#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import os
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import json
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import time
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import math
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import torch
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from torch import nn
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from enum import Enum
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from dataclasses import dataclass
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from funasr.register import tables
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from typing import List, Tuple, Dict, Any, Optional
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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class VadStateMachine(Enum):
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kVadInStateStartPointNotDetected = 1
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kVadInStateInSpeechSegment = 2
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kVadInStateEndPointDetected = 3
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class FrameState(Enum):
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kFrameStateInvalid = -1
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kFrameStateSpeech = 1
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kFrameStateSil = 0
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# final voice/unvoice state per frame
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class AudioChangeState(Enum):
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kChangeStateSpeech2Speech = 0
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kChangeStateSpeech2Sil = 1
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kChangeStateSil2Sil = 2
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kChangeStateSil2Speech = 3
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kChangeStateNoBegin = 4
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kChangeStateInvalid = 5
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class VadDetectMode(Enum):
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kVadSingleUtteranceDetectMode = 0
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kVadMutipleUtteranceDetectMode = 1
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class VADXOptions:
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(
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self,
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sample_rate: int = 16000,
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detect_mode: int = VadDetectMode.kVadMutipleUtteranceDetectMode.value,
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snr_mode: int = 0,
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max_end_silence_time: int = 800,
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max_start_silence_time: int = 3000,
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do_start_point_detection: bool = True,
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do_end_point_detection: bool = True,
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window_size_ms: int = 200,
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sil_to_speech_time_thres: int = 150,
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speech_to_sil_time_thres: int = 150,
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speech_2_noise_ratio: float = 1.0,
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do_extend: int = 1,
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lookback_time_start_point: int = 200,
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lookahead_time_end_point: int = 100,
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max_single_segment_time: int = 60000,
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nn_eval_block_size: int = 8,
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dcd_block_size: int = 4,
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snr_thres: int = -100.0,
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noise_frame_num_used_for_snr: int = 100,
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decibel_thres: int = -100.0,
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speech_noise_thres: float = 0.6,
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fe_prior_thres: float = 1e-4,
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silence_pdf_num: int = 1,
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sil_pdf_ids: List[int] = [0],
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speech_noise_thresh_low: float = -0.1,
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speech_noise_thresh_high: float = 0.3,
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output_frame_probs: bool = False,
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frame_in_ms: int = 10,
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frame_length_ms: int = 25,
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**kwargs,
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):
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self.sample_rate = sample_rate
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self.detect_mode = detect_mode
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self.snr_mode = snr_mode
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self.max_end_silence_time = max_end_silence_time
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self.max_start_silence_time = max_start_silence_time
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self.do_start_point_detection = do_start_point_detection
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self.do_end_point_detection = do_end_point_detection
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self.window_size_ms = window_size_ms
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self.sil_to_speech_time_thres = sil_to_speech_time_thres
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self.speech_to_sil_time_thres = speech_to_sil_time_thres
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self.speech_2_noise_ratio = speech_2_noise_ratio
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self.do_extend = do_extend
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self.lookback_time_start_point = lookback_time_start_point
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self.lookahead_time_end_point = lookahead_time_end_point
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self.max_single_segment_time = max_single_segment_time
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self.nn_eval_block_size = nn_eval_block_size
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self.dcd_block_size = dcd_block_size
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self.snr_thres = snr_thres
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self.noise_frame_num_used_for_snr = noise_frame_num_used_for_snr
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self.decibel_thres = decibel_thres
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self.speech_noise_thres = speech_noise_thres
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self.fe_prior_thres = fe_prior_thres
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self.silence_pdf_num = silence_pdf_num
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self.sil_pdf_ids = sil_pdf_ids
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self.speech_noise_thresh_low = speech_noise_thresh_low
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self.speech_noise_thresh_high = speech_noise_thresh_high
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self.output_frame_probs = output_frame_probs
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self.frame_in_ms = frame_in_ms
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self.frame_length_ms = frame_length_ms
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class E2EVadSpeechBufWithDoa(object):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self):
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self.start_ms = 0
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self.end_ms = 0
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self.buffer = []
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self.contain_seg_start_point = False
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self.contain_seg_end_point = False
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self.doa = 0
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def Reset(self):
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self.start_ms = 0
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self.end_ms = 0
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self.buffer = []
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self.contain_seg_start_point = False
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self.contain_seg_end_point = False
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self.doa = 0
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class E2EVadFrameProb(object):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self):
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self.noise_prob = 0.0
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self.speech_prob = 0.0
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self.score = 0.0
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self.frame_id = 0
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self.frm_state = 0
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class WindowDetector(object):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self, window_size_ms: int,
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sil_to_speech_time: int,
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speech_to_sil_time: int,
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frame_size_ms: int):
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self.window_size_ms = window_size_ms
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self.sil_to_speech_time = sil_to_speech_time
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self.speech_to_sil_time = speech_to_sil_time
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self.frame_size_ms = frame_size_ms
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self.win_size_frame = int(window_size_ms / frame_size_ms)
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self.win_sum = 0
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self.win_state = [0] * self.win_size_frame # 初始化窗
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self.cur_win_pos = 0
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self.pre_frame_state = FrameState.kFrameStateSil
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self.cur_frame_state = FrameState.kFrameStateSil
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self.sil_to_speech_frmcnt_thres = int(sil_to_speech_time / frame_size_ms)
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self.speech_to_sil_frmcnt_thres = int(speech_to_sil_time / frame_size_ms)
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self.voice_last_frame_count = 0
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self.noise_last_frame_count = 0
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self.hydre_frame_count = 0
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def Reset(self) -> None:
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self.cur_win_pos = 0
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self.win_sum = 0
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self.win_state = [0] * self.win_size_frame
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self.pre_frame_state = FrameState.kFrameStateSil
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self.cur_frame_state = FrameState.kFrameStateSil
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self.voice_last_frame_count = 0
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self.noise_last_frame_count = 0
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self.hydre_frame_count = 0
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def GetWinSize(self) -> int:
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return int(self.win_size_frame)
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def DetectOneFrame(self, frameState: FrameState, frame_count: int, cache: dict = {}) -> AudioChangeState:
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cur_frame_state = FrameState.kFrameStateSil
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if frameState == FrameState.kFrameStateSpeech:
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cur_frame_state = 1
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elif frameState == FrameState.kFrameStateSil:
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cur_frame_state = 0
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else:
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return AudioChangeState.kChangeStateInvalid
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self.win_sum -= self.win_state[self.cur_win_pos]
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self.win_sum += cur_frame_state
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self.win_state[self.cur_win_pos] = cur_frame_state
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self.cur_win_pos = (self.cur_win_pos + 1) % self.win_size_frame
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if self.pre_frame_state == FrameState.kFrameStateSil and self.win_sum >= self.sil_to_speech_frmcnt_thres:
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self.pre_frame_state = FrameState.kFrameStateSpeech
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return AudioChangeState.kChangeStateSil2Speech
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if self.pre_frame_state == FrameState.kFrameStateSpeech and self.win_sum <= self.speech_to_sil_frmcnt_thres:
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self.pre_frame_state = FrameState.kFrameStateSil
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return AudioChangeState.kChangeStateSpeech2Sil
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if self.pre_frame_state == FrameState.kFrameStateSil:
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return AudioChangeState.kChangeStateSil2Sil
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if self.pre_frame_state == FrameState.kFrameStateSpeech:
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return AudioChangeState.kChangeStateSpeech2Speech
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return AudioChangeState.kChangeStateInvalid
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def FrameSizeMs(self) -> int:
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return int(self.frame_size_ms)
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class Stats(object):
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def __init__(self,
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sil_pdf_ids,
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max_end_sil_frame_cnt_thresh,
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speech_noise_thres,
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):
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self.data_buf_start_frame = 0
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self.frm_cnt = 0
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self.latest_confirmed_speech_frame = 0
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self.lastest_confirmed_silence_frame = -1
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self.continous_silence_frame_count = 0
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self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
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self.confirmed_start_frame = -1
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self.confirmed_end_frame = -1
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self.number_end_time_detected = 0
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self.sil_frame = 0
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self.sil_pdf_ids = sil_pdf_ids
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self.noise_average_decibel = -100.0
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self.pre_end_silence_detected = False
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self.next_seg = True
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self.output_data_buf = []
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self.output_data_buf_offset = 0
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self.frame_probs = []
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self.max_end_sil_frame_cnt_thresh = max_end_sil_frame_cnt_thresh
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self.speech_noise_thres = speech_noise_thres
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self.scores = None
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self.max_time_out = False
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self.decibel = []
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self.data_buf = None
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self.data_buf_all = None
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self.waveform = None
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self.last_drop_frames = 0
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@tables.register("model_classes", "FsmnVADStreaming")
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class FsmnVADStreaming(nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self,
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encoder: str = None,
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encoder_conf: Optional[Dict] = None,
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vad_post_args: Dict[str, Any] = None,
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**kwargs,
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):
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super().__init__()
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self.vad_opts = VADXOptions(**kwargs)
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encoder_class = tables.encoder_classes.get(encoder)
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encoder = encoder_class(**encoder_conf)
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self.encoder = encoder
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self.encoder_conf = encoder_conf
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def ResetDetection(self, cache: dict = {}):
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cache["stats"].continous_silence_frame_count = 0
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cache["stats"].latest_confirmed_speech_frame = 0
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cache["stats"].lastest_confirmed_silence_frame = -1
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cache["stats"].confirmed_start_frame = -1
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cache["stats"].confirmed_end_frame = -1
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cache["stats"].vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
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cache["windows_detector"].Reset()
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cache["stats"].sil_frame = 0
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cache["stats"].frame_probs = []
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if cache["stats"].output_data_buf:
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assert cache["stats"].output_data_buf[-1].contain_seg_end_point == True
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drop_frames = int(cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
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real_drop_frames = drop_frames - cache["stats"].last_drop_frames
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cache["stats"].last_drop_frames = drop_frames
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cache["stats"].data_buf_all = cache["stats"].data_buf_all[real_drop_frames * int(
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self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
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cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:]
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cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :]
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def ComputeDecibel(self, cache: dict = {}) -> None:
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frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000)
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frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
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if cache["stats"].data_buf_all is None:
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cache["stats"].data_buf_all = cache["stats"].waveform[
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0] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
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cache["stats"].data_buf = cache["stats"].data_buf_all
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else:
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cache["stats"].data_buf_all = torch.cat((cache["stats"].data_buf_all, cache["stats"].waveform[0]))
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for offset in range(0, cache["stats"].waveform.shape[1] - frame_sample_length + 1, frame_shift_length):
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cache["stats"].decibel.append(
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10 * math.log10((cache["stats"].waveform[0][offset: offset + frame_sample_length]).square().sum() + \
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0.000001))
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def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None:
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scores = self.encoder(feats, cache=cache["encoder"]).to('cpu') # return B * T * D
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assert scores.shape[1] == feats.shape[1], "The shape between feats and scores does not match"
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self.vad_opts.nn_eval_block_size = scores.shape[1]
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cache["stats"].frm_cnt += scores.shape[1] # count total frames
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if cache["stats"].scores is None:
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cache["stats"].scores = scores # the first calculation
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else:
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cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
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def PopDataBufTillFrame(self, frame_idx: int, cache: dict = {}) -> None: # need check again
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while cache["stats"].data_buf_start_frame < frame_idx:
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if len(cache["stats"].data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
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cache["stats"].data_buf_start_frame += 1
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cache["stats"].data_buf = cache["stats"].data_buf_all[
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(cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames) * int(
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self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
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def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool,
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last_frm_is_end_point: bool, end_point_is_sent_end: bool, cache: dict = {}) -> None:
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self.PopDataBufTillFrame(start_frm, cache=cache)
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expected_sample_number = int(frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)
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if last_frm_is_end_point:
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extra_sample = max(0, int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000 - \
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self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000))
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expected_sample_number += int(extra_sample)
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if end_point_is_sent_end:
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expected_sample_number = max(expected_sample_number, len(cache["stats"].data_buf))
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if len(cache["stats"].data_buf) < expected_sample_number:
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print('error in calling pop data_buf\n')
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if len(cache["stats"].output_data_buf) == 0 or first_frm_is_start_point:
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cache["stats"].output_data_buf.append(E2EVadSpeechBufWithDoa())
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cache["stats"].output_data_buf[-1].Reset()
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cache["stats"].output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
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cache["stats"].output_data_buf[-1].end_ms = cache["stats"].output_data_buf[-1].start_ms
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cache["stats"].output_data_buf[-1].doa = 0
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cur_seg = cache["stats"].output_data_buf[-1]
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if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
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print('warning\n')
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out_pos = len(cur_seg.buffer) # cur_seg.buff现在没做任何操作
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data_to_pop = 0
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if end_point_is_sent_end:
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data_to_pop = expected_sample_number
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else:
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data_to_pop = int(frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
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if data_to_pop > len(cache["stats"].data_buf):
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print('VAD data_to_pop is bigger than cache["stats"].data_buf.size()!!!\n')
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data_to_pop = len(cache["stats"].data_buf)
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expected_sample_number = len(cache["stats"].data_buf)
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cur_seg.doa = 0
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for sample_cpy_out in range(0, data_to_pop):
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# cur_seg.buffer[out_pos ++] = data_buf_.back();
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out_pos += 1
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for sample_cpy_out in range(data_to_pop, expected_sample_number):
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# cur_seg.buffer[out_pos++] = data_buf_.back()
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out_pos += 1
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if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
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print('Something wrong with the VAD algorithm\n')
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cache["stats"].data_buf_start_frame += frm_cnt
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cur_seg.end_ms = (start_frm + frm_cnt) * self.vad_opts.frame_in_ms
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if first_frm_is_start_point:
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cur_seg.contain_seg_start_point = True
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if last_frm_is_end_point:
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cur_seg.contain_seg_end_point = True
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def OnSilenceDetected(self, valid_frame: int, cache: dict = {}):
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cache["stats"].lastest_confirmed_silence_frame = valid_frame
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if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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self.PopDataBufTillFrame(valid_frame, cache=cache)
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# silence_detected_callback_
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# pass
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def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None:
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cache["stats"].latest_confirmed_speech_frame = valid_frame
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self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache)
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def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache: dict = {}) -> None:
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if self.vad_opts.do_start_point_detection:
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pass
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if cache["stats"].confirmed_start_frame != -1:
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print('not reset vad properly\n')
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else:
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cache["stats"].confirmed_start_frame = start_frame
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if not fake_result and cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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self.PopDataToOutputBuf(cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache)
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def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {}) -> None:
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for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame):
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self.OnVoiceDetected(t, cache=cache)
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if self.vad_opts.do_end_point_detection:
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pass
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if cache["stats"].confirmed_end_frame != -1:
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print('not reset vad properly\n')
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else:
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cache["stats"].confirmed_end_frame = end_frame
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if not fake_result:
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cache["stats"].sil_frame = 0
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self.PopDataToOutputBuf(cache["stats"].confirmed_end_frame, 1, False, True, is_last_frame, cache=cache)
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cache["stats"].number_end_time_detected += 1
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def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int, cache: dict = {}) -> None:
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if is_final_frame:
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self.OnVoiceEnd(cur_frm_idx, False, True, cache=cache)
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cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
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def GetLatency(self, cache: dict = {}) -> int:
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return int(self.LatencyFrmNumAtStartPoint(cache=cache) * self.vad_opts.frame_in_ms)
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def LatencyFrmNumAtStartPoint(self, cache: dict = {}) -> int:
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vad_latency = cache["windows_detector"].GetWinSize()
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if self.vad_opts.do_extend:
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vad_latency += int(self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms)
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return vad_latency
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def GetFrameState(self, t: int, cache: dict = {}):
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frame_state = FrameState.kFrameStateInvalid
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cur_decibel = cache["stats"].decibel[t]
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cur_snr = cur_decibel - cache["stats"].noise_average_decibel
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# for each frame, calc log posterior probability of each state
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if cur_decibel < self.vad_opts.decibel_thres:
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frame_state = FrameState.kFrameStateSil
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self.DetectOneFrame(frame_state, t, False, cache=cache)
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return frame_state
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sum_score = 0.0
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noise_prob = 0.0
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assert len(cache["stats"].sil_pdf_ids) == self.vad_opts.silence_pdf_num
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if len(cache["stats"].sil_pdf_ids) > 0:
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assert len(cache["stats"].scores) == 1 # 只支持batch_size = 1的测试
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sil_pdf_scores = [cache["stats"].scores[0][t][sil_pdf_id] for sil_pdf_id in cache["stats"].sil_pdf_ids]
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sum_score = sum(sil_pdf_scores)
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noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio
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total_score = 1.0
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sum_score = total_score - sum_score
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speech_prob = math.log(sum_score)
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if self.vad_opts.output_frame_probs:
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frame_prob = E2EVadFrameProb()
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frame_prob.noise_prob = noise_prob
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frame_prob.speech_prob = speech_prob
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frame_prob.score = sum_score
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frame_prob.frame_id = t
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cache["stats"].frame_probs.append(frame_prob)
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if math.exp(speech_prob) >= math.exp(noise_prob) + cache["stats"].speech_noise_thres:
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if cur_snr >= self.vad_opts.snr_thres and cur_decibel >= self.vad_opts.decibel_thres:
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frame_state = FrameState.kFrameStateSpeech
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else:
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frame_state = FrameState.kFrameStateSil
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else:
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frame_state = FrameState.kFrameStateSil
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if cache["stats"].noise_average_decibel < -99.9:
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cache["stats"].noise_average_decibel = cur_decibel
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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):
|
is_onnx = kwargs.get("type", "onnx") == "onnx"
|
encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
|
self.encoder = encoder_class(self.encoder, onnx=is_onnx)
|
self.forward = self._export_forward
|
|
return self
|
|
def _export_forward(self, feats: torch.Tensor, *args, **kwargs):
|
|
scores, out_caches = self.encoder(feats, *args)
|
|
return scores, out_caches
|
|
def export_dummy_inputs(self, data_in=None, frame=30):
|
if data_in is None:
|
speech = torch.randn(1, frame, self.encoder_conf.get("input_dim"))
|
else:
|
speech = None # Undo
|
|
cache_frames = self.encoder_conf.get("lorder") + self.encoder_conf.get("rorder") - 1
|
in_cache0 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
|
in_cache1 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
|
in_cache2 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
|
in_cache3 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
|
|
return (speech, in_cache0, in_cache1, in_cache2, in_cache3)
|
|
def export_input_names(self):
|
return ['speech', 'in_cache0', 'in_cache1', 'in_cache2', 'in_cache3']
|
|
def export_output_names(self):
|
return ['logits', 'out_cache0', 'out_cache1', 'out_cache2', 'out_cache3']
|
|
def export_dynamic_axes(self):
|
return {
|
'speech': {
|
1: 'feats_length'
|
},
|
}
|
|
def export_name(self, ):
|
return "model.onnx"
|
|
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)
|
