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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large SSD superU
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 SSD superU
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4312
- Wer: 120.4531
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 3.1192 | 3.125 | 100 | 2.9964 | 142.5654 |
| 1.7195 | 6.25 | 200 | 2.7478 | 122.2719 |
| 0.6545 | 9.375 | 300 | 3.0978 | 127.3772 |
| 0.1503 | 12.5 | 400 | 3.6068 | 139.0874 |
| 0.0827 | 15.625 | 500 | 3.6768 | 115.5712 |
| 0.0556 | 18.75 | 600 | 3.7650 | 114.9968 |
| 0.0441 | 21.875 | 700 | 3.7594 | 125.3350 |
| 0.0346 | 25.0 | 800 | 3.8227 | 147.7026 |
| 0.0205 | 28.125 | 900 | 3.9344 | 120.8998 |
| 0.0166 | 31.25 | 1000 | 3.9918 | 109.6682 |
| 0.0127 | 34.375 | 1100 | 3.9241 | 109.3491 |
| 0.009 | 37.5 | 1200 | 4.1503 | 110.7211 |
| 0.0029 | 40.625 | 1300 | 4.1240 | 134.5246 |
| 0.0007 | 43.75 | 1400 | 4.3018 | 105.5520 |
| 0.0007 | 46.875 | 1500 | 4.3464 | 106.8283 |
| 0.0004 | 50.0 | 1600 | 4.3809 | 115.5712 |
| 0.0003 | 53.125 | 1700 | 4.4061 | 120.8998 |
| 0.0002 | 56.25 | 1800 | 4.4205 | 120.2936 |
| 0.0002 | 59.375 | 1900 | 4.4289 | 120.4212 |
| 0.0002 | 62.5 | 2000 | 4.4312 | 120.4531 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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