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---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: vc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vc
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.9951 | 2.0851 | 50 | 0.9152 |
| 0.9256 | 4.1702 | 100 | 0.8480 |
| 0.9001 | 6.2553 | 150 | 0.8114 |
| 0.8567 | 8.3404 | 200 | 0.7857 |
| 0.8139 | 10.4255 | 250 | 0.7477 |
| 0.7437 | 12.5106 | 300 | 0.6724 |
| 0.6937 | 14.5957 | 350 | 0.6352 |
| 0.6686 | 16.6809 | 400 | 0.6194 |
| 0.6487 | 18.7660 | 450 | 0.6070 |
| 0.6411 | 20.8511 | 500 | 0.6009 |
| 0.643 | 22.9362 | 550 | 0.5970 |
| 0.6158 | 25.0 | 600 | 0.5893 |
| 0.632 | 27.0851 | 650 | 0.5871 |
| 0.6152 | 29.1702 | 700 | 0.5855 |
| 0.6066 | 31.2553 | 750 | 0.5833 |
| 0.615 | 33.3404 | 800 | 0.5817 |
| 0.6011 | 35.4255 | 850 | 0.5812 |
| 0.598 | 37.5106 | 900 | 0.5788 |
| 0.6023 | 39.5957 | 950 | 0.5764 |
| 0.6031 | 41.6809 | 1000 | 0.5775 |
| 0.5976 | 43.7660 | 1050 | 0.5764 |
| 0.597 | 45.8511 | 1100 | 0.5772 |
| 0.5923 | 47.9362 | 1150 | 0.5727 |
| 0.5793 | 50.0 | 1200 | 0.5746 |
| 0.5879 | 52.0851 | 1250 | 0.5757 |
| 0.5908 | 54.1702 | 1300 | 0.5727 |
| 0.5838 | 56.2553 | 1350 | 0.5745 |
| 0.5852 | 58.3404 | 1400 | 0.5709 |
| 0.5869 | 60.4255 | 1450 | 0.5753 |
| 0.585 | 62.5106 | 1500 | 0.5720 |
| 0.5875 | 64.5957 | 1550 | 0.5715 |
| 0.5807 | 66.6809 | 1600 | 0.5729 |
| 0.5886 | 68.7660 | 1650 | 0.5730 |
| 0.5831 | 70.8511 | 1700 | 0.5753 |
| 0.5812 | 72.9362 | 1750 | 0.5711 |
| 0.5736 | 75.0 | 1800 | 0.5768 |
| 0.5761 | 77.0851 | 1850 | 0.5735 |
| 0.5767 | 79.1702 | 1900 | 0.5759 |
| 0.5777 | 81.2553 | 1950 | 0.5720 |
| 0.5759 | 83.3404 | 2000 | 0.5712 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
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