Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Seyfelislem/whisper-medium-arabic-suite-II with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Seyfelislem/whisper-medium-arabic-suite-II with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Seyfelislem/whisper-medium-arabic-suite-II")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Seyfelislem/whisper-medium-arabic-suite-II") model = AutoModelForSpeechSeq2Seq.from_pretrained("Seyfelislem/whisper-medium-arabic-suite-II") - Notebooks
- Google Colab
- Kaggle
whisper-medium-arabic-suite-II
This model is a fine-tuned version of Seyfelislem/whisper-medium-arabic-suite on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1897
- Wer: 15.6083
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 800
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.088 | 0.67 | 800 | 0.1897 | 15.6083 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.0
- Datasets 2.10.2.dev0
- Tokenizers 0.13.2
- Downloads last month
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Evaluation results
- Wer on Common Voice 11.0test set self-reported15.608