metadata
base_model: openai/whisper-base
datasets:
- fleurs
language:
- ar
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Arabic - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: ar_eg
split: None
args: 'config: ar split: test'
metrics:
- type: wer
value: 41.818636022982766
name: Wer
Whisper Base Arabic - Chee Li
This model is a fine-tuned version of openai/whisper-base on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.8130
- Wer: 41.8186
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|---|---|---|---|---|
| 0.1475 | 6.6667 | 1000 | 0.5516 | 41.1441 |
| 0.0072 | 13.3333 | 2000 | 0.6801 | 40.6570 |
| 0.0023 | 20.0 | 3000 | 0.7548 | 40.9443 |
| 0.0013 | 26.6667 | 4000 | 0.7970 | 41.4439 |
| 0.0009 | 33.3333 | 5000 | 0.8130 | 41.8186 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.1