metadata
language:
- ar
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whitefox123/tashkeel
metrics:
- wer
model-index:
- name: Whisper large - tuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: CLARtts
type: whitefox123/tashkeel
config: default
split: None
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 128.72072072072072
Whisper large - tuned
This model is a fine-tuned version of openai/whisper-large-v3 on the CLARtts dataset. It achieves the following results on the evaluation set:
- Loss: 0.1841
- Wer: 128.7207
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: 6250
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0864 | 1.6 | 1000 | 0.1155 | 165.5135 |
| 0.0291 | 3.2 | 2000 | 0.1192 | 268.0360 |
| 0.0196 | 4.8 | 3000 | 0.1317 | 217.9820 |
| 0.0024 | 6.4 | 4000 | 0.1583 | 136.1802 |
| 0.0012 | 8.0 | 5000 | 0.1708 | 136.3604 |
| 0.0004 | 9.6 | 6000 | 0.1841 | 128.7207 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2