From d6fdd1c79364668afbf171bd18586dd1d7570d20 Mon Sep 17 00:00:00 2001
From: 凌匀 <ailsa.zly@alibaba-inc.com>
Date: 星期四, 16 二月 2023 14:56:32 +0800
Subject: [PATCH] support vad streaming decoder
---
funasr/models/e2e_vad.py | 117 ++++++++++++++++++++++++++++++++++++++++------------------
1 files changed, 81 insertions(+), 36 deletions(-)
diff --git a/funasr/models/e2e_vad.py b/funasr/models/e2e_vad.py
index 98504d6..8afc8db 100755
--- a/funasr/models/e2e_vad.py
+++ b/funasr/models/e2e_vad.py
@@ -5,7 +5,6 @@
from torch import nn
import math
from funasr.models.encoder.fsmn_encoder import FSMN
-# from checkpoint import load_checkpoint
class VadStateMachine(Enum):
@@ -136,7 +135,7 @@
self.win_size_frame = int(window_size_ms / frame_size_ms)
self.win_sum = 0
- self.win_state = [0 for i in range(0, self.win_size_frame)] # 鍒濆鍖栫獥
+ self.win_state = [0] * self.win_size_frame # 鍒濆鍖栫獥
self.cur_win_pos = 0
self.pre_frame_state = FrameState.kFrameStateSil
@@ -151,7 +150,7 @@
def Reset(self) -> None:
self.cur_win_pos = 0
self.win_sum = 0
- self.win_state = [0 for i in range(0, self.win_size_frame)]
+ self.win_state = [0] * self.win_size_frame
self.pre_frame_state = FrameState.kFrameStateSil
self.cur_frame_state = FrameState.kFrameStateSil
self.voice_last_frame_count = 0
@@ -192,8 +191,8 @@
return int(self.frame_size_ms)
-class E2EVadModel(torch.nn.Module):
- def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]):
+class E2EVadModel(nn.Module):
+ def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], streaming=False):
super(E2EVadModel, self).__init__()
self.vad_opts = VADXOptions(**vad_post_args)
self.windows_detector = WindowDetector(self.vad_opts.window_size_ms,
@@ -212,13 +211,13 @@
self.confirmed_start_frame = -1
self.confirmed_end_frame = -1
self.number_end_time_detected = 0
- self.is_callback_with_sign = False
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.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
@@ -226,10 +225,13 @@
self.max_time_out = False
self.decibel = []
self.data_buf = None
+ self.data_buf_all = None
self.waveform = None
+ self.streaming = streaming
self.ResetDetection()
def AllResetDetection(self):
+ self.encoder.cache_reset() # reset the in_cache in self.encoder for next query or next long sentence
self.is_final_send = False
self.data_buf_start_frame = 0
self.frm_cnt = 0
@@ -240,13 +242,13 @@
self.confirmed_start_frame = -1
self.confirmed_end_frame = -1
self.number_end_time_detected = 0
- self.is_callback_with_sign = False
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.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
@@ -254,6 +256,7 @@
self.max_time_out = False
self.decibel = []
self.data_buf = None
+ self.data_buf_all = None
self.waveform = None
self.ResetDetection()
@@ -271,26 +274,32 @@
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)
- self.data_buf = self.waveform[0] # 鎸囧悜self.waveform[0]
+ 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, feats_lengths: int) -> None:
- self.scores = self.encoder(feats) # return B * T * D
- self.frm_cnt = feats_lengths # frame
- # return self.scores
+ def ComputeScores(self, feats: torch.Tensor) -> None:
+ scores = self.encoder(feats) # 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.waveform[0][self.data_buf_start_frame * int(
+ self.data_buf = self.data_buf_all[self.data_buf_start_frame * int(
self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
- # for i in range(0, int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)):
- # self.data_buf.popleft()
- # self.data_buf_start_frame += 1
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:
@@ -301,8 +310,9 @@
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))
- pass
+ 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())
@@ -312,15 +322,18 @@
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')
+ print('warning\n')
out_pos = len(cur_seg.buffer) # cur_seg.buff鐜板湪娌″仛浠讳綍鎿嶄綔
data_to_pop = 0
if end_point_is_sent_end:
data_to_pop = expected_sample_number
else:
data_to_pop = int(frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
- # if data_to_pop > len(self.data_buf_)
- # pass
+ 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();
@@ -329,7 +342,7 @@
# 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('warning')
+ 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:
@@ -346,14 +359,13 @@
def OnVoiceDetected(self, valid_frame: int) -> None:
self.latest_confirmed_speech_frame = valid_frame
- if True: # is_new_api_enable_ = True
- self.PopDataToOutputBuf(valid_frame, 1, False, False, False)
+ 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('warning')
+ print('not reset vad properly\n')
else:
self.confirmed_start_frame = start_frame
@@ -366,7 +378,7 @@
if self.vad_opts.do_end_point_detection:
pass
if self.confirmed_end_frame != -1:
- print('warning')
+ print('not reset vad properly\n')
else:
self.confirmed_end_frame = end_frame
if not fake_result:
@@ -406,7 +418,6 @@
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 = sum(self.scores[0][t][:])
total_score = 1.0
sum_score = total_score - sum_score
speech_prob = math.log(sum_score)
@@ -433,25 +444,59 @@
return frame_state
- def forward(self, feats: torch.Tensor, feats_lengths: int, waveform: torch.tensor) -> List[List[List[int]]]:
- self.AllResetDetection()
+ def forward(self, feats: torch.Tensor, waveform: torch.tensor, is_final_send: bool = False) -> List[List[List[int]]]:
self.waveform = waveform # compute decibel for each frame
self.ComputeDecibel()
- self.ComputeScores(feats, feats_lengths)
- assert len(self.decibel) == len(self.scores[0]) # 淇濊瘉甯ф暟涓�鑷�
- self.DetectLastFrames()
+ self.ComputeScores(feats)
+ if not is_final_send:
+ self.DetectCommonFrames()
+ else:
+ if self.streaming:
+ self.DetectLastFrames()
+ else:
+ self.AllResetDetection()
+ self.DetectAllFrames() # offline decode and is_final_send == True
segments = []
for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
segment_batch = []
- for i in range(0, len(self.output_data_buf)):
- segment = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms]
- segment_batch.append(segment)
- segments.append(segment_batch)
+ if len(self.output_data_buf) > 0:
+ for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
+ if self.output_data_buf[i].contain_seg_start_point and self.output_data_buf[
+ i].contain_seg_end_point:
+ 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)
+
return segments
+ 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.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)
+ 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 DetectAllFrames(self) -> int:
+ if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
+ return 0
if self.vad_opts.nn_eval_block_size != self.vad_opts.dcd_block_size:
frame_state = FrameState.kFrameStateInvalid
for t in range(0, self.frm_cnt):
--
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