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End of training

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README.md CHANGED
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4932
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  ## Model description
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@@ -43,23 +43,23 @@ The following hyperparameters were used during training:
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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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- - training_steps: 1000
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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 |
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  |:-------------:|:-------:|:----:|:---------------:|
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- | 0.8772 | 6.8966 | 100 | 0.7399 |
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- | 0.8105 | 13.7931 | 200 | 0.6896 |
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- | 0.6876 | 20.6897 | 300 | 0.5928 |
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- | 0.5714 | 27.5862 | 400 | 0.5120 |
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- | 0.5572 | 34.4828 | 500 | 0.4993 |
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- | 0.539 | 41.3793 | 600 | 0.4985 |
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- | 0.5276 | 48.2759 | 700 | 0.4917 |
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- | 0.5219 | 55.1724 | 800 | 0.4915 |
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- | 0.506 | 62.0690 | 900 | 0.4913 |
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- | 0.51 | 68.9655 | 1000 | 0.4932 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5028
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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: 500
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+ - training_steps: 5000
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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 |
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  |:-------------:|:-------:|:----:|:---------------:|
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+ | 0.6106 | 8.8496 | 500 | 0.5241 |
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+ | 0.5243 | 17.6991 | 1000 | 0.4949 |
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+ | 0.5004 | 26.5487 | 1500 | 0.4873 |
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+ | 0.4918 | 35.3982 | 2000 | 0.4871 |
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+ | 0.4927 | 44.2478 | 2500 | 0.4922 |
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+ | 0.4715 | 53.0973 | 3000 | 0.4964 |
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+ | 0.4832 | 61.9469 | 3500 | 0.4996 |
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+ | 0.4744 | 70.7965 | 4000 | 0.5028 |
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+ | 0.4677 | 79.6460 | 4500 | 0.5046 |
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+ | 0.4568 | 88.4956 | 5000 | 0.5028 |
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  ### Framework versions
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