Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use arbml/whisper-medium-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbml/whisper-medium-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arbml/whisper-medium-ar")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arbml/whisper-medium-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("arbml/whisper-medium-ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload config
Verified signaturecb43629
-
Update README.md
646f54e
-
Update README.md
5d57040
-
End of training
c03b4be -
update model card README.md
06c684e -
Model save
30f1b4a -
Training in progress, step 10000
de5bb56 -
Training in progress, step 9000
ff53392 -
Training in progress, step 8000
9f0678f -
Training in progress, step 7000
c492764 -
Training in progress, step 6000
ff8f1b4 -
Training in progress, step 5000
c820d6b
-
Training in progress, step 4000
638dd7e -
Training in progress, step 3000
6ee2877 -
Training in progress, step 2000
b16f3b6 -
Training in progress, step 1000
b530ab2 -
Training in progress, step 1000
f8ff705
-
initial commit
de448d3