Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
English
hubert
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/hubert-large-ls960-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/hubert-large-ls960-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/hubert-large-ls960-ft")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft") model = AutoModelForCTC.from_pretrained("facebook/hubert-large-ls960-ft") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9536479d212bac31a092ded35f97cc13ccfa1c63f91019b2c67d9877f1144ddc
- Size of remote file:
- 1.26 GB
- SHA256:
- 9cf43abec3f0410ad6854afa4d376c69ccb364b48ddddfd25c4c5aa16398eab0
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