Instructions to use Siphh/wabLab2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siphh/wabLab2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Siphh/wabLab2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Siphh/wabLab2") model = AutoModelForSpeechSeq2Seq.from_pretrained("Siphh/wabLab2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Siphh/wabLab2: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Siphh/wabLab2/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Siphh/wabLab2@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Siphh/wabLab2/resolve/refs%2Fpr%2F1/training_args.bin
4.73 kB
- Xet hash:
- 7da76458f9afd98fd3cdb01600bbc967942230da58f0b94a159905f115c91288
- Size of remote file:
- 4.73 kB
- SHA256:
- fb08d15d2ed4dd39ae42c09b1c78b6f4f38a2452597c92e3c628d28f44e7c887
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