Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into ...

雾聪

2023-06-27 69dcdbcfc0c21627c40fb8f7c435136c11691574

 funasr/models/encoder/conformer_encoder.py

@@ -1081,7 +1081,10 @@
        mask = make_source_mask(x_len).to(x.device)

        if self.unified_model_training:
            chunk_size = self.default_chunk_size + torch.randint(-self.jitter_range, self.jitter_range+1, (1,)).item()
            if self.training:
                chunk_size = self.default_chunk_size + torch.randint(-self.jitter_range, self.jitter_range+1, (1,)).item()
            else:
                chunk_size = self.default_chunk_size
            x, mask = self.embed(x, mask, chunk_size)
            pos_enc = self.pos_enc(x)
            chunk_mask = make_chunk_mask(
@@ -1113,12 +1116,15 @@

        elif self.dynamic_chunk_training:
            max_len = x.size(1)
            chunk_size = torch.randint(1, max_len, (1,)).item()
            if self.training:
                chunk_size = torch.randint(1, max_len, (1,)).item()

            if chunk_size > (max_len * self.short_chunk_threshold):
                chunk_size = max_len
                if chunk_size > (max_len * self.short_chunk_threshold):
                    chunk_size = max_len
                else:
                    chunk_size = (chunk_size % self.short_chunk_size) + 1
            else:
                chunk_size = (chunk_size % self.short_chunk_size) + 1
                chunk_size = self.default_chunk_size

            x, mask = self.embed(x, mask, chunk_size)
            pos_enc = self.pos_enc(x)
@@ -1147,6 +1153,45 @@

        return x, olens, None

    def full_utt_forward(
        self,
        x: torch.Tensor,
        x_len: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Encode input sequences.
        Args:
            x: Encoder input features. (B, T_in, F)
            x_len: Encoder input features lengths. (B,)
        Returns:
           x: Encoder outputs. (B, T_out, D_enc)
           x_len: Encoder outputs lenghts. (B,)
        """
        short_status, limit_size = check_short_utt(
            self.embed.subsampling_factor, x.size(1)
        )

        if short_status:
            raise TooShortUttError(
                f"has {x.size(1)} frames and is too short for subsampling "
                + f"(it needs more than {limit_size} frames), return empty results",
                x.size(1),
                limit_size,
            )

        mask = make_source_mask(x_len).to(x.device)
        x, mask = self.embed(x, mask, None)
        pos_enc = self.pos_enc(x)
        x_utt = self.encoders(
            x,
            pos_enc,
            mask,
            chunk_mask=None,
        )

        if self.time_reduction_factor > 1:
            x_utt = x_utt[:,::self.time_reduction_factor,:]
        return x_utt

    def simu_chunk_forward(
        self,
        x: torch.Tensor,