Automatic Speech Recognition
Transformers
PyTorch
English
data2vec-audio
speech
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/data2vec-audio-base-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-audio-base-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/data2vec-audio-base-960h")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-audio-base-960h") model = AutoModelForCTC.from_pretrained("facebook/data2vec-audio-base-960h") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f69ae6bba19e896802c0166b7bbabe4914af971abe64b54ece3aefb178c1ab5
- Size of remote file:
- 373 MB
- SHA256:
- ba0f29b6b713f88df166a3edfe8db2657fcc9f728d32d68793de5109efacc52d
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