metadata
language:
- fa
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: whisper_small-fa_v03
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0 fa
type: mozilla-foundation/common_voice_11_0
config: fa
split: test
args: fa
metrics:
- type: wer
value: 27.1515
name: Wer
whisper_small-fa_v03
This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 fa dataset. We also did data augmentation using audiomentations library along with hyperparameter tuning to acquire the best parameters. It achieves the following results on the evaluation set:
- Loss: 0.1813
- Wer: 23.1451
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
You can Find the notebooks here.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.15044e-05
- train_batch_size: 8
- eval_batch_size: 4
- 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
- mixed_precision_training: Native AMP
Training results
| Step | Training Loss | Validation Loss | Wer |
|---|---|---|---|
| 500 | 1.210100 | 0.439317 | 44.17001 |
| 1000 | 0.717500 | 0.385981 | 40.53219 |
| 1500 | 0.585800 | 0.312391 | 35.52059 |
| 2000 | 0.508400 | 0.274010 | 31.00885 |
| 2500 | 0.443500 | 0.244815 | 29.79515 |
| 3000 | 0.392700 | 0.216328 | 27.24362 |
| 3500 | 0.340100 | 0.213681 | 26.00705 |
| 4000 | 0.236700 | 0.198893 | 28.51612 |
| 4500 | 0.212000 | 0.186622 | 25.88944 |
| 5000 | 0.183800 | 0.181340 | 23.14515 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.3