Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
Safetensors
English
hubert
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/hubert-xlarge-ls960-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/hubert-xlarge-ls960-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/hubert-xlarge-ls960-ft")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/hubert-xlarge-ls960-ft") model = AutoModelForCTC.from_pretrained("facebook/hubert-xlarge-ls960-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/hubert-xlarge-ls960-ft: direct link, hf CLI and curl.
- Browser
- Download file 3.85 GB
-
https://huggingface.co/facebook/hubert-xlarge-ls960-ft/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/hubert-xlarge-ls960-ft/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/hubert-xlarge-ls960-ft/resolve/main/pytorch_model.bin
3.85 GB
- Xet hash:
- 87c2ef4bd6442bbbef67587666179b9f50d7e5bc2fda4013c7718b1634cd7144
- Size of remote file:
- 3.85 GB
- SHA256:
- e50f16d74714b7ff6d535af7316f25462555faeacf3725efe60024db99be071f
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