| --- |
| language: |
| - tr |
| license: apache-2.0 |
| tags: |
| - hf-asr-leaderboard |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: base Turkish Whisper (bTW) |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # base Turkish Whisper (bTW) |
|
|
| This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ermetal Meetings dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.0576 |
| - Wer: 1.1825 |
| - Cer: 1.0651 |
|
|
| ## 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: 4 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - training_steps: 1000 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
| | 1.6978 | 3.33 | 100 | 1.3610 | 0.7852 | 0.4184 | |
| | 0.6547 | 6.66 | 200 | 0.8659 | 0.7226 | 0.4379 | |
| | 0.3805 | 9.99 | 300 | 0.8060 | 0.7256 | 0.4330 | |
| | 0.1886 | 13.33 | 400 | 0.8382 | 0.6395 | 0.4164 | |
| | 0.0745 | 16.66 | 500 | 0.9106 | 0.8185 | 0.6747 | |
| | 0.0303 | 19.99 | 600 | 0.9697 | 0.8509 | 0.5685 | |
| | 0.0139 | 23.33 | 700 | 1.0096 | 0.8773 | 0.6483 | |
| | 0.0069 | 26.66 | 800 | 1.0367 | 1.2781 | 1.2923 | |
| | 0.0054 | 29.99 | 900 | 1.0518 | 1.2363 | 1.1066 | |
| | 0.0048 | 33.33 | 1000 | 1.0576 | 1.1825 | 1.0651 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
