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MT_RN
Siyong/MT_RN
MT_RN is a automatic speech recognition model from Siyong. 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
- Jul 19, 2022
- Updated
- Jul 20, 2022
- SHA
- 554b6a31
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. -->
run1
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6666
- Wer: 0.6375
- Cer: 0.3170
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: 2000
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 1.0564 | 2.36 | 2000 | 2.3456 | 0.9628 | 0.5549 |
| 0.5071 | 4.73 | 4000 | 2.0652 | 0.9071 | 0.5115 |
| 0.3952 | 7.09 | 6000 | 2.3649 | 0.9108 | 0.4628 |
| 0.3367 | 9.46 | 8000 | 1.7615 | 0.8253 | 0.4348 |
| 0.2765 | 11.82 | 10000 | 1.6151 | 0.7937 | 0.4087 |
| 0.2493 | 14.18 | 12000 | 1.4976 | 0.7881 | 0.3905 |
| 0.2318 | 16.55 | 14000 | 1.6731 | 0.8160 | 0.3925 |
| 0.2074 | 18.91 | 16000 | 1.5822 | 0.7658 | 0.3913 |
| 0.1825 | 21.28 | 18000 | 1.5442 | 0.7361 | 0.3704 |
| 0.1824 | 23.64 | 20000 | 1.5988 | 0.7621 | 0.3711 |
| 0.1699 | 26.0 | 22000 | 1.4261 | 0.7119 | 0.3490 |
| 0.158 | 28.37 | 24000 | 1.7482 | 0.7658 | 0.3648 |
| 0.1385 | 30.73 | 26000 | 1.4103 | 0.6784 | 0.3348 |
| 0.1199 | 33.1 | 28000 | 1.5214 | 0.6636 | 0.3273 |
| 0.116 | 35.46 | 30000 | 1.4288 | 0.7212 | 0.3486 |
| 0.1071 | 37.83 | 32000 | 1.5344 | 0.7138 | 0.3411 |
| 0.1007 | 40.19 | 34000 | 1.4501 | 0.6691 | 0.3237 |
| 0.0943 | 42.55 | 36000 | 1.5367 | 0.6859 | 0.3265 |
| 0.0844 | 44.92 | 38000 | 1.5321 | 0.6599 | 0.3273 |
| 0.0762 | 47.28 | 40000 | 1.6721 | 0.6264 | 0.3142 |
| 0.0778 | 49.65 | 42000 | 1.6666 | 0.6375 | 0.3170 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0+cu113
- Datasets 2.0.0
- Tokenizers 0.12.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Jul 19, 2022
- Updated
- Jul 20, 2022