metadata

language:
  - tr
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17
metrics:
  - wer
model-index:
  - name: 'Whisper Large v2 TR '
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13
          type: mozilla-foundation/common_voice_17
          config: tr
          split: None
          args: tr
        metrics:
          - name: Wer
            type: wer
            value: 9.018929438770417

Whisper Large v2 TR

This model is a fine-tuned version of openai/whisper-large-v2 on the Common Voice 17 dataset. It achieves the following results on the evaluation set:

Loss: 0.1568
Wer: 9.0189

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: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
0.1437	0.9997	1450	0.1550	9.9787
0.0766	2.0	2901	0.1470	9.3616
0.0371	2.9990	4350	0.1568	9.0189

Framework versions

Transformers 4.40.0
Pytorch 2.2.2+cu121
Datasets 2.17.1
Tokenizers 0.19.1