From 937e507977cc9e49ce323f8b2933087d0fe52698 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 16 四月 2023 22:29:32 +0800
Subject: [PATCH] Merge pull request #363 from alibaba-damo-academy/main

---
 funasr/models/e2e_vad.py |   35 +++++++++++++++++++++++++++++++----
 1 files changed, 31 insertions(+), 4 deletions(-)

diff --git a/funasr/models/e2e_vad.py b/funasr/models/e2e_vad.py
index e6cd7c0..50ec475 100644
--- a/funasr/models/e2e_vad.py
+++ b/funasr/models/e2e_vad.py
@@ -35,6 +35,11 @@
 
 
 class VADXOptions:
+    """
+    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,
             sample_rate: int = 16000,
@@ -99,6 +104,11 @@
 
 
 class E2EVadSpeechBufWithDoa(object):
+    """
+    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):
         self.start_ms = 0
         self.end_ms = 0
@@ -117,6 +127,11 @@
 
 
 class E2EVadFrameProb(object):
+    """
+    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):
         self.noise_prob = 0.0
         self.speech_prob = 0.0
@@ -126,6 +141,11 @@
 
 
 class WindowDetector(object):
+    """
+    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, window_size_ms: int, sil_to_speech_time: int,
                  speech_to_sil_time: int, frame_size_ms: int):
         self.window_size_ms = window_size_ms
@@ -192,7 +212,12 @@
 
 
 class E2EVadModel(nn.Module):
-    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]):
+    """
+    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: FSMN, vad_post_args: Dict[str, Any], frontend=None):
         super(E2EVadModel, self).__init__()
         self.vad_opts = VADXOptions(**vad_post_args)
         self.windows_detector = WindowDetector(self.vad_opts.window_size_ms,
@@ -229,6 +254,7 @@
         self.data_buf_all = None
         self.waveform = None
         self.ResetDetection()
+        self.frontend = frontend
 
     def AllResetDetection(self):
         self.is_final = False
@@ -459,8 +485,8 @@
             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 or not self.output_data_buf[
-                        i].contain_seg_end_point:
+                    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)
@@ -477,8 +503,9 @@
                 ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
         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.ComputeDecibel()
+        
         self.ComputeScores(feats, in_cache)
+        self.ComputeDecibel()
         if not is_final:
             self.DetectCommonFrames()
         else:

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