Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hebrew
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use NS-Y/whisper-tiny-he-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NS-Y/whisper-tiny-he-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NS-Y/whisper-tiny-he-2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NS-Y/whisper-tiny-he-2") model = AutoModelForSpeechSeq2Seq.from_pretrained("NS-Y/whisper-tiny-he-2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c64bd67785338880113081e2c88de086c52f2698140a45b4846bad9ba264ca1
- Size of remote file:
- 151 MB
- SHA256:
- 23268927afeabdbd35a0b2f9038a13e4d837347ea305171b980e8ed3510dc3af
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.