---
library_name: transformers
language:
- fa
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper-large-v3-turbo-fa - Sadegh Karimi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: mozilla-foundation/common_voice_17_0
      config: fa
      split: test
      args: 'config: hi, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 9.627528266117483
---

<!-- 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-large-v3-turbo-fa - Sadegh Karimi

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0839
- Wer: 9.6275

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1789        | 0.0217 | 500   | 0.2427          | 26.4099 |
| 0.2077        | 0.0435 | 1000  | 0.2296          | 27.1873 |
| 0.1928        | 0.0652 | 1500  | 0.2320          | 27.5951 |
| 0.1801        | 0.0869 | 2000  | 0.2026          | 24.0409 |
| 0.1865        | 0.1086 | 2500  | 0.1925          | 22.3742 |
| 0.1535        | 0.1304 | 3000  | 0.1872          | 22.9511 |
| 0.1463        | 0.1521 | 3500  | 0.1786          | 21.5436 |
| 0.0935        | 0.1738 | 4000  | 0.1749          | 20.5330 |
| 0.1052        | 0.1956 | 4500  | 0.1597          | 19.0314 |
| 0.091         | 0.2173 | 5000  | 0.1553          | 20.2125 |
| 0.0743        | 0.2390 | 5500  | 0.1474          | 16.9160 |
| 0.096         | 0.2607 | 6000  | 0.1352          | 15.9027 |
| 0.111         | 0.2825 | 6500  | 0.1259          | 14.9071 |
| 0.089         | 0.3042 | 7000  | 0.1179          | 14.1146 |
| 0.0813        | 0.3259 | 7500  | 0.1101          | 12.8653 |
| 0.072         | 0.3477 | 8000  | 0.1012          | 11.8138 |
| 0.0715        | 0.3694 | 8500  | 0.0948          | 10.9791 |
| 0.0683        | 0.3911 | 9000  | 0.0903          | 10.2563 |
| 0.0634        | 0.4128 | 9500  | 0.0861          | 9.6616  |
| 0.0739        | 0.4346 | 10000 | 0.0839          | 9.6275  |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.1.0+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0