| | |
|---|
| | | self.sil_pdf_ids = self.vad_opts.sil_pdf_ids |
|---|
| | | self.noise_average_decibel = -100.0 |
|---|
| | | self.pre_end_silence_detected = False |
|---|
| | | self.next_seg = True |
|---|
| | | |
|---|
| | | self.output_data_buf = [] |
|---|
| | | self.output_data_buf_offset = 0 |
|---|
| | |
|---|
| | | self.sil_pdf_ids = self.vad_opts.sil_pdf_ids |
|---|
| | | self.noise_average_decibel = -100.0 |
|---|
| | | self.pre_end_silence_detected = False |
|---|
| | | self.next_seg = True |
|---|
| | | |
|---|
| | | self.output_data_buf = [] |
|---|
| | | self.output_data_buf_offset = 0 |
|---|
| | |
|---|
| | | - 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]]: |
|---|
| | |
|---|
| | | self.AllResetDetection() |
|---|
| | | 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 |
|---|
| | | ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]: |
|---|
| | | self.waveform = waveform # compute decibel for each frame |
|---|
| | | self.ComputeDecibel() |
|---|
| | | self.ComputeScores(feats, in_cache) |
|---|
| | | if not is_final: |
|---|
| | | self.DetectCommonFrames() |
|---|
| | | else: |
|---|
| | | self.DetectLastFrames() |
|---|
| | | segments = [] |
|---|
| | | for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now |
|---|
| | | 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: |
|---|
| | | continue |
|---|
| | | if not self.next_seg and not self.output_data_buf[i].contain_seg_end_point: |
|---|
| | | continue |
|---|
| | | start_ms = self.output_data_buf[i].start_ms if self.next_seg else -1 |
|---|
| | | if self.output_data_buf[i].contain_seg_end_point: |
|---|
| | | end_ms = self.output_data_buf[i].end_ms |
|---|
| | | self.next_seg = True |
|---|
| | | self.output_data_buf_offset += 1 |
|---|
| | | else: |
|---|
| | | end_ms = -1 |
|---|
| | | self.next_seg = False |
|---|
| | | segment = [start_ms, end_ms] |
|---|
| | | segment_batch.append(segment) |
|---|
| | | if segment_batch: |
|---|
| | | segments.append(segment_batch) |
|---|
| | | if is_final: |
|---|
| | | # reset class variables and clear the dict for the next query |
|---|
| | | self.AllResetDetection() |
|---|
| | | return segments, in_cache |
|---|
| | | |
|---|
| | | def DetectCommonFrames(self) -> int: |
|---|
| | | if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected: |
|---|
| | | return 0 |
|---|
