from enum import Enum from typing import List, Tuple, Dict, Any import torch from torch import nn import math from funasr.models.encoder.fsmn_encoder import FSMN class VadStateMachine(Enum): kVadInStateStartPointNotDetected = 1 kVadInStateInSpeechSegment = 2 kVadInStateEndPointDetected = 3 class FrameState(Enum): kFrameStateInvalid = -1 kFrameStateSpeech = 1 kFrameStateSil = 0 # final voice/unvoice state per frame class AudioChangeState(Enum): kChangeStateSpeech2Speech = 0 kChangeStateSpeech2Sil = 1 kChangeStateSil2Sil = 2 kChangeStateSil2Speech = 3 kChangeStateNoBegin = 4 kChangeStateInvalid = 5 class VadDetectMode(Enum): kVadSingleUtteranceDetectMode = 0 kVadMutipleUtteranceDetectMode = 1 class VADXOptions: def __init__( self, sample_rate: int = 16000, detect_mode: int = VadDetectMode.kVadMutipleUtteranceDetectMode.value, snr_mode: int = 0, max_end_silence_time: int = 800, max_start_silence_time: int = 3000, do_start_point_detection: bool = True, do_end_point_detection: bool = True, window_size_ms: int = 200, sil_to_speech_time_thres: int = 150, speech_to_sil_time_thres: int = 150, speech_2_noise_ratio: float = 1.0, do_extend: int = 1, lookback_time_start_point: int = 200, lookahead_time_end_point: int = 100, max_single_segment_time: int = 60000, nn_eval_block_size: int = 8, dcd_block_size: int = 4, snr_thres: int = -100.0, noise_frame_num_used_for_snr: int = 100, decibel_thres: int = -100.0, speech_noise_thres: float = 0.6, fe_prior_thres: float = 1e-4, silence_pdf_num: int = 1, sil_pdf_ids: List[int] = [0], speech_noise_thresh_low: float = -0.1, speech_noise_thresh_high: float = 0.3, output_frame_probs: bool = False, frame_in_ms: int = 10, frame_length_ms: int = 25, ): self.sample_rate = sample_rate self.detect_mode = detect_mode self.snr_mode = snr_mode self.max_end_silence_time = max_end_silence_time self.max_start_silence_time = max_start_silence_time self.do_start_point_detection = do_start_point_detection self.do_end_point_detection = do_end_point_detection self.window_size_ms = window_size_ms self.sil_to_speech_time_thres = sil_to_speech_time_thres self.speech_to_sil_time_thres = speech_to_sil_time_thres self.speech_2_noise_ratio = speech_2_noise_ratio self.do_extend = do_extend self.lookback_time_start_point = lookback_time_start_point self.lookahead_time_end_point = lookahead_time_end_point self.max_single_segment_time = max_single_segment_time self.nn_eval_block_size = nn_eval_block_size self.dcd_block_size = dcd_block_size self.snr_thres = snr_thres self.noise_frame_num_used_for_snr = noise_frame_num_used_for_snr self.decibel_thres = decibel_thres self.speech_noise_thres = speech_noise_thres self.fe_prior_thres = fe_prior_thres self.silence_pdf_num = silence_pdf_num self.sil_pdf_ids = sil_pdf_ids self.speech_noise_thresh_low = speech_noise_thresh_low self.speech_noise_thresh_high = speech_noise_thresh_high self.output_frame_probs = output_frame_probs self.frame_in_ms = frame_in_ms self.frame_length_ms = frame_length_ms class E2EVadSpeechBufWithDoa(object): def __init__(self): self.start_ms = 0 self.end_ms = 0 self.buffer = [] self.contain_seg_start_point = False self.contain_seg_end_point = False self.doa = 0 def Reset(self): self.start_ms = 0 self.end_ms = 0 self.buffer = [] self.contain_seg_start_point = False self.contain_seg_end_point = False self.doa = 0 class E2EVadFrameProb(object): def __init__(self): self.noise_prob = 0.0 self.speech_prob = 0.0 self.score = 0.0 self.frame_id = 0 self.frm_state = 0 class WindowDetector(object): 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 self.sil_to_speech_time = sil_to_speech_time self.speech_to_sil_time = speech_to_sil_time self.frame_size_ms = frame_size_ms self.win_size_frame = int(window_size_ms / frame_size_ms) self.win_sum = 0 self.win_state = [0] * self.win_size_frame # 初始化窗 self.cur_win_pos = 0 self.pre_frame_state = FrameState.kFrameStateSil self.cur_frame_state = FrameState.kFrameStateSil self.sil_to_speech_frmcnt_thres = int(sil_to_speech_time / frame_size_ms) self.speech_to_sil_frmcnt_thres = int(speech_to_sil_time / frame_size_ms) self.voice_last_frame_count = 0 