Audio Classification
Transformers
PyTorch
hubert
feature-extraction
music
audio
speech
audio-representation-learning
arch-benchmark
general-audio
Instructions to use ALM/hubert-large-audioset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM/hubert-large-audioset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ALM/hubert-large-audioset")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ALM/hubert-large-audioset") model = AutoModel.from_pretrained("ALM/hubert-large-audioset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a5f52aa0790bc8e45acc462917a503db944a583a6bf1c19a6f6060b870ef56a
- Size of remote file:
- 1.26 GB
- SHA256:
- 2d05997fa3b397d9a4e81a3c20a04e9d80b13cca4a90f6175945e750fb79fccd
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