Automatic Speech Recognition
Transformers
PyTorch
Hebrew
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Alex2575/aleksis_heb_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alex2575/aleksis_heb_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Alex2575/aleksis_heb_small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Alex2575/aleksis_heb_small") model = AutoModelForSpeechSeq2Seq.from_pretrained("Alex2575/aleksis_heb_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Alex2575/aleksis_heb_small: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/Alex2575/aleksis_heb_small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Alex2575/aleksis_heb_small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Alex2575/aleksis_heb_small/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- 9e70000eefcf63de74a7c888c0d1773ff229685e28c9f23de565ba7444972779
- Size of remote file:
- 967 MB
- SHA256:
- c9398f1af0ec59490134271f6674d6c6a1e62fd773ef508385fe9f9b43b27f9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.