| --- |
| 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: 2.0552 |
| - Wer: 1.3802 |
| - Cer: 0.8297 |
|
|
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:------:| |
| | 1.3477 | 33.33 | 100 | 1.8981 | 1.2433 | 0.8110 | |
| | 0.0238 | 66.67 | 200 | 1.7919 | 0.9340 | 0.5818 | |
| | 0.0032 | 100.0 | 300 | 1.8780 | 0.9756 | 0.6155 | |
| | 0.0014 | 133.33 | 400 | 1.9332 | 1.3582 | 0.8039 | |
| | 0.0008 | 166.67 | 500 | 1.9769 | 1.6333 | 0.9329 | |
| | 0.0005 | 200.0 | 600 | 2.0099 | 1.3790 | 0.8230 | |
| | 0.0004 | 233.33 | 700 | 2.0307 | 1.3851 | 0.8270 | |
| | 0.0004 | 266.67 | 800 | 2.0442 | 1.3851 | 0.8286 | |
| | 0.0003 | 300.0 | 900 | 2.0523 | 1.3814 | 0.8303 | |
| | 0.0003 | 333.33 | 1000 | 2.0552 | 1.3802 | 0.8297 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
