fprintf(stderr, "%s: warning: empty-string token in vocab, i = %d\n", __func__, i);
//log("%s: warning: empty-string token in vocab, i = %d\n", __func__, i);
word = "";
}
@ -988,7 +1004,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
}
if (n_vocab < model.hparams.n_vocab) {
fprintf(stderr, "%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
log("%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) + "]";
@ -1127,7 +1143,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*512; // object overhead
fprintf(stderr, "%s: model ctx = %7.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
log("%s: model ctx = %7.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
@ -1140,7 +1156,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
model.ctx = ggml_init(params);
if (!model.ctx) {
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
log("%s: ggml_init() failed\n", __func__);
return false;
}
}
@ -1373,20 +1389,20 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
name.assign(&tmp[0], tmp.size());
if (model.tensors.find(name) == model.tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
log("%s: unknown tensor '%s' in model file\n", __func__, name.data());
return false;
}
auto tensor = model.tensors[name.data()];
if (ggml_nelements(tensor) != nelements) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
fprintf(stderr, "%s: shape: [%d, %d, %d], expected: [%d, %d, %d]\n",
log("%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
log("%s: shape: [%d, %d, %d], expected: [%d, %d, %d]\n",
__func__, ne[0], ne[1], ne[2], (int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2]);
return false;
}
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1] || tensor->ne[2] != ne[2]) {
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d, %d], expected [%d, %d, %d]\n",
log("%s: tensor '%s' has wrong shape in model file: got [%d, %d, %d], expected [%d, %d, %d]\n",
__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], ne[0], ne[1], ne[2]);
return false;
}
@ -1394,7 +1410,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
const size_t bpe = ggml_type_size(ggml_type(ttype));
if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
log("%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
return false;
}
@ -1407,12 +1423,12 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
model.n_loaded++;
}
fprintf(stderr, "%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
log("%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
if (model.n_loaded == 0) {
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
log("%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
} else if (model.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(), model.n_loaded);
log("%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
return false;
}
}
@ -2616,7 +2632,7 @@ static std::vector<whisper_vocab::id> tokenize(const whisper_vocab & vocab, cons
--j;
}
if (!found) {
fprintf(stderr, "unknown token \n");
log("unknown token\n");
++i;
}
}
@ -2683,41 +2699,41 @@ struct whisper_state * whisper_init_state(whisper_context * ctx) {
const size_t scale = ctx->model.hparams.ftype ? 1 : 2;
if (!kv_cache_init(ctx->model.hparams, scale * MEM_REQ_KV_SELF.at(ctx->model.type), state->decoders[0].kv_self, ctx->itype, ctx->model.hparams.n_text_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
log("%s: kv_cache_init() failed for self-attention cache\n", __func__);
delete state;
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(state->decoders[0].kv_self.k) + ggml_nbytes(state->decoders[0].kv_self.v);
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
log("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
if (!kv_cache_init(ctx->model.hparams, scale * MEM_REQ_KV_CROSS.at(ctx->model.type), state->kv_cross, ctx->itype, ctx->model.hparams.n_audio_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for cross-attention cache\n", __func__);
log("%s: kv_cache_init() failed for cross-attention cache\n", __func__);
delete state;
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(state->kv_cross.k) + ggml_nbytes(state->kv_cross.v);
fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
log("%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
#ifdef WHISPER_USE_COREML
const auto path_coreml = whisper_get_coreml_path_encoder(ctx->path_model);
fprintf(stderr, "%s: loading Core ML model from '%s'\n", __func__, path_coreml.c_str());
fprintf(stderr, "%s: first run on a device may take a while ...\n", __func__);
log("%s: loading Core ML model from '%s'\n", __func__, path_coreml.c_str());
log("%s: first run on a device may take a while ...\n", __func__);
state->ctx_coreml = whisper_coreml_init(path_coreml.c_str());
if (!state->ctx_coreml) {
fprintf(stderr, "%s: failed to load Core ML model from '%s'\n", __func__, path_coreml.c_str());
log("%s: failed to load Core ML model from '%s'\n", __func__, path_coreml.c_str());
#ifndef WHISPER_COREML_ALLOW_FALLBACK
return nullptr;
#endif
} else {
fprintf(stderr, "%s: Core ML model loaded\n", __func__);
log("%s: Core ML model loaded\n", __func__);
}
#endif
@ -2757,7 +2773,7 @@ int whisper_ctx_init_openvino_encoder(
return 1;
#else
if (!model_path && ctx->path_model.empty()) {
fprintf(stderr, "%s: model_path is nullptr, and ctx has no model_path set.\n", __func__);
log("%s: model_path is nullptr, and ctx has no model_path set.\n", __func__);
return 1;
}
@ -2777,15 +2793,15 @@ int whisper_ctx_init_openvino_encoder(
path_cache = cache_dir;
}
fprintf(stderr, "%s: loading OpenVINO model from '%s'\n", __func__, path_encoder.c_str());
fprintf(stderr, "%s: first run on a device may take a while ...\n", __func__);
log("%s: loading OpenVINO model from '%s'\n", __func__, path_encoder.c_str());
log("%s: first run on a device may take a while ...\n", __func__);
ctx->state->ctx_openvino = whisper_openvino_init(path_encoder.c_str(), device, path_cache.c_str());
if (!ctx->state->ctx_openvino) {
fprintf(stderr, "%s: failed to init OpenVINO encoder from '%s'\n", __func__, path_encoder.c_str());
log("%s: failed to init OpenVINO encoder from '%s'\n", __func__, path_encoder.c_str());
return 1;
} else {
fprintf(stderr, "%s: OpenVINO model loaded\n", __func__);
log("%s: OpenVINO model loaded\n", __func__);
}
return 0;
@ -2794,11 +2810,11 @@ int whisper_ctx_init_openvino_encoder(
struct whisper_context * whisper_init_from_file_no_state(const char * path_model) {
fprintf(stderr, "%s: loading model from '%s'\n", __func__, path_model);
log("%s: loading model from '%s'\n", __func__, path_model);
auto fin = std::ifstream(path_model, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, path_model);
log("%s: failed to open '%s'\n", __func__, path_model);
return nullptr;
}
@ -2840,7 +2856,7 @@ struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t
buf_context ctx = { reinterpret_cast<uint8_t*>(buffer), buffer_size, 0 };
fprintf(stderr, "%s: loading model from buffer\n", __func__);
log("%s: loading model from buffer\n", __func__);
whisper_model_loader loader = {};
@ -2875,7 +2891,7 @@ struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loa
if (!whisper_model_load(loader, *ctx)) {
loader->close(loader->context);
fprintf(stderr, "%s: failed to load model\n", __func__);
log("%s: failed to load model\n", __func__);
delete ctx;
return nullptr;
}
@ -2980,7 +2996,7 @@ void whisper_free_params(struct whisper_full_params * params) {
int whisper_pcm_to_mel_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, WHISPER_N_MEL, n_threads, ctx->model.filters, false, state->mel)) {
fprintf(stderr, "%s: failed to compute mel spectrogram\n", __func__);
log("%s: failed to compute mel spectrogram\n", __func__);
return -1;
}
@ -2994,7 +3010,7 @@ int whisper_pcm_to_mel(struct whisper_context * ctx, const float * samples, int
// same as whisper_pcm_to_mel, but applies a Phase Vocoder to speed up the audio x2
int whisper_pcm_to_mel_phase_vocoder_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, 2 * WHISPER_N_FFT, 2 * WHISPER_HOP_LENGTH, WHISPER_N_MEL, n_threads, ctx->model.filters, true, state->mel)) {
fprintf(stderr, "%s: failed to compute mel spectrogram\n", __func__);
log("%s: failed to compute mel spectrogram\n", __func__);
