#!/usr/bin/env python3
"""Extract ECAPA-TDNN embeddings using SpeechBrain.

Model: speechbrain/spkrec-ecapa-voxceleb (supervised, AAM-Softmax, 192-dim)
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 ECAPA-TDNN on {args.device}...")
    classifier = EncoderClassifier.from_hparams(
        source="speechbrain/spkrec-ecapa-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, "ecapa_tdnn", args.base_dir, args.output_dir)


if __name__ == "__main__":
    main()