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whisper-lt-finetune
daniel-rdt/whisper-lt-finetune
whisper-lt-finetune is a automatic speech recognition model from daniel-rdt. 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 5, 2022
- Updated
- Dec 7, 2022
- SHA
- c153683a
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-lt-finetune
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3634
- Wer: 28.1159
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: 5e-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: 250
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.399 | 0.9 | 500 | 0.4877 | 49.1790 |
| 0.1925 | 1.8 | 1000 | 0.4019 | 39.1325 |
| 0.0734 | 2.7 | 1500 | 0.3989 | 37.5581 |
| 0.0324 | 3.6 | 2000 | 0.3947 | 32.9662 |
| 0.0053 | 5.4 | 3000 | 0.3708 | 29.2808 |
| 0.0007 | 7.19 | 4000 | 0.3634 | 28.1159 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- whisper
- License
- apache-2.0
- Languages
- lt
- Created
- Dec 5, 2022
- Updated
- Dec 7, 2022