Instructions to use 7ocho/WMAC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 7ocho/WMAC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="7ocho/WMAC")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("7ocho/WMAC") model = AutoModelForSpeechSeq2Seq.from_pretrained("7ocho/WMAC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from 7ocho/WMAC: direct link, hf CLI and curl.
- Browser
- Download file 5.39 kB
-
https://huggingface.co/7ocho/WMAC/resolve/main/training_args.bin
- Command line
-
hf download hf://7ocho/WMAC/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/7ocho/WMAC/resolve/main/training_args.bin
5.39 kB
- Xet hash:
- cd018888df7dc1d98db380d9deec4b8a6da38e50fe6ad93aac94b7627b0b8414
- Size of remote file:
- 5.39 kB
- SHA256:
- 1666045af696dbf83ca4ad3c2c1cfcafefdb38d1f31d8461cec41d401a007741
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.