#!/usr/bin/env python3
"""Extract TitaNet embeddings using NVIDIA NeMo.

Model: nvidia/speakerverification_en_titanet_large (supervised, 192-dim)
Install: pip install nemo_toolkit[asr]
"""

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()

    import nemo.collections.asr as nemo_asr

    print(f"Loading TitaNet on {args.device}...")
    model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(
        "nvidia/speakerverification_en_titanet_large"
    )
    model = model.to(args.device)
    model.eval()

    def model_fn(audio_path):
        # NeMo's get_embedding works directly with file paths
        emb = model.get_embedding(str(audio_path))
        return emb.squeeze().cpu().numpy()

    extract_all(model_fn, "titanet", args.base_dir, args.output_dir)


if __name__ == "__main__":
    main()