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@@ -89,6 +89,12 @@ After cleaning the dataset and normalizing the `ñ` character to `ny`, we have u
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  ```python
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  ['c', ' ', 'ó', 'g', 'a', 'o', 'ü', 'v', 'p', 't', "'", '—', 'f', 'k', 'à', 'ï', 'í', 'ú', 'd', 'l', 'z', 'é', 'w', 'm', 'r', 'n', 'y', '-', 'u', 'i', 'h', 'ç', 'e', '·', 'q', 'è', 'ò', 'j', 'x', 's', 'b']
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  ```
 
 
 
 
 
 
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  ## Evaluation
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  After evaluation on the test split of Common Voice 11.0 we have obtained a WER of 6.684.
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  ```python
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  ['c', ' ', 'ó', 'g', 'a', 'o', 'ü', 'v', 'p', 't', "'", '—', 'f', 'k', 'à', 'ï', 'í', 'ú', 'd', 'l', 'z', 'é', 'w', 'm', 'r', 'n', 'y', '-', 'u', 'i', 'h', 'ç', 'e', '·', 'q', 'è', 'ò', 'j', 'x', 's', 'b']
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  ```
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+
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+ ### Training procedure
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+ This model was trained starting from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model. The initial learning rate was set to 0.005 and the minimum lr for weight decay was set to 1e-7.
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+
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+ The model was trained for 90 steps and then continued training for another 90 steps starting from a learning rate of 0.0001.
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+
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  ## Evaluation
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  After evaluation on the test split of Common Voice 11.0 we have obtained a WER of 6.684.
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