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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 43 44 45 46 | #!/usr/bin/env python3
"""Extract wav2vec 2.0 embeddings using HuggingFace Transformers.
Model: facebook/wav2vec2-base (self-supervised ONLY, 768-dim)
- Pre-trained with contrastive loss on LibriSpeech 960h (Baevski et al., NeurIPS 2020)
- NOT the ASR-fine-tuned version (wav2vec2-base-960h has CTC fine-tuning)
Frame-level output is mean-pooled to get utterance-level embeddings.
Install: pip install transformers torchaudio
"""
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 transformers import Wav2Vec2Model, Wav2Vec2FeatureExtractor
print(f"Loading wav2vec2-base (self-supervised, no ASR fine-tuning) on {args.device}...")
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base")
model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base").to(args.device)
model.eval()
def model_fn(audio_path):
audio = load_audio(audio_path, target_sr=16000)
inputs = feature_extractor(audio, sampling_rate=16000, return_tensors="pt")
inputs = {k: v.to(args.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
hidden = outputs.last_hidden_state # (1, T, 768)
embedding = hidden.mean(dim=1).squeeze().cpu().numpy() # (768,)
return embedding
extract_all(model_fn, "wav2vec2", args.base_dir, args.output_dir)
if __name__ == "__main__":
main()
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