| --- |
| license: apache-2.0 |
| base_model: facebook/wav2vec2-xls-r-300m |
| tags: |
| - generated_from_trainer |
| metrics: |
| - wer |
| model-index: |
| - name: XLS-R_Synthesis_ALL_v2 |
| 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. --> |
|
|
| # XLS-R_Synthesis_ALL_v2 |
| |
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1577 |
| - Wer: 0.1671 |
| |
| ## 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: 18 |
| - eval_batch_size: 9 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 72 |
| - total_eval_batch_size: 18 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: polynomial |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 100.0 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:| |
| | 4.5648 | 1.0 | 2611 | 1.3992 | 0.9921 | |
| | 0.7741 | 2.0 | 5223 | 0.3761 | 0.4506 | |
| | 0.431 | 3.0 | 7834 | 0.2574 | 0.3203 | |
| | 0.3323 | 4.0 | 10446 | 0.2205 | 0.2609 | |
| | 0.2818 | 5.0 | 13057 | 0.2050 | 0.2260 | |
| | 0.2514 | 6.0 | 15669 | 0.1896 | 0.2105 | |
| | 0.2318 | 7.0 | 18280 | 0.1766 | 0.2024 | |
| | 0.2189 | 8.0 | 20892 | 0.1736 | 0.1968 | |
| | 0.2098 | 9.0 | 23503 | 0.1755 | 0.1917 | |
| | 0.2024 | 10.0 | 26115 | 0.1707 | 0.1931 | |
| | 0.1966 | 11.0 | 28726 | 0.1636 | 0.1871 | |
| | 0.1893 | 12.0 | 31338 | 0.1719 | 0.1839 | |
| | 0.1808 | 13.0 | 33949 | 0.1684 | 0.1815 | |
| | 0.1756 | 14.0 | 36561 | 0.1631 | 0.1768 | |
| | 0.1702 | 15.0 | 39172 | 0.1670 | 0.1757 | |
| | 0.1651 | 16.0 | 41784 | 0.1627 | 0.1718 | |
| | 0.1596 | 17.0 | 44395 | 0.1572 | 0.1683 | |
| | 0.1553 | 18.0 | 47007 | 0.1614 | 0.1675 | |
| | 0.1528 | 19.0 | 49618 | 0.1701 | 0.1723 | |
| | 0.1502 | 20.0 | 52230 | 0.1598 | 0.1654 | |
| | 0.1839 | 21.0 | 54841 | 0.2059 | 0.2144 | |
| | 0.2445 | 22.0 | 57453 | 0.2215 | 0.2463 | |
| | 0.2002 | 23.0 | 60064 | 0.1703 | 0.1788 | |
| | 0.1534 | 24.0 | 62676 | 0.1634 | 0.1698 | |
| | 0.1449 | 25.0 | 65287 | 0.1577 | 0.1671 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.1+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
