| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: ASCEND_Dataset_Model |
| 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. --> |
|
|
| # ASCEND_Dataset_Model |
|
|
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 3.9199 |
| - Wer: 0.9540 |
| - Cer: 0.9868 |
|
|
| ## Model description |
|
|
| More information needed |
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|
| ## Intended uses & limitations |
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|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
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|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0003 |
| - train_batch_size: 8 |
| - eval_batch_size: 1 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
| | 16.9063 | 1.0 | 687 | 4.7768 | 1.0 | 1.0 | |
| | 5.0252 | 2.0 | 1374 | 4.7004 | 1.0 | 1.0 | |
| | 4.9378 | 3.0 | 2061 | 4.6715 | 1.0 | 1.0 | |
| | 5.1468 | 4.0 | 2748 | 4.6605 | 1.0 | 1.0 | |
| | 4.9353 | 5.0 | 3435 | 4.6470 | 1.0 | 1.0 | |
| | 4.913 | 6.0 | 4122 | 4.6177 | 1.0 | 1.0 | |
| | 4.8034 | 7.0 | 4809 | 4.7699 | 1.0 | 1.0 | |
| | 4.6905 | 8.0 | 5496 | 4.3596 | 1.0 | 1.0 | |
| | 4.5251 | 9.0 | 6183 | 4.2670 | 1.0 | 1.0 | |
| | 4.4527 | 10.0 | 6870 | 4.2087 | 1.0 | 1.0 | |
| | 4.3731 | 11.0 | 7557 | 4.1950 | 0.9982 | 0.9997 | |
| | 4.3461 | 12.0 | 8244 | 4.2287 | 0.9928 | 0.9988 | |
| | 4.3224 | 13.0 | 8931 | 4.1565 | 0.9802 | 0.9971 | |
| | 4.2504 | 14.0 | 9618 | 4.1254 | 0.9619 | 0.9937 | |
| | 4.2196 | 15.0 | 10305 | 4.0377 | 0.9562 | 0.9913 | |
| | 4.1911 | 16.0 | 10992 | 4.0576 | 0.9601 | 0.9887 | |
| | 4.1079 | 17.0 | 11679 | 4.0630 | 0.9544 | 0.9857 | |
| | 4.1117 | 18.0 | 12366 | 4.0009 | 0.9558 | 0.9880 | |
| | 4.0324 | 19.0 | 13053 | 3.9245 | 0.9540 | 0.9877 | |
| | 3.9871 | 20.0 | 13740 | 3.9199 | 0.9540 | 0.9868 | |
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| ### Framework versions |
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|
| - Transformers 4.17.0 |
| - Pytorch 1.10.0+cu111 |
| - Datasets 2.0.0 |
| - Tokenizers 0.11.6 |
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|
