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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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base_model: facebook/wav2vec2-xls-r-300m |
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datasets: |
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- zeroth_korean |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xlrs-korean-v5 |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: zeroth_korean |
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type: zeroth_korean |
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config: clean |
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split: None |
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args: clean |
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metrics: |
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- type: wer |
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value: 0.2433368468604126 |
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name: Wer |
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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-xlrs-korean-v5 |
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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 zeroth_korean dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1300 |
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- Wer: 0.2433 |
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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: 32 |
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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: 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: 1000 |
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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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| 5.1453 | 1.4368 | 500 | 3.1530 | 1.0 | |
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| 2.4287 | 2.8736 | 1000 | 0.6084 | 0.8317 | |
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| 0.5556 | 4.3103 | 1500 | 0.3414 | 0.6165 | |
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| 0.3929 | 5.7471 | 2000 | 0.2729 | 0.5386 | |
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| 0.3211 | 7.1839 | 2500 | 0.2294 | 0.4794 | |
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| 0.281 | 8.6207 | 3000 | 0.2052 | 0.4298 | |
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| 0.2483 | 10.0575 | 3500 | 0.1911 | 0.4061 | |
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| 0.2243 | 11.4943 | 4000 | 0.1685 | 0.3873 | |
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| 0.2023 | 12.9310 | 4500 | 0.1627 | 0.3524 | |
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| 0.188 | 14.3678 | 5000 | 0.1572 | 0.3272 | |
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| 0.1784 | 15.8046 | 5500 | 0.1495 | 0.3131 | |
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| 0.1677 | 17.2414 | 6000 | 0.1424 | 0.2881 | |
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| 0.1533 | 18.6782 | 6500 | 0.1418 | 0.2709 | |
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| 0.1501 | 20.1149 | 7000 | 0.1387 | 0.2822 | |
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| 0.1402 | 21.5517 | 7500 | 0.1401 | 0.2697 | |
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| 0.1353 | 22.9885 | 8000 | 0.1367 | 0.2643 | |
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| 0.133 | 24.4253 | 8500 | 0.1337 | 0.2578 | |
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| 0.1254 | 25.8621 | 9000 | 0.1355 | 0.2560 | |
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| 0.1262 | 27.2989 | 9500 | 0.1339 | 0.2474 | |
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| 0.121 | 28.7356 | 10000 | 0.1300 | 0.2433 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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