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#!/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[ ]:
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