Instructions to use ctranslate2-4you/whisper-medium-ct2-float32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctranslate2-4you/whisper-medium-ct2-float32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ctranslate2-4you/whisper-medium-ct2-float32")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ctranslate2-4you/whisper-medium-ct2-float32", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d96741c22343c3b79252b35c9bbc67fd0032fbf484b97d2599cde256c323fb4
- Size of remote file:
- 3.06 GB
- SHA256:
- 2f6c5caae00c11327d3baccd3f49a1146b1b2e21e4efe6392c03e0b61cefc1bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.