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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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- generated_from_trainer |
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datasets: |
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- fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xls-r-300m-lg-CV-Fleurs_filtered-100hrs-v12 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: fleurs |
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type: fleurs |
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config: lg_ug |
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split: None |
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args: lg_ug |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.45568513119533527 |
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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-xls-r-300m-lg-CV-Fleurs_filtered-100hrs-v12 |
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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 fleurs dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5693 |
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- Wer: 0.4557 |
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- Cer: 0.0926 |
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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: 4 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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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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- num_epochs: 70 |
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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 | Cer | |
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|:-------------:|:-----:|:------:|:---------------:|:------:|:------:| |
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| 0.8586 | 1.0 | 7125 | 0.4810 | 0.5615 | 0.1256 | |
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| 0.423 | 2.0 | 14250 | 0.4661 | 0.5633 | 0.1450 | |
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| 0.3588 | 3.0 | 21375 | 0.4151 | 0.5 | 0.1075 | |
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| 0.32 | 4.0 | 28500 | 0.3937 | 0.4999 | 0.1084 | |
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| 0.291 | 5.0 | 35625 | 0.3880 | 0.4866 | 0.1026 | |
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| 0.2685 | 6.0 | 42750 | 0.3779 | 0.4860 | 0.1035 | |
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| 0.2498 | 7.0 | 49875 | 0.3598 | 0.4660 | 0.0973 | |
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| 0.2347 | 8.0 | 57000 | 0.3553 | 0.4573 | 0.0936 | |
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| 0.2198 | 9.0 | 64125 | 0.3584 | 0.4630 | 0.0950 | |
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| 0.2072 | 10.0 | 71250 | 0.3571 | 0.4742 | 0.0983 | |
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| 0.1954 | 11.0 | 78375 | 0.3596 | 0.4580 | 0.0955 | |
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| 0.1861 | 12.0 | 85500 | 0.3626 | 0.4573 | 0.0952 | |
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| 0.1746 | 13.0 | 92625 | 0.3656 | 0.4972 | 0.1014 | |
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| 0.1656 | 14.0 | 99750 | 0.3712 | 0.4566 | 0.0918 | |
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| 0.1572 | 15.0 | 106875 | 0.3874 | 0.4569 | 0.0933 | |
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| 0.149 | 16.0 | 114000 | 0.3919 | 0.4809 | 0.0966 | |
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| 0.142 | 17.0 | 121125 | 0.3837 | 0.4424 | 0.0907 | |
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| 0.1332 | 18.0 | 128250 | 0.3843 | 0.4635 | 0.0941 | |
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| 0.1271 | 19.0 | 135375 | 0.4080 | 0.4560 | 0.0942 | |
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| 0.1203 | 20.0 | 142500 | 0.4209 | 0.4673 | 0.0929 | |
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| 0.1136 | 21.0 | 149625 | 0.4188 | 0.4632 | 0.0934 | |
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| 0.1084 | 22.0 | 156750 | 0.4369 | 0.4588 | 0.0930 | |
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| 0.1029 | 23.0 | 163875 | 0.4553 | 0.4735 | 0.0944 | |
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| 0.0993 | 24.0 | 171000 | 0.4547 | 0.4654 | 0.0941 | |
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| 0.0943 | 25.0 | 178125 | 0.4775 | 0.4561 | 0.0925 | |
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| 0.0902 | 26.0 | 185250 | 0.5074 | 0.4649 | 0.0935 | |
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| 0.0867 | 27.0 | 192375 | 0.5073 | 0.4509 | 0.0912 | |
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| 0.0833 | 28.0 | 199500 | 0.5150 | 0.4749 | 0.0953 | |
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| 0.0799 | 29.0 | 206625 | 0.5624 | 0.4725 | 0.0944 | |
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| 0.0771 | 30.0 | 213750 | 0.5769 | 0.4552 | 0.0918 | |
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| 0.0739 | 31.0 | 220875 | 0.5697 | 0.4533 | 0.0917 | |
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| 0.0704 | 32.0 | 228000 | 0.5693 | 0.4557 | 0.0926 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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