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Audio-Audio CLAP SAE (32x expansion, k=5)
Sparse Autoencoder trained on 155M CLAP embeddings from 6 audio datasets.
Architecture
- Input dimension: 512
- Hidden dimension: 16384 (32x expansion)
- Top-k: 5
- Alive features: 14,127 / 16,384
- Dead features: 2,257
Training Data
- MLS, CommonVoice, AudioSnippets, Maestrino, Emolia, Podcast
- Total samples: 86,729,608
Usage
from sae import SparseAutoencoder
sae = SparseAutoencoder.load_from_disk("path/to/model")
latents = sae.encode(embeddings) # (batch, 512) -> (batch, 16384)
Stats
- Total feature firings: 433,648,040
- Mean firings per sample: 5.0
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