Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use akhooli/whisper-small-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akhooli/whisper-small-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="akhooli/whisper-small-ar")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("akhooli/whisper-small-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("akhooli/whisper-small-ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from akhooli/whisper-small-ar: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/akhooli/whisper-small-ar/resolve/main/training_args.bin
- Command line
-
hf download hf://akhooli/whisper-small-ar/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/akhooli/whisper-small-ar/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 1174b47d49f3ec0710fceb02c789bf475063732ee2902f3ec4538ffff3614eb6
- Size of remote file:
- 5.37 kB
- SHA256:
- b456ca8f030d9888bcd6303bef3c8f9fc4dd911c70fa91b68fdb2e5352ed9ec2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.