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README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: Model_G_S_D_Wav2Vec2
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results: []
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This model was trained from scratch on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Wer: 0.
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- Cer: 0.
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## Model description
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 30
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
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### Framework versions
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- Transformers 4.
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- Pytorch
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- Datasets 1.18.3
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- Tokenizers 0.
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---
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: Model_G_S_D_Wav2Vec2
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results: []
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This model was trained from scratch on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0425
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- Wer: 0.0310
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- Cer: 0.0095
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## Model description
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
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| 0.4434 | 0.85 | 400 | 0.0763 | 0.0984 | 0.0254 |
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| 0.1737 | 1.71 | 800 | 0.0639 | 0.0781 | 0.0199 |
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| 0.1293 | 2.56 | 1200 | 0.0522 | 0.0653 | 0.0167 |
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| 0.0965 | 3.41 | 1600 | 0.0471 | 0.0659 | 0.0163 |
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| 0.0874 | 4.26 | 2000 | 0.0464 | 0.0535 | 0.0139 |
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| 0.0679 | 5.12 | 2400 | 0.0395 | 0.0490 | 0.0132 |
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| 0.0618 | 5.97 | 2800 | 0.0424 | 0.0533 | 0.0143 |
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| 0.056 | 6.82 | 3200 | 0.0471 | 0.0511 | 0.0132 |
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| 0.0568 | 7.68 | 3600 | 0.0432 | 0.0468 | 0.0123 |
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| 0.046 | 8.53 | 4000 | 0.0425 | 0.0472 | 0.0130 |
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| 0.0459 | 9.38 | 4400 | 0.0502 | 0.0499 | 0.0134 |
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| 0.0408 | 10.23 | 4800 | 0.0450 | 0.0488 | 0.0131 |
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| 0.0436 | 11.09 | 5200 | 0.0431 | 0.0420 | 0.0119 |
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| 0.0375 | 11.94 | 5600 | 0.0463 | 0.0484 | 0.0132 |
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| 0.0327 | 12.79 | 6000 | 0.0412 | 0.0424 | 0.0116 |
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| 0.0322 | 13.65 | 6400 | 0.0381 | 0.0382 | 0.0111 |
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| 0.0316 | 14.5 | 6800 | 0.0441 | 0.0460 | 0.0128 |
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| 0.0296 | 15.35 | 7200 | 0.0426 | 0.0415 | 0.0119 |
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| 0.0274 | 16.2 | 7600 | 0.0421 | 0.0383 | 0.0106 |
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| 0.0247 | 17.06 | 8000 | 0.0442 | 0.0391 | 0.0120 |
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| 0.0235 | 17.91 | 8400 | 0.0449 | 0.0409 | 0.0116 |
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| 0.0219 | 18.76 | 8800 | 0.0394 | 0.0353 | 0.0106 |
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| 0.0174 | 19.62 | 9200 | 0.0489 | 0.0393 | 0.0117 |
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| 0.0161 | 20.47 | 9600 | 0.0421 | 0.0347 | 0.0099 |
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| 0.0158 | 21.32 | 10000 | 0.0425 | 0.0349 | 0.0108 |
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| 0.0141 | 22.17 | 10400 | 0.0436 | 0.0397 | 0.0116 |
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| 0.0156 | 23.03 | 10800 | 0.0432 | 0.0375 | 0.0114 |
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| 0.0138 | 23.88 | 11200 | 0.0438 | 0.0364 | 0.0110 |
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| 0.0116 | 24.73 | 11600 | 0.0420 | 0.0368 | 0.0108 |
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| 0.0108 | 25.59 | 12000 | 0.0407 | 0.0341 | 0.0103 |
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| 0.0073 | 26.44 | 12400 | 0.0428 | 0.0336 | 0.0101 |
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| 0.0085 | 27.29 | 12800 | 0.0432 | 0.0328 | 0.0101 |
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| 0.0078 | 28.14 | 13200 | 0.0416 | 0.0318 | 0.0096 |
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| 0.0065 | 29.0 | 13600 | 0.0423 | 0.0310 | 0.0097 |
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| 0.0062 | 29.85 | 14000 | 0.0425 | 0.0310 | 0.0095 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu117
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- Datasets 1.18.3
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- Tokenizers 0.13.3
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