| | |
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| | | |
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| | | |
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| | | class VADXOptions: |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__( |
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| | | self, |
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| | | sample_rate: int = 16000, |
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| | |
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| | | |
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| | | |
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| | | class E2EVadSpeechBufWithDoa(object): |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__(self): |
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| | | self.start_ms = 0 |
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| | | self.end_ms = 0 |
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| | |
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| | | |
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| | | |
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| | | class E2EVadFrameProb(object): |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__(self): |
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| | | self.noise_prob = 0.0 |
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| | | self.speech_prob = 0.0 |
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| | |
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| | | |
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| | | |
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| | | class WindowDetector(object): |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__(self, window_size_ms: int, sil_to_speech_time: int, |
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| | | speech_to_sil_time: int, frame_size_ms: int): |
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| | | self.window_size_ms = window_size_ms |
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| | |
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| | | |
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| | | |
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| | | class E2EVadModel(nn.Module): |
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| | | def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]): |
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| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
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| | | https://arxiv.org/abs/1803.05030 |
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| | | """ |
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| | | def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], frontend=None): |
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| | | super(E2EVadModel, self).__init__() |
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| | | self.vad_opts = VADXOptions(**vad_post_args) |
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| | | self.windows_detector = WindowDetector(self.vad_opts.window_size_ms, |
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| | |
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| | | self.data_buf_all = None |
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| | | self.waveform = None |
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| | | self.ResetDetection() |
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| | | self.frontend = frontend |
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| | | |
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| | | def AllResetDetection(self): |
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| | | self.is_final = False |
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| | |
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| | | segment_batch = [] |
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| | | if len(self.output_data_buf) > 0: |
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| | | for i in range(self.output_data_buf_offset, len(self.output_data_buf)): |
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| | | if not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[ |
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| | | i].contain_seg_end_point: |
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| | | if not is_final and (not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[ |
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| | | i].contain_seg_end_point): |
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| | | continue |
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| | | segment = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms] |
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| | | segment_batch.append(segment) |
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| | |
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| | | ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]: |
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| | | self.max_end_sil_frame_cnt_thresh = max_end_sil - self.vad_opts.speech_to_sil_time_thres |
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| | | self.waveform = waveform # compute decibel for each frame |
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| | | self.ComputeDecibel() |
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| | | |
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| | | self.ComputeScores(feats, in_cache) |
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| | | self.ComputeDecibel() |
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| | | if not is_final: |
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| | | self.DetectCommonFrames() |
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| | | else: |
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