| | --- |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: Model_G_S_D_Wav2Vec2 |
| | results: [] |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # Model_G_S_D_Wav2Vec2 |
| |
|
| | This model was trained from scratch on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0425 |
| | - Wer: 0.0310 |
| | - Cer: 0.0095 |
| |
|
| | ## 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: 0.0003 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 8 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 2 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 500 |
| | - num_epochs: 30 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
| | | 0.4434 | 0.85 | 400 | 0.0763 | 0.0984 | 0.0254 | |
| | | 0.1737 | 1.71 | 800 | 0.0639 | 0.0781 | 0.0199 | |
| | | 0.1293 | 2.56 | 1200 | 0.0522 | 0.0653 | 0.0167 | |
| | | 0.0965 | 3.41 | 1600 | 0.0471 | 0.0659 | 0.0163 | |
| | | 0.0874 | 4.26 | 2000 | 0.0464 | 0.0535 | 0.0139 | |
| | | 0.0679 | 5.12 | 2400 | 0.0395 | 0.0490 | 0.0132 | |
| | | 0.0618 | 5.97 | 2800 | 0.0424 | 0.0533 | 0.0143 | |
| | | 0.056 | 6.82 | 3200 | 0.0471 | 0.0511 | 0.0132 | |
| | | 0.0568 | 7.68 | 3600 | 0.0432 | 0.0468 | 0.0123 | |
| | | 0.046 | 8.53 | 4000 | 0.0425 | 0.0472 | 0.0130 | |
| | | 0.0459 | 9.38 | 4400 | 0.0502 | 0.0499 | 0.0134 | |
| | | 0.0408 | 10.23 | 4800 | 0.0450 | 0.0488 | 0.0131 | |
| | | 0.0436 | 11.09 | 5200 | 0.0431 | 0.0420 | 0.0119 | |
| | | 0.0375 | 11.94 | 5600 | 0.0463 | 0.0484 | 0.0132 | |
| | | 0.0327 | 12.79 | 6000 | 0.0412 | 0.0424 | 0.0116 | |
| | | 0.0322 | 13.65 | 6400 | 0.0381 | 0.0382 | 0.0111 | |
| | | 0.0316 | 14.5 | 6800 | 0.0441 | 0.0460 | 0.0128 | |
| | | 0.0296 | 15.35 | 7200 | 0.0426 | 0.0415 | 0.0119 | |
| | | 0.0274 | 16.2 | 7600 | 0.0421 | 0.0383 | 0.0106 | |
| | | 0.0247 | 17.06 | 8000 | 0.0442 | 0.0391 | 0.0120 | |
| | | 0.0235 | 17.91 | 8400 | 0.0449 | 0.0409 | 0.0116 | |
| | | 0.0219 | 18.76 | 8800 | 0.0394 | 0.0353 | 0.0106 | |
| | | 0.0174 | 19.62 | 9200 | 0.0489 | 0.0393 | 0.0117 | |
| | | 0.0161 | 20.47 | 9600 | 0.0421 | 0.0347 | 0.0099 | |
| | | 0.0158 | 21.32 | 10000 | 0.0425 | 0.0349 | 0.0108 | |
| | | 0.0141 | 22.17 | 10400 | 0.0436 | 0.0397 | 0.0116 | |
| | | 0.0156 | 23.03 | 10800 | 0.0432 | 0.0375 | 0.0114 | |
| | | 0.0138 | 23.88 | 11200 | 0.0438 | 0.0364 | 0.0110 | |
| | | 0.0116 | 24.73 | 11600 | 0.0420 | 0.0368 | 0.0108 | |
| | | 0.0108 | 25.59 | 12000 | 0.0407 | 0.0341 | 0.0103 | |
| | | 0.0073 | 26.44 | 12400 | 0.0428 | 0.0336 | 0.0101 | |
| | | 0.0085 | 27.29 | 12800 | 0.0432 | 0.0328 | 0.0101 | |
| | | 0.0078 | 28.14 | 13200 | 0.0416 | 0.0318 | 0.0096 | |
| | | 0.0065 | 29.0 | 13600 | 0.0423 | 0.0310 | 0.0097 | |
| | | 0.0062 | 29.85 | 14000 | 0.0425 | 0.0310 | 0.0095 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.31.0 |
| | - Pytorch 2.0.1+cu117 |
| | - Datasets 1.18.3 |
| | - Tokenizers 0.13.3 |
| |
|
