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:
- a6334f26e20bbeaae66cf919505f9e63dd395097934e24e49eb07b436884e9c9
- Size of remote file:
- 3.5 kB
- SHA256:
- 10ad7c8bbf23d62175247824f597aa688969fb42f3f7d491b810b220a702a086
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.