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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 |