add paraforme infer code

haoneng.lhn

2023-05-22 a2a70f776ac46dc8987a05459de260ff2825ffbc

 funasr/models/e2e_asr_paraformer.py

@@ -153,6 +153,7 @@
            speech_lengths: torch.Tensor,
            text: torch.Tensor,
            text_lengths: torch.Tensor,
            decoding_ind: int = None,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        """Frontend + Encoder + Decoder + Calc loss
        Args:
@@ -176,6 +177,10 @@
        speech = speech[:, :speech_lengths.max()]

        # 1. Encoder
        if hasattr(self.encoder, "overlap_chunk_cls"):
            ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
            encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
        else:
        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
        intermediate_outs = None
        if isinstance(encoder_out, tuple):
@@ -272,7 +277,7 @@
        return {"feats": feats, "feats_lengths": feats_lengths}

    def encode(
            self, speech: torch.Tensor, speech_lengths: torch.Tensor
            self, speech: torch.Tensor, speech_lengths: torch.Tensor, ind: int = 0,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Frontend + Encoder. Note that this method is used by asr_inference.py
        Args:
@@ -299,10 +304,24 @@
        # feats: (Batch, Length, Dim)
        # -> encoder_out: (Batch, Length2, Dim2)
        if self.encoder.interctc_use_conditioning:
            if hasattr(self.encoder, "overlap_chunk_cls"):
                encoder_out, encoder_out_lens, _ = self.encoder(
                    feats, feats_lengths, ctc=self.ctc, ind=ind
                )
                encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
                                                                                            encoder_out_lens,
                                                                                            chunk_outs=None)
            else:
            encoder_out, encoder_out_lens, _ = self.encoder(
                feats, feats_lengths, ctc=self.ctc
            )
        else:
            if hasattr(self.encoder, "overlap_chunk_cls"):
                encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
                encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
                                                                                            encoder_out_lens,
                                                                                            chunk_outs=None)
            else:
            encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
        intermediate_outs = None
        if isinstance(encoder_out, tuple):