metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: whisper_small-fa_v02
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0 fa
type: mozilla-foundation/common_voice_11_0
config: fa
split: test
metrics:
- name: Wer
type: wer
value: 30.9315
language:
- fa
whisper_small-fa_v02
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. It achieves the following results on the evaluation set:
- Loss: 0.2291
- Wer: 30.3423
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: 1e-05
- train_batch_size: 8
- 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
- mixed_precision_training: Native AMP
Training results
| Step | Training Loss | Validation Loss | Wer |
|---|---|---|---|
| 500 | 1.770700 | 0.476709 | 52.29181 |
| 1000 | 0.762300 | 0.368512 | 41.83410 |
| 1500 | 0.645000 | 0.323680 | 37.57881 |
| 2000 | 0.601900 | 0.297370 | 36.43209 |
| 2500 | 0.529700 | 0.276422 | 33.52608 |
| 3000 | 0.523200 | 0.260825 | 31.94485 |
| 3500 | 0.488400 | 0.249957 | 33.11771 |
| 4000 | 0.464800 | 0.241462 | 30.34238 |
| 4500 | 0.440500 | 0.233215 | 31.04969 |
| 5000 | 0.440500 | 0.229116 | 30.73605 |
Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.3