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README.md
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metrics:
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- name: Wer
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type: wer
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value: 0.
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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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This model is a fine-tuned version of [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.
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- Wer: 0.
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## Model description
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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: 30
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- num_epochs:
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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.
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| 3.
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### Framework versions
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metrics:
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- name: Wer
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type: wer
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value: 0.4536376604850214
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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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This model is a fine-tuned version of [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th) on the common_voice dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2174
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- Wer: 0.4536
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## Model description
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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: 30
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- num_epochs: 50
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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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| 5.3619 | 3.23 | 100 | 3.2891 | 1.0 |
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| 3.299 | 6.45 | 200 | 3.1670 | 1.0 |
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| 2.1179 | 9.68 | 300 | 1.1747 | 0.5221 |
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| 1.1047 | 12.9 | 400 | 1.0323 | 0.5849 |
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| 0.8974 | 16.13 | 500 | 1.0128 | 0.5029 |
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| 0.769 | 19.35 | 600 | 1.0402 | 0.4957 |
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| 0.6659 | 22.58 | 700 | 1.0902 | 0.4729 |
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| 0.6114 | 25.81 | 800 | 1.1412 | 0.4629 |
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| 0.5511 | 29.03 | 900 | 1.1156 | 0.4643 |
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| 0.5137 | 32.26 | 1000 | 1.1556 | 0.4679 |
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| 0.5132 | 35.48 | 1100 | 1.1851 | 0.4515 |
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| 0.4583 | 38.71 | 1200 | 1.1971 | 0.4529 |
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| 0.4523 | 41.94 | 1300 | 1.2182 | 0.4579 |
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| 0.4329 | 45.16 | 1400 | 1.2178 | 0.4586 |
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| 0.4502 | 48.39 | 1500 | 1.2174 | 0.4536 |
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### Framework versions
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