Automatic Speech Recognition
Transformers
PyTorch
Hebrew
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Shiry/Whisper_hebrew_medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shiry/Whisper_hebrew_medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Shiry/Whisper_hebrew_medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Shiry/Whisper_hebrew_medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("Shiry/Whisper_hebrew_medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Shiry/Whisper_hebrew_medium: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/Shiry/Whisper_hebrew_medium/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Shiry/Whisper_hebrew_medium/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Shiry/Whisper_hebrew_medium/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- cc8a881a0eed12f84adcfc97eaa7a65952450cdbd81303082df1f8a1fd8bbed0
- Size of remote file:
- 3.06 GB
- SHA256:
- 78368b345765897fef02d37e6afae00c83116bfe0ac49393e5898f25e26a988e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.