---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# whisper_small-fa_v03

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/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](https://github.com/mohammadh128/Persian_ASR).

### 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