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whisper-small-nl-dy-noaugmentation
wangpuupup/whisper-small-nl-dy-noaugmentation
whisper-small-nl-dy-noaugmentation is a automatic speech recognition model from wangpuupup. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- whisper
- Access
- Public
- Created
- Dec 22, 2022
- Updated
- Jan 2, 2023
- SHA
- aa54b78a
Datasets
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 Small dysarthric Dutch
This model is a fine-tuned version of qmeeus/whisper-small-nl on the data/copas copas-full dataset. It achieves the following results on the evaluation set:
- Loss: 0.4891
- Wer: 22.8706
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1493 | 2.02 | 500 | 0.3960 | 28.9779 |
| 0.0383 | 5.02 | 1000 | 0.4041 | 26.5132 |
| 0.0264 | 8.01 | 1500 | 0.4274 | 25.5890 |
| 0.0155 | 11.01 | 2000 | 0.4437 | 24.7735 |
| 0.0041 | 14.01 | 2500 | 0.4454 | 25.0453 |
| 0.0044 | 17.01 | 3000 | 0.4444 | 23.9761 |
| 0.0044 | 20.01 | 3500 | 0.4394 | 23.4868 |
| 0.0022 | 23.01 | 4000 | 0.4415 | 22.8525 |
| 0.0034 | 26.01 | 4500 | 0.4602 | 23.6499 |
| 0.0027 | 29.01 | 5000 | 0.4577 | 23.3780 |
| 0.0072 | 32.01 | 5500 | 0.4573 | 23.3962 |
| 0.0002 | 35.01 | 6000 | 0.4673 | 23.1062 |
| 0.0001 | 38.01 | 6500 | 0.4723 | 22.9975 |
| 0.0001 | 41.01 | 7000 | 0.4770 | 23.0881 |
| 0.0 | 44.01 | 7500 | 0.4807 | 23.0518 |
| 0.0 | 47.01 | 8000 | 0.4835 | 22.9612 |
| 0.0 | 50.01 | 8500 | 0.4857 | 22.9250 |
| 0.0 | 53.0 | 9000 | 0.4874 | 22.9069 |
| 0.0 | 56.0 | 9500 | 0.4887 | 22.9069 |
| 0.0 | 59.0 | 10000 | 0.4891 | 22.8706 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Created
- Dec 22, 2022
- Updated
- Jan 2, 2023