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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-Vocals-Kor |
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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-Vocals-Kor |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4436 |
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- Cer: 0.2135 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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: 12 |
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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 | Cer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 10.0761 | 0.1181 | 300 | 3.5931 | 0.9861 | |
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| 2.8824 | 0.2361 | 600 | 1.9956 | 0.6491 | |
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| 1.1701 | 0.3542 | 900 | 0.8263 | 0.2735 | |
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| 0.8015 | 0.4723 | 1200 | 0.6946 | 0.2530 | |
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| 0.7235 | 0.5903 | 1500 | 0.6638 | 0.2380 | |
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| 0.6747 | 0.7084 | 1800 | 0.6288 | 0.2399 | |
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| 0.6528 | 0.8264 | 2100 | 0.5963 | 0.2382 | |
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| 0.6185 | 0.9445 | 2400 | 0.6014 | 0.2412 | |
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| 0.5861 | 1.0626 | 2700 | 0.5747 | 0.2388 | |
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| 0.5668 | 1.1806 | 3000 | 0.5561 | 0.2199 | |
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| 0.5628 | 1.2987 | 3300 | 0.5335 | 0.2235 | |
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| 0.5521 | 1.4168 | 3600 | 0.5489 | 0.2290 | |
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| 0.5309 | 1.5348 | 3900 | 0.4995 | 0.2125 | |
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| 0.5033 | 1.6529 | 4200 | 0.4905 | 0.2171 | |
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| 0.5018 | 1.7710 | 4500 | 0.4853 | 0.2129 | |
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| 0.5011 | 1.8890 | 4800 | 0.4901 | 0.2171 | |
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| 0.4907 | 2.0071 | 5100 | 0.4828 | 0.2135 | |
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| 0.4578 | 2.1251 | 5400 | 0.4855 | 0.2180 | |
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| 0.4552 | 2.2432 | 5700 | 0.4621 | 0.2216 | |
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| 0.4345 | 2.3613 | 6000 | 0.4669 | 0.2152 | |
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| 0.4332 | 2.4793 | 6300 | 0.4639 | 0.2171 | |
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| 0.4338 | 2.5974 | 6600 | 0.4517 | 0.2180 | |
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| 0.4181 | 2.7155 | 6900 | 0.4407 | 0.2117 | |
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| 0.4048 | 2.8335 | 7200 | 0.4394 | 0.2063 | |
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| 0.4003 | 2.9516 | 7500 | 0.4478 | 0.2100 | |
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| 0.3847 | 3.0697 | 7800 | 0.4478 | 0.2159 | |
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| 0.3634 | 3.1877 | 8100 | 0.4378 | 0.2145 | |
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| 0.3629 | 3.3058 | 8400 | 0.4386 | 0.2060 | |
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| 0.3603 | 3.4238 | 8700 | 0.4411 | 0.2127 | |
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| 0.361 | 3.5419 | 9000 | 0.4436 | 0.2135 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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