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
- Xet hash:
- acc85cfdafaa86dac6983ab6115e03de65baf5b7141e80be13a161ef0619a4b8
- Size of remote file:
- 1.26 GB
- SHA256:
- 0bcd66194749b66ea9b6af7bb07923cca18e3579865125c3b2c258729a3a530a
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