self.noise_last_frame_count = 0 self.hydre_frame_count = 0 def Reset(self) -> None: self.cur_win_pos = 0 self.win_sum = 0 self.win_state = [0] * self.win_size_frame self.pre_frame_state = FrameState.kFrameStateSil self.cur_frame_state = FrameState.kFrameStateSil self.voice_last_frame_count = 0 self.noise_last_frame_count = 0 self.hydre_frame_count = 0 def GetWinSize(self) -> int: return int(self.win_size_frame) def DetectOneFrame(self, frameState: FrameState, frame_count: int) -> AudioChangeState: cur_frame_state = FrameState.kFrameStateSil if frameState == FrameState.kFrameStateSpeech: cur_frame_state = 1 elif frameState == FrameState.kFrameStateSil: cur_frame_state = 0 else: return AudioChangeState.kChangeStateInvalid self.win_sum -= self.win_state[self.cur_win_pos] self.win_sum += cur_frame_state self.win_state[self.cur_win_pos] = cur_frame_state self.cur_win_pos = (self.cur_win_pos + 1) % self.win_size_frame if self.pre_frame_state == FrameState.kFrameStateSil and self.win_sum >= self.sil_to_speech_frmcnt_thres: self.pre_frame_state = FrameState.kFrameStateSpeech return AudioChangeState.kChangeStateSil2Speech if self.pre_frame_state == FrameState.kFrameStateSpeech and self.win_sum <= self.speech_to_sil_frmcnt_thres: self.pre_frame_state = FrameState.kFrameStateSil return AudioChangeState.kChangeStateSpeech2Sil if self.pre_frame_state == FrameState.kFrameStateSil: return AudioChangeState.kChangeStateSil2Sil if self.pre_frame_state == FrameState.kFrameStateSpeech: return AudioChangeState.kChangeStateSpeech2Speech return AudioChangeState.kChangeStateInvalid def FrameSizeMs(self) -> int: return int(self.frame_size_ms) class E2EVadModel(nn.Module): def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]): super(E2EVadModel, self).__init__() self.vad_opts = VADXOptions(**vad_post_args) self.windows_detector = WindowDetector(self.vad_opts.window_size_ms, self.vad_opts.sil_to_speech_time_thres, self.vad_opts.speech_to_sil_time_thres, self.vad_opts.frame_in_ms) self.encoder = encoder # init variables self.is_final_send = False self.data_buf_start_frame = 0 self.frm_cnt = 0 self.latest_confirmed_speech_frame = 0 self.lastest_confirmed_silence_frame = -1 self.continous_silence_frame_count = 0 self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected self.confirmed_start_frame = -1 self.confirmed_end_frame = -1 self.number_end_time_detected = 0 self.sil_frame = 0 self.sil_pdf_ids = self.vad_opts.sil_pdf_ids self.noise_average_decibel = -100.0 self.pre_end_silence_detected = False self.output_data_buf = [] self.output_data_buf_offset = 0 self.frame_probs = [] self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres self.speech_noise_thres = self.vad_opts.speech_noise_thres self.scores = None self.max_time_out = False self.decibel = [] self.data_buf = None self.data_buf_all = None self.waveform = None self.ResetDetection() def AllResetDetection(self): self.encoder.cache_reset() # reset the in_cache in self.encoder for next query or next long sentence self.is_final_send = False self.data_buf_start_frame = 0 self.frm_cnt = 0 self.latest_confirmed_speech_frame = 0 self.lastest_confirmed_silence_frame = -1 self.continous_silence_frame_count = 0 self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected self.confirmed_start_frame = -1 self.confirmed_end_frame = -1 self.number_end_time_detected = 0 self.sil_frame = 0 self.sil_pdf_ids = self.vad_opts.sil_pdf_ids self.noise_average_decibel = -100.0 self.pre_end_silence_detected = False self.output_data_buf = [] self.output_data_buf_offset = 0 self.frame_probs = [] self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres self.speech_noise_thres = self.vad_opts.speech_noise_thres self.scores = None self.max_time_out = False self.decibel = [] self.data_buf = None self.data_buf_all = None self.waveform = None self.ResetDetection() def ResetDetection(self): self.continous_silence_frame_count = 0 self.latest_confirmed_speech_frame = 0 self.lastest_confirmed_silence_frame = -1 self.confirmed_start_frame = -1 self.confirmed_end_frame = -1 self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected self.windows_detector.Reset() self.sil_frame = 0 self.frame_probs = [] def ComputeDecibel(self) -> None: frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000) frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) if self.data_buf_all is None: self.data_buf_all = self.waveform[0] # self.data_buf is pointed to self.waveform[0] self.data_buf = self.data_buf_all else: self.data_buf_all = torch.cat((self.data_buf_all, self.waveform[0])) for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length): self.decibel.append( 10 * math.log10((self.waveform[0][offset: offset + frame_sample_length]).square().sum() + \ 0.000001)) def ComputeScores(self, feats: torch.Tensor) -> None: scores = self.encoder(feats) # 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 if self.scores is None: self.scores = scores # the first calculation else: self.scores = torch.cat((self.scores, scores), dim=1) def PopDataBufTillFrame(self, frame_idx: int) -> None: # need check again while self.data_buf_start_frame < frame_idx: if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000): self.data_buf_start_frame += 1 self.data_buf = self.data_buf_all[self.data_buf_start_frame * int( self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):] def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool, last_frm_is_end_point: bool, end_point_is_sent_end: bool) -> None: self.PopDataBufTillFrame(start_frm) expected_sample_number = int(frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000) if last_frm_is_end_point: extra_sample = max(0, int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000 - \ self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)) expected_sample_number += int(extra_sample) if end_point_is_sent_end: expected_sample_number = max(expected_sample_number, len(self.data_buf)) if len(self.data_buf) < expected_sample_number: print('error in calling pop data_buf\n') if len(self.output_data_buf) == 0 or first_frm_is_start_point: self.output_data_buf.append(E2EVadSpeechBufWithDoa()) self.output_data_buf[-1].Reset() self.output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms self.output_data_buf[-1].end_ms = self.output_data_buf[-1].start_ms self.output_data_buf[-1].doa = 0 cur_seg = self.output_data_buf[-1] if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms: print('warning\n') out_pos = len(cur_seg.buffer) # cur_seg.buff现在没做任何操作 data_to_pop = 0 if end_point_is_sent_end: data_to_pop = expected_sample_number else: data_to_pop = int(frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) if data_to_pop > len(self.data_buf): print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n') data_to_pop = len(self.data_buf) expected_sample_number = len(self.data_buf) cur_seg.doa = 0 for sample_cpy_out in range(0, data_to_pop): # cur_seg.buffer[out_pos ++] = data_buf_.back(); out_pos += 1 for sample_cpy_out in range(data_to_pop, expected_sample_number): # cur_seg.buffer[out_pos++] = data_buf_.back() out_pos += 1 if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms: print('Something wrong with the VAD algorithm\n') self.data_buf_start_frame += frm_cnt cur_seg.end_ms = (start_frm + frm_cnt) * self.vad_opts.frame_in_ms if first_frm_is_start_point: cur_seg.contain_seg_start_point = True if last_frm_is_end_point: cur_seg.contain_seg_end_point = True def OnSilenceDetected(self, valid_frame: int): self.lastest_confirmed_silence_frame = valid_frame if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: self.PopDataBufTillFrame(valid_frame) # silence_detected_callback_ # pass def OnVoiceDetected(self, valid_frame: int) -> None: self.latest_confirmed_speech_frame = valid_frame self.PopDataToOutputBuf(valid_frame, 1, False, False, False) def OnVoiceStart(self, start_frame: int, fake_result: bool = False) -> None: if self.vad_opts.do_start_point_detection: pass if self.confirmed_start_frame != -1: print('not reset vad properly\n') else: self.confirmed_start_frame = start_frame if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False) def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool) -> None: for t in range(self.latest_confirmed_speech_frame + 