fix vad results bug

egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/README.md

@@ -0,0 +1,24 @@
+# ModelScope Model
+
+## How to finetune and infer using a pretrained ModelScope Model
+
+### Inference
+
+Or you can use the finetuned model for inference directly.
+
+- Setting parameters in `infer.py`
+    - <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format.
+    - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
+
+- Then you can run the pipeline to infer with:
+```python
+    python infer.py
+```
+
+
+Modify inference related parameters in vad.yaml.
+
+- max_end_silence_time: The end-point silence duration  to judge the end of sentence, the parameter range is 500ms~6000ms, and the default value is 800ms
+- speech_noise_thres:  The balance of speech and silence scores, the parameter range is (-1,1)
+    - The value tends to -1, the greater probability of noise being judged as speech
+    - The value tends to 1, the greater probability of speech being judged as noise

egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/infer.py

@@ -0,0 +1,15 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+if __name__ == '__main__':
+    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav'
+    output_dir = None
+    inference_pipline = pipeline(
+        task=Tasks.voice_activity_detection,
+        model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+        model_revision=None,
+        output_dir=output_dir,
+        batch_size=1,
+    )
+    segments_result = inference_pipline(audio_in=audio_in)
+    print(segments_result)

egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/README.md

@@ -0,0 +1,24 @@
+# ModelScope Model
+
+## How to finetune and infer using a pretrained ModelScope Model
+
+### Inference
+
+Or you can use the finetuned model for inference directly.
+
+- Setting parameters in `infer.py`
+    - <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format.
+    - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set.
+
+- Then you can run the pipeline to infer with:
+```python
+    python infer.py
+```
+
+
+Modify inference related parameters in vad.yaml.
+
+- max_end_silence_time: The end-point silence duration  to judge the end of sentence, the parameter range is 500ms~6000ms, and the default value is 800ms
+- speech_noise_thres:  The balance of speech and silence scores, the parameter range is (-1,1)
+    - The value tends to -1, the greater probability of noise being judged as speech
+    - The value tends to 1, the greater probability of speech being judged as noise

egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/infer.py

@@ -0,0 +1,15 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+if __name__ == '__main__':
+    audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example_8k.wav'
+    output_dir = None
+    inference_pipline = pipeline(
+        task=Tasks.voice_activity_detection,
+        model="damo/speech_fsmn_vad_zh-cn-8k-common",
+        model_revision='v1.1.1',
+        output_dir='./output_dir',
+        batch_size=1,
+    )
+    segments_result = inference_pipline(audio_in=audio_in)
+    print(segments_result)

funasr/bin/vad_inference.py

@@ -81,6 +81,7 @@ class Speech2VadSegment:
         self.device = device
         self.dtype = dtype
         self.frontend = frontend
+        self.batch_size = batch_size
 
     @torch.no_grad()
     def __call__(
@@ -110,10 +111,9 @@ class Speech2VadSegment:
         # segments = self.vad_model(**batch)
 
         # b. Forward Encoder sreaming
-        segments = []
-        segments_tmp = []
-        step = 6000
         t_offset = 0
+        step = min(feats_len, 6000)
+        segments = [[]] * self.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
@@ -129,8 +129,8 @@ class Speech2VadSegment:
             batch = to_device(batch, device=self.device)
             segments_part = self.vad_model(**batch)
             if segments_part:
-                segments_tmp += segments_part[0]
-        segments.append(segments_tmp)
+                for batch_num in range(0, self.batch_size):
+                    segments[batch_num] += segments_part[batch_num]
         return segments
 
 
@@ -254,7 +254,6 @@ def inference_modelscope(
             assert all(isinstance(s, str) for s in keys), keys
             _bs = len(next(iter(batch.values())))
             assert len(keys) == _bs, f"{len(keys)} != {_bs}"
-            # batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
 
             # do vad segment
             results = speech2vadsegment(**batch)

funasr/models/e2e_vad.py

@@ -192,7 +192,7 @@ class WindowDetector(object):
 
 
 class E2EVadModel(nn.Module):
-    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any], streaming=False):
+    def __init__(self, encoder: FSMN, vad_post_args: Dict[str, Any]):
         super(E2EVadModel, self).__init__()
         self.vad_opts = VADXOptions(**vad_post_args)
         self.windows_detector = WindowDetector(self.vad_opts.window_size_ms,
@@ -227,7 +227,6 @@ class E2EVadModel(nn.Module):
         self.data_buf = None
         self.data_buf_all = None
         self.waveform = None
-        self.streaming = streaming
         self.ResetDetection()
 
     def AllResetDetection(self):
@@ -451,11 +450,7 @@ class E2EVadModel(nn.Module):
         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
+            self.DetectLastFrames()
         segments = []
         for batch_num in range(0, feats.shape[0]):  # only support batch_size = 1 now
             segment_batch = []
@@ -468,7 +463,8 @@ class E2EVadModel(nn.Module):
                         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
 
     def DetectCommonFrames(self) -> int:
@@ -494,18 +490,6 @@ class E2EVadModel(nn.Module):
 
         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):
-                frame_state = self.GetFrameState(t)
-                self.DetectOneFrame(frame_state, t, t == self.frm_cnt - 1)
-        else:
-            pass
-        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:

funasr/tasks/vad.py

@@ -291,8 +291,7 @@ class VADTask(AbsTask):
             model_class = model_choices.get_class(args.model)
         except AttributeError:
             model_class = model_choices.get_class("e2evad")
-        model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf,
-                            streaming=args.encoder_conf.get('streaming', False))
+        model = model_class(encoder=encoder, vad_post_args=args.vad_post_conf)
 
         return model