update

嘉渊

2023-04-27 607073619cedf2c114e1589aa6d5953d171f33bf

 funasr/models/e2e_vad.py

@@ -35,6 +35,12 @@


class VADXOptions:
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """

    def __init__(
            self,
            sample_rate: int = 16000,
@@ -99,6 +105,12 @@


class E2EVadSpeechBufWithDoa(object):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """

    def __init__(self):
        self.start_ms = 0
        self.end_ms = 0
@@ -117,6 +129,12 @@


class E2EVadFrameProb(object):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """

    def __init__(self):
        self.noise_prob = 0.0
        self.speech_prob = 0.0
@@ -126,6 +144,12 @@


class WindowDetector(object):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """

    def __init__(self, window_size_ms: int, sil_to_speech_time: int,
                 speech_to_sil_time: int, frame_size_ms: int):
        self.window_size_ms = window_size_ms
@@ -192,6 +216,12 @@


class E2EVadModel(nn.Module):
    """
    Author: Speech Lab of DAMO Academy, Alibaba Group
    Deep-FSMN for Large Vocabulary Continuous Speech Recognition
    https://arxiv.org/abs/1803.05030
    """

    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], frontend=None):
        super(E2EVadModel, self).__init__()
        self.vad_opts = VADXOptions(**vad_post_args)
@@ -286,7 +316,7 @@
                                0.000001))

    def ComputeScores(self, feats: torch.Tensor, in_cache: Dict[str, torch.Tensor]) -> None:
        scores = self.encoder(feats, in_cache)  # return B * T * D
        scores = self.encoder(feats, in_cache).to('cpu')  # return B * T * D
        assert scores.shape[1] == feats.shape[1], "The shape between feats and scores does not match"
        self.vad_opts.nn_eval_block_size = scores.shape[1]
        self.frm_cnt += scores.shape[1]  # count total frames
@@ -444,7 +474,7 @@
                        - 1)) / self.vad_opts.noise_frame_num_used_for_snr

        return frame_state
     

    def forward(self, feats: torch.Tensor, waveform: torch.tensor, in_cache: Dict[str, torch.Tensor] = dict(),
                is_final: bool = False
                ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
@@ -460,8 +490,9 @@
            segment_batch = []
            if len(self.output_data_buf) > 0:
                for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
                    if not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[
                        i].contain_seg_end_point:
                    if not is_final and (
                            not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[
                        i].contain_seg_end_point):
                        continue
                    segment = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms]
                    segment_batch.append(segment)
@@ -474,11 +505,11 @@
        return segments, in_cache

    def forward_online(self, feats: torch.Tensor, waveform: torch.tensor, in_cache: Dict[str, torch.Tensor] = dict(),
                is_final: bool = False, max_end_sil: int = 800
                ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
                       is_final: bool = False, max_end_sil: int = 800
                       ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
        self.max_end_sil_frame_cnt_thresh = max_end_sil - self.vad_opts.speech_to_sil_time_thres
        self.waveform = waveform  # compute decibel for each frame
        

        self.ComputeScores(feats, in_cache)
        self.ComputeDecibel()
        if not is_final: