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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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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xls-r-300m-th-cv11_0 |
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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-xls-r-300m-th-cv11_0 |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3391 |
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- Wer: 0.2915 |
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- Cer: 0.0651 |
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- Clean Cer: 0.0508 |
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- Learning Rate: 0.0000 |
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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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- 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: 500 |
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- num_epochs: 10 |
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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 | Cer | Clean Cer | Rate | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:| |
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| 7.5397 | 0.37 | 500 | 3.5716 | 1.0 | 0.9811 | 0.9774 | 0.0001 | |
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| 1.7478 | 0.75 | 1000 | 0.7702 | 0.8097 | 0.2296 | 0.1746 | 0.0001 | |
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| 0.7687 | 1.12 | 1500 | 0.4997 | 0.5392 | 0.1415 | 0.1182 | 0.0001 | |
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| 0.6064 | 1.5 | 2000 | 0.4270 | 0.4956 | 0.1238 | 0.1001 | 0.0001 | |
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| 0.5473 | 1.87 | 2500 | 0.3809 | 0.4489 | 0.1105 | 0.0898 | 0.0001 | |
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| 0.454 | 2.24 | 3000 | 0.3585 | 0.4256 | 0.1021 | 0.0813 | 0.0001 | |
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| 0.4219 | 2.62 | 3500 | 0.3375 | 0.4063 | 0.0974 | 0.0777 | 0.0001 | |
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| 0.4075 | 2.99 | 4000 | 0.3274 | 0.4036 | 0.0948 | 0.0746 | 0.0001 | |
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| 0.3355 | 3.37 | 4500 | 0.3257 | 0.3782 | 0.0898 | 0.0729 | 0.0001 | |
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| 0.3203 | 3.74 | 5000 | 0.3024 | 0.3561 | 0.0830 | 0.0659 | 0.0001 | |
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| 0.3151 | 4.11 | 5500 | 0.3038 | 0.3606 | 0.0830 | 0.0653 | 0.0001 | |
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| 0.2713 | 4.49 | 6000 | 0.3052 | 0.3595 | 0.0832 | 0.0655 | 0.0001 | |
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| 0.2685 | 4.86 | 6500 | 0.2933 | 0.3436 | 0.0796 | 0.0628 | 0.0001 | |
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| 0.2379 | 5.24 | 7000 | 0.3020 | 0.3362 | 0.0763 | 0.0608 | 0.0000 | |
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| 0.224 | 5.61 | 7500 | 0.2874 | 0.3265 | 0.0745 | 0.0589 | 0.0000 | |
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| 0.2204 | 5.98 | 8000 | 0.2922 | 0.3191 | 0.0724 | 0.0576 | 0.0000 | |
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| 0.1927 | 6.36 | 8500 | 0.3107 | 0.3163 | 0.0719 | 0.0568 | 0.0000 | |
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| 0.1875 | 6.73 | 9000 | 0.3034 | 0.3084 | 0.0703 | 0.0554 | 0.0000 | |
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| 0.1786 | 7.11 | 9500 | 0.3210 | 0.3107 | 0.0702 | 0.0553 | 0.0000 | |
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| 0.1606 | 7.48 | 10000 | 0.3231 | 0.3062 | 0.0688 | 0.0541 | 0.0000 | |
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| 0.1594 | 7.85 | 10500 | 0.3234 | 0.3033 | 0.0680 | 0.0535 | 0.0000 | |
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| 0.1498 | 8.23 | 11000 | 0.3276 | 0.3035 | 0.0680 | 0.0530 | 0.0000 | |
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| 0.1396 | 8.6 | 11500 | 0.3265 | 0.2975 | 0.0668 | 0.0520 | 0.0000 | |
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| 0.142 | 8.98 | 12000 | 0.3236 | 0.2930 | 0.0659 | 0.0515 | 0.0000 | |
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| 0.1242 | 9.35 | 12500 | 0.3403 | 0.2921 | 0.0655 | 0.0511 | 0.0000 | |
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| 0.1225 | 9.72 | 13000 | 0.3391 | 0.2915 | 0.0651 | 0.0508 | 0.0000 | |
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### Framework versions |
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- Transformers 4.27.0.dev0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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