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---
library_name: transformers
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper_large_v3_turbo_v2
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. -->
# Whisper_large_v3_turbo_v2
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6363
- Wer: 31.7384
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.8106 | 1.1834 | 500 | 1.0268 | 93.4686 |
| 0.5518 | 2.3669 | 1000 | 0.8523 | 56.8544 |
| 0.4203 | 3.5503 | 1500 | 0.7787 | 52.2696 |
| 0.2934 | 4.7337 | 2000 | 0.7357 | 48.8402 |
| 0.2243 | 5.9172 | 2500 | 0.7544 | 49.3678 |
| 0.1262 | 7.1006 | 3000 | 0.7770 | 49.9682 |
| 0.1038 | 8.2840 | 3500 | 0.7445 | 43.7824 |
| 0.0791 | 9.4675 | 4000 | 0.7615 | 44.6193 |
| 0.057 | 10.6509 | 4500 | 0.7432 | 41.0079 |
| 0.0441 | 11.8343 | 5000 | 0.7307 | 40.3166 |
| 0.0313 | 13.0178 | 5500 | 0.7222 | 38.7519 |
| 0.0147 | 14.2012 | 6000 | 0.7173 | 37.2965 |
| 0.0091 | 15.3846 | 6500 | 0.6866 | 34.8949 |
| 0.0022 | 16.5680 | 7000 | 0.6540 | 33.5031 |
| 0.0025 | 17.7515 | 7500 | 0.6488 | 32.5298 |
| 0.0004 | 18.9349 | 8000 | 0.6363 | 31.7384 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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