Instructions to use devkyle/whisper-tiny-pure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devkyle/whisper-tiny-pure with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="devkyle/whisper-tiny-pure")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("devkyle/whisper-tiny-pure") model = AutoModelForSpeechSeq2Seq.from_pretrained("devkyle/whisper-tiny-pure") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed6421de0b761e2d5d7ff798e0e06b8b483b2272bc54f919ce777e475f0a2eee
- Size of remote file:
- 151 MB
- SHA256:
- 5519ff83bda20d783e9e7928f1aea90523001ac5275d4523f0703dbebc6b62fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.