| |
| """Extract x-vector embeddings using SpeechBrain. |
| |
| Model: speechbrain/spkrec-xvect-voxceleb (supervised, softmax, 512-dim) |
| - The classic TDNN x-vector architecture (Snyder et al., ICASSP 2018) |
| - Trained on VoxCeleb1+2 with softmax loss |
| - Statistics pooling (mean + std) for utterance-level embeddings |
| Install: pip install speechbrain |
| """ |
|
|
| import argparse |
| import torch |
| import numpy as np |
| from extraction_utils import load_audio, extract_all |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") |
| parser.add_argument("--base-dir", default=None) |
| parser.add_argument("--output-dir", default=None) |
| args = parser.parse_args() |
|
|
| from speechbrain.inference.speaker import EncoderClassifier |
|
|
| print(f"Loading x-vector on {args.device}...") |
| classifier = EncoderClassifier.from_hparams( |
| source="speechbrain/spkrec-xvect-voxceleb", |
| run_opts={"device": args.device}, |
| ) |
|
|
| def model_fn(audio_path): |
| audio = load_audio(audio_path, target_sr=16000) |
| signal = torch.tensor(audio).unsqueeze(0).to(args.device) |
| embedding = classifier.encode_batch(signal) |
| return embedding.squeeze().cpu().numpy() |
|
|
| extract_all(model_fn, "xvector", args.base_dir, args.output_dir) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
