Instructions to use vipul22/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vipul22/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vipul22/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vipul22/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("vipul22/whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90ca5754094b89c693a23315d81cc9da5349c772a139e3b6f72f2983a75b40e9
- Size of remote file:
- 151 MB
- SHA256:
- 584eb3421b231c27bf8f4be78e753aa58d7f89ccc4b63084f9da50649f8cfc39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.