| | |
|---|
| | | filelist = [".scp", ".txt", ".json", ".jsonl", ".text"] |
|---|
| | | |
|---|
| | | chars = string.ascii_letters + string.digits |
|---|
| | | if isinstance(data_in, str) and data_in.startswith("http"): # url |
|---|
| | | data_in = download_from_url(data_in) |
|---|
| | | if isinstance(data_in, str): |
|---|
| | | if data_in.startswith("http://") or data_in.startswith("https://"): # url |
|---|
| | | data_in = download_from_url(data_in) |
|---|
| | | |
|---|
| | | if isinstance(data_in, str) and os.path.exists( |
|---|
| | | data_in |
|---|
| | |
|---|
| | | with torch.no_grad(): |
|---|
| | | res = model.inference(**batch, **kwargs) |
|---|
| | | if isinstance(res, (list, tuple)): |
|---|
| | | results = res[0] |
|---|
| | | results = res[0] if len(res) > 0 else [{"text": ""}] |
|---|
| | | meta_data = res[1] if len(res) > 1 else {} |
|---|
| | | time2 = time.perf_counter() |
|---|
| | | |
|---|
| | |
|---|
| | | results_sorted = [] |
|---|
| | | |
|---|
| | | if not len(sorted_data): |
|---|
| | | results_ret_list.append({"key": key, "text": "", "timestamp": []}) |
|---|
| | | logging.info("decoding, utt: {}, empty speech".format(key)) |
|---|
| | | continue |
|---|
| | | |
|---|
| | | if len(sorted_data) > 0 and len(sorted_data[0]) > 0: |
|---|
| | | batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0]) |
|---|
| | | |
|---|
| | | batch_size_ms_cum = 0 |
|---|
| | | beg_idx = 0 |
|---|
| | | beg_asr_total = time.time() |
|---|
| | | time_speech_total_per_sample = speech_lengths / 16000 |
|---|
| | |
|---|
| | | # pbar_sample = tqdm(colour="blue", total=n, dynamic_ncols=True) |
|---|
| | | |
|---|
| | | all_segments = [] |
|---|
| | | max_len_in_batch = 0 |
|---|
| | | end_idx = 1 |
|---|
| | | 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] |
|---|
| | | sample_length = sorted_data[j][0][1] - sorted_data[j][0][0] |
|---|
| | | potential_batch_length = max(max_len_in_batch, sample_length) * (j + 1 - beg_idx) |
|---|
| | | # 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 (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) |
|---|
| | | < batch_size_threshold_ms |
|---|
| | | and sample_length < batch_size_threshold_ms |
|---|
| | | and potential_batch_length < batch_size |
|---|
| | | ): |
|---|
| | | max_len_in_batch = max(max_len_in_batch, sample_length) |
|---|
| | | end_idx += 1 |
|---|
| | | continue |
|---|
| | | 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[_b]["spk_embedding"] = spk_res[0]["spk_embedding"] |
|---|
| | | beg_idx = end_idx |
|---|
| | | end_idx += 1 |
|---|
| | | max_len_in_batch = sample_length |
|---|
| | | if len(results) < 1: |
|---|
| | | continue |
|---|
| | | results_sorted.extend(results) |
|---|
