v0.8.0

游雁

2023-10-10 580b11b57ac4b62f7e2acda73813a4e10e8e4cd3

 funasr/bin/asr_infer.py

@@ -399,7 +399,7 @@
    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
            begin_time: int = 0, end_time: int = None,
            decoding_ind: int = None, begin_time: int = 0, end_time: int = None,
    ):
        """Inference

@@ -429,7 +429,9 @@
        batch = to_device(batch, device=self.device)

        # b. Forward Encoder
        enc, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
        if decoding_ind is None:
            decoding_ind = 0 if self.decoding_ind is None else self.decoding_ind
        enc, enc_len = self.asr_model.encode(**batch, ind=decoding_ind)
        if isinstance(enc, tuple):
            enc = enc[0]
        # assert len(enc) == 1, len(enc)
@@ -1335,7 +1337,7 @@
            quantize_dtype: str = "qint8",
            nbest: int = 1,
            streaming: bool = False,
            simu_streaming: bool = False,
            fake_streaming: bool = False,
            full_utt: bool = False,
            chunk_size: int = 16,
            left_context: int = 32,
@@ -1430,7 +1432,7 @@

        self.beam_search = beam_search
        self.streaming = streaming
        self.simu_streaming = simu_streaming
        self.fake_streaming = fake_streaming
        self.full_utt = full_utt
        self.chunk_size = max(chunk_size, 0)
        self.left_context = left_context
@@ -1440,8 +1442,8 @@
            self.streaming = False
            self.asr_model.encoder.dynamic_chunk_training = False

        if not simu_streaming or chunk_size == 0:
            self.simu_streaming = False
        if not fake_streaming or chunk_size == 0:
            self.fake_streaming = False
            self.asr_model.encoder.dynamic_chunk_training = False

        self.frontend = frontend
@@ -1518,7 +1520,7 @@
        return nbest_hyps

    @torch.no_grad()
    def simu_streaming_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]:
    def fake_streaming_decode(self, speech: Union[torch.Tensor, np.ndarray]) -> List[HypothesisTransducer]:
        """Speech2Text call.
        Args:
            speech: Speech data. (S)