| | |
|---|
| | | |
|---|
| | | import numpy as np |
|---|
| | | import torch |
|---|
| | | from typeguard import check_argument_types |
|---|
| | | from funasr.build_utils.build_streaming_iterator import build_streaming_iterator |
|---|
| | | from funasr.fileio.datadir_writer import DatadirWriter |
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| | | from funasr.torch_utils.set_all_random_seed import set_all_random_seed |
|---|
| | |
|---|
| | | num_workers: int = 1, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | assert check_argument_types() |
|---|
| | | if batch_size > 1: |
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| | | raise NotImplementedError("batch decoding is not implemented") |
|---|
| | | |
|---|
| | |
|---|
| | | vad_results.append(item) |
|---|
| | | if writer is not None: |
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| | | ibest_writer["text"][keys[i]] = "{}".format(results[i]) |
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| | | |
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| | | torch.cuda.empty_cache() |
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| | | return vad_results |
|---|
| | | |
|---|
| | | return _forward |
|---|
| | |
|---|
| | | num_workers: int = 1, |
|---|
| | | **kwargs, |
|---|
| | | ): |
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| | | assert check_argument_types() |
|---|
| | | |
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| | | logging.basicConfig( |
|---|
| | | level=log_level, |
|---|
