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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- automatic-speech-recognition |
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- sudoping01/malian-languages-dataset |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-malian-languages-minianka-dataset |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-malian-languages-minianka-dataset |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the SUDOPING01/MALIAN-LANGUAGES-DATASET - MINIANKA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1794 |
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- Wer: 0.1271 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 15.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-------:|:----:|:---------------:|:------:| |
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| No log | 0.4706 | 100 | 3.8351 | 1.0 | |
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| No log | 0.9412 | 200 | 2.8794 | 1.0 | |
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| No log | 1.4094 | 300 | 0.6950 | 0.5312 | |
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| No log | 1.88 | 400 | 0.3555 | 0.3603 | |
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| 2.6845 | 2.3482 | 500 | 0.2666 | 0.2772 | |
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| 2.6845 | 2.8188 | 600 | 0.2253 | 0.2401 | |
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| 2.6845 | 3.2871 | 700 | 0.2025 | 0.2246 | |
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| 2.6845 | 3.7576 | 800 | 0.1880 | 0.1979 | |
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| 2.6845 | 4.2259 | 900 | 0.1853 | 0.1959 | |
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| 0.2121 | 4.6965 | 1000 | 0.1887 | 0.1909 | |
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| 0.2121 | 5.1647 | 1100 | 0.1745 | 0.1709 | |
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| 0.2121 | 5.6353 | 1200 | 0.1602 | 0.1674 | |
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| 0.2121 | 6.1035 | 1300 | 0.1627 | 0.1588 | |
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| 0.2121 | 6.5741 | 1400 | 0.1593 | 0.1563 | |
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| 0.1148 | 7.0424 | 1500 | 0.1660 | 0.1593 | |
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| 0.1148 | 7.5129 | 1600 | 0.1655 | 0.1551 | |
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| 0.1148 | 7.9835 | 1700 | 0.1581 | 0.1520 | |
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| 0.1148 | 8.4518 | 1800 | 0.1771 | 0.1493 | |
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| 0.1148 | 8.9224 | 1900 | 0.1778 | 0.1482 | |
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| 0.0723 | 9.3906 | 2000 | 0.1683 | 0.1402 | |
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| 0.0723 | 9.8612 | 2100 | 0.1676 | 0.1378 | |
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| 0.0723 | 10.3294 | 2200 | 0.1672 | 0.1365 | |
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| 0.0723 | 10.8 | 2300 | 0.1646 | 0.1332 | |
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| 0.0723 | 11.2682 | 2400 | 0.1751 | 0.1337 | |
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| 0.0504 | 11.7388 | 2500 | 0.1742 | 0.1353 | |
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| 0.0504 | 12.2071 | 2600 | 0.1809 | 0.1329 | |
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| 0.0504 | 12.6776 | 2700 | 0.1769 | 0.1298 | |
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| 0.0504 | 13.1459 | 2800 | 0.1752 | 0.1289 | |
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| 0.0504 | 13.6165 | 2900 | 0.1782 | 0.1275 | |
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| 0.0382 | 14.0847 | 3000 | 0.1789 | 0.1289 | |
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| 0.0382 | 14.5553 | 3100 | 0.1783 | 0.1275 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.3.1 |
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- Tokenizers 0.21.0 |
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