metadata
license: apache-2.0
base_model: arun100/whisper-small-ar-1
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs ar_eg
type: google/fleurs
config: ar_eg
split: test
args: ar_eg
metrics:
- name: Wer
type: wer
value: 28.809032414714096
Whisper Small Arabic
This model is a fine-tuned version of arun100/whisper-small-ar-1 on the google/fleurs ar_eg dataset. It achieves the following results on the evaluation set:
- Loss: 0.4548
- Wer: 28.8090
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: 5e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2414 | 52.0 | 500 | 0.3988 | 30.5694 |
| 0.0412 | 105.0 | 1000 | 0.4284 | 30.5694 |
| 0.0147 | 157.0 | 1500 | 0.4548 | 28.8090 |
| 0.0084 | 210.0 | 2000 | 0.4738 | 29.1125 |
| 0.0057 | 263.0 | 2500 | 0.4888 | 29.3553 |
| 0.0043 | 315.0 | 3000 | 0.5010 | 29.2218 |
| 0.0034 | 368.0 | 3500 | 0.5108 | 29.4889 |
| 0.0029 | 421.0 | 4000 | 0.5185 | 29.5010 |
| 0.0026 | 473.0 | 4500 | 0.5236 | 29.4889 |
| 0.0024 | 526.0 | 5000 | 0.5256 | 29.5375 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0