---
language:
- fa
license: apache-2.0
base_model: makhataei/Whisper-Small-Ctejarat
tags:
- fa-asr
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Persian
  results: []
---

<!-- 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 Persian

This model is a fine-tuned version of [makhataei/Whisper-Small-Ctejarat](https://huggingface.co/makhataei/Whisper-Small-Ctejarat) on the Ctejarat dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0031
- Wer: 13.3353

## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0032        | 9.52   | 100  | 0.0031          | 13.1328 |
| 0.0027        | 19.05  | 200  | 0.0031          | 13.0171 |
| 0.002         | 28.57  | 300  | 0.0031          | 13.0171 |
| 0.0015        | 38.1   | 400  | 0.0031          | 13.0749 |
| 0.0005        | 47.62  | 500  | 0.0031          | 13.3642 |
| 0.0003        | 57.14  | 600  | 0.0031          | 13.3353 |
| 0.0002        | 66.67  | 700  | 0.0031          | 13.3353 |
| 0.0002        | 76.19  | 800  | 0.0031          | 13.3353 |
| 0.0001        | 85.71  | 900  | 0.0031          | 13.3063 |
| 0.0001        | 95.24  | 1000 | 0.0031          | 13.3063 |
| 0.0001        | 104.76 | 1100 | 0.0031          | 13.3063 |
| 0.0001        | 114.29 | 1200 | 0.0031          | 13.3931 |
| 0.0001        | 123.81 | 1300 | 0.0031          | 13.3931 |
| 0.0001        | 133.33 | 1400 | 0.0031          | 13.3642 |
| 0.0001        | 142.86 | 1500 | 0.0031          | 13.3931 |
| 0.0001        | 152.38 | 1600 | 0.0031          | 13.3931 |
| 0.0001        | 161.9  | 1700 | 0.0031          | 13.3642 |
| 0.0           | 171.43 | 1800 | 0.0031          | 13.3642 |
| 0.0001        | 180.95 | 1900 | 0.0031          | 13.3642 |
| 0.0           | 190.48 | 2000 | 0.0031          | 13.3642 |
| 0.0           | 200.0  | 2100 | 0.0031          | 13.3642 |
| 0.0           | 209.52 | 2200 | 0.0031          | 13.3642 |
| 0.0           | 219.05 | 2300 | 0.0031          | 13.3642 |
| 0.0           | 228.57 | 2400 | 0.0031          | 13.3642 |
| 0.0           | 238.1  | 2500 | 0.0031          | 13.3353 |
| 0.0           | 247.62 | 2600 | 0.0031          | 13.3353 |
| 0.0           | 257.14 | 2700 | 0.0031          | 13.3353 |
| 0.0           | 266.67 | 2800 | 0.0031          | 13.3353 |
| 0.0           | 276.19 | 2900 | 0.0031          | 13.3353 |
| 0.0           | 285.71 | 3000 | 0.0031          | 13.3353 |
| 0.0           | 295.24 | 3100 | 0.0031          | 13.3353 |
| 0.0           | 304.76 | 3200 | 0.0031          | 13.3353 |
| 0.0           | 314.29 | 3300 | 0.0031          | 13.3642 |
| 0.0           | 323.81 | 3400 | 0.0031          | 13.3642 |
| 0.0           | 333.33 | 3500 | 0.0031          | 13.3642 |
| 0.0           | 342.86 | 3600 | 0.0031          | 13.3642 |
| 0.0           | 352.38 | 3700 | 0.0031          | 13.3353 |
| 0.0           | 361.9  | 3800 | 0.0031          | 13.3353 |
| 0.0           | 371.43 | 3900 | 0.0031          | 13.3353 |
| 0.0           | 380.95 | 4000 | 0.0031          | 13.3353 |
| 0.0           | 390.48 | 4100 | 0.0031          | 13.3353 |
| 0.0           | 400.0  | 4200 | 0.0031          | 13.3353 |
| 0.0           | 409.52 | 4300 | 0.0031          | 13.3353 |
| 0.0           | 419.05 | 4400 | 0.0031          | 13.3353 |
| 0.0           | 428.57 | 4500 | 0.0031          | 13.3353 |
| 0.0           | 438.1  | 4600 | 0.0031          | 13.3353 |
| 0.0           | 447.62 | 4700 | 0.0031          | 13.3353 |
| 0.0           | 457.14 | 4800 | 0.0031          | 13.3353 |
| 0.0           | 466.67 | 4900 | 0.0031          | 13.3353 |
| 0.0           | 476.19 | 5000 | 0.0031          | 13.3353 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0