Instructions to use bgstud/whisper-tiny-libirAugm-vs-commonAccentAug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgstud/whisper-tiny-libirAugm-vs-commonAccentAug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-tiny-libirAugm-vs-commonAccentAug")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bgstud/whisper-tiny-libirAugm-vs-commonAccentAug") model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-tiny-libirAugm-vs-commonAccentAug") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11f53105e1470a125e5f936c070d9aa59e198caf79c2201d00a1ea3180dedb0e
- Size of remote file:
- 151 MB
- SHA256:
- 4bc7621fdc6bb7eef054f3970ff2f7b5ad1cccd5b4d678a9e7dcac0b0a67e31b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.