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update model card README.md
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README.md
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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 MULTILINGUAL_LIBRISPEECH - GERMAN dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Wer: 0.
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## Model description
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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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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- 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:
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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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### Framework versions
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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 MULTILINGUAL_LIBRISPEECH - GERMAN dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1936
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- Wer: 0.1316
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## Model description
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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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- 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: 100.0
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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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| 2.9888 | 7.25 | 500 | 2.9192 | 1.0 |
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| 2.9313 | 14.49 | 1000 | 2.8698 | 1.0 |
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| 1.068 | 21.74 | 1500 | 0.2647 | 0.2565 |
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| 0.8151 | 28.99 | 2000 | 0.2067 | 0.1719 |
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| 0.764 | 36.23 | 2500 | 0.1975 | 0.1568 |
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| 0.7332 | 43.48 | 3000 | 0.1812 | 0.1463 |
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| 0.5952 | 50.72 | 3500 | 0.1923 | 0.1428 |
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| 0.6655 | 57.97 | 4000 | 0.1900 | 0.1404 |
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| 0.574 | 65.22 | 4500 | 0.1822 | 0.1370 |
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| 0.6211 | 72.46 | 5000 | 0.1937 | 0.1355 |
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| 0.5883 | 79.71 | 5500 | 0.1872 | 0.1335 |
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| 0.5666 | 86.96 | 6000 | 0.1874 | 0.1324 |
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| 0.5436 | 94.2 | 6500 | 0.1924 | 0.1311 |
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### Framework versions
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