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 pytorch_model.bin from Vkt/model-dataaugmentationpipe: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/404ca090244635807d2f809aa22029f0835b7fc2/pytorch_model.bin
- Command line
-
hf download hf://Vkt/model-dataaugmentationpipe@404ca090244635807d2f809aa22029f0835b7fc2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/404ca090244635807d2f809aa22029f0835b7fc2/pytorch_model.bin
1.26 GB
- Xet hash:
- 5302aad0ba5ba8004180e71c363028e63988e6071ff29ddd01f7250edf21b3d6
- Size of remote file:
- 1.26 GB
- SHA256:
- 1486ef5c8cbfd428fb8b5fedde13a30804a365c01f7fbc360a3e6d9622fac804
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