Instructions to use emre/whisper-medium-turkish-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emre/whisper-medium-turkish-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="emre/whisper-medium-turkish-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("emre/whisper-medium-turkish-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("emre/whisper-medium-turkish-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b68a7847ca253d83e966eb8b7c1d73342ef6f5bbebcf628795003c8ec9ae4bca
- Size of remote file:
- 3.06 GB
- SHA256:
- 2fcc98a173391531c37aa9a664f10cac54e6183a983248b080d1b8438259400e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.