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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-irish-colab_test |
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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-irish-colab_test |
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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: 1.7839 |
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- Wer: 0.6220 |
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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: 100 |
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- num_epochs: 90 |
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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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| 10.0428 | 2.94 | 50 | 4.1311 | 1.0 | |
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| 3.2917 | 5.88 | 100 | 3.1468 | 1.0 | |
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| 3.0221 | 8.82 | 150 | 2.9848 | 1.0 | |
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| 2.9795 | 11.76 | 200 | 2.9567 | 1.0 | |
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| 2.9379 | 14.71 | 250 | 2.9463 | 1.0 | |
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| 2.9068 | 17.65 | 300 | 2.8330 | 1.0 | |
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| 2.5088 | 20.59 | 350 | 1.9807 | 0.9535 | |
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| 1.6188 | 23.53 | 400 | 1.4254 | 0.8398 | |
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| 1.0435 | 26.47 | 450 | 1.3668 | 0.7807 | |
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| 0.7212 | 29.41 | 500 | 1.3914 | 0.7476 | |
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| 0.5456 | 32.35 | 550 | 1.5495 | 0.7470 | |
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| 0.4297 | 35.29 | 600 | 1.4751 | 0.6960 | |
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| 0.3533 | 38.24 | 650 | 1.5157 | 0.6909 | |
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| 0.2899 | 41.18 | 700 | 1.5394 | 0.6879 | |
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| 0.2529 | 44.12 | 750 | 1.6186 | 0.6903 | |
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| 0.2413 | 47.06 | 800 | 1.6386 | 0.6954 | |
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| 0.2113 | 50.0 | 850 | 1.6906 | 0.6778 | |
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| 0.1769 | 52.94 | 900 | 1.6918 | 0.6575 | |
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| 0.1622 | 55.88 | 950 | 1.7313 | 0.6572 | |
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| 0.1564 | 58.82 | 1000 | 1.7701 | 0.6510 | |
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| 0.1637 | 61.76 | 1050 | 1.6800 | 0.6444 | |
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| 0.148 | 64.71 | 1100 | 1.7306 | 0.6477 | |
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| 0.1385 | 67.65 | 1150 | 1.7605 | 0.6408 | |
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| 0.1264 | 70.59 | 1200 | 1.7534 | 0.6244 | |
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| 0.1157 | 73.53 | 1250 | 1.7906 | 0.6381 | |
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| 0.1027 | 76.47 | 1300 | 1.7803 | 0.6265 | |
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| 0.1061 | 79.41 | 1350 | 1.7617 | 0.6259 | |
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| 0.0934 | 82.35 | 1400 | 1.7649 | 0.6253 | |
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| 0.0904 | 85.29 | 1450 | 1.7713 | 0.6187 | |
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| 0.0911 | 88.24 | 1500 | 1.7839 | 0.6220 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.10.3 |
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