# -*- coding:utf-8 -*- # @FileName :e2e_vad.py # @Time :2023/4/3 17:02 # @Author :lovemefan # @Email :lovemefan@outlook.com import logging import math from enum import Enum from pathlib import Path from typing import Any, Dict, List, Tuple import numpy as np from paraformer.runtime.python.utils.logger import logger from paraformer.runtime.python.utils.vadOrtInferRuntimeSession import \ VadOrtInferRuntimeSession 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 get_win_size(self) -> int: return int(self.win_size_frame) def detect_one_frame( 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 frame_size_ms(self) -> int: return int(self.frame_size_ms) class E2EVadModel: def __init__(self, config, vad_post_args: Dict[str, Any], root_dir: Path): 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.model = VadOrtInferRuntimeSession(config, root_dir) # init variables self.is_final = 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.next_seg = True 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_size = 0 self.data_buf_all_size = 0 self.waveform = None self.reset_detection() def all_reset_detection(self): self.is_final = 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.next_seg = True 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 = 0 self.data_buf_all = 0 self.waveform = None self.reset_detection() def reset_detection(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 compute_decibel(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_size == 0: self.data_buf_all_size = len(self.waveform[0]) self.data_buf_size = self.data_buf_all_size else: self.data_buf_all_size += len(self.waveform[0]) for offset in range( 0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length ): self.decibel.append( 10 * np.log10( np.square( self.waveform[0][offset : offset + frame_sample_length] ).sum() + 1e-6 ) ) def compute_scores(self, feats: np.ndarray) -> None: scores = self.model(feats) self.vad_opts.nn_eval_block_size = scores[0].shape[1] self.frm_cnt += scores[0].shape[1] # count total frames if isinstance(feats, list): # return B * T * D feats = feats[0] assert ( scores[0].shape[1] == feats.shape[1] ), "The shape between feats and scores does not match" if self.scores is None: self.scores = scores[0] # the first calculation else: self.scores = np.concatenate((self.scores, scores[0]), axis=1) return scores[1:] def pop_data_buf_till_frame(self, frame_idx: int) -> None: # need check again while self.data_buf_start_frame < frame_idx: if self.data_buf_size >= int( self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000 ): self.data_buf_start_frame += 1 self.data_buf_size = ( self.data_buf_all_size - self.data_buf_start_frame * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) ) def pop_data_to_output_buf( 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.pop_data_buf_till_frame(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, self.data_buf_size) if self.data_buf_size < expected_sample_number: logging.error("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: logging.error("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 > self.data_buf_size: logging.error("VAD data_to_pop is bigger than self.data_buf.size()!!!\n") data_to_pop = self.data_buf_size expected_sample_number = self.data_buf_size 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: logging.error("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 on_silence_detected(self, valid_frame: int): self.lastest_confirmed_silence_frame = valid_frame if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: self.pop_data_buf_till_frame(valid_frame) # silence_detected_callback_ # pass def on_voice_detected(self, valid_frame: int) -> None: self.latest_confirmed_speech_frame = valid_frame self.pop_data_to_output_buf(valid_frame, 1, False, False, False) def on_voice_start(self, start_frame: int, fake_result: bool = False) -> None: if self.vad_opts.do_start_point_detection: pass if self.confirmed_start_frame != -1: logging.error("not reset vad properly\n") else: self.confirmed_start_frame = start_frame if ( not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected ): self.pop_data_to_output_buf( self.confirmed_start_frame, 1, True, False, False ) def on_voice_end( 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.on_voice_detected(t) if self.vad_opts.do_end_point_detection: pass if self.confirmed_end_frame != -1: logging.error("not reset vad properly\n") else: self.confirmed_end_frame = end_frame if not fake_result: self.sil_frame = 0 self.pop_data_to_output_buf( self.confirmed_end_frame, 1, False, True, is_last_frame ) self.number_end_time_detected += 1 def maybe_on_voice_end_last_frame( self, is_final_frame: bool, cur_frm_idx: int ) -> None: if is_final_frame: self.on_voice_end(cur_frm_idx, False, True) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected def get_latency(self) -> int: return int(self.latency_frm_num_at_start_point() * self.vad_opts.frame_in_ms) def latency_frm_num_at_start_point(self) -> int: vad_latency = self.windows_detector.get_win_size() 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 