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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-transcript
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-transcript
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: 0.7130
- Wer: 43.1693
## 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: 8
- seed: 42
- 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.7784 | 0.4292 | 100 | 0.4158 | 34.8757 |
| 0.4942 | 0.8584 | 200 | 0.4306 | 33.8295 |
| 0.4017 | 1.2876 | 300 | 0.4313 | 38.3124 |
| 0.3677 | 1.7167 | 400 | 0.4539 | 39.1020 |
| 0.3498 | 2.1459 | 500 | 0.4611 | 41.6343 |
| 0.2632 | 2.5751 | 600 | 0.4645 | 37.8113 |
| 0.2701 | 3.0043 | 700 | 0.4461 | 37.3347 |
| 0.1499 | 3.4335 | 800 | 0.5147 | 40.4414 |
| 0.1596 | 3.8627 | 900 | 0.5218 | 41.5292 |
| 0.1073 | 4.2918 | 1000 | 0.5668 | 39.3977 |
| 0.0888 | 4.7210 | 1100 | 0.5665 | 39.4393 |
| 0.0738 | 5.1502 | 1200 | 0.6428 | 39.6104 |
| 0.0495 | 5.5794 | 1300 | 0.5914 | 41.9007 |
| 0.0512 | 6.0086 | 1400 | 0.6297 | 41.4950 |
| 0.0315 | 6.4378 | 1500 | 0.6753 | 44.4477 |
| 0.034 | 6.8670 | 1600 | 0.6906 | 38.4151 |
| 0.023 | 7.2961 | 1700 | 0.6998 | 40.0821 |
| 0.0251 | 7.7253 | 1800 | 0.7130 | 43.1693 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.1
- Datasets 3.0.0
- Tokenizers 0.19.1
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