Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
Safetensors
English
wav2vec2
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/wav2vec2-base-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-base-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h") model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c6bac2e31f0e29f0ff362ce3ca85e6e13878fc442f68e53e12ceb6181b827457
- Size of remote file:
- 378 MB
- SHA256:
- 412742825972a6e2e877255ccd8b3416e618df15a7f1e5e4f736aa3632ce33b5
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