metadata
language:
- ara
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- AsemBadr/GP
metrics:
- wer
model-index:
- name: >-
Whisper Small for Arabic Automatic Speech Recognition with keeping
diacritics
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Quran_Reciters
type: AsemBadr/GP
config: default
split: test
args: 'config: default, split: train'
metrics:
- name: Wer
type: wer
value: 16.91285
Whisper Small for Arabic ASR with diacritics
This model is a fine-tuned version of openai/whisper-small on the Quran_Reciters dataset. It achieves the following results on the evaluation set:
- Loss: 0.188
- Wer: 16.9
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 |
|---|---|---|---|---|
| 0.0059 | 1.62 | 500 | 0.0259 | 18.8277 |
| 0.0019 | 3.24 | 1000 | 0.0223 | 17.1430 |
| 0.0007 | 4.85 | 1500 | 0.0211 | 17.0055 |
| 0.0003 | 6.47 | 2000 | 0.0198 | 16.4726 |
| 0.0 | 8.09 | 2500 | 0.0191 | 16.3351 |
| 0.0 | 9.71 | 3000 | 0.0187 | 16.3007 |
| 0.0 | 11.33 | 3500 | 0.0188 | 16.2491 |
| 0.0 | 12.94 | 4000 | 0.0188 | 16.9128 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.2