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whisper.cpp
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ref #17 : print whisper logs to stderr

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Only the transcribed/translted text is printed to stdout.
This way, one can redirect the result to a file.
pull/31/head
Georgi Gerganov 2 years ago
parent 8c7c018893
commit 2ca8cc77b2
2 changed files with 38 additions and 38 deletions
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22
main.cpp
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@ -192,21 +192,21 @@ int main(int argc, char ** argv) {
// print some info about the processing
{
printf("\n");
fprintf(stderr, "\n");
if (!whisper_is_multilingual(ctx)) {
if (params.language != "en" || params.translate) {
params.language = "en";
params.translate = false;
printf("%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
}
}
printf("%s: processing '%s' (%d samples, %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
__func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE, params.n_threads,
params.language.c_str(),
params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1);
printf("\n");
fprintf(stderr, "\n");
}
@ -230,25 +230,25 @@ int main(int argc, char ** argv) {
// print result
if (!wparams.print_realtime) {
printf("\n");
fprintf(stderr, "\n");
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 (params.no_timestamps) {
printf ("%s", text);
fprintf(stderr, "%s", text);
fflush(stdout);
} else {
const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
printf ("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
fprintf(stderr, "[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
}
}
}
printf("\n");
fprintf(stderr, "\n");
// output to text file
if (params.output_txt) {
@ -260,7 +260,7 @@ int main(int argc, char ** argv) {
return 8;
}
printf("%s: saving output to '%s.txt'\n", __func__, fname_inp.c_str());
fprintf(stderr, "%s: saving output to '%s.txt'\n", __func__, fname_inp.c_str());
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
@ -279,7 +279,7 @@ int main(int argc, char ** argv) {
return 9;
}
printf("%s: saving output to '%s.vtt'\n", __func__, fname_inp.c_str());
fprintf(stderr, "%s: saving output to '%s.vtt'\n", __func__, fname_inp.c_str());
fout_vtt << "WEBVTT\n\n";
@ -304,7 +304,7 @@ int main(int argc, char ** argv) {
return 10;
}
printf("%s: saving output to '%s.srt'\n", __func__, fname_inp.c_str());
fprintf(stderr, "%s: saving output to '%s.srt'\n", __func__, fname_inp.c_str());
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {

54
whisper.cpp
Unescape View File

@ -421,7 +421,7 @@ struct whisper_context {
// see the convert-pt-to-ggml.py script for details
//
bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str());
fprintf(stderr, "%s: loading model from '%s'\n", __func__, fname.c_str());
auto & model = wctx.model;
auto & vocab = wctx.vocab;
@ -480,18 +480,18 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
model.type = e_model::MODEL_LARGE;
}
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
printf("%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
printf("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
printf("%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
printf("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
printf("%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
printf("%s: n_text_state = %d\n", __func__, hparams.n_text_state);
printf("%s: n_text_head = %d\n", __func__, hparams.n_text_head);
printf("%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
printf("%s: n_mels = %d\n", __func__, hparams.n_mels);
printf("%s: f16 = %d\n", __func__, hparams.f16);
printf("%s: type = %d\n", __func__, model.type);
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: f16 = %d\n", __func__, hparams.f16);
fprintf(stderr, "%s: type = %d\n", __func__, model.type);
wctx.buf_model.resize(MEM_REQ_MODEL.at(model.type));
wctx.buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
@ -503,7 +503,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
wctx.buf_compute.size() +
wctx.buf_compute_layer.size();
printf("%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
fprintf(stderr, "%s: mem_required = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
}
// load mel filters
@ -553,7 +553,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
}
if (n_vocab < model.hparams.n_vocab) {
printf("%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
fprintf(stderr, "%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) + "]";
@ -698,7 +698,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
fprintf(stderr, "%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
}
// create the ggml context
@ -945,7 +945,7 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v) +
ggml_nbytes(model.memory_cross_k) + ggml_nbytes(model.memory_cross_v);
printf("%s: memory size = %8.2f MB \n", __func__, memory_size/1024.0/1024.0);
fprintf(stderr, "%s: memory size = %8.2f MB \n", __func__, memory_size/1024.0/1024.0);
}
// load weights
@ -1008,10 +1008,10 @@ bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
n_loaded++;
}
printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
fprintf(stderr, "%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
if (n_loaded == 0) {
printf("%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
} else if (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(), n_loaded);
return false;
@ -2242,13 +2242,13 @@ whisper_token whisper_token_transcribe() {
void whisper_print_timings(struct whisper_context * ctx) {
const int64_t t_end_us = ggml_time_us();
printf("\n");
printf("%s: load time = %8.2f ms\n", __func__, ctx->t_load_us/1000.0f);
printf("%s: mel time = %8.2f ms\n", __func__, ctx->t_mel_us/1000.0f);
printf("%s: sample time = %8.2f ms\n", __func__, ctx->t_sample_us/1000.0f);
printf("%s: encode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_encode_us/1000.0f, ctx->t_encode_us/1000.0f/ctx->model.hparams.n_audio_layer);
printf("%s: decode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_decode_us/1000.0f, ctx->t_decode_us/1000.0f/ctx->model.hparams.n_text_layer);
printf("%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
fprintf(stderr, "\n");
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us/1000.0f);
fprintf(stderr, "%s: mel time = %8.2f ms\n", __func__, ctx->t_mel_us/1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, ctx->t_sample_us/1000.0f);
fprintf(stderr, "%s: encode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_encode_us/1000.0f, ctx->t_encode_us/1000.0f/ctx->model.hparams.n_audio_layer);
fprintf(stderr, "%s: decode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_decode_us/1000.0f, ctx->t_decode_us/1000.0f/ctx->model.hparams.n_text_layer);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
}
////////////////////////////////////////////////////////////////////////////
@ -2349,7 +2349,7 @@ int whisper_full(
while (progress_cur >= progress_prev + progress_step) {
progress_prev += progress_step;
if (params.print_progress) {
printf("%s: progress = %3d%%\n", __func__, progress_prev);
fprintf(stderr, "%s: progress = %3d%%\n", __func__, progress_prev);
}
}

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