Automatic Speech Recognition
Transformers
PyTorch
Hebrew
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Alex2575/aleksis_heb_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alex2575/aleksis_heb_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Alex2575/aleksis_heb_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Alex2575/aleksis_heb_base") model = AutoModelForSpeechSeq2Seq.from_pretrained("Alex2575/aleksis_heb_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Alex2575/aleksis_heb_base: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/Alex2575/aleksis_heb_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Alex2575/aleksis_heb_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Alex2575/aleksis_heb_base/resolve/main/pytorch_model.bin
290 MB
- Xet hash:
- 51aad5a6547eacc01bba22a0c36dab18b4ee783adc0cf2b6681d7dc30d0a1588
- Size of remote file:
- 290 MB
- SHA256:
- f4cf760f66dc2f9ca4fcec6e19833f7533d79e911a09e40213e288749b4b972f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.