From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords
---
funasr/models/fsmn_vad_streaming/model.py | 1594 +++++++++++++++++++++++++++++++---------------------------
1 files changed, 862 insertions(+), 732 deletions(-)
diff --git a/funasr/models/fsmn_vad_streaming/model.py b/funasr/models/fsmn_vad_streaming/model.py
index 7d71faa..04689be 100644
--- a/funasr/models/fsmn_vad_streaming/model.py
+++ b/funasr/models/fsmn_vad_streaming/model.py
@@ -19,768 +19,898 @@
class VadStateMachine(Enum):
- kVadInStateStartPointNotDetected = 1
- kVadInStateInSpeechSegment = 2
- kVadInStateEndPointDetected = 3
+ kVadInStateStartPointNotDetected = 1
+ kVadInStateInSpeechSegment = 2
+ kVadInStateEndPointDetected = 3
class FrameState(Enum):
- kFrameStateInvalid = -1
- kFrameStateSpeech = 1
- kFrameStateSil = 0
+ 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
+ kChangeStateSpeech2Speech = 0
+ kChangeStateSpeech2Sil = 1
+ kChangeStateSil2Sil = 2
+ kChangeStateSil2Speech = 3
+ kChangeStateNoBegin = 4
+ kChangeStateInvalid = 5
class VadDetectMode(Enum):
- kVadSingleUtteranceDetectMode = 0
- kVadMutipleUtteranceDetectMode = 1
+ kVadSingleUtteranceDetectMode = 0
+ kVadMutipleUtteranceDetectMode = 1
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,
- 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,
- **kwargs,
- ):
- 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
+ """
+ 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,
+ 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,
+ **kwargs,
+ ):
+ 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):
- """
- 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
- 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
+ """
+ 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
+ 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):
- """
- 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
- self.score = 0.0
- self.frame_id = 0
- self.frm_state = 0
+ """
+ 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
+ self.score = 0.0
+ self.frame_id = 0
+ self.frm_state = 0
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
- 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 # 鍒濆鍖栫獥
-
- 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, cache: dict = {}) -> 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)
+ """
+ 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
+ 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 # 鍒濆鍖栫獥
+
+ 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, cache: dict = {}
+ ) -> 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 Stats(object):
- def __init__(self,
- sil_pdf_ids,
- max_end_sil_frame_cnt_thresh,
- speech_noise_thres,
- ):
- 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 = 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 = max_end_sil_frame_cnt_thresh
- self.speech_noise_thres = 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.last_drop_frames = 0
+ def __init__(
+ self,
+ sil_pdf_ids,
+ max_end_sil_frame_cnt_thresh,
+ speech_noise_thres,
+ ):
+ 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 = 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 = max_end_sil_frame_cnt_thresh
+ self.speech_noise_thres = 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.last_drop_frames = 0
@tables.register("model_classes", "FsmnVADStreaming")
class FsmnVADStreaming(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: str = None,
- encoder_conf: Optional[Dict] = None,
- vad_post_args: Dict[str, Any] = None,
- **kwargs,
- ):
- super().__init__()
- self.vad_opts = VADXOptions(**kwargs)
-
- encoder_class = tables.encoder_classes.get(encoder)
- encoder = encoder_class(**encoder_conf)
- self.encoder = encoder
- self.encoder_conf = encoder_conf
-
- def ResetDetection(self, cache: dict = {}):
- cache["stats"].continous_silence_frame_count = 0
- cache["stats"].latest_confirmed_speech_frame = 0
- cache["stats"].lastest_confirmed_silence_frame = -1
- cache["stats"].confirmed_start_frame = -1
- cache["stats"].confirmed_end_frame = -1
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
- cache["windows_detector"].Reset()
- cache["stats"].sil_frame = 0
- cache["stats"].frame_probs = []
-
- if cache["stats"].output_data_buf:
- assert cache["stats"].output_data_buf[-1].contain_seg_end_point == True
- drop_frames = int(cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
- real_drop_frames = drop_frames - cache["stats"].last_drop_frames
- cache["stats"].last_drop_frames = drop_frames
- cache["stats"].data_buf_all = cache["stats"].data_buf_all[real_drop_frames * int(
- self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
- cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:]
- cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :]
-
- def ComputeDecibel(self, cache: dict = {}) -> 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 cache["stats"].data_buf_all is None:
- cache["stats"].data_buf_all = cache["stats"].waveform[
- 0] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
- cache["stats"].data_buf = cache["stats"].data_buf_all
- else:
- cache["stats"].data_buf_all = torch.cat((cache["stats"].data_buf_all, cache["stats"].waveform[0]))
- for offset in range(0, cache["stats"].waveform.shape[1] - frame_sample_length + 1, frame_shift_length):
- cache["stats"].decibel.append(
- 10 * math.log10((cache["stats"].waveform[0][offset: offset + frame_sample_length]).square().sum() + \
- 0.000001))
-
- def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None:
- scores = self.encoder(feats, cache=cache["encoder"]).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]
- cache["stats"].frm_cnt += scores.shape[1] # count total frames
- if cache["stats"].scores is None:
- cache["stats"].scores = scores # the first calculation
- else:
- cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
-
- def PopDataBufTillFrame(self, frame_idx: int, cache: dict = {}) -> None: # need check again
- while cache["stats"].data_buf_start_frame < frame_idx:
- if len(cache["stats"].data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
- cache["stats"].data_buf_start_frame += 1
- cache["stats"].data_buf = cache["stats"].data_buf_all[
- (cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames) * 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, cache: dict = {}) -> None:
- self.PopDataBufTillFrame(start_frm, cache=cache)
- 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(cache["stats"].data_buf))
- if len(cache["stats"].data_buf) < expected_sample_number:
- print('error in calling pop data_buf\n')
-
- if len(cache["stats"].output_data_buf) == 0 or first_frm_is_start_point:
- cache["stats"].output_data_buf.append(E2EVadSpeechBufWithDoa())
- cache["stats"].output_data_buf[-1].Reset()
- cache["stats"].output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
- cache["stats"].output_data_buf[-1].end_ms = cache["stats"].output_data_buf[-1].start_ms
- cache["stats"].output_data_buf[-1].doa = 0
- cur_seg = cache["stats"].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鐜板湪娌″仛浠讳綍鎿嶄綔
- 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(cache["stats"].data_buf):
- print('VAD data_to_pop is bigger than cache["stats"].data_buf.size()!!!\n')
- data_to_pop = len(cache["stats"].data_buf)
- expected_sample_number = len(cache["stats"].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')
- cache["stats"].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, cache: dict = {}):
- cache["stats"].lastest_confirmed_silence_frame = valid_frame
- if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- self.PopDataBufTillFrame(valid_frame, cache=cache)
-
- # silence_detected_callback_
- # pass
-
- def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None:
- cache["stats"].latest_confirmed_speech_frame = valid_frame
- self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache)
-
- def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache: dict = {}) -> None:
- if self.vad_opts.do_start_point_detection:
- pass
- if cache["stats"].confirmed_start_frame != -1:
- print('not reset vad properly\n')
- else:
- cache["stats"].confirmed_start_frame = start_frame
-
- if not fake_result and cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- self.PopDataToOutputBuf(cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache)
-
- def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {}) -> None:
- for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame):
- self.OnVoiceDetected(t, cache=cache)
- if self.vad_opts.do_end_point_detection:
- pass
- if cache["stats"].confirmed_end_frame != -1:
- print('not reset vad properly\n')
- else:
- cache["stats"].confirmed_end_frame = end_frame
- if not fake_result:
- cache["stats"].sil_frame = 0
- self.PopDataToOutputBuf(cache["stats"].confirmed_end_frame, 1, False, True, is_last_frame, cache=cache)
- cache["stats"].number_end_time_detected += 1
-
- def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int, cache: dict = {}) -> None:
- if is_final_frame:
- self.OnVoiceEnd(cur_frm_idx, False, True, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
-
- def GetLatency(self, cache: dict = {}) -> int:
- return int(self.LatencyFrmNumAtStartPoint(cache=cache) * self.vad_opts.frame_in_ms)
-
- def LatencyFrmNumAtStartPoint(self, cache: dict = {}) -> int:
- vad_latency = cache["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, cache: dict = {}):
- frame_state = FrameState.kFrameStateInvalid
- cur_decibel = cache["stats"].decibel[t]
- cur_snr = cur_decibel - cache["stats"].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, cache=cache)
- return frame_state
-
- sum_score = 0.0
- noise_prob = 0.0
- assert len(cache["stats"].sil_pdf_ids) == self.vad_opts.silence_pdf_num
- if len(cache["stats"].sil_pdf_ids) > 0:
- assert len(cache["stats"].scores) == 1 # 鍙敮鎸乥atch_size = 1鐨勬祴璇�
- sil_pdf_scores = [cache["stats"].scores[0][t][sil_pdf_id] for sil_pdf_id in cache["stats"].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
- cache["stats"].frame_probs.append(frame_prob)
- if math.exp(speech_prob) >= math.exp(noise_prob) + cache["stats"].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 cache["stats"].noise_average_decibel < -99.9:
- cache["stats"].noise_average_decibel = cur_decibel
- else:
- cache["stats"].noise_average_decibel = (cur_decibel + cache["stats"].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,
- cache: dict = {},
- is_final: bool = False,
- **kwargs,
- ):
- # if len(cache) == 0:
- # self.AllResetDetection()
- # self.waveform = waveform # compute decibel for each frame
- cache["stats"].waveform = waveform
- is_streaming_input = kwargs.get("is_streaming_input", True)
- self.ComputeDecibel(cache=cache)
- self.ComputeScores(feats, cache=cache)
- if not is_final:
- self.DetectCommonFrames(cache=cache)
- else:
- self.DetectLastFrames(cache=cache)
- segments = []
- for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
- segment_batch = []
- if len(cache["stats"].output_data_buf) > 0:
- for i in range(cache["stats"].output_data_buf_offset, len(cache["stats"].output_data_buf)):
- if is_streaming_input: # in this case, return [beg, -1], [], [-1, end], [beg, end]
- if not cache["stats"].output_data_buf[i].contain_seg_start_point:
- continue
- if not cache["stats"].next_seg and not cache["stats"].output_data_buf[i].contain_seg_end_point:
- continue
- start_ms = cache["stats"].output_data_buf[i].start_ms if cache["stats"].next_seg else -1
- if cache["stats"].output_data_buf[i].contain_seg_end_point:
- end_ms = cache["stats"].output_data_buf[i].end_ms
- cache["stats"].next_seg = True
- cache["stats"].output_data_buf_offset += 1
- else:
- end_ms = -1
- cache["stats"].next_seg = False
- segment = [start_ms, end_ms]
-
- else: # in this case, return [beg, end]
-
- if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not
- cache["stats"].output_data_buf[
- i].contain_seg_end_point):
- continue
- segment = [cache["stats"].output_data_buf[i].start_ms, cache["stats"].output_data_buf[i].end_ms]
- cache["stats"].output_data_buf_offset += 1 # need update this parameter
-
- 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
-
- def init_cache(self, cache: dict = {}, **kwargs):
-
- cache["frontend"] = {}
- cache["prev_samples"] = torch.empty(0)
- cache["encoder"] = {}
- 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)
- windows_detector.Reset()
-
- stats = Stats(sil_pdf_ids=self.vad_opts.sil_pdf_ids,
- max_end_sil_frame_cnt_thresh=self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres,
- speech_noise_thres=self.vad_opts.speech_noise_thres
- )
- cache["windows_detector"] = windows_detector
- cache["stats"] = stats
- return cache
-
- def inference(self,
- data_in,
- data_lengths=None,
- key: list = None,
- tokenizer=None,
- frontend=None,
- cache: dict = {},
- **kwargs,
- ):
-
- if len(cache) == 0:
- self.init_cache(cache, **kwargs)
-
- meta_data = {}
- chunk_size = kwargs.get("chunk_size", 60000) # 50ms
- chunk_stride_samples = int(chunk_size * frontend.fs / 1000)
-
- time1 = time.perf_counter()
- is_streaming_input = kwargs.get("is_streaming_input", False) if chunk_size >= 15000 else kwargs.get("is_streaming_input", True)
- is_final = kwargs.get("is_final", False) if is_streaming_input else kwargs.get("is_final", True)
- cfg = {"is_final": is_final, "is_streaming_input": is_streaming_input}
- audio_sample_list = load_audio_text_image_video(data_in,
- fs=frontend.fs,
- audio_fs=kwargs.get("fs", 16000),
- data_type=kwargs.get("data_type", "sound"),
- tokenizer=tokenizer,
- cache=cfg,
- )
- _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
- is_streaming_input = cfg["is_streaming_input"]
- time2 = time.perf_counter()
- meta_data["load_data"] = f"{time2 - time1:0.3f}"
- assert len(audio_sample_list) == 1, "batch_size must be set 1"
-
- audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
-
- n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
- m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
- segments = []
- for i in range(n):
- kwargs["is_final"] = _is_final and i == n - 1
- audio_sample_i = audio_sample[i * chunk_stride_samples:(i + 1) * chunk_stride_samples]
-
- # extract fbank feats
- speech, speech_lengths = extract_fbank([audio_sample_i], data_type=kwargs.get("data_type", "sound"),
- frontend=frontend, cache=cache["frontend"],
- is_final=kwargs["is_final"])
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
- speech = speech.to(device=kwargs["device"])
- speech_lengths = speech_lengths.to(device=kwargs["device"])
-
- batch = {
- "feats": speech,
- "waveform": cache["frontend"]["waveforms"],
- "is_final": kwargs["is_final"],
- "cache": cache,
- "is_streaming_input": is_streaming_input
- }
- segments_i = self.forward(**batch)
- if len(segments_i) > 0:
- segments.extend(*segments_i)
-
- cache["prev_samples"] = audio_sample[:-m]
- if _is_final:
- self.init_cache(cache)
-
- ibest_writer = None
- if kwargs.get("output_dir") is not None:
- if not hasattr(self, "writer"):
- self.writer = DatadirWriter(kwargs.get("output_dir"))
- ibest_writer = self.writer[f"{1}best_recog"]
-
- results = []
- result_i = {"key": key[0], "value": segments}
- # if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
- # result_i = json.dumps(result_i)
-
- results.append(result_i)
-
- if ibest_writer is not None:
- ibest_writer["text"][key[0]] = segments
-
- return results, meta_data
-
- def export(self, **kwargs):
+ """
+ Author: Speech Lab of DAMO Academy, Alibaba Group
+ Deep-FSMN for Large Vocabulary Continuous Speech Recognition
+ https://arxiv.org/abs/1803.05030
+ """
- from .export_meta import export_rebuild_model
- models = export_rebuild_model(model=self, **kwargs)
- return models
+ def __init__(
+ self,
+ encoder: str = None,
+ encoder_conf: Optional[Dict] = None,
+ vad_post_args: Dict[str, Any] = None,
+ **kwargs,
+ ):
+ super().__init__()
+ self.vad_opts = VADXOptions(**kwargs)
- def DetectCommonFrames(self, cache: dict = {}) -> int:
- if cache["stats"].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(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames,
- cache=cache)
- self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
-
- return 0
-
- def DetectLastFrames(self, cache: dict = {}) -> int:
- if cache["stats"].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(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames,
- cache=cache)
- if i != 0:
- self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
- else:
- self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1, True, cache=cache)
-
- return 0
-
- def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool,
- cache: dict = {}) -> 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 = cache["windows_detector"].DetectOneFrame(tmp_cur_frm_state, cur_frm_idx, cache=cache)
- frm_shift_in_ms = self.vad_opts.frame_in_ms
- if AudioChangeState.kChangeStateSil2Speech == state_change:
- silence_frame_count = cache["stats"].continous_silence_frame_count
- cache["stats"].continous_silence_frame_count = 0
- cache["stats"].pre_end_silence_detected = False
- start_frame = 0
- if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- start_frame = max(cache["stats"].data_buf_start_frame,
- cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache))
- self.OnVoiceStart(start_frame, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
- for t in range(start_frame + 1, cur_frm_idx + 1):
- self.OnVoiceDetected(t, cache=cache)
- elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- for t in range(cache["stats"].latest_confirmed_speech_frame + 1, cur_frm_idx):
- self.OnVoiceDetected(t, cache=cache)
- if cur_frm_idx - cache["stats"].confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif not is_final_frame:
- self.OnVoiceDetected(cur_frm_idx, cache=cache)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
- else:
- pass
- elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
- cache["stats"].continous_silence_frame_count = 0
- if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- pass
- elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cur_frm_idx - cache["stats"].confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif not is_final_frame:
- self.OnVoiceDetected(cur_frm_idx, cache=cache)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
- else:
- pass
- elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
- cache["stats"].continous_silence_frame_count = 0
- if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cur_frm_idx - cache["stats"].confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- cache["stats"].max_time_out = True
- self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif not is_final_frame:
- self.OnVoiceDetected(cur_frm_idx, cache=cache)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
- else:
- pass
- elif AudioChangeState.kChangeStateSil2Sil == state_change:
- cache["stats"].continous_silence_frame_count += 1
- if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- # silence timeout, return zero length decision
- if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and (
- cache[
- "stats"].continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
- or (is_final_frame and cache["stats"].number_end_time_detected == 0):
- for t in range(cache["stats"].lastest_confirmed_silence_frame + 1, cur_frm_idx):
- self.OnSilenceDetected(t, cache=cache)
- self.OnVoiceStart(0, True, cache=cache)
- self.OnVoiceEnd(0, True, False, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- else:
- if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(cache=cache):
- self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), cache=cache)
- elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cache["stats"].continous_silence_frame_count * frm_shift_in_ms >= cache[
- "stats"].max_end_sil_frame_cnt_thresh:
- lookback_frame = int(cache["stats"].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, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif cur_frm_idx - cache["stats"].confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
- cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif self.vad_opts.do_extend and not is_final_frame:
- if cache["stats"].continous_silence_frame_count <= int(
- self.vad_opts.lookahead_time_end_point / frm_shift_in_ms):
- self.OnVoiceDetected(cur_frm_idx, cache=cache)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
- else:
- pass
-
- if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
- self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
- self.ResetDetection(cache=cache)
+ encoder_class = tables.encoder_classes.get(encoder)
+ encoder = encoder_class(**encoder_conf)
+ self.encoder = encoder
+ self.encoder_conf = encoder_conf
+ def ResetDetection(self, cache: dict = {}):
+ cache["stats"].continous_silence_frame_count = 0
+ cache["stats"].latest_confirmed_speech_frame = 0
+ cache["stats"].lastest_confirmed_silence_frame = -1
+ cache["stats"].confirmed_start_frame = -1
+ cache["stats"].confirmed_end_frame = -1
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
+ cache["windows_detector"].Reset()
+ cache["stats"].sil_frame = 0
+ cache["stats"].frame_probs = []
+ if cache["stats"].output_data_buf:
+ assert cache["stats"].output_data_buf[-1].contain_seg_end_point == True
+ drop_frames = int(cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
+ real_drop_frames = drop_frames - cache["stats"].last_drop_frames
+ cache["stats"].last_drop_frames = drop_frames
+ cache["stats"].data_buf_all = cache["stats"].data_buf_all[
+ real_drop_frames
+ * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) :
+ ]
+ cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:]
+ cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :]
+
+ def ComputeDecibel(self, cache: dict = {}) -> 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 cache["stats"].data_buf_all is None:
+ cache["stats"].data_buf_all = cache["stats"].waveform[
+ 0
+ ] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
+ cache["stats"].data_buf = cache["stats"].data_buf_all
+ else:
+ cache["stats"].data_buf_all = torch.cat(
+ (cache["stats"].data_buf_all, cache["stats"].waveform[0])
+ )
+ for offset in range(
+ 0, cache["stats"].waveform.shape[1] - frame_sample_length + 1, frame_shift_length
+ ):
+ cache["stats"].decibel.append(
+ 10
+ * math.log10(
+ (cache["stats"].waveform[0][offset : offset + frame_sample_length])
+ .square()
+ .sum()
+ + 0.000001
+ )
+ )
+
+ def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None:
+ scores = self.encoder(feats, cache=cache["encoder"]).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]
+ cache["stats"].frm_cnt += scores.shape[1] # count total frames
+ if cache["stats"].scores is None:
+ cache["stats"].scores = scores # the first calculation
+ else:
+ cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
+
+ def PopDataBufTillFrame(self, frame_idx: int, cache: dict = {}) -> None: # need check again
+ while cache["stats"].data_buf_start_frame < frame_idx:
+ if len(cache["stats"].data_buf) >= int(
+ self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000
+ ):
+ cache["stats"].data_buf_start_frame += 1
+ cache["stats"].data_buf = cache["stats"].data_buf_all[
+ (cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames)
+ * 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,
+ cache: dict = {},
+ ) -> None:
+ self.PopDataBufTillFrame(start_frm, cache=cache)
+ 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(cache["stats"].data_buf))
+ if len(cache["stats"].data_buf) < expected_sample_number:
+ print("error in calling pop data_buf\n")
+
+ if len(cache["stats"].output_data_buf) == 0 or first_frm_is_start_point:
+ cache["stats"].output_data_buf.append(E2EVadSpeechBufWithDoa())
+ cache["stats"].output_data_buf[-1].Reset()
+ cache["stats"].output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
+ cache["stats"].output_data_buf[-1].end_ms = cache["stats"].output_data_buf[-1].start_ms
+ cache["stats"].output_data_buf[-1].doa = 0
+ cur_seg = cache["stats"].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鐜板湪娌″仛浠讳綍鎿嶄綔
+ 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(cache["stats"].data_buf):
+ print('VAD data_to_pop is bigger than cache["stats"].data_buf.size()!!!\n')
+ data_to_pop = len(cache["stats"].data_buf)
+ expected_sample_number = len(cache["stats"].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")
+ cache["stats"].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, cache: dict = {}):
+ cache["stats"].lastest_confirmed_silence_frame = valid_frame
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+ self.PopDataBufTillFrame(valid_frame, cache=cache)
+
+ # silence_detected_callback_
+ # pass
+
+ def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None:
+ cache["stats"].latest_confirmed_speech_frame = valid_frame
+ self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache)
+
+ def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache: dict = {}) -> None:
+ if self.vad_opts.do_start_point_detection:
+ pass
+ if cache["stats"].confirmed_start_frame != -1:
+ print("not reset vad properly\n")
+ else:
+ cache["stats"].confirmed_start_frame = start_frame
+
+ if (
+ not fake_result
+ and cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected
+ ):
+ self.PopDataToOutputBuf(
+ cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache
+ )
+
+ def OnVoiceEnd(
+ self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {}
+ ) -> None:
+ for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame):
+ self.OnVoiceDetected(t, cache=cache)
+ if self.vad_opts.do_end_point_detection:
+ pass
+ if cache["stats"].confirmed_end_frame != -1:
+ print("not reset vad properly\n")
+ else:
+ cache["stats"].confirmed_end_frame = end_frame
+ if not fake_result:
+ cache["stats"].sil_frame = 0
+ self.PopDataToOutputBuf(
+ cache["stats"].confirmed_end_frame, 1, False, True, is_last_frame, cache=cache
+ )
+ cache["stats"].number_end_time_detected += 1
+
+ def MaybeOnVoiceEndIfLastFrame(
+ self, is_final_frame: bool, cur_frm_idx: int, cache: dict = {}
+ ) -> None:
+ if is_final_frame:
+ self.OnVoiceEnd(cur_frm_idx, False, True, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+
+ def GetLatency(self, cache: dict = {}) -> int:
+ return int(self.LatencyFrmNumAtStartPoint(cache=cache) * self.vad_opts.frame_in_ms)
+
+ def LatencyFrmNumAtStartPoint(self, cache: dict = {}) -> int:
+ vad_latency = cache["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, cache: dict = {}):
+ frame_state = FrameState.kFrameStateInvalid
+ cur_decibel = cache["stats"].decibel[t]
+ cur_snr = cur_decibel - cache["stats"].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, cache=cache)
+ return frame_state
+
+ sum_score = 0.0
+ noise_prob = 0.0
+ assert len(cache["stats"].sil_pdf_ids) == self.vad_opts.silence_pdf_num
+ if len(cache["stats"].sil_pdf_ids) > 0:
+ assert len(cache["stats"].scores) == 1 # 鍙敮鎸乥atch_size = 1鐨勬祴璇�
+ sil_pdf_scores = [
+ cache["stats"].scores[0][t][sil_pdf_id] for sil_pdf_id in cache["stats"].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
+ cache["stats"].frame_probs.append(frame_prob)
+ if math.exp(speech_prob) >= math.exp(noise_prob) + cache["stats"].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 cache["stats"].noise_average_decibel < -99.9:
+ cache["stats"].noise_average_decibel = cur_decibel
+ else:
+ cache["stats"].noise_average_decibel = (
+ cur_decibel
+ + cache["stats"].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,
+ cache: dict = {},
+ is_final: bool = False,
+ **kwargs,
+ ):
+ # if len(cache) == 0:
+ # self.AllResetDetection()
+ # self.waveform = waveform # compute decibel for each frame
+ cache["stats"].waveform = waveform
+ is_streaming_input = kwargs.get("is_streaming_input", True)
+ self.ComputeDecibel(cache=cache)
+ self.ComputeScores(feats, cache=cache)
+ if not is_final:
+ self.DetectCommonFrames(cache=cache)
+ else:
+ self.DetectLastFrames(cache=cache)
+ segments = []
+ for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
+ segment_batch = []
+ if len(cache["stats"].output_data_buf) > 0:
+ for i in range(
+ cache["stats"].output_data_buf_offset, len(cache["stats"].output_data_buf)
+ ):
+ if (
+ is_streaming_input
+ ): # in this case, return [beg, -1], [], [-1, end], [beg, end]
+ if not cache["stats"].output_data_buf[i].contain_seg_start_point:
+ continue
+ if (
+ not cache["stats"].next_seg
+ and not cache["stats"].output_data_buf[i].contain_seg_end_point
+ ):
+ continue
+ start_ms = (
+ cache["stats"].output_data_buf[i].start_ms
+ if cache["stats"].next_seg
+ else -1
+ )
+ if cache["stats"].output_data_buf[i].contain_seg_end_point:
+ end_ms = cache["stats"].output_data_buf[i].end_ms
+ cache["stats"].next_seg = True
+ cache["stats"].output_data_buf_offset += 1
+ else:
+ end_ms = -1
+ cache["stats"].next_seg = False
+ segment = [start_ms, end_ms]
+
+ else: # in this case, return [beg, end]
+
+ if not is_final and (
+ not cache["stats"].output_data_buf[i].contain_seg_start_point
+ or not cache["stats"].output_data_buf[i].contain_seg_end_point
+ ):
+ continue
+ segment = [
+ cache["stats"].output_data_buf[i].start_ms,
+ cache["stats"].output_data_buf[i].end_ms,
+ ]
+ cache["stats"].output_data_buf_offset += 1 # need update this parameter
+
+ 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
+
+ def init_cache(self, cache: dict = {}, **kwargs):
+
+ cache["frontend"] = {}
+ cache["prev_samples"] = torch.empty(0)
+ cache["encoder"] = {}
+
+ if kwargs.get("max_end_silence_time") is not None:
+ # update the max_end_silence_time
+ self.vad_opts.max_end_silence_time = kwargs.get("max_end_silence_time")
+
+ 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,
+ )
+ windows_detector.Reset()
+
+ stats = Stats(
+ sil_pdf_ids=self.vad_opts.sil_pdf_ids,
+ max_end_sil_frame_cnt_thresh=self.vad_opts.max_end_silence_time
+ - self.vad_opts.speech_to_sil_time_thres,
+ speech_noise_thres=self.vad_opts.speech_noise_thres,
+ )
+ cache["windows_detector"] = windows_detector
+ cache["stats"] = stats
+ return cache
+
+ def inference(
+ self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ cache: dict = {},
+ **kwargs,
+ ):
+
+ if len(cache) == 0:
+ self.init_cache(cache, **kwargs)
+
+ meta_data = {}
+ chunk_size = kwargs.get("chunk_size", 60000) # 50ms
+ chunk_stride_samples = int(chunk_size * frontend.fs / 1000)
+
+ time1 = time.perf_counter()
+ is_streaming_input = (
+ kwargs.get("is_streaming_input", False)
+ if chunk_size >= 15000
+ else kwargs.get("is_streaming_input", True)
+ )
+ is_final = (
+ kwargs.get("is_final", False) if is_streaming_input else kwargs.get("is_final", True)
+ )
+ cfg = {"is_final": is_final, "is_streaming_input": is_streaming_input}
+ audio_sample_list = load_audio_text_image_video(
+ data_in,
+ fs=frontend.fs,
+ audio_fs=kwargs.get("fs", 16000),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer,
+ cache=cfg,
+ )
+ _is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
+ is_streaming_input = cfg["is_streaming_input"]
+ time2 = time.perf_counter()
+ meta_data["load_data"] = f"{time2 - time1:0.3f}"
+ assert len(audio_sample_list) == 1, "batch_size must be set 1"
+
+ audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
+
+ n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
+ m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
+ segments = []
+ for i in range(n):
+ kwargs["is_final"] = _is_final and i == n - 1
+ audio_sample_i = audio_sample[i * chunk_stride_samples : (i + 1) * chunk_stride_samples]
+
+ # extract fbank feats
+ speech, speech_lengths = extract_fbank(
+ [audio_sample_i],
+ data_type=kwargs.get("data_type", "sound"),
+ frontend=frontend,
+ cache=cache["frontend"],
+ is_final=kwargs["is_final"],
+ )
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+ )
+ speech = speech.to(device=kwargs["device"])
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+ batch = {
+ "feats": speech,
+ "waveform": cache["frontend"]["waveforms"],
+ "is_final": kwargs["is_final"],
+ "cache": cache,
+ "is_streaming_input": is_streaming_input,
+ }
+ segments_i = self.forward(**batch)
+ if len(segments_i) > 0:
+ segments.extend(*segments_i)
+
+ cache["prev_samples"] = audio_sample[:-m]
+ if _is_final:
+ self.init_cache(cache)
+
+ ibest_writer = None
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.writer[f"{1}best_recog"]
+
+ results = []
+ result_i = {"key": key[0], "value": segments}
+ # if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
+ # result_i = json.dumps(result_i)
+
+ results.append(result_i)
+
+ if ibest_writer is not None:
+ ibest_writer["text"][key[0]] = segments
+
+ return results, meta_data
+
+ def export(self, **kwargs):
+
+ from .export_meta import export_rebuild_model
+
+ models = export_rebuild_model(model=self, **kwargs)
+ return models
+
+ def DetectCommonFrames(self, cache: dict = {}) -> int:
+ if cache["stats"].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(
+ cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache
+ )
+ self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
+
+ return 0
+
+ def DetectLastFrames(self, cache: dict = {}) -> int:
+ if cache["stats"].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(
+ cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache
+ )
+ if i != 0:
+ self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
+ else:
+ self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1, True, cache=cache)
+
+ return 0
+
+ def DetectOneFrame(
+ self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool, cache: dict = {}
+ ) -> 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 = cache["windows_detector"].DetectOneFrame(
+ tmp_cur_frm_state, cur_frm_idx, cache=cache
+ )
+ frm_shift_in_ms = self.vad_opts.frame_in_ms
+ if AudioChangeState.kChangeStateSil2Speech == state_change:
+ silence_frame_count = cache["stats"].continous_silence_frame_count
+ cache["stats"].continous_silence_frame_count = 0
+ cache["stats"].pre_end_silence_detected = False
+ start_frame = 0
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+ start_frame = max(
+ cache["stats"].data_buf_start_frame,
+ cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache),
+ )
+ self.OnVoiceStart(start_frame, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
+ for t in range(start_frame + 1, cur_frm_idx + 1):
+ self.OnVoiceDetected(t, cache=cache)
+ elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+ for t in range(cache["stats"].latest_confirmed_speech_frame + 1, cur_frm_idx):
+ self.OnVoiceDetected(t, cache=cache)
+ if (
+ cur_frm_idx - cache["stats"].confirmed_start_frame + 1
+ > self.vad_opts.max_single_segment_time / frm_shift_in_ms
+ ):
+ self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ elif not is_final_frame:
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
+ else:
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
+ else:
+ pass
+ elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
+ cache["stats"].continous_silence_frame_count = 0
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+ pass
+ elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+ if (
+ cur_frm_idx - cache["stats"].confirmed_start_frame + 1
+ > self.vad_opts.max_single_segment_time / frm_shift_in_ms
+ ):
+ self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ elif not is_final_frame:
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
+ else:
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
+ else:
+ pass
+ elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
+ cache["stats"].continous_silence_frame_count = 0
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+ if (
+ cur_frm_idx - cache["stats"].confirmed_start_frame + 1
+ > self.vad_opts.max_single_segment_time / frm_shift_in_ms
+ ):
+ cache["stats"].max_time_out = True
+ self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ elif not is_final_frame:
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
+ else:
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
+ else:
+ pass
+ elif AudioChangeState.kChangeStateSil2Sil == state_change:
+ cache["stats"].continous_silence_frame_count += 1
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+ # silence timeout, return zero length decision
+ if (
+ (self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value)
+ and (
+ cache["stats"].continous_silence_frame_count * frm_shift_in_ms
+ > self.vad_opts.max_start_silence_time
+ )
+ ) or (is_final_frame and cache["stats"].number_end_time_detected == 0):
+ for t in range(cache["stats"].lastest_confirmed_silence_frame + 1, cur_frm_idx):
+ self.OnSilenceDetected(t, cache=cache)
+ self.OnVoiceStart(0, True, cache=cache)
+ self.OnVoiceEnd(0, True, False, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ else:
+ if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(cache=cache):
+ self.OnSilenceDetected(
+ cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), cache=cache
+ )
+ elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
+ if (
+ cache["stats"].continous_silence_frame_count * frm_shift_in_ms
+ >= cache["stats"].max_end_sil_frame_cnt_thresh
+ ):
+ lookback_frame = int(
+ cache["stats"].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, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ elif (
+ cur_frm_idx - cache["stats"].confirmed_start_frame + 1
+ > self.vad_opts.max_single_segment_time / frm_shift_in_ms
+ ):
+ self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ elif self.vad_opts.do_extend and not is_final_frame:
+ if cache["stats"].continous_silence_frame_count <= int(
+ self.vad_opts.lookahead_time_end_point / frm_shift_in_ms
+ ):
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
+ else:
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
+ else:
+ pass
+
+ if (
+ cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected
+ and self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value
+ ):
+ self.ResetDetection(cache=cache)
--
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