| | |
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| | | |
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| | | _bs = len(next(iter(batch.values()))) |
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| | | assert len(keys) == _bs, f"{len(keys)} != {_bs}" |
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| | | <<<<<<< HEAD |
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| | | batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")} |
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| | | assert len(batch.keys()) == 1 |
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| | | |
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| | |
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| | | |
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| | | if text is not None: |
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| | | ibest_writer["text"][key] = text |
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| | | ======= |
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| | | # batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")} |
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| | | |
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| | | logging.info("decoding, utt_id: {}".format(keys)) |
|---|
| | | # N-best list of (text, token, token_int, hyp_object) |
|---|
| | | |
|---|
| | | time_beg = time.time() |
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| | | results = speech2text(cache=cache, **batch) |
|---|
| | | if len(results) < 1: |
|---|
| | | hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[]) |
|---|
| | | results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest |
|---|
| | | time_end = time.time() |
|---|
| | | forward_time = time_end - time_beg |
|---|
| | | lfr_factor = results[0][-1] |
|---|
| | | length = results[0][-2] |
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| | | forward_time_total += forward_time |
|---|
| | | length_total += length |
|---|
| | | rtf_cur = "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".format(length, forward_time, 100 * forward_time / (length * lfr_factor)) |
|---|
| | | logging.info(rtf_cur) |
|---|
| | | |
|---|
| | | for batch_id in range(_bs): |
|---|
| | | result = [results[batch_id][:-2]] |
|---|
| | | |
|---|
| | | key = keys[batch_id] |
|---|
| | | for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result): |
|---|
| | | # Create a directory: outdir/{n}best_recog |
|---|
| | | if writer is not None: |
|---|
| | | ibest_writer = writer[f"{n}best_recog"] |
|---|
| | | |
|---|
| | | # Write the result to each file |
|---|
| | | ibest_writer["token"][key] = " ".join(token) |
|---|
| | | # ibest_writer["token_int"][key] = " ".join(map(str, token_int)) |
|---|
| | | ibest_writer["score"][key] = str(hyp.score) |
|---|
| | | ibest_writer["rtf"][key] = rtf_cur |
|---|
| | | |
|---|
| | | if text is not None: |
|---|
| | | text_postprocessed, word_lists = postprocess_utils.sentence_postprocess(token) |
|---|
| | | item = {'key': key, 'value': text_postprocessed} |
|---|
| | | asr_result_list.append(item) |
|---|
| | | finish_count += 1 |
|---|
| | | # asr_utils.print_progress(finish_count / file_count) |
|---|
| | | if writer is not None: |
|---|
| | | ibest_writer["text"][key] = " ".join(word_lists) |
|---|
| | | |
|---|
| | | logging.info("decoding, utt: {}, predictions: {}".format(key, text)) |
|---|
| | | rtf_avg = "decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor)) |
|---|
| | | logging.info(rtf_avg) |
|---|
| | | if writer is not None: |
|---|
| | | ibest_writer["rtf"]["rtf_avf"] = rtf_avg |
|---|
| | | return asr_result_list |
|---|
| | | |
|---|
| | | return _forward |
|---|
| | | >>>>>>> main |
|---|
| | | |
|---|
| | | |
|---|
| | | def get_parser(): |
|---|
