Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use arbml/whisper-largev2-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arbml/whisper-largev2-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="arbml/whisper-largev2-ar")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("arbml/whisper-largev2-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("arbml/whisper-largev2-ar") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 1.0, | |
| "eval_loss": 0.80908203125, | |
| "eval_runtime": 2081.0807, | |
| "eval_samples_per_second": 2.717, | |
| "eval_steps_per_second": 0.17, | |
| "eval_wer": 17.787465782193706, | |
| "train_loss": 0.2545709289550781, | |
| "train_runtime": 95170.9301, | |
| "train_samples_per_second": 3.362, | |
| "train_steps_per_second": 0.105 | |
| } |