Instructions to use notlober/whisper-multi-en-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use notlober/whisper-multi-en-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="notlober/whisper-multi-en-tr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("notlober/whisper-multi-en-tr") model = AutoModelForSpeechSeq2Seq.from_pretrained("notlober/whisper-multi-en-tr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fafbbefd45e7617e3175157f87daadec3fe74eb432fcc8a29841078ef0ae7ef3
- Size of remote file:
- 5.24 kB
- SHA256:
- 3f74e0369efbee20a00e384252274f5faf874c95c2771a28b706ba9c6e5eb918
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.