| --- |
| 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: 1.8564 |
| - Wer: 1.2482 |
| - Cer: 0.7381 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
| | 1.6604 | 2.86 | 100 | 1.9378 | 1.1296 | 0.6334 | |
| | 0.6453 | 5.71 | 200 | 1.4655 | 0.9878 | 0.5974 | |
| | 0.3912 | 8.57 | 300 | 1.4669 | 1.2543 | 0.7557 | |
| | 0.2081 | 11.43 | 400 | 1.4622 | 0.8203 | 0.5123 | |
| | 0.094 | 14.29 | 500 | 1.6592 | 0.9535 | 0.6367 | |
| | 0.039 | 17.14 | 600 | 1.6946 | 0.9658 | 0.5706 | |
| | 0.0172 | 20.0 | 700 | 1.8271 | 1.4046 | 1.0027 | |
| | 0.0086 | 22.86 | 800 | 1.8149 | 1.2567 | 0.7530 | |
| | 0.0064 | 25.71 | 900 | 1.8478 | 1.2311 | 0.7279 | |
| | 0.0061 | 28.57 | 1000 | 1.8564 | 1.2482 | 0.7381 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
