Instructions to use bartelds/gos-gpum-cp0_adp0_96m-silver_96-orig_1e-4_cp-11000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bartelds/gos-gpum-cp0_adp0_96m-silver_96-orig_1e-4_cp-11000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bartelds/gos-gpum-cp0_adp0_96m-silver_96-orig_1e-4_cp-11000")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("bartelds/gos-gpum-cp0_adp0_96m-silver_96-orig_1e-4_cp-11000") model = AutoModelForCTC.from_pretrained("bartelds/gos-gpum-cp0_adp0_96m-silver_96-orig_1e-4_cp-11000") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- gos
A Gronings Wav2Vec2 model. This model is created by fine-tuning the multilingual XLS-R model on Gronings speech.
This model is part of the paper: Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation. More information on GitHub.