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torgo_xlsr_finetune-F03-2
yip-i/torgo_xlsr_finetune-F03-2
torgo_xlsr_finetune-F03-2 is a automatic speech recognition model from yip-i. 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
- wav2vec2
- Access
- Public
- Created
- Mar 8, 2023
- Updated
- Mar 9, 2023
- SHA
- 7b6db62f
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. -->
torgo_xlsr_finetune-F03-2
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5454
- Wer: 0.8555
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: 8
- 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: 1000
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 25.1806 | 0.97 | 500 | 3.3512 | 1.0 |
| 3.3648 | 1.94 | 1000 | 3.1566 | 1.0 |
| 2.9865 | 2.91 | 1500 | 2.8249 | 1.0 |
| 2.8219 | 3.88 | 2000 | 2.7880 | 1.0 |
| 2.62 | 4.85 | 2500 | 2.4134 | 1.1793 |
| 2.0129 | 5.83 | 3000 | 1.7735 | 1.3777 |
| 1.3439 | 6.8 | 3500 | 1.4148 | 1.3656 |
| 0.9587 | 7.77 | 4000 | 1.3914 | 1.2437 |
| 0.7532 | 8.74 | 4500 | 1.2565 | 1.2957 |
| 0.6204 | 9.71 | 5000 | 1.2621 | 1.1074 |
| 0.5367 | 10.68 | 5500 | 1.3255 | 1.1199 |
| 0.4471 | 11.65 | 6000 | 1.2730 | 1.0789 |
| 0.3989 | 12.62 | 6500 | 1.2627 | 1.0258 |
| 0.3562 | 13.59 | 7000 | 1.3006 | 0.9754 |
| 0.3346 | 14.56 | 7500 | 1.2739 | 0.9598 |
| 0.2949 | 15.53 | 8000 | 1.3260 | 0.9238 |
| 0.2816 | 16.5 | 8500 | 1.3446 | 0.9152 |
| 0.2552 | 17.48 | 9000 | 1.3537 | 0.8848 |
| 0.2434 | 18.45 | 9500 | 1.3288 | 0.9258 |
| 0.2156 | 19.42 | 10000 | 1.3863 | 0.8812 |
| 0.2126 | 20.39 | 10500 | 1.3466 | 0.8867 |
| 0.1939 | 21.36 | 11000 | 1.4522 | 0.9113 |
| 0.1829 | 22.33 | 11500 | 1.5253 | 0.8922 |
| 0.179 | 23.3 | 12000 | 1.4589 | 0.8543 |
| 0.1684 | 24.27 | 12500 | 1.5436 | 0.8664 |
| 0.1516 | 25.24 | 13000 | 1.5324 | 0.8668 |
| 0.1472 | 26.21 | 13500 | 1.5561 | 0.8711 |
| 0.1399 | 27.18 | 14000 | 1.5400 | 0.8605 |
| 0.1405 | 28.16 | 14500 | 1.5626 | 0.8512 |
| 0.1349 | 29.13 | 15000 | 1.5454 | 0.8555 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Mar 8, 2023
- Updated
- Mar 9, 2023