code/extract_whisper.py

#!/usr/bin/env python3
"""Extract Whisper encoder embeddings.

Model: openai/whisper-base (encoder only, 512-dim after mean pooling)
Frame-level encoder output is mean-pooled to get utterance-level embeddings.
Install: pip install openai-whisper
"""

import argparse
import torch
import numpy as np
from extraction_utils import extract_all


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
    parser.add_argument("--model-size", default="base", choices=["tiny", "base", "small", "medium"])
    parser.add_argument("--base-dir", default=None)
    parser.add_argument("--output-dir", default=None)
    args = parser.parse_args()

    import whisper

    print(f"Loading Whisper {args.model_size} on {args.device}...")
    model = whisper.load_model(args.model_size, device=args.device)

    def model_fn(audio_path):
        audio = whisper.load_audio(str(audio_path))
        audio = whisper.pad_or_trim(audio)
        mel = whisper.log_mel_spectrogram(audio).to(args.device)
        with torch.no_grad():
            enc_output = model.encoder(mel.unsqueeze(0))  # (1, T, D)
        embedding = enc_output.mean(dim=1).squeeze().cpu().numpy()
        return embedding

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


if __name__ == "__main__":
    main()