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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: 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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# 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: 0.5712 |
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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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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 2000 |
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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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| 0.9951 | 2.0851 | 50 | 0.9152 | |
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| 0.9256 | 4.1702 | 100 | 0.8480 | |
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| 0.9001 | 6.2553 | 150 | 0.8114 | |
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| 0.8567 | 8.3404 | 200 | 0.7857 | |
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| 0.8139 | 10.4255 | 250 | 0.7477 | |
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| 0.7437 | 12.5106 | 300 | 0.6724 | |
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| 0.6937 | 14.5957 | 350 | 0.6352 | |
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| 0.6686 | 16.6809 | 400 | 0.6194 | |
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| 0.6487 | 18.7660 | 450 | 0.6070 | |
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| 0.6411 | 20.8511 | 500 | 0.6009 | |
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| 0.643 | 22.9362 | 550 | 0.5970 | |
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| 0.6158 | 25.0 | 600 | 0.5893 | |
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| 0.632 | 27.0851 | 650 | 0.5871 | |
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| 0.6152 | 29.1702 | 700 | 0.5855 | |
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| 0.6066 | 31.2553 | 750 | 0.5833 | |
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| 0.615 | 33.3404 | 800 | 0.5817 | |
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| 0.6011 | 35.4255 | 850 | 0.5812 | |
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| 0.598 | 37.5106 | 900 | 0.5788 | |
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| 0.6023 | 39.5957 | 950 | 0.5764 | |
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| 0.6031 | 41.6809 | 1000 | 0.5775 | |
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| 0.5976 | 43.7660 | 1050 | 0.5764 | |
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| 0.597 | 45.8511 | 1100 | 0.5772 | |
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| 0.5923 | 47.9362 | 1150 | 0.5727 | |
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| 0.5793 | 50.0 | 1200 | 0.5746 | |
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| 0.5879 | 52.0851 | 1250 | 0.5757 | |
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| 0.5908 | 54.1702 | 1300 | 0.5727 | |
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| 0.5838 | 56.2553 | 1350 | 0.5745 | |
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| 0.5852 | 58.3404 | 1400 | 0.5709 | |
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| 0.5869 | 60.4255 | 1450 | 0.5753 | |
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| 0.585 | 62.5106 | 1500 | 0.5720 | |
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| 0.5875 | 64.5957 | 1550 | 0.5715 | |
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| 0.5807 | 66.6809 | 1600 | 0.5729 | |
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| 0.5886 | 68.7660 | 1650 | 0.5730 | |
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| 0.5831 | 70.8511 | 1700 | 0.5753 | |
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| 0.5812 | 72.9362 | 1750 | 0.5711 | |
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| 0.5736 | 75.0 | 1800 | 0.5768 | |
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| 0.5761 | 77.0851 | 1850 | 0.5735 | |
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| 0.5767 | 79.1702 | 1900 | 0.5759 | |
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| 0.5777 | 81.2553 | 1950 | 0.5720 | |
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| 0.5759 | 83.3404 | 2000 | 0.5712 | |
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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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