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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 | #!/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()
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