| | |
|---|
| | | ibest_writer["rtf"][key] = rtf_cur |
|---|
| | | |
|---|
| | | if text is not None: |
|---|
| | | if use_timestamp and timestamp is not None: |
|---|
| | | if use_timestamp and timestamp is not None and len(timestamp): |
|---|
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp) |
|---|
| | | else: |
|---|
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token) |
|---|
| | |
|---|
| | | text, token, token_int = result[0], result[1], result[2] |
|---|
| | | time_stamp = result[4] if len(result[4]) > 0 else None |
|---|
| | | |
|---|
| | | if use_timestamp and time_stamp is not None: |
|---|
| | | if use_timestamp and time_stamp is not None and len(time_stamp): |
|---|
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp) |
|---|
| | | else: |
|---|
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token) |
|---|
| | |
|---|
| | | type=int, |
|---|
| | | default=1, |
|---|
| | | help="The batch size for inference", |
|---|
| | | ) |
|---|
| | | group.add_argument( |
|---|
| | | "--decoding_ind", |
|---|
| | | type=int, |
|---|
| | | default=0, |
|---|
| | | help="chunk select for chunk encoder", |
|---|
| | | ) |
|---|
| | | group.add_argument("--nbest", type=int, default=5, help="Output N-best hypotheses") |
|---|
| | | group.add_argument("--beam_size", type=int, default=20, help="Beam size") |
|---|
