Feature Extraction
Transformers
audio
speech
sparse-autoencoder
sae
interpretability
mechanistic-interpretability
hubert
Instructions to use Egorgij21/Audio-SAE-HuBERT-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Egorgij21/Audio-SAE-HuBERT-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Egorgij21/Audio-SAE-HuBERT-large")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Egorgij21/Audio-SAE-HuBERT-large", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -66,7 +66,7 @@ hubert = MyHubert("facebook/hubert-large-ll60k", sae_after_layer=layer).to(devic
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# 2. Matching SAE
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ckpt = hf_hub_download(
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repo_id="Egorgij21/Audio-SAE-
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filename=f"layer_{layer}/ae.pt",
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)
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sae = BatchTopKSAE.from_pretrained(ckpt, device=device)
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# 2. Matching SAE
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ckpt = hf_hub_download(
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repo_id="Egorgij21/Audio-SAE-hubert-large",
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filename=f"layer_{layer}/ae.pt",
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)
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sae = BatchTopKSAE.from_pretrained(ckpt, device=device)
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