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import torch
import numpy as np
from speechbrain.pretrained import EncoderClassifier
from pydub import AudioSegment
from sklearn.cluster import DBSCAN
import librosa
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb", run_opts={"device": device})
def extract_voice_embedding(audio_segment):
signal = np.array(audio_segment.get_array_of_samples())
signal = signal.astype(np.float32) / 32768.0 # Normalize to [-1, 1]
embedding = classifier.encode_batch(torch.tensor(signal).unsqueeze(0))
return embedding.squeeze().cpu().numpy()
def process_audio(audio_path, segment_duration=1000):
audio = AudioSegment.from_file(audio_path)
segments = [audio[i:i+segment_duration] for i in range(0, len(audio), segment_duration)]
embeddings = [extract_voice_embedding(segment) for segment in segments]
return embeddings
def cluster_voices(embeddings):
if len(embeddings) < 2:
print("Not enough voice segments for clustering. Assigning all to one cluster.")
return np.zeros(len(embeddings), dtype=int)
X = np.stack(embeddings)
dbscan = DBSCAN(eps=0.3, min_samples=5, metric='cosine')
clusters = dbscan.fit_predict(X)
if np.all(clusters == -1):
print("DBSCAN assigned all to noise. Considering as one cluster.")
return np.zeros(len(embeddings), dtype=int)
return clusters
def get_most_frequent_voice(embeddings, clusters):
largest_cluster = max(set(clusters), key=list(clusters).count)
return [emb for emb, cluster in zip(embeddings, clusters) if cluster == largest_cluster] |