voice_analysis.py

import numpy as np
import librosa
from sklearn.cluster import DBSCAN
from pydub import AudioSegment

def extract_voice_features(audio_path, segment_duration=1000):
    # Load the audio file
    y, sr = librosa.load(audio_path)
    
    # Extract MFCC features
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    
    # Segment the MFCCs
    segment_length = int(segment_duration * sr / 1000)
    num_segments = len(y) // segment_length
    
    segments = []
    for i in range(num_segments):
        start = i * segment_length
        end = start + segment_length
        segment = mfccs[:, start:end]
        segments.append(np.mean(segment, axis=1))
    
    return np.array(segments)

def remove_nan_features(features):
    return features[~np.isnan(features).any(axis=1)]

def cluster_voices(features):
    # Remove NaN values
    features = remove_nan_features(features)
    
    if len(features) < 2:
        print("Not enough voice segments for clustering. Assigning all to one cluster.")
        return np.zeros(len(features), dtype=int)

    dbscan = DBSCAN(eps=0.5, min_samples=5, metric='euclidean')
    clusters = dbscan.fit_predict(features)

    if np.all(clusters == -1):
        print("DBSCAN assigned all to noise. Considering as one cluster.")
        return np.zeros(len(features), dtype=int)

    return clusters

def get_most_frequent_voice(features, clusters):
    largest_cluster = max(set(clusters), key=list(clusters).count)
    return features[clusters == largest_cluster]

def process_audio(audio_path, segment_duration=1000):
    features = extract_voice_features(audio_path, segment_duration)
    features = remove_nan_features(features)
    clusters = cluster_voices(features)
    most_frequent_voice = get_most_frequent_voice(features, clusters)
    return most_frequent_voice, features, clusters