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whisper-finetuned-v3_2e
tranha/whisper-finetuned-v3_2e
whisper-finetuned-v3_2e is a automatic speech recognition model from tranha. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- whisper
- Access
- Public
- Created
- May 2, 2025
- Updated
- May 2, 2025
- SHA
- 32f6934e
Base models
Model card
From the Hugging Face model README
<!-- 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-finetuned-v3_2e
This model is a fine-tuned version of openai/whisper-large-v3-turbo on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0648
- Wer: 60.0
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use 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 |
|---|---|---|---|---|
| 1.6639 | 0.1120 | 100 | 1.1985 | 235.1515 |
| 0.9978 | 0.2240 | 200 | 0.6341 | 150.1299 |
| 0.4115 | 0.3359 | 300 | 0.2679 | 88.0952 |
| 0.26 | 0.4479 | 400 | 0.2050 | 83.2468 |
| 0.2076 | 0.5599 | 500 | 0.1975 | 77.7922 |
| 0.1776 | 0.6719 | 600 | 0.1711 | 70.8658 |
| 0.1589 | 0.7839 | 700 | 0.1450 | 74.8485 |
| 0.1366 | 0.8959 | 800 | 0.1362 | 68.0519 |
| 0.124 | 1.0078 | 900 | 0.1124 | 62.1645 |
| 0.0944 | 1.1198 | 1000 | 0.1125 | 63.8528 |
| 0.0841 | 1.2318 | 1100 | 0.1039 | 61.6017 |
| 0.0759 | 1.3438 | 1200 | 0.0972 | 75.1082 |
| 0.0773 | 1.4558 | 1300 | 0.0892 | 60.4329 |
| 0.0709 | 1.5677 | 1400 | 0.0806 | 58.3983 |
| 0.0632 | 1.6797 | 1500 | 0.0813 | 58.0519 |
| 0.0613 | 1.7917 | 1600 | 0.0745 | 56.9697 |
| 0.0603 | 1.9037 | 1700 | 0.0689 | 55.1515 |
| 0.0477 | 2.0157 | 1800 | 0.0683 | 54.0693 |
| 0.0294 | 2.1277 | 1900 | 0.0660 | 60.4329 |
| 0.029 | 2.2396 | 2000 | 0.0648 | 60.0 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- mit
- Created
- May 2, 2025
- Updated
- May 2, 2025