metadata

language:
  - tr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_9
metrics:
  - wer
model-index:
  - name: 'Whisper Large v2 TR '
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 9
          type: mozilla-foundation/common_voice_9
          config: tr
          split: None
          args: tr
        metrics:
          - name: Wer
            type: wer
            value: 9.500538020086083

Whisper Large v2 TR

This model is a fine-tuned version of openai/whisper-large-v2 on the Common Voice 9 dataset. It achieves the following results on the evaluation set:

Loss: 0.1784
Wer: 9.5005

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: 8
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_ratio: 0.1
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1558	1.0	3147	0.1813	11.0406
0.0705	2.0	6294	0.1675	9.8256
0.029	3.0	9441	0.1784	9.5005

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2