| | |
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| | | |
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| | | # for data-parallel |
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| | | text = text[:, : text_lengths.max()] |
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| | | speech = speech[:, :speech_lengths.max(), :] |
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| | | speech = speech[:, :speech_lengths.max()] |
|---|
| | | |
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| | | ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind) |
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| | | speech_raw = speech.clone().to(speech.device) |
|---|
| | | # 1. Encoder |
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| | | if self.enable_maas_finetune: |
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| | | with torch.no_grad(): |
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| | | encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind) |
|---|
| | | speech_raw, encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind) |
|---|
| | | else: |
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| | | encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind) |
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| | | speech_raw, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind) |
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| | | |
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| | | intermediate_outs = None |
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| | | if isinstance(encoder_out, tuple): |
|---|
| | |
|---|
| | | # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN |
|---|
| | | if self.normalize is not None: |
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| | | feats, feats_lengths = self.normalize(feats, feats_lengths) |
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| | | |
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| | | speech_raw = feats.clone().to(feats.device) |
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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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| | |
|---|
| | | if intermediate_outs is not None: |
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| | | return (encoder_out, intermediate_outs), encoder_out_lens |
|---|
| | | |
|---|
| | | return encoder_out, encoder_out_lens |
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| | | return speech_raw, encoder_out, encoder_out_lens |
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| | | |
|---|
| | | def encode2( |
|---|
| | | self, |
|---|
