File size: 1,381 Bytes
6351b36 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | #!/usr/bin/env python3
"""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()
|