Instructions to use StofEzz/whisper-tiny-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StofEzz/whisper-tiny-fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="StofEzz/whisper-tiny-fr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("StofEzz/whisper-tiny-fr") model = AutoModelForSpeechSeq2Seq.from_pretrained("StofEzz/whisper-tiny-fr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fce7602133019084f4f30006c9928b197e25c8211ab370e413915a6363e27873
- Size of remote file:
- 4.16 kB
- SHA256:
- ab3e686ec17a076f4554e12008945d25cc62bf714a80885c814ec78d7cf96ce4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.