emre's picture
Librarian Bot: Add base_model information to model (#3)
  - tr
license: apache-2.0
  - mozilla-foundation/common_voice_11_0
  - wer
base_model: openai/whisper-medium
  - name: Whisper Medium TR - Emre Tasar
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: tr
          split: test[:10%]
          args: 'config: tr, split: test'
          - type: wer
            value: 18.51
            name: Wer

Whisper Medium TR

This model is a fine-tuned version of openai/whisper-medium on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.211673
  • Wer: 18.51

Model description

This model is the openai whisper medium transformer adapted for Turkish audio to text transcription. This model has weight decay set to 0.1 to cope with overfitting.

Intended uses & limitations

The model is available through its HuggingFace web app

Training and evaluation data

Data used for training is the initial 10% of train and validation of Turkish Common Voice 11.0 from Mozilla Foundation.

Weight decay showed to have slightly better result also on the evaluation dataset.

Training procedure

After loading the pre trained model, it has been trained on the dataset.

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: 500
  • training_steps: 4000
  • mixed_precision_training: Native AMP
  • weight_decay: 0.1

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.12.1+cu113
  • Datasets 2.7.1
  • Tokenizers 0.13.2