Automatic Speech Recognition
Transformers
PyTorch
Hebrew
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Alex2575/aleksis_heb_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Alex2575/aleksis_heb_tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Alex2575/aleksis_heb_tiny")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Alex2575/aleksis_heb_tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("Alex2575/aleksis_heb_tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Alex2575/aleksis_heb_tiny: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/Alex2575/aleksis_heb_tiny/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Alex2575/aleksis_heb_tiny/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Alex2575/aleksis_heb_tiny/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- b8a1d257248e37e09b3358d6ec28392ac183f1c5390994cfc08c56660f2e4108
- Size of remote file:
- 151 MB
- SHA256:
- 33bca741d79b55d00a2f9766e95e0440a87ef95e7647579016811873e254a7b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.