---
library_name: peft
language:
- tr
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- asr
- whisper
- lora
- Turkish
- tr
- generated_from_trainer
datasets:
- dcl-ai-team/CommonVoice-17_tr_bandpass_filter_telephonic
metrics:
- wer
model-index:
- name: v3-turbo-cv17-telephonic-lora
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: CommonVoice-17_tr_bandpass_filter_telephonic
      type: dcl-ai-team/CommonVoice-17_tr_bandpass_filter_telephonic
    metrics:
    - type: wer
      value: 14.208987174831321
      name: Wer
---

<!-- 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. -->

# v3-turbo-cv17-telephonic-lora

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the CommonVoice-17_tr_bandpass_filter_telephonic dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1411
- Wer: 14.2090

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: cosine
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.454         | 0.1138 | 500  | 0.1633          | 15.3845 |
| 0.1463        | 0.2276 | 1000 | 0.1525          | 14.9965 |
| 0.1393        | 0.3414 | 1500 | 0.1482          | 14.7002 |
| 0.1344        | 0.4552 | 2000 | 0.1466          | 14.4383 |
| 0.1305        | 0.5690 | 2500 | 0.1442          | 14.3084 |
| 0.1235        | 0.6828 | 3000 | 0.1427          | 14.2510 |
| 0.129         | 0.7966 | 3500 | 0.1418          | 14.2434 |
| 0.1259        | 0.9104 | 4000 | 0.1416          | 14.1765 |
| 0.1169        | 1.0241 | 4500 | 0.1412          | 14.2185 |
| 0.1103        | 1.1379 | 5000 | 0.1411          | 14.2090 |


### Framework versions

- PEFT 0.14.0
- Transformers 4.48.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0