Instructions to use notlober/whisper-large-en-tr-multi-3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use notlober/whisper-large-en-tr-multi-3.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="notlober/whisper-large-en-tr-multi-3.0")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("notlober/whisper-large-en-tr-multi-3.0") model = AutoModelForSpeechSeq2Seq.from_pretrained("notlober/whisper-large-en-tr-multi-3.0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d67f6e81a4e7ffb5e2ef3ca6a73201c5579f06189f4d10358ec1001231c00876
- Size of remote file:
- 5.3 kB
- SHA256:
- 855b3824dd18b20ed26b026eb81761546891a2b2a6b76627c82380c2968f621c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.