Instructions to use bartelds/gos-gpu6-cp0_adp0_2x168m-silver_24-orig_1e-5_cp-11000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartelds/gos-gpu6-cp0_adp0_2x168m-silver_24-orig_1e-5_cp-11000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bartelds/gos-gpu6-cp0_adp0_2x168m-silver_24-orig_1e-5_cp-11000")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("bartelds/gos-gpu6-cp0_adp0_2x168m-silver_24-orig_1e-5_cp-11000") model = AutoModelForCTC.from_pretrained("bartelds/gos-gpu6-cp0_adp0_2x168m-silver_24-orig_1e-5_cp-11000") - Notebooks
- Google Colab
- Kaggle
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README.md
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A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
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A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual [XLS-R](https://huggingface.co/facebook/wav2vec2-xls-r-300m) model on Gronings speech.
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This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation.
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More information on [GitHub](https://github.com/Bartelds/asr-augmentation).
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