Update applyVad.py

applyVad.py

@@ -1,105 +1,85 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[3]:
-
-
-# import webrtcvad
-# import numpy as np
-# import librosa
-# def apply_vad(audio, sr, frame_duration=30, aggressiveness=3):
-#     '''
-#      Voice Activity Detection (VAD): It is a technique used to determine whether a segment of audio contains speech. 
-#      This is useful in noisy environments where you want to filter out non-speech parts of the audio.
-#      webrtcvad: This is a Python package based on the VAD from the WebRTC (Web Real-Time Communication) project. 
-#      It helps detect speech in small chunks of audio.
-#      '''
-#     vad = webrtcvad.Vad()
-#     audio_int16 = np.int16(audio * 32767)
-#     frame_size = int(sr * frame_duration / 1000)
-#     frames = [audio_int16[i:i + frame_size] for i in range(0, len(audio_int16), frame_size)]
-#     voiced_audio = np.concatenate([frame for frame in frames if vad.is_speech(frame.tobytes(), sample_rate=sr)])
-#     voiced_audio = np.float32(voiced_audio) / 32767
-#     return voiced_audio
-
-
-# In[1]:
-
-
-# import webrtcvad
-# import librosa
-# import numpy as np
-# def apply_vad(audio, sr, frame_duration_ms=30):
-#     # Initialize WebRTC VAD
-#     vad = webrtcvad.Vad()
-#     vad.set_mode(1)  # Set aggressiveness mode (0-3)
-
-#     # Convert to 16kHz if not already
-#     if sr != 16000:
-#         audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
-#         sr = 16000
-
-#     # Convert to 16-bit PCM
-#     audio = (audio * 32768).astype(np.int16)
-
-#     frame_length = int(sr * (frame_duration_ms / 1000.0))  # Calculate fram
-#     e length in samples
-#     bytes_per_frame = frame_length * 2  # 16-bit audio has 2 bytes per sample
-
-#     # Apply VAD to the audio
-#     voiced_frames = []
-#     for i in range(0, len(audio), frame_length):
-#         frame = audio[i:i + frame_length].tobytes()
-#         if len(frame) == bytes_per_frame and vad.is_speech(frame, sr):
-#             voiced_frames.extend(audio[i:i + frame_length])
-
-#     # Return the VAD-filtered audio
-#     return np.array(voiced_frames)
-
-
-# In[4]:
-
-
-import webrtcvad
-import numpy as np
-import librosa
-
-def apply_vad(audio, sr, frame_duration=30, aggressiveness=3):
-    '''
-    Voice Activity Detection (VAD): Detects speech in audio.
-    '''
-    vad = webrtcvad.Vad(aggressiveness)
-    
-    # Resample to 16000 Hz if not already (recommended for better compatibility)
-    if sr != 16000:
-        audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
-        sr = 16000
-    
-    # Convert to 16-bit PCM format expected by webrtcvad
-    audio_int16 = np.int16(audio * 32767)
-    
-    # Ensure frame size matches WebRTC's expected lengths
-    frame_size = int(sr * frame_duration / 1000)
-    if frame_size % 2 != 0:
-        frame_size -= 1  # Make sure it's even to avoid processing issues
-    
-    frames = [audio_int16[i:i + frame_size] for i in range(0, len(audio_int16), frame_size)]
-    
-    # Filter out non-speech frames
-    voiced_frames = []
-    for frame in frames:
-        if len(frame) == frame_size and vad.is_speech(frame.tobytes(), sample_rate=sr):
-            voiced_frames.append(frame)
-    
-    # Concatenate the voiced frames
-    voiced_audio = np.concatenate(voiced_frames)
-    voiced_audio = np.float32(voiced_audio) / 32767
-    
-    return voiced_audio
-
-
-# In[ ]:
-
-
-
-
+
+import webrtcvad
+import numpy as np
+import librosa
+def apply_vad(audio, sr, frame_duration=30, aggressiveness=3):
+    '''
+     Voice Activity Detection (VAD): It is a technique used to determine whether a segment of audio contains speech. 
+     This is useful in noisy environments where you want to filter out non-speech parts of the audio.
+     webrtcvad: This is a Python package based on the VAD from the WebRTC (Web Real-Time Communication) project. 
+     It helps detect speech in small chunks of audio.
+     '''
+    vad = webrtcvad.Vad()
+    audio_int16 = np.int16(audio * 32767)
+    frame_size = int(sr * frame_duration / 1000)
+    frames = [audio_int16[i:i + frame_size] for i in range(0, len(audio_int16), frame_size)]
+    voiced_audio = np.concatenate([frame for frame in frames if vad.is_speech(frame.tobytes(), sample_rate=sr)])
+    voiced_audio = np.float32(voiced_audio) / 32767
+    return voiced_audio
+
+# import webrtcvad
+# import librosa
+# import numpy as np
+# def apply_vad(audio, sr, frame_duration_ms=30):
+#     # Initialize WebRTC VAD
+#     vad = webrtcvad.Vad()
+#     vad.set_mode(1)  # Set aggressiveness mode (0-3)
+
+#     # Convert to 16kHz if not already
+#     if sr != 16000:
+#         audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
+#         sr = 16000
+
+#     # Convert to 16-bit PCM
+#     audio = (audio * 32768).astype(np.int16)
+
+#     frame_length = int(sr * (frame_duration_ms / 1000.0))  # Calculate fram
+#     e length in samples
+#     bytes_per_frame = frame_length * 2  # 16-bit audio has 2 bytes per sample
+
+#     # Apply VAD to the audio
+#     voiced_frames = []
+#     for i in range(0, len(audio), frame_length):
+#         frame = audio[i:i + frame_length].tobytes()
+#         if len(frame) == bytes_per_frame and vad.is_speech(frame, sr):
+#             voiced_frames.extend(audio[i:i + frame_length])
+
+#     # Return the VAD-filtered audio
+#     return np.array(voiced_frames)
+
+# import webrtcvad
+# import numpy as np
+# import librosa
+
+# def apply_vad(audio, sr, frame_duration=30, aggressiveness=3):
+#     '''
+#     Voice Activity Detection (VAD): Detects speech in audio.
+#     '''
+#     vad = webrtcvad.Vad(aggressiveness)
+    
+#     # Resample to 16000 Hz if not already (recommended for better compatibility)
+#     if sr != 16000:
+#         audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
+#         sr = 16000
+    
+#     # Convert to 16-bit PCM format expected by webrtcvad
+#     audio_int16 = np.int16(audio * 32767)
+    
+#     # Ensure frame size matches WebRTC's expected lengths
+#     frame_size = int(sr * frame_duration / 1000)
+#     if frame_size % 2 != 0:
+#         frame_size -= 1  # Make sure it's even to avoid processing issues
+    
+#     frames = [audio_int16[i:i + frame_size] for i in range(0, len(audio_int16), frame_size)]
+    
+#     # Filter out non-speech frames
+#     voiced_frames = []
+#     for frame in frames:
+#         if len(frame) == frame_size and vad.is_speech(frame.tobytes(), sample_rate=sr):
+#             voiced_frames.append(frame)
+    
+#     # Concatenate the voiced frames
+#     voiced_audio = np.concatenate(voiced_frames)
+#     voiced_audio = np.float32(voiced_audio) / 32767
+    
+#     return voiced_audio