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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Vkt/model-dataaugmentationpipe") model = AutoModelForCTC.from_pretrained("Vkt/model-dataaugmentationpipe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Vkt/model-dataaugmentationpipe: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/404ca090244635807d2f809aa22029f0835b7fc2/training_args.bin
- Command line
-
hf download hf://Vkt/model-dataaugmentationpipe@404ca090244635807d2f809aa22029f0835b7fc2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/404ca090244635807d2f809aa22029f0835b7fc2/training_args.bin
3.06 kB
- Xet hash:
- 3626776edd3cf6747a5b6153fc909a673bf96ad2cc3a1f704e3e3505316a2c09
- Size of remote file:
- 3.06 kB
- SHA256:
- 077ba5ba1ac56aa096d1f3994a9be73512b0b40848b09ea864da0c0c89d7ba85
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