auto frontend

游雁

2024-06-07 162efb747f21f305851c682e5f7f0f3050d545a9

auto frontend

 funasr/models/llm_asr/adaptor.py

@@ -83,25 +83,27 @@
        from funasr.models.transformer.attention import MultiHeadedAttention
        from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward

        self.blocks = nn.ModuleList(
            [
                EncoderLayer(
                    llm_dim,
                    MultiHeadedAttention(
                        kwargs.get("attention_heads", 8),
        self.blocks = None
        if kwargs.get("n_layer", 2) > 0:
            self.blocks = nn.ModuleList(
                [
                    EncoderLayer(
                        llm_dim,
                        kwargs.get("attention_dropout_rate", 0.0),
                    ),
                    PositionwiseFeedForward(
                        llm_dim,
                        llm_dim // 4,
                        MultiHeadedAttention(
                            kwargs.get("attention_heads", 8),
                            llm_dim,
                            kwargs.get("attention_dropout_rate", 0.0),
                        ),
                        PositionwiseFeedForward(
                            llm_dim,
                            llm_dim // 4,
                            kwargs.get("dropout_rate", 0.0),
                        ),
                        kwargs.get("dropout_rate", 0.0),
                    ),
                    kwargs.get("dropout_rate", 0.0),
                )
                for i in range(kwargs.get("n_layer", 2))
            ]
        )
                    )
                    for i in range(kwargs.get("n_layer", 2))
                ]
            )

    def forward(self, x, ilens=None):

@@ -123,6 +125,7 @@
        olens = None
        olens = (ilens - 1) // self.k + 1
        masks = (~make_pad_mask(olens)[:, None, :]).to(x.device)
        for layer, block in enumerate(self.blocks):
            x, masks = block(x, masks)
        if self.blocks is not None:
            for layer, block in enumerate(self.blocks):
                x, masks = block(x, masks)
        return x, olens

 funasr/models/llm_asr/model.py

@@ -481,7 +481,7 @@

        batch_size, token_num, dims = inputs_embeds.shape
        fbank_mask[fbank_mask < 0] = 0
        fbank_fake_lens = fbank_mask.sum(-1)
        fbank_fake_lens = fbank_mask.sum(-1).to(torch.int32)
        # _, l, _ = encoder_out.shape
        for batch_idx in range(batch_size):