| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: testlaibasettsgopdata |
| 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. --> |
|
|
| # testlaibasettsgopdata |
|
|
| This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0930 |
| - Wer: 0.1682 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 6.2716 | 1.05 | 500 | 3.0550 | 1.0 | |
| | 1.8262 | 2.11 | 1000 | 0.2669 | 0.3023 | |
| | 0.5469 | 3.16 | 1500 | 0.1809 | 0.2281 | |
| | 0.3541 | 4.21 | 2000 | 0.1541 | 0.2185 | |
| | 0.3367 | 5.26 | 2500 | 0.1432 | 0.2054 | |
| | 0.2792 | 6.32 | 3000 | 0.1218 | 0.2023 | |
| | 0.2411 | 7.37 | 3500 | 0.1136 | 0.2029 | |
| | 0.2041 | 8.42 | 4000 | 0.1423 | 0.2025 | |
| | 0.2262 | 9.47 | 4500 | 0.1294 | 0.1968 | |
| | 0.1921 | 10.53 | 5000 | 0.1237 | 0.1952 | |
| | 0.1877 | 11.58 | 5500 | 0.1043 | 0.1890 | |
| | 0.176 | 12.63 | 6000 | 0.1272 | 0.1935 | |
| | 0.1236 | 13.68 | 6500 | 0.1352 | 0.1902 | |
| | 0.1473 | 14.74 | 7000 | 0.1257 | 0.1874 | |
| | 0.1748 | 15.79 | 7500 | 0.1190 | 0.1854 | |
| | 0.1147 | 16.84 | 8000 | 0.1213 | 0.1914 | |
| | 0.1508 | 17.89 | 8500 | 0.1262 | 0.1813 | |
| | 0.1061 | 18.95 | 9000 | 0.1148 | 0.1802 | |
| | 0.1182 | 20.0 | 9500 | 0.1034 | 0.1758 | |
| | 0.1144 | 21.05 | 10000 | 0.1123 | 0.1769 | |
| | 0.0885 | 22.11 | 10500 | 0.1043 | 0.1735 | |
| | 0.0797 | 23.16 | 11000 | 0.1004 | 0.1712 | |
| | 0.0729 | 24.21 | 11500 | 0.1045 | 0.1703 | |
| | 0.0718 | 25.26 | 12000 | 0.1064 | 0.1712 | |
| | 0.0668 | 26.32 | 12500 | 0.1050 | 0.1687 | |
| | 0.0599 | 27.37 | 13000 | 0.0965 | 0.1677 | |
| | 0.0702 | 28.42 | 13500 | 0.0930 | 0.1682 | |
| | 0.0942 | 29.47 | 14000 | 0.0959 | 0.1674 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.17.0 |
| - Pytorch 2.5.1+cu121 |
| - Datasets 1.18.3 |
| - Tokenizers 0.20.3 |
| |