1, end_frame): self.OnVoiceDetected(t) if self.vad_opts.do_end_point_detection: pass if self.confirmed_end_frame != -1: print('not reset vad properly\n') else: self.confirmed_end_frame = end_frame if not fake_result: self.sil_frame = 0 self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame) self.number_end_time_detected += 1 def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int) -> None: if is_final_frame: self.OnVoiceEnd(cur_frm_idx, False, True) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected def GetLatency(self) -> int: return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms) def LatencyFrmNumAtStartPoint(self) -> int: vad_latency = self.windows_detector.GetWinSize() if self.vad_opts.do_extend: vad_latency += int(self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms) return vad_latency def GetFrameState(self, t: int) -> FrameState: frame_state = FrameState.kFrameStateInvalid cur_decibel = self.decibel[t] cur_snr = cur_decibel - self.noise_average_decibel # for each frame, calc log posterior probability of each state if cur_decibel < self.vad_opts.decibel_thres: frame_state = FrameState.kFrameStateSil self.DetectOneFrame(frame_state, t, False) return frame_state sum_score = 0.0 noise_prob = 0.0 assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num if len(self.sil_pdf_ids) > 0: assert len(self.scores) == 1 # 只支持batch_size = 1的测试 sil_pdf_scores = [self.scores[0][t][sil_pdf_id] for sil_pdf_id in self.sil_pdf_ids] sum_score = sum(sil_pdf_scores) noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio total_score = 1.0 sum_score = total_score - sum_score speech_prob = math.log(sum_score) if self.vad_opts.output_frame_probs: frame_prob = E2EVadFrameProb() frame_prob.noise_prob = noise_prob frame_prob.speech_prob = speech_prob frame_prob.score = sum_score frame_prob.frame_id = t self.frame_probs.append(frame_prob) if math.exp(speech_prob) >= math.exp(noise_prob) + self.speech_noise_thres: if cur_snr >= self.vad_opts.snr_thres and cur_decibel >= self.vad_opts.decibel_thres: frame_state = FrameState.kFrameStateSpeech else: frame_state = FrameState.kFrameStateSil else: frame_state = FrameState.kFrameStateSil if self.noise_average_decibel < -99.9: self.noise_average_decibel = cur_decibel else: self.noise_average_decibel = (cur_decibel + self.noise_average_decibel * ( self.vad_opts.noise_frame_num_used_for_snr - 1)) / self.vad_opts.noise_frame_num_used_for_snr return frame_state def forward(self, feats: torch.Tensor, waveform: torch.tensor, is_final_send: bool = False) -> List[List[List[int]]]: self.waveform = waveform # compute decibel for each frame self.ComputeDecibel() self.ComputeScores(feats) if not is_final_send: 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 self.output_data_buf[i].contain_seg_start_point and self.output_data_buf[ i].contain_seg_end_point: segment = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms] segment_batch.append(segment) self.output_data_buf_offset += 1 # need update this parameter if segment_batch: segments.append(segment_batch) if is_final_send: self.AllResetDetection() return segments def DetectCommonFrames(self) -> int: if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected: return 0 for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1): frame_state = FrameState.kFrameStateInvalid frame_state = self.GetFrameState(self.frm_cnt - 1 - i) self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False) return 0 def DetectLastFrames(self) -> int: if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected: return 0 for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1): frame_state = FrameState.kFrameStateInvalid frame_state = self.GetFrameState(self.frm_cnt - 1 - i) if i != 0: self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False) else: self.DetectOneFrame(frame_state, self.frm_cnt - 1, True) return 0 def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool) -> None: tmp_cur_frm_state = FrameState.kFrameStateInvalid if cur_frm_state == FrameState.kFrameStateSpeech: if math.fabs(1.0) > self.vad_opts.fe_prior_thres: tmp_cur_frm_state = FrameState.kFrameStateSpeech