| --- |
| 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: 1.8804 |
| - Wer: 2.0146 |
| - Cer: 1.4030 |
|
|
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:------:| |
| | 1.4622 | 16.67 | 100 | 1.5376 | 0.8662 | 0.7357 | |
| | 0.297 | 33.33 | 200 | 1.2979 | 0.8675 | 0.6481 | |
| | 0.0163 | 50.0 | 300 | 1.5699 | 1.4066 | 1.0449 | |
| | 0.0034 | 66.67 | 400 | 1.6919 | 1.6416 | 1.1817 | |
| | 0.0017 | 83.33 | 500 | 1.7654 | 1.6943 | 1.2587 | |
| | 0.0011 | 100.0 | 600 | 1.8153 | 1.9908 | 1.4084 | |
| | 0.0008 | 116.67 | 700 | 1.8455 | 1.9817 | 1.3867 | |
| | 0.0007 | 133.33 | 800 | 1.8647 | 2.0479 | 1.4215 | |
| | 0.0006 | 150.0 | 900 | 1.8764 | 2.0489 | 1.4253 | |
| | 0.0006 | 166.67 | 1000 | 1.8804 | 2.0146 | 1.4030 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
