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游雁

2023-03-13 fc08b62d05723cdc1ce021bb8ba044ca014fb1f7

 funasr/punctuation/espnet_model.py

@@ -13,15 +13,19 @@


class ESPnetPunctuationModel(AbsESPnetModel):
    def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0):

    def __init__(self, punc_model: AbsPunctuation, vocab_size: int, ignore_id: int = 0, punc_weight: list = None):
        assert check_argument_types()
        super().__init__()
        self.punc_model = punc_model
        self.punc_weight = torch.Tensor(punc_weight)
        self.sos = 1
        self.eos = 2

        # ignore_id may be assumed as 0, shared with CTC-blank symbol for ASR.
        self.ignore_id = ignore_id
        if self.punc_model.with_vad():
            print("This is a vad puncuation model.")

    def nll(
        self,
@@ -30,6 +34,8 @@
        text_lengths: torch.Tensor,
        punc_lengths: torch.Tensor,
        max_length: Optional[int] = None,
        vad_indexes: Optional[torch.Tensor] = None,
        vad_indexes_lengths: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute negative log likelihood(nll)

@@ -43,33 +49,33 @@
        batch_size = text.size(0)
        # For data parallel
        if max_length is None:
            text = text[:, : text_lengths.max()]
            punc = punc[:, : text_lengths.max()]
            text = text[:, :text_lengths.max()]
            punc = punc[:, :text_lengths.max()]
        else:
            text = text[:, :max_length]
            punc = punc[:, :max_length]
        # 1. Create a sentence pair like '<sos> w1 w2 w3' and 'w1 w2 w3 <eos>'
        # text: (Batch, Length) -> x, y: (Batch, Length + 1)
        #x = F.pad(text, [1, 0], "constant", self.eos)
        #t = F.pad(text, [0, 1], "constant", self.ignore_id)
        #for i, l in enumerate(text_lengths):
        #    t[i, l] = self.sos
        #x_lengths = text_lengths + 1
       
        if self.punc_model.with_vad():
            # Should be VadRealtimeTransformer
            assert vad_indexes is not None
            y, _ = self.punc_model(text, text_lengths, vad_indexes)
        else:
            # Should be TargetDelayTransformer,
            y, _ = self.punc_model(text, text_lengths)

        # 2. Forward Language model
        # x: (Batch, Length) -> y: (Batch, Length, NVocab)
        y, _ = self.punc_model(text, text_lengths)

        # 3. Calc negative log likelihood
        # Calc negative log likelihood
        # nll: (BxL,)
        if self.training == False:
            _, indices = y.view(-1, y.shape[-1]).topk(1,dim=1)
            _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
            from sklearn.metrics import f1_score
            f1_score = f1_score(punc.view(-1).detach().cpu().numpy(), indices.squeeze(-1).detach().cpu().numpy(), average='micro')
            f1_score = f1_score(punc.view(-1).detach().cpu().numpy(),
                                indices.squeeze(-1).detach().cpu().numpy(),
                                average='micro')
            nll = torch.Tensor([f1_score]).repeat(text_lengths.sum())
            return nll, text_lengths
        else:
            nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), reduction="none", ignore_index=self.ignore_id)
            self.punc_weight = self.punc_weight.to(punc.device)
            nll = F.cross_entropy(y.view(-1, y.shape[-1]), punc.view(-1), self.punc_weight, reduction="none", ignore_index=self.ignore_id)
        # nll: (BxL,) -> (BxL,)
        if max_length is None:
            nll.masked_fill_(make_pad_mask(text_lengths).to(nll.device).view(-1), 0.0)
@@ -82,14 +88,12 @@
        nll = nll.view(batch_size, -1)
        return nll, text_lengths

    def batchify_nll(
        self,
        text: torch.Tensor,
        punc: torch.Tensor,
        text_lengths: torch.Tensor,
        punc_lengths: torch.Tensor,
        batch_size: int = 100
    ) -> Tuple[torch.Tensor, torch.Tensor]:
    def batchify_nll(self,
                     text: torch.Tensor,
                     punc: torch.Tensor,
                     text_lengths: torch.Tensor,
                     punc_lengths: torch.Tensor,
                     batch_size: int = 100) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute negative log likelihood(nll) from transformer language model

        To avoid OOM, this fuction seperate the input into batches.
@@ -117,9 +121,7 @@
                batch_punc = punc[start_idx:end_idx, :]
                batch_text_lengths = text_lengths[start_idx:end_idx]
                # batch_nll: [B * T]
                batch_nll, batch_x_lengths = self.nll(
                    batch_text, batch_punc, batch_text_lengths, max_length=max_length
                )
                batch_nll, batch_x_lengths = self.nll(batch_text, batch_punc, batch_text_lengths, max_length=max_length)
                nlls.append(batch_nll)
                x_lengths.append(batch_x_lengths)
                start_idx = end_idx
@@ -132,21 +134,33 @@
        return nll, x_lengths

    def forward(
        self, text: torch.Tensor, punc: torch.Tensor, text_lengths: torch.Tensor, punc_lengths: torch.Tensor
        self,
        text: torch.Tensor,
        punc: torch.Tensor,
        text_lengths: torch.Tensor,
        punc_lengths: torch.Tensor,
        vad_indexes: Optional[torch.Tensor] = None,
        vad_indexes_lengths: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
        nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths)
        nll, y_lengths = self.nll(text, punc, text_lengths, punc_lengths, vad_indexes=vad_indexes)
        ntokens = y_lengths.sum()
        loss = nll.sum() / ntokens
        stats = dict(loss=loss.detach())
        

        # force_gatherable: to-device and to-tensor if scalar for DataParallel
        loss, stats, weight = force_gatherable((loss, stats, ntokens), loss.device)
        return loss, stats, weight

    def collect_feats(
        self, text: torch.Tensor, punc: torch.Tensor, text_lengths: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
    def collect_feats(self, text: torch.Tensor, punc: torch.Tensor,
                      text_lengths: torch.Tensor) -> Dict[str, torch.Tensor]:
        return {}

    def inference(self, text: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
        return self.punc_model(text, text_lengths)
    def inference(self,
                  text: torch.Tensor,
                  text_lengths: torch.Tensor,
                  vad_indexes: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, None]:
        if self.punc_model.with_vad():
            assert vad_indexes is not None
            return self.punc_model(text, text_lengths, vad_indexes)
        else:
            return self.punc_model(text, text_lengths)