| | |
|---|
| | | from enum import Enum |
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| | | from typing import List, Tuple, Dict, Any |
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| | | |
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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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| | | from funasr.models.encoder.fsmn_encoder import FSMN |
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| | | from funasr.models.base_model import FunASRModel |
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| | | from funasr.models.model_class_factory import * |
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| | | |
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| | | |
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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, 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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| | | frontend=None): |
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| | | super().__init__() |
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| | | self.vad_opts = VADXOptions(**vad_post_args) |
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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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| | | |
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| | | encoder_class = encoder_choices.get_class(encoder) |
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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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| | | |
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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.frontend = frontend |
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| | | self.last_drop_frames = 0 |
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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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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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| | | |
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| | | def OnVoiceDetected(self, valid_frame: int) -> None: |
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| | | self.latest_confirmed_speech_frame = valid_frame |
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| | | self.PopDataToOutputBuf(valid_frame, 1, False, False, False) |
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| | | |
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| | | def OnVoiceStart(self, start_frame: int, fake_result: bool = False) -> None: |
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| | | if self.vad_opts.do_start_point_detection: |
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| | | pass |
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| | | if self.confirmed_start_frame != -1: |
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| | | print('not reset vad properly\n') |
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| | | else: |
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| | | self.confirmed_start_frame = start_frame |
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| | | |
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| | | if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: |
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| | | self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False) |
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| | | |
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| | | def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool) -> None: |
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| | | for t in range(self.latest_confirmed_speech_frame + 1, end_frame): |
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| | | self.OnVoiceDetected(t) |
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| | | if self.vad_opts.do_end_point_detection: |
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| | | pass |
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| | | if self.confirmed_end_frame != -1: |
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| | | print('not reset vad properly\n') |
|---|
| | | else: |
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| | | self.confirmed_end_frame = end_frame |
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| | | if not fake_result: |
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| | | self.sil_frame = 0 |
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| | | self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame) |
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| | | self.number_end_time_detected += 1 |
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| | | |
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| | | def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int) -> None: |
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| | | if is_final_frame: |
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| | | self.OnVoiceEnd(cur_frm_idx, False, True) |
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| | | self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected |
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| | | |
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| | | def GetLatency(self) -> int: |
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| | | return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms) |
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| | | |
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| | | def LatencyFrmNumAtStartPoint(self) -> int: |
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| | | vad_latency = self.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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| | | |
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| | | def GetFrameState(self, t: int) -> FrameState: |
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| | | frame_state = FrameState.kFrameStateInvalid |
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| | | cur_decibel = self.decibel[t] |
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| | | cur_snr = cur_decibel - self.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) |
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| | | return frame_state |
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| | | |
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| | | sum_score = 0.0 |
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| | | noise_prob = 0.0 |
|---|
| | | assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num |
|---|
| | | if len(self.sil_pdf_ids) > 0: |
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| | | 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 |
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| | | self.frame_probs.append(frame_prob) |
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| | | 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 * ( |
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| | | 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 |
|---|
| | | ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]: |
|---|
| | | 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 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() |
|---|
| | | |
|---|
| | | |
|---|
| | | import time |
|---|
| | | from funasr.register import tables |
|---|
| | | from funasr.utils.load_utils import load_audio_text_image_video,extract_fbank |
|---|
| | | from funasr.utils.datadir_writer import DatadirWriter |
|---|
| | | from torch.nn.utils.rnn import pad_sequence |
|---|
| | | |
|---|
| | | class VadStateMachine(Enum): |
|---|
| | | kVadInStateStartPointNotDetected = 1 |
|---|
| | |
|---|
| | | output_frame_probs: bool = False, |
|---|
| | | frame_in_ms: int = 10, |
|---|
| | | frame_length_ms: int = 25, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | self.sample_rate = sample_rate |
|---|
| | | self.detect_mode = detect_mode |
|---|
| | |
|---|
| | | return int(self.frame_size_ms) |
|---|
| | | |
|---|
| | | |
|---|
| | | @tables.register("model_classes", "FsmnVAD") |
|---|
| | | class FsmnVAD(nn.Module): |
|---|
| | | """ |
|---|
| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
|---|
| | | Deep-FSMN for Large Vocabulary Continuous Speech Recognition |
|---|
| | | https://arxiv.org/abs/1803.05030 |
|---|
| | | """ |
|---|
| | | def __init__(self, |
|---|
| | | encoder: str = None, |
|---|
| | | encoder_conf: Optional[Dict] = None, |
|---|
| | | vad_post_args: Dict[str, Any] = None, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | super().__init__() |
|---|
| | | self.vad_opts = VADXOptions(**kwargs) |
|---|
| | | self.windows_detector = WindowDetector(self.vad_opts.window_size_ms, |
|---|
| | | self.vad_opts.sil_to_speech_time_thres, |
|---|
| | | self.vad_opts.speech_to_sil_time_thres, |
|---|
| | | self.vad_opts.frame_in_ms) |
|---|
| | | |
|---|
| | | encoder_class = tables.encoder_classes.get(encoder.lower()) |
|---|
| | | encoder = encoder_class(**encoder_conf) |
|---|
| | | self.encoder = encoder |
|---|
| | | # init variables |
|---|
| | | self.data_buf_start_frame = 0 |
|---|
| | | self.frm_cnt = 0 |
|---|
| | | self.latest_confirmed_speech_frame = 0 |
|---|
| | | self.lastest_confirmed_silence_frame = -1 |
|---|
| | | self.continous_silence_frame_count = 0 |
|---|
| | | self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected |
|---|
| | | self.confirmed_start_frame = -1 |
|---|
| | | self.confirmed_end_frame = -1 |
|---|
| | | self.number_end_time_detected = 0 |
|---|
| | | self.sil_frame = 0 |
|---|
| | | self.sil_pdf_ids = self.vad_opts.sil_pdf_ids |
|---|
| | | self.noise_average_decibel = -100.0 |
|---|
| | | self.pre_end_silence_detected = False |
|---|
| | | self.next_seg = True |
|---|
| | | |
|---|
| | | self.output_data_buf = [] |
|---|
| | | self.output_data_buf_offset = 0 |
|---|
| | | self.frame_probs = [] |
|---|
| | | self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres |
|---|
| | | self.speech_noise_thres = self.vad_opts.speech_noise_thres |
|---|
| | | self.scores = None |
|---|
| | | self.max_time_out = False |
|---|
| | | self.decibel = [] |
|---|
| | | self.data_buf = None |
|---|
| | | self.data_buf_all = None |
|---|
| | | self.waveform = None |
|---|
