Instructions to use Vkt/model-dataaugmentationpipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vkt/model-dataaugmentationpipe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Vkt/model-dataaugmentationpipe")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Vkt/model-dataaugmentationpipe") model = AutoModelForCTC.from_pretrained("Vkt/model-dataaugmentationpipe") - Notebooks
- Google Colab
- Kaggle
Training in progress, step 900
Browse files
pytorch_model.bin
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runs/Jul04_20-17-45_dsbrwavvec2-0/events.out.tfevents.1656967828.dsbrwavvec2-0.282365.10
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