Instructions to use aadel4/omniASR-W2V-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aadel4/omniASR-W2V-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aadel4/omniASR-W2V-1B")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("aadel4/omniASR-W2V-1B") model = AutoModel.from_pretrained("aadel4/omniASR-W2V-1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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This is the **pre-trained encoder backbone without a CTC head**, suitable for feature extraction, probing, and fine-tuning on downstream speech tasks.
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## Model details
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This is the **pre-trained encoder backbone without a CTC head**, suitable for feature extraction, probing, and fine-tuning on downstream speech tasks.
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# Code Base
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The code base for the conversion can be found [here](https://github.com/ahmedadelattia/omnilingual_to_hf). I was only able to convert the 300M and 1B models due to GPU limitations. Contributions are welcome.
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## Model details
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