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tun_msa_wav2vec2
Myriam123/tun_msa_wav2vec2
tun_msa_wav2vec2 is a automatic speech recognition model from Myriam123. Use it when you need speech turned into text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- Model type
- wav2vec2
- Access
- Public
- Created
- Jul 1, 2024
- Updated
- Jul 2, 2024
- SHA
- c329c325
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. -->
tun_msa_wav2vec2
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5373
- Wer: 0.5598
- Cer: 0.1779
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 4.1696 | 2.2727 | 700 | 1.2685 | 0.8366 | 0.3108 |
| 1.4028 | 4.5455 | 1400 | 0.7872 | 0.7136 | 0.2375 |
| 1.0872 | 6.8182 | 2100 | 0.6787 | 0.6778 | 0.2198 |
| 0.9573 | 9.0909 | 2800 | 0.6569 | 0.6605 | 0.2121 |
| 0.939 | 11.3636 | 3500 | 0.6115 | 0.6409 | 0.2040 |
| 0.8378 | 13.6364 | 4200 | 0.6097 | 0.6288 | 0.2000 |
| 0.8026 | 15.9091 | 4900 | 0.5843 | 0.6125 | 0.1949 |
| 0.7712 | 18.1818 | 5600 | 0.5830 | 0.6080 | 0.1942 |
| 0.7561 | 20.4545 | 6300 | 0.5637 | 0.5951 | 0.1889 |
| 0.7255 | 22.7273 | 7000 | 0.5630 | 0.5888 | 0.1869 |
| 0.7051 | 25.0 | 7700 | 0.5573 | 0.5852 | 0.1848 |
| 0.6873 | 27.2727 | 8400 | 0.5577 | 0.5799 | 0.1841 |
| 0.6722 | 29.5455 | 9100 | 0.5460 | 0.5748 | 0.1827 |
| 0.6673 | 31.8182 | 9800 | 0.5495 | 0.5725 | 0.1827 |
| 0.6359 | 34.0909 | 10500 | 0.5509 | 0.5720 | 0.1831 |
| 0.6513 | 36.3636 | 11200 | 0.5455 | 0.5676 | 0.1804 |
| 0.6402 | 38.6364 | 11900 | 0.5373 | 0.5649 | 0.1797 |
| 0.6302 | 40.9091 | 12600 | 0.5399 | 0.5643 | 0.1797 |
| 0.617 | 43.1818 | 13300 | 0.5368 | 0.5628 | 0.1793 |
| 0.6353 | 45.4545 | 14000 | 0.5369 | 0.5596 | 0.1786 |
| 0.6277 | 47.7273 | 14700 | 0.5361 | 0.5597 | 0.1783 |
| 0.5956 | 50.0 | 15400 | 0.5373 | 0.5598 | 0.1779 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- Created
- Jul 1, 2024
- Updated
- Jul 2, 2024