Instructions to use rosyvs/whisat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rosyvs/whisat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rosyvs/whisat")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("rosyvs/whisat") model = AutoModelForSpeechSeq2Seq.from_pretrained("rosyvs/whisat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4516511a85bfb75c430896380a27d1a3a18b6ef575c05f2c19743940240c8e8
- Size of remote file:
- 63.1 MB
- SHA256:
- fb47e80f941df6f0e2d3c14f28af956d42a7b08442a7581c3a5dd8bd03b19f99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.