| --- |
| license: apache-2.0 |
| base_model: facebook/wav2vec2-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: output2 |
| 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. --> |
|
|
| # output2 |
|
|
| This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7711 |
| - Wer: 0.3693 |
|
|
| ## 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 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 0.9858 | 0.5 | 500 | 0.8322 | 0.6842 | |
| | 0.7867 | 1.0 | 1000 | 0.6777 | 0.6137 | |
| | 0.6252 | 1.5 | 1500 | 0.6082 | 0.5503 | |
| | 0.5833 | 2.0 | 2000 | 0.5441 | 0.5066 | |
| | 0.4611 | 2.5 | 2500 | 0.5498 | 0.4922 | |
| | 0.4511 | 3.0 | 3000 | 0.5262 | 0.4654 | |
| | 0.37 | 3.5 | 3500 | 0.5422 | 0.4554 | |
| | 0.375 | 4.0 | 4000 | 0.6414 | 0.4659 | |
| | 0.3149 | 4.5 | 4500 | 0.5149 | 0.4353 | |
| | 0.3103 | 5.0 | 5000 | 0.5329 | 0.4423 | |
| | 0.2735 | 5.5 | 5500 | 0.9014 | 0.4359 | |
| | 0.2711 | 6.0 | 6000 | 3.1838 | 0.4374 | |
| | 0.26 | 6.5 | 6500 | 0.5987 | 0.4288 | |
| | 0.2451 | 7.0 | 7000 | 0.5245 | 0.4206 | |
| | 0.2184 | 7.5 | 7500 | 0.5627 | 0.4138 | |
| | 0.2115 | 8.0 | 8000 | 0.6408 | 0.4245 | |
| | 0.187 | 8.5 | 8500 | 0.5788 | 0.4093 | |
| | 0.1955 | 9.0 | 9000 | 0.5591 | 0.4214 | |
| | 0.1725 | 9.5 | 9500 | 0.5812 | 0.4135 | |
| | 0.1758 | 10.0 | 10000 | 0.5863 | 0.4051 | |
| | 0.1592 | 10.5 | 10500 | 0.6263 | 0.4116 | |
| | 0.1576 | 11.0 | 11000 | 0.5829 | 0.4028 | |
| | 0.1427 | 11.5 | 11500 | 0.6378 | 0.4016 | |
| | 0.1476 | 12.0 | 12000 | 0.5706 | 0.3988 | |
| | 0.1289 | 12.5 | 12500 | 0.6381 | 0.4104 | |
| | 0.1366 | 13.0 | 13000 | 0.6326 | 0.3975 | |
| | 0.1183 | 13.5 | 13500 | 0.6256 | 0.3916 | |
| | 0.1225 | 14.0 | 14000 | 0.6376 | 0.3971 | |
| | 0.1083 | 14.5 | 14500 | 0.6493 | 0.3905 | |
| | 0.1134 | 15.0 | 15000 | 0.6686 | 0.3951 | |
| | 0.1003 | 15.5 | 15500 | 0.6983 | 0.3967 | |
| | 0.104 | 16.0 | 16000 | 0.6324 | 0.3927 | |
| | 0.0928 | 16.5 | 16500 | 0.6482 | 0.3907 | |
| | 0.0944 | 17.0 | 17000 | 0.6790 | 0.3912 | |
| | 0.0925 | 17.5 | 17500 | 0.6877 | 0.3902 | |
| | 0.0847 | 18.0 | 18000 | 0.6572 | 0.3845 | |
| | 0.0808 | 18.5 | 18500 | 0.6551 | 0.3910 | |
| | 0.0836 | 19.0 | 19000 | 0.6832 | 0.3859 | |
| | 0.0757 | 19.5 | 19500 | 0.7594 | 0.3905 | |
| | 0.0751 | 20.0 | 20000 | 0.6960 | 0.3880 | |
| | 0.0715 | 20.5 | 20500 | 0.7244 | 0.3840 | |
| | 0.07 | 21.0 | 21000 | 0.7233 | 0.3848 | |
| | 0.0654 | 21.5 | 21500 | 0.7428 | 0.3833 | |
| | 0.0657 | 22.0 | 22000 | 0.7014 | 0.3842 | |
| | 0.0641 | 22.5 | 22500 | 0.7357 | 0.3796 | |
| | 0.0624 | 23.0 | 23000 | 0.7338 | 0.3796 | |
| | 0.0575 | 23.5 | 23500 | 0.7375 | 0.3804 | |
| | 0.0578 | 24.0 | 24000 | 0.7386 | 0.3782 | |
| | 0.0542 | 24.5 | 24500 | 0.7405 | 0.3758 | |
| | 0.0509 | 25.0 | 25000 | 0.7719 | 0.3774 | |
| | 0.0495 | 25.5 | 25500 | 0.7505 | 0.3763 | |
| | 0.0521 | 26.0 | 26000 | 0.7345 | 0.3742 | |
| | 0.0477 | 26.5 | 26500 | 0.7776 | 0.3740 | |
| | 0.0442 | 27.0 | 27000 | 0.7742 | 0.3738 | |
| | 0.0473 | 27.5 | 27500 | 0.7695 | 0.3719 | |
| | 0.0452 | 28.0 | 28000 | 0.7737 | 0.3705 | |
| | 0.0425 | 28.5 | 28500 | 0.7937 | 0.3702 | |
| | 0.0415 | 29.0 | 29000 | 0.7970 | 0.3713 | |
| | 0.0432 | 29.5 | 29500 | 0.7714 | 0.3700 | |
| | 0.041 | 30.0 | 30000 | 0.7711 | 0.3693 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
| |
