---
language:
- tr
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- custom
metrics:
- wer
model-index:
- name: Whisper large tr - baki
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: custom
      type: custom
      args: 'config: tr, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 90.93493367024637
---

<!-- 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 tr - baki

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0105
- Wer: 90.9349

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- training_steps: 300
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.1523        | 0.9615 | 100  | 2.1371          | 117.2773 |
| 1.5102        | 1.9231 | 200  | 1.9995          | 93.6829  |
| 1.1534        | 2.8846 | 300  | 2.0105          | 90.9349  |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1