| | |
|---|
| | | ind: int |
|---|
| | | """ |
|---|
| | | with autocast(False): |
|---|
| | | pdb.set_trace() |
|---|
| | | # Data augmentation |
|---|
| | | if self.specaug is not None and self.training: |
|---|
| | | speech, speech_lengths = self.specaug(speech, speech_lengths) |
|---|
| | | pdb.set_trace() |
|---|
| | | # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN |
|---|
| | | if self.normalize is not None: |
|---|
| | | speech, speech_lengths = self.normalize(speech, speech_lengths) |
|---|
| | | pdb.set_trace() |
|---|
| | | # Forward encoder |
|---|
| | | # feats: (Batch, Length, Dim) |
|---|
| | | # -> encoder_out: (Batch, Length2, Dim2) |
|---|
| | |
|---|
| | | |
|---|
| | | if intermediate_outs is not None: |
|---|
| | | return (encoder_out, intermediate_outs), encoder_out_lens |
|---|
| | | pdb.set_trace() |
|---|
| | | return encoder_out, encoder_out_lens |
|---|
| | | |
|---|
| | | def _calc_att_loss( |
|---|
| | |
|---|
| | | encoder_out, encoder_out_lens = self.encode(speech, speech_lengths) |
|---|
| | | if isinstance(encoder_out, tuple): |
|---|
| | | encoder_out = encoder_out[0] |
|---|
| | | |
|---|
| | | pdb.set_trace() |
|---|
| | | ocr = ocr_sample_list[0] |
|---|
| | | ocr_lengths = ocr.new_full([1], dtype=torch.long, fill_value=ocr.size(1)) |
|---|
| | | ocr, ocr_lens, _ = self.text_encoder(ocr, ocr_lengths) |
|---|
| | | pdb.set_trace() |
|---|
| | | # c. Passed the encoder result and the beam search |
|---|
| | | nbest_hyps = self.beam_search( |
|---|
| | | x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0) |
|---|
