metadata

language:
  - tr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: 'Whisper Large v2 TR '
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 16_1
          type: mozilla-foundation/common_voice_16_1
          config: tr
          split: None
          args: tr
        metrics:
          - name: Wer
            type: wer
            value: 9.159607746652139

Whisper Large v2 TR

This model is a fine-tuned version of openai/whisper-large-v2 on the Common Voice 16_1 dataset. It achieves the following results on the evaluation set:

Loss: 0.1739
Wer: 9.1596

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
0.1425	1.0	5475	0.1734	10.7764
0.0714	2.0	10950	0.1627	9.6763
0.0283	3.0	16425	0.1739	9.1596

Framework versions

Transformers 4.38.1
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2