metadata

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

Whisper large tr - baki

This model is a fine-tuned version of 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