| | | self.last_drop_frames = 0 |
|---|
| | | |
|---|
| | | def AllResetDetection(self): |
|---|
| | | self.data_buf_start_frame = 0 |
|---|
| | | self.frm_cnt = 0 |
|---|
| | | self.latest_confirmed_speech_frame = 0 |
|---|
| | | self.lastest_confirmed_silence_frame = -1 |
|---|
| | | self.continous_silence_frame_count = 0 |
|---|
| | | self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected |
|---|
| | | self.confirmed_start_frame = -1 |
|---|
| | | self.confirmed_end_frame = -1 |
|---|
| | | self.number_end_time_detected = 0 |
|---|
| | | self.sil_frame = 0 |
|---|
| | | self.sil_pdf_ids = self.vad_opts.sil_pdf_ids |
|---|
| | | self.noise_average_decibel = -100.0 |
|---|
| | | self.pre_end_silence_detected = False |
|---|
| | | self.next_seg = True |
|---|
| | | |
|---|
| | | self.output_data_buf = [] |
|---|
| | | self.output_data_buf_offset = 0 |
|---|
| | | self.frame_probs = [] |
|---|
| | | self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres |
|---|
| | | self.speech_noise_thres = self.vad_opts.speech_noise_thres |
|---|
| | | self.scores = None |
|---|
| | | self.max_time_out = False |
|---|
| | | self.decibel = [] |
|---|
| | | self.data_buf = None |
|---|
| | | self.data_buf_all = None |
|---|
| | | self.waveform = None |
|---|
| | | self.last_drop_frames = 0 |
|---|
| | | self.windows_detector.Reset() |
|---|
| | | |
|---|
| | | def ResetDetection(self): |
|---|
| | | self.continous_silence_frame_count = 0 |
|---|
| | | self.latest_confirmed_speech_frame = 0 |
|---|
| | | self.lastest_confirmed_silence_frame = -1 |
|---|
| | | self.confirmed_start_frame = -1 |
|---|
| | | self.confirmed_end_frame = -1 |
|---|
| | | self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected |
|---|
| | | self.windows_detector.Reset() |
|---|
| | | self.sil_frame = 0 |
|---|
| | | self.frame_probs = [] |
|---|
| | | |
|---|
| | | if self.output_data_buf: |
|---|
| | | assert self.output_data_buf[-1].contain_seg_end_point == True |
|---|
| | | drop_frames = int(self.output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms) |
|---|
| | | real_drop_frames = drop_frames - self.last_drop_frames |
|---|
| | | self.last_drop_frames = drop_frames |
|---|
| | | self.data_buf_all = self.data_buf_all[real_drop_frames * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):] |
|---|
| | | self.decibel = self.decibel[real_drop_frames:] |
|---|
| | | self.scores = self.scores[:, real_drop_frames:, :] |
|---|
| | | |
|---|
| | | def ComputeDecibel(self) -> None: |
|---|
| | | frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000) |
|---|
| | | frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) |
|---|
| | | if self.data_buf_all is None: |
|---|
| | | self.data_buf_all = self.waveform[0] # self.data_buf is pointed to self.waveform[0] |
|---|
| | | self.data_buf = self.data_buf_all |
|---|
| | | else: |
|---|
| | | self.data_buf_all = torch.cat((self.data_buf_all, self.waveform[0])) |
|---|
| | | for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length): |
|---|
| | | self.decibel.append( |
|---|
| | | 10 * math.log10((self.waveform[0][offset: offset + frame_sample_length]).square().sum() + \ |
|---|
| | | 0.000001)) |
|---|
| | | |
|---|
| | | def ComputeScores(self, feats: torch.Tensor, cache: Dict[str, torch.Tensor]) -> None: |
|---|
| | | scores = self.encoder(feats, cache).to('cpu') # return B * T * D |
|---|
| | | assert scores.shape[1] == feats.shape[1], "The shape between feats and scores does not match" |
|---|
| | | self.vad_opts.nn_eval_block_size = scores.shape[1] |
|---|
| | | self.frm_cnt += scores.shape[1] # count total frames |
|---|
| | | if self.scores is None: |
|---|
| | | self.scores = scores # the first calculation |
|---|
| | | else: |
|---|
| | | self.scores = torch.cat((self.scores, scores), dim=1) |
|---|
| | | |
|---|
| | | def PopDataBufTillFrame(self, frame_idx: int) -> None: # need check again |
|---|
| | | while self.data_buf_start_frame < frame_idx: |
|---|
| | | if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000): |
|---|
| | | self.data_buf_start_frame += 1 |
|---|
| | | self.data_buf = self.data_buf_all[(self.data_buf_start_frame - self.last_drop_frames) * int( |
|---|
| | | self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):] |
|---|
| | | |
|---|
| | | def PopDataToOutputBuf(self, start_frm: int, frm_cnt: int, first_frm_is_start_point: bool, |
|---|
| | | last_frm_is_end_point: bool, end_point_is_sent_end: bool) -> None: |
|---|
| | | self.PopDataBufTillFrame(start_frm) |
|---|
| | | expected_sample_number = int(frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000) |
|---|
| | | if last_frm_is_end_point: |
|---|
| | | extra_sample = max(0, int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000 - \ |
|---|
| | | self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000)) |
|---|
| | | expected_sample_number += int(extra_sample) |
|---|
| | | if end_point_is_sent_end: |
|---|
| | | expected_sample_number = max(expected_sample_number, len(self.data_buf)) |
|---|
| | | if len(self.data_buf) < expected_sample_number: |
|---|
| | | print('error in calling pop data_buf\n') |
|---|
| | | |
|---|
| | | if len(self.output_data_buf) == 0 or first_frm_is_start_point: |
