alihan/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.
This commit is contained in:
Georgi Gerganov
2022-10-08 17:28:06 +03:00
parent 8c7c018893
commit 2ca8cc77b2
2 changed files with 38 additions and 38 deletions
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54
whisper.cpp
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@@ -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);
}
}