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--- |
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license: mit |
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base_model: microsoft/speecht5_tts |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: speecht5_improved_data_less_steps |
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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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# speecht5_improved_data_less_steps |
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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: 0.6468 |
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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: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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: 250 |
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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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| 1.4475 | 0.1533 | 25 | 1.1070 | |
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| 1.3754 | 0.3065 | 50 | 1.0239 | |
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| 1.2957 | 0.4598 | 75 | 0.9667 | |
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| 1.1821 | 0.6130 | 100 | 0.9231 | |
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| 1.1414 | 0.7663 | 125 | 0.8946 | |
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| 1.0555 | 0.9195 | 150 | 0.8335 | |
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| 0.9565 | 1.0728 | 175 | 0.7764 | |
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| 0.9189 | 1.2261 | 200 | 0.7485 | |
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| 0.8751 | 1.3793 | 225 | 0.7332 | |
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| 0.8533 | 1.5326 | 250 | 0.7197 | |
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| 0.8219 | 1.6858 | 275 | 0.7133 | |
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| 0.8171 | 1.8391 | 300 | 0.7009 | |
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| 0.8088 | 1.9923 | 325 | 0.6926 | |
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| 0.785 | 2.1456 | 350 | 0.6877 | |
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| 0.7989 | 2.2989 | 375 | 0.6824 | |
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| 0.7755 | 2.4521 | 400 | 0.6784 | |
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| 0.7914 | 2.6054 | 425 | 0.6738 | |
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| 0.7696 | 2.7586 | 450 | 0.6694 | |
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| 0.7741 | 2.9119 | 475 | 0.6680 | |
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| 0.7613 | 3.0651 | 500 | 0.6659 | |
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| 0.7733 | 3.2184 | 525 | 0.6654 | |
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| 0.7605 | 3.3716 | 550 | 0.6623 | |
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| 0.7538 | 3.5249 | 575 | 0.6606 | |
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| 0.7626 | 3.6782 | 600 | 0.6596 | |
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| 0.7573 | 3.8314 | 625 | 0.6577 | |
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| 0.7469 | 3.9847 | 650 | 0.6556 | |
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| 0.7524 | 4.1379 | 675 | 0.6537 | |
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| 0.7342 | 4.2912 | 700 | 0.6491 | |
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| 0.7305 | 4.4444 | 725 | 0.6511 | |
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| 0.7433 | 4.5977 | 750 | 0.6486 | |
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| 0.7438 | 4.7510 | 775 | 0.6487 | |
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| 0.7505 | 4.9042 | 800 | 0.6481 | |
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| 0.7354 | 5.0575 | 825 | 0.6450 | |
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| 0.7354 | 5.2107 | 850 | 0.6439 | |
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| 0.7333 | 5.3640 | 875 | 0.6462 | |
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| 0.7246 | 5.5172 | 900 | 0.6444 | |
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| 0.7289 | 5.6705 | 925 | 0.6417 | |
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| 0.7436 | 5.8238 | 950 | 0.6474 | |
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| 0.741 | 5.9770 | 975 | 0.6428 | |
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| 0.7443 | 6.1303 | 1000 | 0.6468 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.17.0 |
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- Tokenizers 0.19.1 |
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