| | |
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| | | speech_lengths: (Batch, ) |
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| | | """ |
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| | | # with autocast(False): |
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| | | # # 1. Extract feats |
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| | | # feats, feats_lengths = self._extract_feats(speech, speech_lengths) |
|---|
| | | # # 1. Extract feats |
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| | | # feats, feats_lengths = self._extract_feats(speech, speech_lengths) |
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| | | # |
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| | | # # 2. Data augmentation |
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| | | # if self.specaug is not None and self.training: |
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| | | # feats, feats_lengths = self.specaug(feats, feats_lengths) |
|---|
| | | # # 2. Data augmentation |
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| | | # if self.specaug is not None and self.training: |
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| | | # feats, feats_lengths = self.specaug(feats, feats_lengths) |
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| | | # |
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| | | # # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN |
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| | | # if self.normalize is not None: |
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| | | # feats, feats_lengths = self.normalize(feats, feats_lengths) |
|---|
| | | # # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN |
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| | | # if self.normalize is not None: |
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| | | # feats, feats_lengths = self.normalize(feats, feats_lengths) |
|---|
| | | |
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| | | # Pre-encoder, e.g. used for raw input data |
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| | | # if self.preencoder is not None: |
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| | | # feats, feats_lengths = self.preencoder(feats, feats_lengths) |
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| | | # feats, feats_lengths = self.preencoder(feats, feats_lengths) |
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| | | encoder_out_rm, encoder_out_lens_rm = self.encoder.overlap_chunk_cls.remove_chunk( |
|---|
| | | encoder_out, |
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| | | encoder_out_lens, |
|---|
| | |
|---|
| | | |
|---|
| | | # # Post-encoder, e.g. NLU |
|---|
| | | # if self.postencoder is not None: |
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| | | # encoder_out, encoder_out_lens = self.postencoder( |
|---|
| | | # encoder_out, encoder_out_lens |
|---|
| | | # ) |
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| | | # encoder_out, encoder_out_lens = self.postencoder( |
|---|
| | | # encoder_out, encoder_out_lens |
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| | | # ) |
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| | | |
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| | | assert encoder_out.size(0) == speech.size(0), ( |
|---|
| | | encoder_out.size(), |
|---|
