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")# 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
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README.md
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[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/).
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This model is a fine-tuned version of the
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It has been pretrained on:
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- [Libri-Light](https://github.com/facebookresearch/libri-light): open-source audio books from the LibriVox project; clean, read-out audio data
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[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/).
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This model is a fine-tuned version of the [wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) model.
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It has been pretrained on:
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- [Libri-Light](https://github.com/facebookresearch/libri-light): open-source audio books from the LibriVox project; clean, read-out audio data
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