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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- automatic-speech-recognition |
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- ./sample_speech.py |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: ko-xlsr2 |
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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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# ko-xlsr2 |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the ./SAMPLE_SPEECH.PY - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4239 |
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- Cer: 0.1113 |
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- Wer: 0.3038 |
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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: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 16 |
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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: 1000 |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| |
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| 1.7721 | 0.94 | 2000 | 1.1368 | 0.2903 | 0.6589 | |
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| 1.3501 | 1.89 | 4000 | 0.8561 | 0.2240 | 0.5451 | |
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| 1.2133 | 2.83 | 6000 | 0.7505 | 0.2003 | 0.4974 | |
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| 1.0981 | 3.77 | 8000 | 0.6768 | 0.1842 | 0.4686 | |
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| 1.0375 | 4.72 | 10000 | 0.6413 | 0.1707 | 0.4404 | |
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| 0.9927 | 5.66 | 12000 | 0.6106 | 0.1634 | 0.4246 | |
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| 0.9439 | 6.6 | 14000 | 0.5999 | 0.1613 | 0.4159 | |
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| 0.9059 | 7.55 | 16000 | 0.5740 | 0.1535 | 0.3985 | |
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| 0.8772 | 8.49 | 18000 | 0.5569 | 0.1478 | 0.3954 | |
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| 0.8483 | 9.43 | 20000 | 0.5407 | 0.1427 | 0.3784 | |
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| 0.81 | 10.37 | 22000 | 0.5283 | 0.1415 | 0.3744 | |
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| 0.793 | 11.32 | 24000 | 0.5179 | 0.1366 | 0.3663 | |
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| 0.7577 | 12.26 | 26000 | 0.5059 | 0.1359 | 0.3595 | |
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| 0.7379 | 13.2 | 28000 | 0.4969 | 0.1333 | 0.3532 | |
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| 0.7328 | 14.15 | 30000 | 0.4908 | 0.1308 | 0.3475 | |
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| 0.7119 | 15.09 | 32000 | 0.4887 | 0.1286 | 0.3478 | |
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| 0.7572 | 16.03 | 34000 | 0.5170 | 0.1327 | 0.3577 | |
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| 0.8198 | 16.98 | 36000 | 0.5839 | 0.1432 | 0.3825 | |
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| 0.8008 | 17.92 | 38000 | 0.5447 | 0.1376 | 0.3661 | |
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| 0.759 | 18.86 | 40000 | 0.4998 | 0.1337 | 0.3534 | |
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| 0.6907 | 19.81 | 42000 | 0.4710 | 0.1288 | 0.3412 | |
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| 0.659 | 20.75 | 44000 | 0.4578 | 0.1242 | 0.3325 | |
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| 0.6345 | 21.69 | 46000 | 0.4531 | 0.1221 | 0.3257 | |
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| 0.6242 | 22.64 | 48000 | 0.4498 | 0.1209 | 0.3218 | |
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| 0.6163 | 23.58 | 50000 | 0.4552 | 0.1194 | 0.3188 | |
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| 0.6121 | 24.52 | 52000 | 0.4633 | 0.1154 | 0.3137 | |
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| 0.6054 | 25.47 | 54000 | 0.4623 | 0.1176 | 0.3171 | |
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| 0.591 | 26.41 | 56000 | 0.4413 | 0.1146 | 0.3116 | |
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| 0.5713 | 27.35 | 58000 | 0.4338 | 0.1135 | 0.3093 | |
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| 0.5703 | 28.3 | 60000 | 0.4280 | 0.1121 | 0.3061 | |
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| 0.5576 | 29.24 | 62000 | 0.4248 | 0.1119 | 0.3047 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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