return -1;
}
@ -3013,7 +3029,7 @@ int whisper_set_mel_with_state(
int n_len,
int n_mel) {
if (n_mel != WHISPER_N_MEL) {
fprintf(stderr, "%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, WHISPER_N_MEL);
log("%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, WHISPER_N_MEL);
return -1;
}
@ -3037,7 +3053,7 @@ int whisper_set_mel(
int whisper_encode_with_state(struct whisper_context * ctx, struct whisper_state * state, int offset, int n_threads) {
if (!whisper_encode_internal(*ctx, *state, offset, n_threads)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
log("%s: failed to eval\n", __func__);
return -1;
}
@ -3046,7 +3062,7 @@ int whisper_encode_with_state(struct whisper_context * ctx, struct whisper_state
int whisper_encode(struct whisper_context * ctx, int offset, int n_threads) {
if (!whisper_encode_internal(*ctx, *ctx->state, offset, n_threads)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
log("%s: failed to eval\n", __func__);
return -1;
}
@ -3057,7 +3073,7 @@ int whisper_decode_with_state(struct whisper_context * ctx, struct whisper_state
const int selected_decoder_id = 0;
if (!whisper_decode_internal(*ctx, *state, state->decoders[selected_decoder_id], tokens, n_tokens, n_past, n_threads)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
log("%s: failed to eval\n", __func__);
return 1;
}
@ -3069,13 +3085,13 @@ int whisper_decode(struct whisper_context * ctx, const whisper_token * tokens, i
const int selected_decoder_id = 0;
if (ctx->state == nullptr) {
fprintf(stderr, "%s: ERROR state was not loaded.\n", __func__);
log("%s: ERROR state was not loaded.\n", __func__);
return false;
}
if (!whisper_decode_internal(*ctx, *ctx->state, ctx->state->decoders[selected_decoder_id], tokens, n_tokens, n_past, n_threads)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
log("%s: failed to eval\n", __func__);
return 1;
}
@ -3086,7 +3102,7 @@ int whisper_tokenize(struct whisper_context * ctx, const char * text, whisper_to
const auto res = tokenize(ctx->vocab, text);
if (n_max_tokens < (int) res.size()) {
fprintf(stderr, "%s: too many resulting tokens: %d (max %d)\n", __func__, (int) res.size(), n_max_tokens);
log("%s: too many resulting tokens: %d (max %d)\n", __func__, (int) res.size(), n_max_tokens);
return -1;
}
@ -3114,7 +3130,7 @@ int whisper_lang_id(const char * lang) {
}
}
fprintf(stderr, "%s: unknown language '%s'\n", __func__, lang);
log("%s: unknown language '%s'\n", __func__, lang);
return -1;
}
return g_lang.at(lang).first;
@ -3127,7 +3143,7 @@ const char * whisper_lang_str(int id) {
}
}
fprintf(stderr, "%s: unknown language id %d\n", __func__, id);
log("%s: unknown language id %d\n", __func__, id);
return nullptr;
}
@ -3140,25 +3156,25 @@ int whisper_lang_auto_detect_with_state(
const int seek = offset_ms/10;
if (seek < 0) {
fprintf(stderr, "%s: offset %dms is before the start of the audio\n", __func__, offset_ms);
log("%s: offset %dms is before the start of the audio\n", __func__, offset_ms);
return -1;
}
if (seek >= state->mel.n_len_org) {
fprintf(stderr, "%s: offset %dms is past the end of the audio (%dms)\n", __func__, offset_ms, state->mel.n_len_org*10);
log("%s: offset %dms is past the end of the audio (%dms)\n", __func__, offset_ms, state->mel.n_len_org*10);
return -2;
}
// run the encoder
if (whisper_encode_with_state(ctx, state, seek, n_threads) != 0) {
fprintf(stderr, "%s: failed to encode\n", __func__);
log("%s: failed to encode\n", __func__);
return -6;
}
const std::vector<whisper_token> prompt = { whisper_token_sot(ctx) };
if (whisper_decode_with_state(ctx, state, prompt.data(), prompt.size(), 0, n_threads) != 0) {
fprintf(stderr, "%s: failed to decode\n", __func__);
log("%s: failed to decode\n", __func__);
return -7;
}
@ -3359,21 +3375,21 @@ whisper_token whisper_token_transcribe(struct whisper_context * ctx) {
void whisper_print_timings(struct whisper_context * ctx) {
const int64_t t_end_us = ggml_time_us();
fprintf(stderr, "\n");
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
log("\n");
log("%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
if (ctx->state != nullptr) {
const int32_t n_sample = std::max(1, ctx->state->n_sample);
const int32_t n_encode = std::max(1, ctx->state->n_encode);
const int32_t n_decode = std::max(1, ctx->state->n_decode);
fprintf(stderr, "%s: fallbacks = %3d p / %3d h\n", __func__, ctx->state->n_fail_p, ctx->state->n_fail_h);
fprintf(stderr, "%s: mel time = %8.2f ms\n", __func__, ctx->state->t_mel_us / 1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_sample_us, n_sample, 1e-3f * ctx->state->t_sample_us / n_sample);
fprintf(stderr, "%s: encode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_encode_us, n_encode, 1e-3f * ctx->state->t_encode_us / n_encode);
fprintf(stderr, "%s: decode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_decode_us, n_decode, 1e-3f * ctx->state->t_decode_us / n_decode);
log("%s: fallbacks = %3d p / %3d h\n", __func__, ctx->state->n_fail_p, ctx->state->n_fail_h);
log("%s: mel time = %8.2f ms\n", __func__, ctx->state->t_mel_us / 1000.0f);
log("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_sample_us, n_sample, 1e-3f * ctx->state->t_sample_us / n_sample);
log("%s: encode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_encode_us, n_encode, 1e-3f * ctx->state->t_encode_us / n_encode);
log("%s: decode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_decode_us, n_decode, 1e-3f * ctx->state->t_decode_us / n_decode);
}
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
log("%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
}
void whisper_reset_timings(struct whisper_context * ctx) {
@ -3697,7 +3713,7 @@ static void whisper_process_logits(
const bool last_was_timestamp = tokens_cur.size() > 0 && tokens_cur.back().id >= vocab.token_beg;
const bool penultimate_was_timestamp = tokens_cur.size() < 2 || tokens_cur[tokens_cur.size() - 2].id >= vocab.token_beg;
//fprintf(stderr, "last_was_timestamp=%d penultimate_was_timestamp=%d\n", last_was_timestamp, penultimate_was_timestamp);
//log("last_was_timestamp=%d penultimate_was_timestamp=%d\n", last_was_timestamp, penultimate_was_timestamp);
if (last_was_timestamp) {
if (penultimate_was_timestamp) {
@ -3773,7 +3789,7 @@ static void whisper_process_logits(
const float max_text_token_logprob = *std::max_element(logprobs.begin(), logprobs.begin() + vocab.token_beg);
//fprintf(stderr, "timestamp_logprob=%f max_text_token_logprob=%f\n", timestamp_logprob, max_text_token_logprob);
//log("timestamp_logprob=%f max_text_token_logprob=%f\n", timestamp_logprob, max_text_token_logprob);
if (timestamp_logprob > max_text_token_logprob) {
for (int i = 0; i < vocab.token_beg; ++i) {
@ -4022,12 +4038,12 @@ int whisper_full_with_state(
// compute log mel spectrogram
if (params.speed_up) {
if (whisper_pcm_to_mel_phase_vocoder_with_state(ctx, state, samples, n_samples, params.n_threads) != 0) {
fprintf(stderr, "%s: failed to compute log mel spectrogram\n", __func__);
log("%s: failed to compute log mel spectrogram\n", __func__);
return -1;
}
} else {
if (whisper_pcm_to_mel_with_state(ctx, state, samples, n_samples, params.n_threads) != 0) {
fprintf(stderr, "%s: failed to compute log mel spectrogram\n", __func__);
log("%s: failed to compute log mel spectrogram\n", __func__);
return -2;
}
}
@ -4038,13 +4054,13 @@ int whisper_full_with_state(
const auto lang_id = whisper_lang_auto_detect_with_state(ctx, state, 0, params.n_threads, probs.data());
if (lang_id < 0) {
fprintf(stderr, "%s: failed to auto-detect language\n", __func__);
log("%s: failed to auto-detect language\n", __func__);
return -3;
}
state->lang_id = lang_id;
params.language = whisper_lang_str(lang_id);
fprintf(stderr, "%s: auto-detected language: %s (p = %f)\n", __func__, params.language, probs[whisper_lang_id(params.language)]);
log("%s: auto-detected language: %s (p = %f)\n", __func__, params.language, probs[whisper_lang_id(params.language)]);
if (params.detect_language) {
return 0;
}
@ -4101,7 +4117,7 @@ int whisper_full_with_state(
if (decoder.kv_self.ctx == nullptr) {
decoder.kv_self = state->decoders[0].kv_self;
if (!kv_cache_reinit(decoder.kv_self)) {
