Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use uoseftalaat/whisper-small-final-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uoseftalaat/whisper-small-final-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="uoseftalaat/whisper-small-final-v2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("uoseftalaat/whisper-small-final-v2") model = AutoModelForSpeechSeq2Seq.from_pretrained("uoseftalaat/whisper-small-final-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a6e3281b66e0baa4eeeece3e9f33b168f9ce63be33151b0e3c87d21b92cc0b4
- Size of remote file:
- 5.11 kB
- SHA256:
- bf146e017d05dafd7811f0780216db49691800308c63a69fe1e5a99e42a959c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.