From 41eb0b4b5e15df9ebf2c1308c8b3ae0c2ef4c845 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 17 三月 2023 20:15:19 +0800
Subject: [PATCH] Merge pull request #260 from alibaba-damo-academy/tmp

---
 funasr/models/e2e_vad.py |   45 +++++++++++++++++++++++++++++++++++++++++----
 1 files changed, 41 insertions(+), 4 deletions(-)

diff --git a/funasr/models/e2e_vad.py b/funasr/models/e2e_vad.py
index c21be1b..2c5673c 100755
--- a/funasr/models/e2e_vad.py
+++ b/funasr/models/e2e_vad.py
@@ -215,6 +215,7 @@
         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
@@ -244,6 +245,7 @@
         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
@@ -441,10 +443,10 @@
                         - 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
-                ) -> List[List[List[int]]]:
+                ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
         self.waveform = waveform  # compute decibel for each frame
         self.ComputeDecibel()
         self.ComputeScores(feats, in_cache)
@@ -468,8 +470,43 @@
         if is_final:
             # reset class variables and clear the dict for the next query
             self.AllResetDetection()
-            in_cache.clear()
-        return segments
+        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
+                ) -> Tuple[List[List[List[int]]], Dict[str, torch.Tensor]]:
+        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 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:

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