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--- |
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library_name: transformers |
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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: not_overfited_vc |
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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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# not_overfited_vc |
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This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4675 |
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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: 5e-06 |
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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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- training_steps: 4000 |
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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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| 0.7032 | 0.4002 | 100 | 0.5819 | |
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| 0.6216 | 0.8004 | 200 | 0.5350 | |
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| 0.5847 | 1.2041 | 300 | 0.5165 | |
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| 0.5625 | 1.6043 | 400 | 0.5083 | |
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| 0.5647 | 2.0080 | 500 | 0.5047 | |
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| 0.5529 | 2.4082 | 600 | 0.4992 | |
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| 0.5524 | 2.8084 | 700 | 0.4944 | |
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| 0.54 | 3.2121 | 800 | 0.4924 | |
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| 0.5296 | 3.6123 | 900 | 0.4879 | |
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| 0.5418 | 4.0160 | 1000 | 0.4858 | |
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| 0.5345 | 4.4162 | 1100 | 0.4862 | |
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| 0.5186 | 4.8164 | 1200 | 0.4817 | |
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| 0.5232 | 5.2201 | 1300 | 0.4819 | |
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| 0.5309 | 5.6203 | 1400 | 0.4820 | |
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| 0.5315 | 6.0240 | 1500 | 0.4793 | |
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| 0.5238 | 6.4242 | 1600 | 0.4771 | |
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| 0.5121 | 6.8244 | 1700 | 0.4769 | |
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| 0.5252 | 7.2281 | 1800 | 0.4755 | |
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| 0.5251 | 7.6283 | 1900 | 0.4758 | |
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| 0.5136 | 8.0320 | 2000 | 0.4721 | |
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| 0.5176 | 8.4322 | 2100 | 0.4729 | |
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| 0.5096 | 8.8324 | 2200 | 0.4746 | |
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| 0.5155 | 9.2361 | 2300 | 0.4729 | |
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| 0.5091 | 9.6363 | 2400 | 0.4699 | |
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| 0.5223 | 10.0400 | 2500 | 0.4707 | |
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| 0.5105 | 10.4402 | 2600 | 0.4695 | |
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| 0.5148 | 10.8404 | 2700 | 0.4689 | |
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| 0.5101 | 11.2441 | 2800 | 0.4694 | |
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| 0.5125 | 11.6443 | 2900 | 0.4690 | |
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| 0.5093 | 12.0480 | 3000 | 0.4686 | |
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| 0.5057 | 12.4482 | 3100 | 0.4671 | |
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| 0.5063 | 12.8484 | 3200 | 0.4693 | |
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| 0.5071 | 13.2521 | 3300 | 0.4669 | |
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| 0.5051 | 13.6523 | 3400 | 0.4685 | |
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| 0.5049 | 14.0560 | 3500 | 0.4660 | |
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| 0.5015 | 14.4562 | 3600 | 0.4679 | |
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| 0.5041 | 14.8564 | 3700 | 0.4663 | |
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| 0.5108 | 15.2601 | 3800 | 0.4678 | |
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| 0.5048 | 15.6603 | 3900 | 0.4680 | |
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| 0.508 | 16.0640 | 4000 | 0.4675 | |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.3.1 |
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- Tokenizers 0.21.0 |
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