---
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-c17-avs '
  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: None
      args: 'config: fa, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 27.209121534076186
---

<!-- 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-c17-avs 

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: 0.2583
- Wer: 27.2091

## 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: 200
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2293        | 0.4055 | 1000 | 0.4008          | 40.3172 |
| 0.167         | 0.8110 | 2000 | 0.3385          | 34.0503 |
| 0.0948        | 1.2165 | 3000 | 0.3067          | 31.7494 |
| 0.0669        | 1.6221 | 4000 | 0.2878          | 29.7909 |
| 0.0458        | 2.0276 | 5000 | 0.2583          | 27.2091 |


### Framework versions

- Transformers 4.52.2
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1