| --- |
| 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: 1.5006 |
| - Wer: 1.3698 |
| - Cer: 1.1255 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
| | 1.8141 | 5.53 | 100 | 1.4784 | 0.7680 | 0.4463 | |
| | 0.673 | 11.11 | 200 | 1.0561 | 0.8175 | 0.5889 | |
| | 0.2762 | 16.64 | 300 | 1.0746 | 0.8564 | 0.5887 | |
| | 0.0852 | 22.21 | 400 | 1.2061 | 1.4290 | 0.9567 | |
| | 0.0199 | 27.75 | 500 | 1.2649 | 1.0706 | 0.9168 | |
| | 0.0087 | 33.32 | 600 | 1.4641 | 1.2417 | 1.0328 | |
| | 0.0041 | 38.85 | 700 | 1.4685 | 1.2806 | 0.9546 | |
| | 0.003 | 44.43 | 800 | 1.4830 | 1.3633 | 1.0236 | |
| | 0.0026 | 49.96 | 900 | 1.4964 | 1.3698 | 1.0375 | |
| | 0.0025 | 55.53 | 1000 | 1.5006 | 1.3698 | 1.1255 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
