Audio Classification
Transformers
PyTorch
hubert
feature-extraction
music
audio
speech
audio-representation-learning
arch-benchmark
general-audio
Instructions to use ALM/hubert-base-audioset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM/hubert-base-audioset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ALM/hubert-base-audioset")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ALM/hubert-base-audioset") model = AutoModel.from_pretrained("ALM/hubert-base-audioset") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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oid sha256:f066f19353ffc8a3be4eccf4a22e5d4326e45eb209911fe0b11c7c967af2330c
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size 377510584
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