|---|
| | | self.output_data_buf.append(E2EVadSpeechBufWithDoa()) |
|---|
| | | self.output_data_buf[-1].Reset() |
|---|
| | | self.output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms |
|---|
| | | self.output_data_buf[-1].end_ms = self.output_data_buf[-1].start_ms |
|---|
| | | self.output_data_buf[-1].doa = 0 |
|---|
| | | cur_seg = self.output_data_buf[-1] |
|---|
| | | if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms: |
|---|
| | | print('warning\n') |
|---|
| | | out_pos = len(cur_seg.buffer) # cur_seg.buff现在没做任何操作 |
|---|
| | | data_to_pop = 0 |
|---|
| | | if end_point_is_sent_end: |
|---|
| | | data_to_pop = expected_sample_number |
|---|
| | | else: |
|---|
| | | data_to_pop = int(frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) |
|---|
| | | if data_to_pop > len(self.data_buf): |
|---|
| | | print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n') |
|---|
| | | data_to_pop = len(self.data_buf) |
|---|
| | | expected_sample_number = len(self.data_buf) |
|---|
| | | |
|---|
| | | cur_seg.doa = 0 |
|---|
| | | for sample_cpy_out in range(0, data_to_pop): |
|---|
| | | # cur_seg.buffer[out_pos ++] = data_buf_.back(); |
|---|
| | | out_pos += 1 |
|---|
| | | for sample_cpy_out in range(data_to_pop, expected_sample_number): |
|---|
| | | # cur_seg.buffer[out_pos++] = data_buf_.back() |
|---|
| | | out_pos += 1 |
|---|
| | | if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms: |
|---|
| | | print('Something wrong with the VAD algorithm\n') |
|---|
| | | self.data_buf_start_frame += frm_cnt |
|---|
| | | cur_seg.end_ms = (start_frm + frm_cnt) * self.vad_opts.frame_in_ms |
|---|
| | | if first_frm_is_start_point: |
|---|
| | | cur_seg.contain_seg_start_point = True |
|---|
| | | if last_frm_is_end_point: |
|---|
| | | cur_seg.contain_seg_end_point = True |
|---|
| | | |
|---|
| | | def OnSilenceDetected(self, valid_frame: int): |
|---|
| | | self.lastest_confirmed_silence_frame = valid_frame |
|---|
| | | if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: |
|---|
| | | self.PopDataBufTillFrame(valid_frame) |
|---|
| | | # silence_detected_callback_ |
|---|
| | | # pass |
|---|
| | | |
|---|
| | | def OnVoiceDetected(self, valid_frame: int) -> None: |
|---|
| | | self.latest_confirmed_speech_frame = valid_frame |
|---|
| | | self.PopDataToOutputBuf(valid_frame, 1, False, False, False) |
|---|
| | | |
|---|
| | | def OnVoiceStart(self, start_frame: int, fake_result: bool = False) -> None: |
|---|
| | | if self.vad_opts.do_start_point_detection: |
|---|
| | | pass |
|---|
| | | if self.confirmed_start_frame != -1: |
|---|
| | | print('not reset vad properly\n') |
|---|
| | | else: |
|---|
| | | self.confirmed_start_frame = start_frame |
|---|
| | | |
|---|
| | | if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected: |
|---|
| | | self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False) |
|---|
| | | |
|---|
| | | def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool) -> None: |
|---|
| | | for t in range(self.latest_confirmed_speech_frame + 1, end_frame): |
|---|
| | | self.OnVoiceDetected(t) |
|---|
| | | if self.vad_opts.do_end_point_detection: |
|---|
| | | pass |
|---|
| | | if self.confirmed_end_frame != -1: |
|---|
| | | print('not reset vad properly\n') |
|---|
| | | else: |
|---|
| | | self.confirmed_end_frame = end_frame |
|---|
| | | if not fake_result: |
|---|
| | | self.sil_frame = 0 |
|---|
| | | self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame) |
|---|
| | | self.number_end_time_detected += 1 |
|---|
| | | |
|---|
| | | def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int) -> None: |
|---|
| | | if is_final_frame: |
|---|
| | | self.OnVoiceEnd(cur_frm_idx, False, True) |
|---|
| | | self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected |
|---|
| | | |
|---|
| | | def GetLatency(self) -> int: |
|---|
| | | return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms) |
|---|
| | | |
|---|
| | | def LatencyFrmNumAtStartPoint(self) -> int: |
|---|
| | | vad_latency = self.windows_detector.GetWinSize() |
|---|
| | | if self.vad_opts.do_extend: |
|---|
| | | vad_latency += int(self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms) |
|---|
| | | return vad_latency |
|---|
| | | |
|---|
| | | def GetFrameState(self, t: int): |
|---|
| | | 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, cache: Dict[str, torch.Tensor] = dict(), |
|---|
| | | is_final: bool = False |
|---|
| | | ): |
|---|
| | | if not cache: |
|---|
| | | self.AllResetDetection() |
|---|
| | | self.waveform = waveform # compute decibel for each frame |
|---|
| | | self.ComputeDecibel() |
|---|
| | | self.ComputeScores(feats, 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, 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_text_image_video(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] |
|---|
| | | cache = kwargs.get("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, |
|---|
| | | "cache": cache |
|---|
| | | } |
|---|
| | | |
|---|
| | | |
|---|
| | | segments_part, 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 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() |
|---|
| | | |
|---|
| | | |
|---|
| | | |
|---|
