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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-russian-colab-beam_search_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-russian-colab-beam_search_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: 0.7619 |
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- Wer: 0.4680 |
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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: 4 |
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- total_train_batch_size: 64 |
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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: 800 |
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- num_epochs: 100 |
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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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| 10.0158 | 4.16 | 100 | 5.4134 | 1.0 | |
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| 4.0394 | 8.33 | 200 | 3.4304 | 1.0 | |
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| 3.2721 | 12.49 | 300 | 3.2273 | 1.0 | |
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| 3.1277 | 16.66 | 400 | 2.8023 | 0.9984 | |
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| 1.3791 | 20.82 | 500 | 0.9888 | 0.8546 | |
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| 0.3659 | 24.99 | 600 | 0.7602 | 0.6304 | |
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| 0.1858 | 29.16 | 700 | 0.7965 | 0.6156 | |
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| 0.1403 | 33.33 | 800 | 0.7998 | 0.5839 | |
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| 0.1173 | 37.49 | 900 | 0.8353 | 0.5941 | |
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| 0.0917 | 41.66 | 1000 | 0.8272 | 0.5522 | |
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| 0.0743 | 45.82 | 1100 | 0.8342 | 0.5471 | |
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| 0.063 | 49.99 | 1200 | 0.7988 | 0.5352 | |
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| 0.0528 | 54.16 | 1300 | 0.7740 | 0.5201 | |
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| 0.0456 | 58.33 | 1400 | 0.7636 | 0.5165 | |
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| 0.0389 | 62.49 | 1500 | 0.7922 | 0.5161 | |
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| 0.0329 | 66.66 | 1600 | 0.8035 | 0.5158 | |
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| 0.0283 | 70.82 | 1700 | 0.7873 | 0.4832 | |
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| 0.0255 | 74.99 | 1800 | 0.7853 | 0.4870 | |
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| 0.0236 | 79.16 | 1900 | 0.8236 | 0.5045 | |
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| 0.0202 | 83.33 | 2000 | 0.7661 | 0.4796 | |
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| 0.0165 | 87.49 | 2100 | 0.7584 | 0.4680 | |
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| 0.0156 | 91.66 | 2200 | 0.7685 | 0.4772 | |
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| 0.0149 | 95.82 | 2300 | 0.7519 | 0.4696 | |
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| 0.0126 | 99.99 | 2400 | 0.7619 | 0.4680 | |
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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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