Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
Generated from Trainer
Instructions to use jlvdoorn/whisper-medium-atcosim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jlvdoorn/whisper-medium-atcosim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jlvdoorn/whisper-medium-atcosim")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jlvdoorn/whisper-medium-atcosim") model = AutoModelForSpeechSeq2Seq.from_pretrained("jlvdoorn/whisper-medium-atcosim") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfd8e5502499580c7ec91f50d52baf39772d11dfedf33dac570797290b99e001
- Size of remote file:
- 3.06 GB
- SHA256:
- 4b3e0d6913b6aa9facabf1c47e89b370eae47dacbf298af7568b35e2bc2a63ec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.