Instructions to use WassayS/whisper-tiny-ur-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WassayS/whisper-tiny-ur-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="WassayS/whisper-tiny-ur-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("WassayS/whisper-tiny-ur-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("WassayS/whisper-tiny-ur-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f5726d2e3b5a12fb895611b8e8229b3c78d06613f44d269c5e06c811f89d222
- Size of remote file:
- 4.86 kB
- SHA256:
- c9839a498f7677ec46c0ca2064a53dd868229c2e0c2c3989c5215018458d0be0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.