Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Hebrew
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use NS-Y/whisper-tiny-he-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NS-Y/whisper-tiny-he-5 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-5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NS-Y/whisper-tiny-he-5") model = AutoModelForSpeechSeq2Seq.from_pretrained("NS-Y/whisper-tiny-he-5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NS-Y/whisper-tiny-he-5: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/NS-Y/whisper-tiny-he-5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NS-Y/whisper-tiny-he-5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NS-Y/whisper-tiny-he-5/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- e1757c516fcb5844bb34929d2e335afe649be06a225a3c5135614e3a86acad7e
- Size of remote file:
- 151 MB
- SHA256:
- f6b57958615c3b737077bc10003bf3d48f5a2c620d7bd4628d1f415e5ed87c21
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