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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-pt-colab |
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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-large-xls-r-300m-pt-colab |
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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 common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3637 |
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- Wer: 0.2982 |
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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: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 30 |
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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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| 4.591 | 1.15 | 400 | 0.9128 | 0.6517 | |
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| 0.5049 | 2.31 | 800 | 0.4596 | 0.4437 | |
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| 0.2871 | 3.46 | 1200 | 0.3964 | 0.3905 | |
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| 0.2077 | 4.61 | 1600 | 0.3958 | 0.3744 | |
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| 0.1695 | 5.76 | 2000 | 0.4040 | 0.3720 | |
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| 0.1478 | 6.92 | 2400 | 0.3866 | 0.3651 | |
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| 0.1282 | 8.07 | 2800 | 0.3987 | 0.3674 | |
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| 0.1134 | 9.22 | 3200 | 0.4128 | 0.3688 | |
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| 0.1048 | 10.37 | 3600 | 0.3928 | 0.3561 | |
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| 0.0938 | 11.53 | 4000 | 0.4048 | 0.3619 | |
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| 0.0848 | 12.68 | 4400 | 0.4229 | 0.3555 | |
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| 0.0798 | 13.83 | 4800 | 0.3974 | 0.3468 | |
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| 0.0688 | 14.98 | 5200 | 0.3870 | 0.3503 | |
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| 0.0658 | 16.14 | 5600 | 0.3875 | 0.3351 | |
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| 0.061 | 17.29 | 6000 | 0.4133 | 0.3417 | |
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| 0.0569 | 18.44 | 6400 | 0.3915 | 0.3414 | |
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| 0.0526 | 19.6 | 6800 | 0.3957 | 0.3231 | |
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| 0.0468 | 20.75 | 7200 | 0.4110 | 0.3301 | |
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| 0.0407 | 21.9 | 7600 | 0.3866 | 0.3186 | |
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| 0.0384 | 23.05 | 8000 | 0.3976 | 0.3193 | |
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| 0.0363 | 24.21 | 8400 | 0.3910 | 0.3177 | |
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| 0.0313 | 25.36 | 8800 | 0.3656 | 0.3109 | |
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| 0.0293 | 26.51 | 9200 | 0.3712 | 0.3092 | |
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| 0.0277 | 27.66 | 9600 | 0.3613 | 0.3054 | |
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| 0.0249 | 28.82 | 10000 | 0.3783 | 0.3015 | |
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| 0.0234 | 29.97 | 10400 | 0.3637 | 0.2982 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.13.3 |
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- Tokenizers 0.10.3 |
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