get_frame_state(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.detect_one_frame(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 infer_offline( self, feats: np.ndarray, waveform: np.ndarray, in_cache: Dict[str, np.ndarray] = dict(), is_final: bool = False, ) -> Tuple[List[List[List[int]]], Dict[str, np.ndarray]]: self.waveform = waveform self.compute_decibel() self.compute_scores(feats) if not is_final: self.detect_common_frames() else: self.detect_last_frames() 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 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) self.output_data_buf_offset += 1 # need update this parameter if segment_batch: segments.append(segment_batch) if is_final: # reset class variables and clear the dict for the next query self.all_reset_detection() return segments, in_cache def infer_online( self, feats: np.ndarray, waveform: np.ndarray, in_cache: list = None, is_final: bool = False, max_end_sil: int = 800, ) -> Tuple[List[List[List[int]]], Dict[str, np.ndarray]]: feats = [feats] if in_cache is None: in_cache = [] 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 feats.extend(in_cache) in_cache = self.compute_scores(feats) self.compute_decibel() if is_final: self.detect_last_frames() else: self.detect_common_frames() segments = [] # only support batch_size = 1 now for batch_num in range(0, feats[0].shape[0]): 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 segments.append([start_ms, end_ms]) return segments, in_cache def get_frames_state( self, feats: np.ndarray, waveform: np.ndarray, in_cache: list = None, is_final: bool = False, max_end_sil: int = 800, ): feats = [feats] states = [] if in_cache is None: in_cache = [] 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 feats.extend(in_cache) in_cache = self.compute_scores(feats) self.compute_decibel() if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected: return states for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1): frame_state = FrameState.kFrameStateInvalid frame_state = self.get_frame_state(self.frm_cnt - 1 - i) states.append(frame_state) if i == 0 and is_final: logger.info("last frame detected") self.detect_one_frame(frame_state, self.frm_cnt - 1, True) else: self.detect_one_frame(frame_state, self.frm_cnt - 1 - i, False) return states def detect_common_frames(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.get_frame_state(self.frm_cnt - 1 - i) # print(f"cur frame: {self.frm_cnt - 1 - i}, state is {frame_state}") self.detect_one_frame(frame_state, self.frm_cnt - 1 - i, False) return 0 def detect_last_frames(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.get_frame_state(self.frm_cnt - 1 - i) if i != 0: self.detect_one_frame(frame_state, self.frm_cnt - 1 - i, False) else: self.detect_one_frame(frame_state, self.frm_cnt - 1, True) return 0 def detect_one_frame( 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) > float(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.detect_one_frame( tmp_cur_frm_state, cur_frm_idx ) frm_shift_in_ms = self.vad_opts.frame_in_ms if AudioChangeState.kChangeStateSil2Speech == state_change: self.continous_silence_frame_count = 0 self.pre_end_silence_detected = False if ( self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected ): start_frame = max( self.data_buf_start_frame, cur_frm_idx - self.latency_frm_num_at_start_point(), ) self.on_voice_start(start_frame) self.vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment for t in range(start_frame + 1, cur_frm_idx + 1): self.on_voice_detected(t) elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment: for t in range(self.latest_confirmed_speech_frame + 1, cur_frm_idx): self.on_voice_detected(t) if ( cur_frm_idx - self.confirmed_start_frame + 1 > self.vad_opts.max_single_segment_time / frm_shift_in_ms ): self.on_voice_end(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif not is_final_frame: self.on_voice_detected(cur_frm_idx) else: self.maybe_on_voice_end_last_frame(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.on_voice_end(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif not is_final_frame: self.on_voice_detected(cur_frm_idx) else: self.maybe_on_voice_end_last_frame(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.on_voice_end(cur_frm_idx, False, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected elif not is_final_frame: self.on_voice_detected(cur_frm_idx) else: self.maybe_on_voice_end_last_frame(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.on_silence_detected(t) self.on_voice_start(0, True) self.on_voice_end(0, True, False) self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected else: if cur_frm_idx >= self.latency_frm_num_at_start_point(): self.on_silence_detected( cur_frm_idx - self.latency_frm_num_at_start_point() ) 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.on_voice_end(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.on_voice_end(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.on_voice_detected(cur_frm_idx) else: self.maybe_on_voice_end_last_frame(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.reset_detection()