| | |
|---|
| | | speed_stats = {} |
|---|
| | | asr_result_list = [] |
|---|
| | | num_samples = len(data_list) |
|---|
| | | pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) |
|---|
| | | disable_pbar = kwargs.get("disable_pbar", False) |
|---|
| | | pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) if not disable_pbar else None |
|---|
| | | time_speech_total = 0.0 |
|---|
| | | time_escape_total = 0.0 |
|---|
| | | for beg_idx in range(0, num_samples, batch_size): |
|---|
| | |
|---|
| | | time2 = time.perf_counter() |
|---|
| | | |
|---|
| | | asr_result_list.extend(results) |
|---|
| | | pbar.update(1) |
|---|
| | | |
|---|
| | | |
|---|
| | | # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item() |
|---|
| | | batch_data_time = meta_data.get("batch_data_time", -1) |
|---|
| | | time_escape = time2 - time1 |
|---|
| | |
|---|
| | | description = ( |
|---|
| | | f"{speed_stats}, " |
|---|
| | | ) |
|---|
| | | pbar.set_description(description) |
|---|
| | | if pbar: |
|---|
| | | pbar.update(1) |
|---|
| | | pbar.set_description(description) |
|---|
| | | time_speech_total += batch_data_time |
|---|
| | | time_escape_total += time_escape |
|---|
| | | |
|---|
| | | pbar.update(1) |
|---|
| | | pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}") |
|---|
| | | |
|---|
| | | if pbar: |
|---|
| | | pbar.update(1) |
|---|
| | | pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}") |
|---|
| | | torch.cuda.empty_cache() |
|---|
| | | return asr_result_list |
|---|
| | | |
|---|
| | |
|---|
| | | time_speech_total_per_sample = speech_lengths/16000 |
|---|
| | | time_speech_total_all_samples += time_speech_total_per_sample |
|---|
| | | |
|---|
| | | pbar_sample = tqdm(colour="blue", total=n + 1, dynamic_ncols=True) |
|---|
| | | |
|---|
| | | all_segments = [] |
|---|
| | | for j, _ in enumerate(range(0, n)): |
|---|
| | | pbar_sample.update(1) |
|---|
| | | batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0]) |
|---|
| | | if j < n - 1 and ( |
|---|
| | | batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and ( |
|---|
| | |
|---|
| | | batch_size_ms_cum = 0 |
|---|
| | | end_idx = j + 1 |
|---|
| | | speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx]) |
|---|
| | | results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg) |
|---|
| | | results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg) |
|---|
| | | if self.spk_model is not None: |
|---|
| | | |
|---|
| | | |
|---|
| | | |
|---|
| | | # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]] |
|---|
| | | for _b in range(len(speech_j)): |
|---|
| | | vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \ |
|---|
| | | sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \ |
|---|
| | | vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, |
|---|
| | | sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, |
|---|
| | | speech_j[_b]]] |
|---|
| | | segments = sv_chunk(vad_segments) |
|---|
| | | all_segments.extend(segments) |
|---|
| | |
|---|
| | | results_sorted.extend(results) |
|---|
| | | |
|---|
| | | |
|---|
| | | pbar_total.update(1) |
|---|
| | | |
|---|
| | | end_asr_total = time.time() |
|---|
| | | time_escape_total_per_sample = end_asr_total - beg_asr_total |
|---|
| | | pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, " |
|---|
| | | pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, " |
|---|
| | | f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, " |
|---|
| | | f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}") |
|---|
| | | |
|---|
| | | |
|---|
| | | restored_data = [0] * n |
|---|
| | | for j in range(n): |
|---|
