update gitignore

维石

2024-07-23 fe8ebd746bf0c0f57ef85ed342500cbf0e2c4e9e

 runtime/python/libtorch/funasr_torch/sensevoice_bin.py

@@ -33,14 +33,13 @@
        self,
        model_dir: Union[str, Path] = None,
        batch_size: int = 1,
        device_id: Union[str, int] = "-1",
        plot_timestamp_to: str = "",
        quantize: bool = False,
        intra_op_num_threads: int = 4,
        cache_dir: str = None,
        **kwargs,
    ):

        self.device = kwargs.get("device", "cpu")
        if not Path(model_dir).exists():
            try:
                from modelscope.hub.snapshot_download import snapshot_download
@@ -78,6 +77,10 @@
        self.ort_infer = torch.jit.load(model_file)
        self.batch_size = batch_size
        self.blank_id = 0
        self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
        self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
        self.textnorm_dict = {"withitn": 14, "woitn": 15}
        self.textnorm_int_dict = {25016: 14, 25017: 15}

    def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:

@@ -95,10 +98,10 @@
            end_idx = min(waveform_nums, beg_idx + self.batch_size)
            feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
            ctc_logits, encoder_out_lens = self.ort_infer(
                torch.Tensor(feats),
                torch.Tensor(feats_len),
                torch.tensor([language]),
                torch.tensor([textnorm]),
                torch.Tensor(feats).to(self.device),
                torch.Tensor(feats_len).to(self.device),
                torch.tensor([language]).to(self.device),
                torch.tensor([textnorm]).to(self.device),
            )
            # support batch_size=1 only currently
            x = ctc_logits[0, : encoder_out_lens[0].item(), :]
@@ -108,10 +111,8 @@
            mask = yseq != self.blank_id
            token_int = yseq[mask].tolist()

            if tokenizer is not None:
                asr_res.append(tokenizer.decode(token_int))
            else:
                asr_res.append(token_int)
            asr_res.append(self.tokenizer.decode(token_int))

        return asr_res

    def load_data(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List: