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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-v13 |
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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: 0.44538386783284745 |
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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-v13 |
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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: 0.4759 |
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- Wer: 0.4454 |
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- Cer: 0.0854 |
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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: 0.0001 |
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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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| 0.7277 | 1.0 | 7125 | 0.4018 | 0.4833 | 0.1016 | |
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| 0.3225 | 2.0 | 14250 | 0.3800 | 0.4945 | 0.1067 | |
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| 0.2726 | 3.0 | 21375 | 0.3745 | 0.4588 | 0.0919 | |
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| 0.2416 | 4.0 | 28500 | 0.3439 | 0.4419 | 0.0885 | |
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| 0.2188 | 5.0 | 35625 | 0.3353 | 0.4657 | 0.0906 | |
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| 0.2024 | 6.0 | 42750 | 0.3289 | 0.4563 | 0.0881 | |
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| 0.1888 | 7.0 | 49875 | 0.3272 | 0.4451 | 0.0863 | |
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| 0.1767 | 8.0 | 57000 | 0.3267 | 0.4226 | 0.0830 | |
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| 0.1668 | 9.0 | 64125 | 0.3354 | 0.4305 | 0.0837 | |
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| 0.1568 | 10.0 | 71250 | 0.3277 | 0.4297 | 0.0857 | |
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| 0.1483 | 11.0 | 78375 | 0.3310 | 0.4425 | 0.0857 | |
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| 0.1398 | 12.0 | 85500 | 0.3433 | 0.4299 | 0.0836 | |
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| 0.1323 | 13.0 | 92625 | 0.3448 | 0.4472 | 0.0870 | |
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| 0.125 | 14.0 | 99750 | 0.3585 | 0.4388 | 0.0849 | |
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| 0.1174 | 15.0 | 106875 | 0.3623 | 0.4250 | 0.0828 | |
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| 0.1121 | 16.0 | 114000 | 0.3813 | 0.4333 | 0.0843 | |
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| 0.1059 | 17.0 | 121125 | 0.3788 | 0.4251 | 0.0825 | |
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| 0.0996 | 18.0 | 128250 | 0.3882 | 0.4434 | 0.0863 | |
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| 0.0944 | 19.0 | 135375 | 0.4082 | 0.4444 | 0.0860 | |
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| 0.0889 | 20.0 | 142500 | 0.4227 | 0.4446 | 0.0848 | |
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| 0.0846 | 21.0 | 149625 | 0.4323 | 0.4422 | 0.0852 | |
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| 0.081 | 22.0 | 156750 | 0.4540 | 0.4506 | 0.0881 | |
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| 0.0767 | 23.0 | 163875 | 0.4759 | 0.4454 | 0.0854 | |
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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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