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---
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
|