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parallel : print time of audio boundaries + fix timings
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50
whisper.cpp
50
whisper.cpp
@ -1910,14 +1910,19 @@ whisper_vocab::id whisper_sample_timestamp(
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return probs_id[0].second;
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}
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static std::string to_timestamp(int64_t t) {
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int64_t sec = t/100;
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int64_t msec = t - sec*100;
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int64_t min = sec/60;
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sec = sec - min*60;
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// 500 -> 00:05.000
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// 6000 -> 01:00.000
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std::string to_timestamp(int64_t t, bool comma = false) {
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int64_t msec = t * 10;
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int64_t hr = msec / (1000 * 60 * 60);
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msec = msec - hr * (1000 * 60 * 60);
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int64_t min = msec / (1000 * 60);
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msec = msec - min * (1000 * 60);
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int64_t sec = msec / 1000;
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msec = msec - sec * 1000;
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char buf[32];
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snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
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snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
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return std::string(buf);
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}
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@ -2727,25 +2732,46 @@ int whisper_full_parallel(
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// combine results into ctx->result_all
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for (int i = 0; i < n_processors - 1; ++i) {
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auto & result_all = ctxs[i].result_all;
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auto & results_i = ctxs[i].result_all;
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for (int j = 0; j < (int) result_all.size(); ++j) {
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result_all[j].t0 += 100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t;
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result_all[j].t1 += 100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t;
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for (int j = 0; j < (int) results_i.size(); ++j) {
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// correct the segment timestamp taking into account the offset
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results_i[j].t0 += 100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t;
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results_i[j].t1 += 100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t;
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// make sure that segments are not overlapping
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if (ctx->result_all.size() > 0) {
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result_all[j].t0 = std::max(result_all[j].t0, ctx->result_all.back().t1);
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results_i[j].t0 = std::max(results_i[j].t0, ctx->result_all.back().t1);
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}
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ctx->result_all.push_back(std::move(result_all[j]));
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ctx->result_all.push_back(std::move(results_i[j]));
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// call the new_segment_callback for each segment
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if (params.new_segment_callback) {
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params.new_segment_callback(ctx, params.new_segment_callback_user_data);
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}
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}
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ctx->t_mel_us += ctxs[i].t_mel_us;
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ctx->t_sample_us += ctxs[i].t_sample_us;
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ctx->t_encode_us += ctxs[i].t_encode_us;
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ctx->t_decode_us += ctxs[i].t_decode_us;
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}
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// average the timings
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ctx->t_mel_us /= n_processors;
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ctx->t_sample_us /= n_processors;
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ctx->t_encode_us /= n_processors;
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ctx->t_decode_us /= n_processors;
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// print information about the audio boundaries
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fprintf(stderr, "\n");
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fprintf(stderr, "%s: the audio has been split into %d chunks at the following times:\n", __func__, n_processors);
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for (int i = 0; i < n_processors - 1; ++i) {
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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());
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}
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fprintf(stderr, "%s: the transcription quality may be degraded near these boundaries\n", __func__);
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return ret;
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}
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@ -213,6 +213,9 @@ extern "C" {
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const float * samples,
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int n_samples);
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// Split the input audio in chunks and process each chunk separately using whisper_full()
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// It seems this approach can offer some speedup in some cases.
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// However, the transcription accuracy can be worse at the beginning and end of each chunk.
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WHISPER_API int whisper_full_parallel(
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struct whisper_context * ctx,
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struct whisper_full_params params,
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