Instructions to use bgstud/whisper-tiny-libri with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgstud/whisper-tiny-libri with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bgstud/whisper-tiny-libri")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bgstud/whisper-tiny-libri") model = AutoModelForSpeechSeq2Seq.from_pretrained("bgstud/whisper-tiny-libri") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d366c8674c876bc6675986bca13f4a406a52c09ab339b2336cf082ae859300bd
- Size of remote file:
- 151 MB
- SHA256:
- d4bfb2af683d8e45a4d1bebeb704f8f8136d14852e20434dd27f0fc37c3bd81d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.