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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ALM/hubert-large-audioset") model = AutoModel.from_pretrained("ALM/hubert-large-audioset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ALM/hubert-large-audioset: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/ALM/hubert-large-audioset/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://ALM/hubert-large-audioset@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ALM/hubert-large-audioset/resolve/refs%2Fpr%2F2/pytorch_model.bin
1.26 GB
- Xet hash:
- 9a5f52aa0790bc8e45acc462917a503db944a583a6bf1c19a6f6060b870ef56a
- Size of remote file:
- 1.26 GB
- SHA256:
- 2d05997fa3b397d9a4e81a3c20a04e9d80b13cca4a90f6175945e750fb79fccd
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