| | |
|---|
| | | from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard |
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list |
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| | | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank |
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| | | |
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| | | from funasr.train_utils.device_funcs import to_device |
|---|
| | | |
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| | | if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): |
|---|
| | | from torch.cuda.amp import autocast |
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| | |
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| | | nbest_hyps = [Hypothesis(yseq=yseq, score=score)] |
|---|
| | | for nbest_idx, hyp in enumerate(nbest_hyps): |
|---|
| | | ibest_writer = None |
|---|
| | | if ibest_writer is None and kwargs.get("output_dir") is not None: |
|---|
| | | writer = DatadirWriter(kwargs.get("output_dir")) |
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| | | ibest_writer = writer[f"{nbest_idx + 1}best_recog"] |
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| | | if kwargs.get("output_dir") is not None: |
|---|
| | | if not hasattr(self, "writer"): |
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| | | self.writer = DatadirWriter(kwargs.get("output_dir")) |
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| | | ibest_writer = self.writer[f"{nbest_idx+1}best_recog"] |
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| | | |
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| | | # remove sos/eos and get results |
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| | | last_pos = -1 |
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| | | if isinstance(hyp.yseq, list): |
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| | |
|---|
| | | result_i = {"key": key[i], "token_int": token_int} |
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| | | results.append(result_i) |
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| | | |
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| | | return results, meta_data |
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| | | return results, meta_data |
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| | | |
|---|
| | | def export(self, **kwargs): |
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| | | from .export_meta import export_rebuild_model |
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| | | if 'max_seq_len' not in kwargs: |
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| | | kwargs['max_seq_len'] = 512 |
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| | | models = export_rebuild_model(model=self, **kwargs) |
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| | | return models |
|---|
