| | |
|---|
| | | text_encoder = text_encoder_class(input_size=vocab_size, **text_encoder_conf) |
|---|
| | | fusion_encoder_class = tables.encoder_classes.get(fusion_encoder) |
|---|
| | | fusion_encoder = fusion_encoder_class(**fusion_encoder_conf) |
|---|
| | | bias_predictor_class = tables.encoder_classes.get_class(bias_predictor) |
|---|
| | | bias_predictor = bias_predictor_class(bias_predictor_conf) |
|---|
| | | bias_predictor_class = tables.encoder_classes.get(bias_predictor) |
|---|
| | | bias_predictor = bias_predictor_class(**bias_predictor_conf) |
|---|
| | | |
|---|
| | | if decoder is not None: |
|---|
| | | decoder_class = tables.decoder_classes.get(decoder) |
|---|
| | |
|---|
| | | self.init_beam_search(**kwargs) |
|---|
| | | self.nbest = kwargs.get("nbest", 1) |
|---|
| | | pdb.set_trace() |
|---|
| | | |
|---|
| | | |
|---|
| | | meta_data = {} |
|---|
| | | if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank |
|---|
| | | speech, speech_lengths = data_in, data_lengths |
|---|
