Create voice_analysis.py

voice_analysis.py

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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]