Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Hebrew
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use cantillation/Teamim-medium_Random-True_OriginalData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cantillation/Teamim-medium_Random-True_OriginalData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cantillation/Teamim-medium_Random-True_OriginalData")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("cantillation/Teamim-medium_Random-True_OriginalData") model = AutoModelForSpeechSeq2Seq.from_pretrained("cantillation/Teamim-medium_Random-True_OriginalData", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18c9ec643d9b8b3b7bb4aeb4a5f35ffb4de296ab023da9c396a36bb401112b54
- Size of remote file:
- 5.18 kB
- SHA256:
- ba57f7e5cffcfdb84ff4c07365e42fcebf72219877577b66f6e3cece6cddfe83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.