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---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Sep26-Mixat-whisper-lg-3-transliteration
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. -->
# Sep26-Mixat-whisper-lg-3-transliteration
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7321
- Wer: 40.6571
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.7747 | 0.4292 | 100 | 0.4311 | 36.6994 |
| 0.4882 | 0.8584 | 200 | 0.4418 | 35.7241 |
| 0.3749 | 1.2876 | 300 | 0.4387 | 40.5617 |
| 0.3644 | 1.7167 | 400 | 0.4506 | 40.1608 |
| 0.3451 | 2.1459 | 500 | 0.4571 | 42.6225 |
| 0.2678 | 2.5751 | 600 | 0.4558 | 38.1490 |
| 0.2737 | 3.0043 | 700 | 0.4406 | 38.5621 |
| 0.1576 | 3.4335 | 800 | 0.4937 | 42.0456 |
| 0.1653 | 3.8627 | 900 | 0.4995 | 41.7987 |
| 0.1113 | 4.2918 | 1000 | 0.5667 | 41.4100 |
| 0.0957 | 4.7210 | 1100 | 0.5606 | 39.9237 |
| 0.0817 | 5.1502 | 1200 | 0.6160 | 41.6984 |
| 0.0534 | 5.5794 | 1300 | 0.6003 | 42.2313 |
| 0.0549 | 6.0086 | 1400 | 0.5908 | 40.9724 |
| 0.0315 | 6.4378 | 1500 | 0.6655 | 40.5031 |
| 0.0364 | 6.8670 | 1600 | 0.7179 | 43.4389 |
| 0.0278 | 7.2961 | 1700 | 0.6839 | 42.8009 |
| 0.0251 | 7.7253 | 1800 | 0.6803 | 42.9891 |
| 0.0228 | 8.1545 | 1900 | 0.7166 | 42.3047 |
| 0.0197 | 8.5837 | 2000 | 0.7321 | 40.6571 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.1
- Datasets 3.0.0
- Tokenizers 0.19.1
|