metadata
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
- google/fleurs
- ymoslem/MediaSpeech
metrics:
- wer
model-index:
- name: Whisper Small ar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: ar
split: test
args: ar
metrics:
- name: Wer
type: wer
value: 60.99884137633713
Whisper Small ar
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 2.0345
- Wer: 60.9988
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: 64
- 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: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1333 | 0.2 | 1000 | 1.6477 | 58.9201 |
| 0.0766 | 1.0454 | 2000 | 1.8054 | 62.8257 |
| 0.0554 | 1.2454 | 3000 | 1.8952 | 55.9413 |
| 0.0358 | 2.0908 | 4000 | 1.9780 | 64.4329 |
| 0.0203 | 2.2908 | 5000 | 2.0345 | 60.9988 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1