Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hebrew
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Shiry/whisper-large-v2-he-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shiry/whisper-large-v2-he-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Shiry/whisper-large-v2-he-1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Shiry/whisper-large-v2-he-1") model = AutoModelForSpeechSeq2Seq.from_pretrained("Shiry/whisper-large-v2-he-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Shiry/whisper-large-v2-he-1: direct link, hf CLI and curl.
- Browser
- Download file 6.17 GB
-
https://huggingface.co/Shiry/whisper-large-v2-he-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Shiry/whisper-large-v2-he-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Shiry/whisper-large-v2-he-1/resolve/main/pytorch_model.bin
6.17 GB
- Xet hash:
- 6d0ac78012dcc6087339d75b7bb18526f59c56ec657c52f504a3c3fee8c025d9
- Size of remote file:
- 6.17 GB
- SHA256:
- 7fd8766c124f5106c354a68ff49f565a2a1f895d43758081a28bedbd3365b7f5
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