|
--- |
|
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 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.26.0 |
|
- Pytorch 1.12.0+cu102 |
|
- Datasets 2.9.0 |
|
- Tokenizers 0.13.2 |
|
|
