From 2cdb2d654f2109ef4e648bae6f169143e267e5db Mon Sep 17 00:00:00 2001
From: zhuzizyf <42790740+zhuzizyf@users.noreply.github.com>
Date: 星期六, 11 三月 2023 14:33:14 +0800
Subject: [PATCH] Update dataset.py
---
funasr/models/e2e_vad.py | 35 +++++++++++++++++++----------------
1 files changed, 19 insertions(+), 16 deletions(-)
diff --git a/funasr/models/e2e_vad.py b/funasr/models/e2e_vad.py
index b64c677..b9be89a 100755
--- a/funasr/models/e2e_vad.py
+++ b/funasr/models/e2e_vad.py
@@ -201,7 +201,7 @@
self.vad_opts.frame_in_ms)
self.encoder = encoder
# init variables
- self.is_final_send = False
+ self.is_final = False
self.data_buf_start_frame = 0
self.frm_cnt = 0
self.latest_confirmed_speech_frame = 0
@@ -230,8 +230,7 @@
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.is_final = False
self.data_buf_start_frame = 0
self.frm_cnt = 0
self.latest_confirmed_speech_frame = 0
@@ -283,8 +282,8 @@
10 * math.log10((self.waveform[0][offset: offset + frame_sample_length]).square().sum() + \
0.000001))
- def ComputeScores(self, feats: torch.Tensor) -> None:
- scores = self.encoder(feats) # return B * T * D
+ def ComputeScores(self, feats: torch.Tensor, in_cache: Dict[str, torch.Tensor]) -> None:
+ scores = self.encoder(feats, in_cache) # 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
@@ -306,7 +305,7 @@
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))
+ 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))
@@ -443,11 +442,13 @@
return frame_state
- def forward(self, feats: torch.Tensor, waveform: torch.tensor, is_final_send: bool = False) -> List[List[List[int]]]:
+ 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]]:
self.waveform = waveform # compute decibel for each frame
self.ComputeDecibel()
- self.ComputeScores(feats)
- if not is_final_send:
+ self.ComputeScores(feats, in_cache)
+ if not is_final:
self.DetectCommonFrames()
else:
self.DetectLastFrames()
@@ -456,16 +457,18 @@
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[
+ if not self.output_data_buf[i].contain_seg_start_point or not 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
+ 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_send:
- self.AllResetDetection()
- return segments
+ 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:
--
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