metadata
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- sermonar
metrics:
- wer
model-index:
- name: whisper small ar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: sermonarr
type: sermonar
config: ar
split: test[:2%]
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 150.6172839506173
whisper small ar
This model is a fine-tuned version of openai/whisper-small on the sermonarr dataset. It achieves the following results on the evaluation set:
- Loss: 0.5258
- Wer: 150.6173
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.8749 | 0.32 | 1000 | 0.5855 | 150.4274 |
| 0.6537 | 0.65 | 2000 | 0.5461 | 130.5793 |
| 0.7103 | 0.97 | 3000 | 0.5241 | 278.2526 |
| 0.6544 | 1.29 | 4000 | 0.5258 | 150.6173 |
Framework versions
- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3