Update applyVad.py

applyVad.py

@@ -1,161 +1,3 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# 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
-
-
-# In[1]:
-
-
-# import webrtcvad
-# import numpy as np
-# import librosa
-
-# def apply_vad(audio, sr):
-#     # Ensure that sample rate is supported by webrtcvad
-#     if sr not in [8000, 16000, 32000, 48000]:
-#         raise ValueError("Sample rate must be one of: 8000, 16000, 32000, or 48000 Hz")
-
-#     vad = webrtcvad.Vad(2)  # Aggressiveness mode: 0-3
-#     frame_duration_ms = 30  # Use 10ms, 20ms, or 30ms frames only
-
-#     # Convert to PCM 16-bit and calculate frame length
-#     audio_pcm16 = (audio * 32767).astype(np.int16)
-#     frame_length = int(sr * frame_duration_ms / 1000) * 2  # 2 bytes per sample for 16-bit PCM
-    
-#     # Create frames ensuring correct frame size
-#     frames = [
-#         audio_pcm16[i:i + frame_length].tobytes()
-#         for i in range(0, len(audio_pcm16) - frame_length, frame_length)
-#     ]
-
-#     # Apply VAD
-#     voiced_frames = []
-#     for frame in frames:
-#         try:
-#             if vad.is_speech(frame, sample_rate=sr):
-#                 voiced_frames.append(frame)
-#         except Exception as e:
-#             print(f"Error during VAD frame processing: {e}")
-
-#     if not voiced_frames:
-#         raise Exception("No voiced frames detected.")
-
-#     # Concatenate voiced frames
-#     voiced_audio = b''.join(voiced_frames)
-#     return np.frombuffer(voiced_audio, dtype=np.int16) / 32767.0
-
-
-# In[ ]:
-
-
-# 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[3]:
-
-
-# import webrtcvad
-# import numpy as np
-# import librosa
-
-# def frame_generator(frame_duration_ms, audio, sample_rate):
-#     """
-#     Generates audio frames from PCM audio data.
-#     Takes the desired frame duration in milliseconds, the PCM data, and the sample rate.
-#     """
-#     n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)  # Convert to byte length
-#     offset = 0
-#     while offset + n < len(audio):
-#         yield audio[offset:offset + n]
-#         offset += n
-
-# def apply_vad(audio, sample_rate):
-#     vad = webrtcvad.Vad()
-#     vad.set_mode(1)
-#     print("Applying VAD with mode:", 1)
-#     print("Audio length:", len(audio), "bytes")
-#     print("Sample rate:", sample_rate)
-
-#     # Ensure mono and correct sample rate
-#     if sample_rate != 16000:
-#         print("Sample rate issue detected.")
-#         raise ValueError("Sample rate must be 16000 Hz")
-
-#     frames = frame_generator(30, audio, sample_rate)
-#     frames = list(frames)
-
-#     print("Number of frames:", len(frames))
-#     try:
-#         segments = [frame for frame in frames if vad.is_speech(frame, sample_rate)]
-
-#         if not segments:
-#             raise Exception("No voiced frames detected.")
-
-#         return b''.join(segments)
-
-#     except Exception as e:
-#         print(f"Error during VAD frame processing: {e}")
-#         raise
-
-
-# In[5]:
-
-
 # import torch
 # import torchaudio
 # from silero_vad import get_speech_timestamps, read_audio, save_audio
@@ -240,8 +82,4 @@ def apply_vad(audio, sr, frame_duration=30, aggressiveness=3):
     voiced_audio = np.concatenate(voiced_frames)
     voiced_audio = np.float32(voiced_audio) / 32767
     
-    return voiced_audio
-
-
-
-
+    return voiced_audio