---
library_name: transformers
language:
- tr
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: 'Whisper Large V3 Turbo FT TR Telephonic - Alperitoo '
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: tr
      split: validation
      args: 'config: turkish, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 15.630747366661094
---

<!-- 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 FT TR Telephonic - Alperitoo 

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 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1739
- Wer: 15.6307

## 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.1657        | 0.6154 | 1000 | 0.2519          | 21.1461 |
| 0.0993        | 1.2308 | 2000 | 0.2193          | 19.6685 |
| 0.0838        | 1.8462 | 3000 | 0.2031          | 18.6612 |
| 0.0574        | 2.4615 | 4000 | 0.1923          | 16.5399 |
| 0.0247        | 3.0769 | 5000 | 0.1739          | 15.6307 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0