雾聪
2023-06-28 54931dd4e1a099d7d6f144c4e12e5453deb3aa26
funasr/models/e2e_asr_transducer.py
@@ -108,7 +108,7 @@
        self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
        if self.use_auxiliary_ctc:
            self.ctc_lin = torch.nn.Linear(encoder.output_size, vocab_size)
            self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
            self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
        if self.use_auxiliary_lm_loss:
@@ -162,7 +162,9 @@
        # 1. Encoder
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
            encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
                                                                                        chunk_outs=None)
        # 2. Transducer-related I/O preparation
        decoder_in, target, t_len, u_len = get_transducer_task_io(
            text,
@@ -483,7 +485,7 @@
        return loss_lm
class UnifiedTransducerModel(AbsESPnetModel):
class UnifiedTransducerModel(FunASRModel):
    """ESPnet2ASRTransducerModel module definition.
    Args:
        vocab_size: Size of complete vocabulary (w/ EOS and blank included).
@@ -577,7 +579,7 @@
        self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
        if self.use_auxiliary_ctc:
            self.ctc_lin = torch.nn.Linear(encoder.output_size, vocab_size)
            self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
            self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
        if self.use_auxiliary_att: