mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
777 lines
35 KiB
Python
777 lines
35 KiB
Python
from enum import Enum
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from typing import List, Tuple, Dict, Any
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import logging
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import os
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import json
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import torch
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from torch import nn
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import math
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from typing import Optional
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import time
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from funasr.register import tables
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from funasr.datasets.audio_datasets.load_audio_extract_fbank import load_audio,extract_fbank
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from funasr.utils.datadir_writer import DatadirWriter
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from torch.nn.utils.rnn import pad_sequence
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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, sil_to_speech_time: int,
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speech_to_sil_time: int, 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) -> 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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@tables.register("model_classes", "FsmnVAD")
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class FsmnVAD(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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self.windows_detector = WindowDetector(self.vad_opts.window_size_ms,
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self.vad_opts.sil_to_speech_time_thres,
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self.vad_opts.speech_to_sil_time_thres,
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self.vad_opts.frame_in_ms)
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encoder_class = tables.encoder_classes.get(encoder.lower())
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encoder = encoder_class(**encoder_conf)
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self.encoder = encoder
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# init variables
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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 = self.vad_opts.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 = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
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self.speech_noise_thres = self.vad_opts.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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def AllResetDetection(self):
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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 = self.vad_opts.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 = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
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self.speech_noise_thres = self.vad_opts.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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self.windows_detector.Reset()
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def ResetDetection(self):
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self.continous_silence_frame_count = 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.confirmed_start_frame = -1
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self.confirmed_end_frame = -1
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self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
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self.windows_detector.Reset()
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self.sil_frame = 0
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self.frame_probs = []
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if self.output_data_buf:
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assert self.output_data_buf[-1].contain_seg_end_point == True
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drop_frames = int(self.output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
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real_drop_frames = drop_frames - self.last_drop_frames
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self.last_drop_frames = drop_frames
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self.data_buf_all = self.data_buf_all[real_drop_frames * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
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self.decibel = self.decibel[real_drop_frames:]
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self.scores = self.scores[:, real_drop_frames:, :]
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def ComputeDecibel(self) -> 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 self.data_buf_all is None:
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self.data_buf_all = self.waveform[0] # self.data_buf is pointed to self.waveform[0]
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self.data_buf = self.data_buf_all
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else:
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self.data_buf_all = torch.cat((self.data_buf_all, self.waveform[0]))
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for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length):
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self.decibel.append(
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10 * math.log10((self.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, in_cache: Dict[str, torch.Tensor]) -> None:
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scores = self.encoder(feats, in_cache).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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self.frm_cnt += scores.shape[1] # count total frames
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if self.scores is None:
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self.scores = scores # the first calculation
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else:
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self.scores = torch.cat((self.scores, scores), dim=1)
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def PopDataBufTillFrame(self, frame_idx: int) -> None: # need check again
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while self.data_buf_start_frame < frame_idx:
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if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
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self.data_buf_start_frame += 1
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self.data_buf = self.data_buf_all[(self.data_buf_start_frame - self.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) -> None:
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self.PopDataBufTillFrame(start_frm)
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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(self.data_buf))
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if len(self.data_buf) < expected_sample_number:
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print('error in calling pop data_buf\n')
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if len(self.output_data_buf) == 0 or first_frm_is_start_point:
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self.output_data_buf.append(E2EVadSpeechBufWithDoa())
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self.output_data_buf[-1].Reset()
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self.output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
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self.output_data_buf[-1].end_ms = self.output_data_buf[-1].start_ms
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self.output_data_buf[-1].doa = 0
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cur_seg = self.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(self.data_buf):
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print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n')
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data_to_pop = len(self.data_buf)
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expected_sample_number = len(self.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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self.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):
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self.lastest_confirmed_silence_frame = valid_frame
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if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
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self.PopDataBufTillFrame(valid_frame)
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# silence_detected_callback_
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# pass
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def OnVoiceDetected(self, valid_frame: int) -> None:
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self.latest_confirmed_speech_frame = valid_frame
|
|
self.PopDataToOutputBuf(valid_frame, 1, False, False, False)
|
|
|
|
def OnVoiceStart(self, start_frame: int, fake_result: bool = False) -> None:
|
|
if self.vad_opts.do_start_point_detection:
|
|
pass
|
|
if self.confirmed_start_frame != -1:
|
|
print('not reset vad properly\n')
|
|
else:
|
|
self.confirmed_start_frame = start_frame
|
|
|
|
if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
|
self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False)
|
|
|
|
def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool) -> None:
|
|
for t in range(self.latest_confirmed_speech_frame + 1, end_frame):
|
|
self.OnVoiceDetected(t)
|
|
if self.vad_opts.do_end_point_detection:
|
|
pass
|
|
if self.confirmed_end_frame != -1:
|
|
print('not reset vad properly\n')
|
|
else:
|
|
self.confirmed_end_frame = end_frame
|
|
if not fake_result:
|
|
self.sil_frame = 0
|
|
self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame)
|
|
self.number_end_time_detected += 1
|
|
|
|
def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int) -> None:
|
|
if is_final_frame:
|
|
self.OnVoiceEnd(cur_frm_idx, False, True)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
|
|
def GetLatency(self) -> int:
|
|
return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms)
|
|
|
|
def LatencyFrmNumAtStartPoint(self) -> int:
|
|
vad_latency = self.windows_detector.GetWinSize()
|
|
if self.vad_opts.do_extend:
|
|
vad_latency += int(self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms)
|
|
return vad_latency
|
|
|
|
def GetFrameState(self, t: int):
|
|
frame_state = FrameState.kFrameStateInvalid
|
|
cur_decibel = self.decibel[t]
|
|
cur_snr = cur_decibel - self.noise_average_decibel
|
|
# for each frame, calc log posterior probability of each state
|
|
if cur_decibel < self.vad_opts.decibel_thres:
|
|
frame_state = FrameState.kFrameStateSil
|
|
self.DetectOneFrame(frame_state, t, False)
|
|
return frame_state
|
|
|
|
sum_score = 0.0
|
|
noise_prob = 0.0
|
|
assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num
|
|
if len(self.sil_pdf_ids) > 0:
|
|
assert len(self.scores) == 1 # 只支持batch_size = 1的测试
|
|
sil_pdf_scores = [self.scores[0][t][sil_pdf_id] for sil_pdf_id in self.sil_pdf_ids]
|
|
sum_score = sum(sil_pdf_scores)
|
|
noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio
|
|
total_score = 1.0
|
|
sum_score = total_score - sum_score
|
|
speech_prob = math.log(sum_score)
|
|
if self.vad_opts.output_frame_probs:
|
|
frame_prob = E2EVadFrameProb()
|
|
frame_prob.noise_prob = noise_prob
|
|
frame_prob.speech_prob = speech_prob
|
|
frame_prob.score = sum_score
|
|
frame_prob.frame_id = t
|
|
self.frame_probs.append(frame_prob)
|
|
if math.exp(speech_prob) >= math.exp(noise_prob) + self.speech_noise_thres:
|
|
if cur_snr >= self.vad_opts.snr_thres and cur_decibel >= self.vad_opts.decibel_thres:
|
|
frame_state = FrameState.kFrameStateSpeech
|
|
else:
|
|
frame_state = FrameState.kFrameStateSil
|
|
else:
|
|
frame_state = FrameState.kFrameStateSil
|
|
if self.noise_average_decibel < -99.9:
|
|
self.noise_average_decibel = cur_decibel
|
|
else:
|
|
self.noise_average_decibel = (cur_decibel + self.noise_average_decibel * (
|
|
self.vad_opts.noise_frame_num_used_for_snr
|
|
- 1)) / self.vad_opts.noise_frame_num_used_for_snr
|
|
|
|
return frame_state
|
|
|
|
def forward(self, feats: torch.Tensor, waveform: torch.tensor, in_cache: Dict[str, torch.Tensor] = dict(),
|
|
is_final: bool = False
|
|
):
|
|
if not in_cache:
|
|
self.AllResetDetection()
|
|
self.waveform = waveform # compute decibel for each frame
|
|
self.ComputeDecibel()
|
|
self.ComputeScores(feats, in_cache)
|
|
if not is_final:
|
|
self.DetectCommonFrames()
|
|
else:
|
|
self.DetectLastFrames()
|
|
segments = []
|
|
for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
|
|
segment_batch = []
|
|
if len(self.output_data_buf) > 0:
|
|
for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
|
|
if not is_final and (not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[
|
|
i].contain_seg_end_point):
|
|
continue
|
|
segment = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms]
|
|
segment_batch.append(segment)
|
|
self.output_data_buf_offset += 1 # need update this parameter
|
|
if segment_batch:
|
|
segments.append(segment_batch)
|
|
if is_final:
|
|
# reset class variables and clear the dict for the next query
|
|
self.AllResetDetection()
|
|
return segments, in_cache
|
|
|
|
def generate(self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
|
|
meta_data = {}
|
|
audio_sample_list = [data_in]
|
|
if isinstance(data_in, torch.Tensor): # fbank
|
|
speech, speech_lengths = data_in, data_lengths
|
|
if len(speech.shape) < 3:
|
|
speech = speech[None, :, :]
|
|
if speech_lengths is None:
|
|
speech_lengths = speech.shape[1]
|
|
else:
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
|
|
frontend=frontend)
|
|
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.to(device=kwargs["device"]), speech_lengths.to(device=kwargs["device"])
|
|
|
|
# b. Forward Encoder streaming
|
|
t_offset = 0
|
|
feats = speech
|
|
feats_len = speech_lengths.max().item()
|
|
waveform = pad_sequence(audio_sample_list, batch_first=True).to(device=kwargs["device"]) # data: [batch, N]
|
|
in_cache = kwargs.get("in_cache", {})
|
|
batch_size = kwargs.get("batch_size", 1)
|
|
step = min(feats_len, 6000)
|
|
segments = [[]] * batch_size
|
|
|
|
for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
|
|
if t_offset + step >= feats_len - 1:
|
|
step = feats_len - t_offset
|
|
is_final = True
|
|
else:
|
|
is_final = False
|
|
batch = {
|
|
"feats": feats[:, t_offset:t_offset + step, :],
|
|
"waveform": waveform[:, t_offset * 160:min(waveform.shape[-1], (t_offset + step - 1) * 160 + 400)],
|
|
"is_final": is_final,
|
|
"in_cache": in_cache
|
|
}
|
|
|
|
|
|
segments_part, in_cache = self.forward(**batch)
|
|
if segments_part:
|
|
for batch_num in range(0, batch_size):
|
|
segments[batch_num] += segments_part[batch_num]
|
|
|
|
ibest_writer = None
|
|
if ibest_writer is None and kwargs.get("output_dir") is not None:
|
|
writer = DatadirWriter(kwargs.get("output_dir"))
|
|
ibest_writer = writer[f"{1}best_recog"]
|
|
|
|
results = []
|
|
for i in range(batch_size):
|
|
|
|
if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
|
|
results[i] = json.dumps(results[i])
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["text"][key[i]] = segments[i]
|
|
|
|
result_i = {"key": key[i], "value": segments[i]}
|
|
results.append(result_i)
|
|
|
|
return results, meta_data
|
|
|
|
def forward_online(self, feats: torch.Tensor, waveform: torch.tensor, in_cache: Dict[str, torch.Tensor] = dict(),
|
|
is_final: bool = False, max_end_sil: int = 800
|
|
) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
|
|
if not in_cache:
|
|
self.AllResetDetection()
|
|
self.max_end_sil_frame_cnt_thresh = max_end_sil - self.vad_opts.speech_to_sil_time_thres
|
|
self.waveform = waveform # compute decibel for each frame
|
|
|
|
self.ComputeScores(feats, in_cache)
|
|
self.ComputeDecibel()
|
|
if not is_final:
|
|
self.DetectCommonFrames()
|
|
else:
|
|
self.DetectLastFrames()
|
|
segments = []
|
|
for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
|
|
segment_batch = []
|
|
if len(self.output_data_buf) > 0:
|
|
for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
|
|
if not self.output_data_buf[i].contain_seg_start_point:
|
|
continue
|
|
if not self.next_seg and not self.output_data_buf[i].contain_seg_end_point:
|
|
continue
|
|
start_ms = self.output_data_buf[i].start_ms if self.next_seg else -1
|
|
if self.output_data_buf[i].contain_seg_end_point:
|
|
end_ms = self.output_data_buf[i].end_ms
|
|
self.next_seg = True
|
|
self.output_data_buf_offset += 1
|
|
else:
|
|
end_ms = -1
|
|
self.next_seg = False
|
|
segment = [start_ms, end_ms]
|
|
segment_batch.append(segment)
|
|
if segment_batch:
|
|
segments.append(segment_batch)
|
|
