Whisper Medium Tr - denysdios
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13.0 & Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.1618
- Wer: 14.3825
Model description
The model took about nine hours to train on a single A100 GPU.
Intended uses & limitations
Absolutely no restrictions additional to whisper models. Increasing the Turkish labeled data in whisper, which was 4333/690k (0.0063), was the primary objective. There are just 49.945 hours of data in the fine-tuning dataset, or about 1.1% of the Turkish dataset that has already been trained.
Training and evaluation data
Processing...
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
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1803 | 0.36 | 1000 | 0.2089 | 18.6326 |
| 0.1428 | 0.71 | 2000 | 0.1821 | 16.3912 |
| 0.0535 | 1.07 | 3000 | 0.1693 | 14.9132 |
| 0.0491 | 1.43 | 4000 | 0.1618 | 14.3825 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
- Downloads last month
- 9
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for denysdios/whisper-med-tr-tuned
Base model
openai/whisper-medium