else: tmp_cur_frm_state = FrameState.kFrameStateSil elif cur_frm_state == FrameState.kFrameStateSil: tmp_cur_frm_state = FrameState.kFrameStateSil state_change = self.windows_detector.DetectOneFrame(tmp_cur_frm_state, cur_frm_idx) frm_shift_in_ms = self.vad_opts.frame_in_ms if AudioChangeState.kChangeStateSil2Speech == state_change: silence_frame_count = self.continous_silence_frame_count self.continous_silence_frame_count = 0 self.pre_end_silence_detected = False start_frame = 0 if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: start_frame = max(self.data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint()) self.OnVoiceStart(start_frame) self.vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment for t in range(start_frame + 1, cur_frm_idx + 1): self.OnVoiceDetected(t) elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment: for t in range(self.latest_confirmed_speech_frame + 1, cur_frm_idx): self.OnVoiceDetected(t) if cur_frm_idx - self.confirmed_start_frame + 1 > \ self.vad_opts.max_single_segment_time / frm_shift_in_ms: self.OnVoiceEnd(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif not is_final_frame: self.OnVoiceDetected(cur_frm_idx) else: self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx) else: pass elif AudioChangeState.kChangeStateSpeech2Sil == state_change: self.continous_silence_frame_count = 0 if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: pass elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment: if cur_frm_idx - self.confirmed_start_frame + 1 > \ self.vad_opts.max_single_segment_time / frm_shift_in_ms: self.OnVoiceEnd(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif not is_final_frame: self.OnVoiceDetected(cur_frm_idx) else: self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx) else: pass elif AudioChangeState.kChangeStateSpeech2Speech == state_change: self.continous_silence_frame_count = 0 if self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment: if cur_frm_idx - self.confirmed_start_frame + 1 > \ self.vad_opts.max_single_segment_time / frm_shift_in_ms: self.max_time_out = True self.OnVoiceEnd(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif not is_final_frame: self.OnVoiceDetected(cur_frm_idx) else: self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx) else: pass elif AudioChangeState.kChangeStateSil2Sil == state_change: self.continous_silence_frame_count += 1 if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: # silence timeout, return zero length decision if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and ( self.continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \ or (is_final_frame and self.number_end_time_detected == 0): for t in range(self.lastest_confirmed_silence_frame + 1, cur_frm_idx): self.OnSilenceDetected(t) self.OnVoiceStart(0, True) self.OnVoiceEnd(0, True, False); self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected else: if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(): self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint()) elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment: if self.continous_silence_frame_count * frm_shift_in_ms >= self.max_end_sil_frame_cnt_thresh: lookback_frame = int(self.max_end_sil_frame_cnt_thresh / frm_shift_in_ms) if self.vad_opts.do_extend: lookback_frame -= int(self.vad_opts.lookahead_time_end_point / frm_shift_in_ms) lookback_frame -= 1 lookback_frame = max(0, lookback_frame) self.OnVoiceEnd(cur_frm_idx - lookback_frame, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif cur_frm_idx - self.confirmed_start_frame + 1 > \ self.vad_opts.max_single_segment_time / frm_shift_in_ms: self.OnVoiceEnd(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif self.vad_opts.do_extend and not is_final_frame: if self.continous_silence_frame_count <= int( self.vad_opts.lookahead_time_end_point / frm_shift_in_ms): self.OnVoiceDetected(cur_frm_idx) else: self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx) else: pass if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \ self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value: self.ResetDetection()