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
- Xet hash:
- e0b4326f688b00d9900451a91a8feedb9908e15392807c462285c60eef0c940b
- Size of remote file:
- 201 MB
- SHA256:
- 8099f1fae767787d7a1b559eee78595398750cadd7f8637f50eef32e7a196ed9
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