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