if is_final:
|
|
# reset class variables and clear the dict for the next query
|
|
self.AllResetDetection()
|
|
return segments, in_cache
|
|
|
|
def DetectCommonFrames(self) -> int:
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
|
|
return 0
|
|
for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
|
|
frame_state = FrameState.kFrameStateInvalid
|
|
frame_state = self.GetFrameState(self.frm_cnt - 1 - i - self.last_drop_frames)
|
|
self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
|
|
|
|
return 0
|
|
|
|
def DetectLastFrames(self) -> int:
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
|
|
return 0
|
|
for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
|
|
frame_state = FrameState.kFrameStateInvalid
|
|
frame_state = self.GetFrameState(self.frm_cnt - 1 - i - self.last_drop_frames)
|
|
if i != 0:
|
|
self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
|
|
else:
|
|
self.DetectOneFrame(frame_state, self.frm_cnt - 1, True)
|
|
|
|
return 0
|
|
|
|
def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool) -> None:
|
|
tmp_cur_frm_state = FrameState.kFrameStateInvalid
|
|
if cur_frm_state == FrameState.kFrameStateSpeech:
|
|
if math.fabs(1.0) > self.vad_opts.fe_prior_thres:
|
|
tmp_cur_frm_state = FrameState.kFrameStateSpeech
|
|
else:
|
|
tmp_cur_frm_state = FrameState.kFrameStateSil
|
|
elif cur_frm_state == FrameState.kFrameStateSil:
|
|
tmp_cur_frm_state = FrameState.kFrameStateSil
|
|
state_change = self.windows_detector.DetectOneFrame(tmp_cur_frm_state, cur_frm_idx)
|
|
frm_shift_in_ms = self.vad_opts.frame_in_ms
|
|
if AudioChangeState.kChangeStateSil2Speech == state_change:
|
|
silence_frame_count = self.continous_silence_frame_count
|
|
self.continous_silence_frame_count = 0
|
|
self.pre_end_silence_detected = False
|
|
start_frame = 0
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
|
start_frame = max(self.data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint())
|
|
self.OnVoiceStart(start_frame)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
|
|
for t in range(start_frame + 1, cur_frm_idx + 1):
|
|
self.OnVoiceDetected(t)
|
|
elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
|
for t in range(self.latest_confirmed_speech_frame + 1, cur_frm_idx):
|
|
self.OnVoiceDetected(t)
|
|
if cur_frm_idx - self.confirmed_start_frame + 1 > \
|
|
self.vad_opts.max_single_segment_time / frm_shift_in_ms:
|
|
self.OnVoiceEnd(cur_frm_idx, False, False)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
elif not is_final_frame:
|
|
self.OnVoiceDetected(cur_frm_idx)
|
|
else:
|
|
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
|
|
else:
|
|
pass
|
|
elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
|
|
self.continous_silence_frame_count = 0
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
|
pass
|
|
elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
|
if cur_frm_idx - self.confirmed_start_frame + 1 > \
|
|
self.vad_opts.max_single_segment_time / frm_shift_in_ms:
|
|
self.OnVoiceEnd(cur_frm_idx, False, False)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
elif not is_final_frame:
|
|
self.OnVoiceDetected(cur_frm_idx)
|
|
else:
|
|
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
|
|
else:
|
|
pass
|
|
elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
|
|
self.continous_silence_frame_count = 0
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
|
if cur_frm_idx - self.confirmed_start_frame + 1 > \
|
|
self.vad_opts.max_single_segment_time / frm_shift_in_ms:
|
|
self.max_time_out = True
|
|
self.OnVoiceEnd(cur_frm_idx, False, False)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
elif not is_final_frame:
|
|
self.OnVoiceDetected(cur_frm_idx)
|
|
else:
|
|
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
|
|
else:
|
|
pass
|
|
elif AudioChangeState.kChangeStateSil2Sil == state_change:
|
|
self.continous_silence_frame_count += 1
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
|
# silence timeout, return zero length decision
|
|
if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and (
|
|
self.continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
|
|
or (is_final_frame and self.number_end_time_detected == 0):
|
|
for t in range(self.lastest_confirmed_silence_frame + 1, cur_frm_idx):
|
|
self.OnSilenceDetected(t)
|
|
self.OnVoiceStart(0, True)
|
|
self.OnVoiceEnd(0, True, False);
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
else:
|
|
if cur_frm_idx >= self.LatencyFrmNumAtStartPoint():
|
|
self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint())
|
|
elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
|
if self.continous_silence_frame_count * frm_shift_in_ms >= self.max_end_sil_frame_cnt_thresh:
|
|
lookback_frame = int(self.max_end_sil_frame_cnt_thresh / frm_shift_in_ms)
|
|
if self.vad_opts.do_extend:
|
|
lookback_frame -= int(self.vad_opts.lookahead_time_end_point / frm_shift_in_ms)
|
|
lookback_frame -= 1
|
|
lookback_frame = max(0, lookback_frame)
|
|
self.OnVoiceEnd(cur_frm_idx - lookback_frame, False, False)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
elif cur_frm_idx - self.confirmed_start_frame + 1 > \
|
|
self.vad_opts.max_single_segment_time / frm_shift_in_ms:
|
|
self.OnVoiceEnd(cur_frm_idx, False, False)
|
|
self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
|
elif self.vad_opts.do_extend and not is_final_frame:
|
|
if self.continous_silence_frame_count <= int(
|
|
self.vad_opts.lookahead_time_end_point / frm_shift_in_ms):
|
|
self.OnVoiceDetected(cur_frm_idx)
|
|
else:
|
|
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
|
|
else:
|
|
pass
|
|
|
|
if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
|
|
self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
|
|
self.ResetDetection()
|
|
|
|
|
|
|