Automatic Speech Recognition
Transformers
PyTorch
Safetensors
English
wav2vec2
speech
audio
Eval Results
Instructions to use facebook/wav2vec2-large-robust-ft-libri-960h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-large-robust-ft-libri-960h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-robust-ft-libri-960h")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-robust-ft-libri-960h") model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-large-robust-ft-libri-960h", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/wav2vec2-large-robust-ft-libri-960h: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/facebook/wav2vec2-large-robust-ft-libri-960h/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/wav2vec2-large-robust-ft-libri-960h/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/facebook/wav2vec2-large-robust-ft-libri-960h/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- 8b9a411b3669296a55020e0c13712152189092141df992cae1c1102ab5257773
- Size of remote file:
- 1.26 GB
- SHA256:
- bd3ed266edbe299cf5d91ee899400d30851cc26597eb03aba67d21f31d44cfc3
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