| --- |
| 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: 1.0034 |
| - Wer: 0.9507 |
| - Cer: 0.9543 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
| | 1.6746 | 2.63 | 100 | 1.4311 | 0.8342 | 0.5210 | |
| | 0.7117 | 5.26 | 200 | 0.8645 | 0.9008 | 0.5476 | |
| | 0.4373 | 7.89 | 300 | 0.7748 | 0.7412 | 0.5489 | |
| | 0.2419 | 10.53 | 400 | 0.7788 | 0.6967 | 0.4042 | |
| | 0.1359 | 13.16 | 500 | 0.8320 | 0.6912 | 0.5735 | |
| | 0.055 | 15.79 | 600 | 0.8891 | 0.7571 | 0.7292 | |
| | 0.0268 | 18.42 | 700 | 0.9250 | 0.7480 | 0.6051 | |
| | 0.0133 | 21.05 | 800 | 0.9747 | 0.6906 | 0.7730 | |
| | 0.0088 | 23.68 | 900 | 0.9968 | 0.8349 | 0.8106 | |
| | 0.0077 | 26.32 | 1000 | 1.0034 | 0.9507 | 0.9543 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.12.0+cu102 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