fprintf(stderr, "%s: kv_cache_reinit() failed for self-attention, decoder %d\n", __func__, j);
log("%s: kv_cache_reinit() failed for self-attention, decoder %d\n", __func__, j);
return -4;
}
@ -4145,7 +4161,7 @@ int whisper_full_with_state(
// overwrite audio_ctx, max allowed is hparams.n_audio_ctx
if (params.audio_ctx > whisper_n_audio_ctx(ctx)) {
fprintf(stderr, "%s: audio_ctx is larger than the maximum allowed (%d > %d)\n", __func__, params.audio_ctx, whisper_n_audio_ctx(ctx));
log("%s: audio_ctx is larger than the maximum allowed (%d > %d)\n", __func__, params.audio_ctx, whisper_n_audio_ctx(ctx));
return -5;
}
state->exp_n_audio_ctx = params.audio_ctx;
@ -4189,8 +4205,9 @@ int whisper_full_with_state(
// main loop
while (true) {
const int progress_cur = (100*(seek - seek_start))/(seek_end - seek_start);
if (params.progress_callback) {
const int progress_cur = (100*(seek - seek_start))/(seek_end - seek_start);
params.progress_callback(
ctx, ctx->state, progress_cur, params.progress_callback_user_data);
}
@ -4202,14 +4219,14 @@ int whisper_full_with_state(
if (params.encoder_begin_callback) {
if (params.encoder_begin_callback(ctx, state, params.encoder_begin_callback_user_data) == false) {
fprintf(stderr, "%s: encoder_begin_callback returned false - aborting\n", __func__);
log("%s: encoder_begin_callback returned false - aborting\n", __func__);
break;
}
}
// encode audio features starting at offset seek
if (!whisper_encode_internal(*ctx, *state, seek, params.n_threads)) {
fprintf(stderr, "%s: failed to encode\n", __func__);
log("%s: failed to encode\n", __func__);
return -6;
}
@ -4292,7 +4309,7 @@ int whisper_full_with_state(
WHISPER_PRINT_DEBUG("\n\n");
if (!whisper_decode_internal(*ctx, *state, state->decoders[0], prompt.data(), prompt.size(), 0, params.n_threads)) {
fprintf(stderr, "%s: failed to decode\n", __func__);
log("%s: failed to decode\n", __func__);
return -7;
}
@ -4530,7 +4547,7 @@ int whisper_full_with_state(
//WHISPER_PRINT_DEBUG("%s: decoder %d: token %d, kv_self.n %d, seek_delta %d\n", __func__, j, decoder.tokens_tmp[0], decoder.kv_self.n, decoder.seek_delta);
if (!whisper_decode_internal(*ctx, *state, decoder, decoder.tokens_tmp.data(), decoder.tokens_tmp.size(), decoder.kv_self.n, params.n_threads)) {
fprintf(stderr, "%s: failed to decode\n", __func__);
log("%s: failed to decode\n", __func__);
return -8;
}
@ -4852,12 +4869,12 @@ int whisper_full_parallel(
ctx->state->t_decode_us /= n_processors;
// print information about the audio boundaries
fprintf(stderr, "\n");
fprintf(stderr, "%s: the audio has been split into %d chunks at the following times:\n", __func__, n_processors);
log("\n");
log("%s: the audio has been split into %d chunks at the following times:\n", __func__, n_processors);
for (int i = 0; i < n_processors - 1; ++i) {
fprintf(stderr, "%s: split %d - %s\n", __func__, (i + 1), to_timestamp(100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t).c_str());
log("%s: split %d - %s\n", __func__, (i + 1), to_timestamp(100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t).c_str());
}
fprintf(stderr, "%s: the transcription quality may be degraded near these boundaries\n", __func__);
log("%s: the transcription quality may be degraded near these boundaries\n", __func__);
return ret;
}
@ -5217,7 +5234,7 @@ static void whisper_exp_compute_token_level_timestamps(
const int n_samples = state.energy.size();
if (n_samples == 0) {
fprintf(stderr, "%s: no signal data available\n", __func__);
log("%s: no signal data available\n", __func__);
return;
}
@ -5437,3 +5454,7 @@ static void whisper_exp_compute_token_level_timestamps(
// }
//}
}
void whisper_set_log_callback(whisper_log_callback callback) {
whisper_log = callback;
}

5
whisper.h
View File

@ -517,6 +517,11 @@ extern "C" {
WHISPER_API int whisper_bench_ggml_mul_mat (int n_threads);
WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads);
// Control logging output; default behavior is to print to stderr
typedef void (*whisper_log_callback)(const char * line);
WHISPER_API void whisper_set_log_callback(whisper_log_callback callback);
#ifdef __cplusplus
}
#endif