---
library_name: transformers
language:
- fa
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- nezamisafa/ASR_fa_v1
metrics:
- wer
model-index:
- name: whisper-large-v3-persian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ASR_fa_v1
      type: nezamisafa/ASR_fa_v1
      args: 'config: fa, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 8.7299744601811
---

<!-- 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-persian

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the ASR_fa_v1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0858
- Wer: 8.7300

## 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 OptimizerNames.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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1501        | 0.5970 | 1000 | 0.1537          | 17.1059 |
| 0.081         | 1.1940 | 2000 | 0.1156          | 12.6248 |
| 0.0766        | 1.7910 | 3000 | 0.0965          | 11.1969 |
| 0.0313        | 2.3881 | 4000 | 0.0877          | 9.3975  |
| 0.0263        | 2.9851 | 5000 | 0.0858          | 8.7300  |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1