Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("rahafvii/ABG_STT")
model = AutoModelForSpeechSeq2Seq.from_pretrained("rahafvii/ABG_STT")Quick Links
arabic Whisper Small
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3384
- Wer: 44.4075
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3476 | 0.4148 | 1000 | 0.4130 | 52.3435 |
| 0.2522 | 0.8295 | 2000 | 0.3676 | 49.2305 |
| 0.1606 | 1.2443 | 3000 | 0.3475 | 44.8855 |
| 0.161 | 1.6591 | 4000 | 0.3384 | 44.4075 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for rahafvii/ABG_STT
Base model
openai/whisper-smallDataset used to train rahafvii/ABG_STT
Evaluation results
- Wer on Common Voice 13.0test set self-reported44.407
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rahafvii/ABG_STT")