not_overfited_vc / README.md
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---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: not_overfited_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. -->
# not_overfited_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: 0.4675
## 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
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.7032 | 0.4002 | 100 | 0.5819 |
| 0.6216 | 0.8004 | 200 | 0.5350 |
| 0.5847 | 1.2041 | 300 | 0.5165 |
| 0.5625 | 1.6043 | 400 | 0.5083 |
| 0.5647 | 2.0080 | 500 | 0.5047 |
| 0.5529 | 2.4082 | 600 | 0.4992 |
| 0.5524 | 2.8084 | 700 | 0.4944 |
| 0.54 | 3.2121 | 800 | 0.4924 |
| 0.5296 | 3.6123 | 900 | 0.4879 |
| 0.5418 | 4.0160 | 1000 | 0.4858 |
| 0.5345 | 4.4162 | 1100 | 0.4862 |
| 0.5186 | 4.8164 | 1200 | 0.4817 |
| 0.5232 | 5.2201 | 1300 | 0.4819 |
| 0.5309 | 5.6203 | 1400 | 0.4820 |
| 0.5315 | 6.0240 | 1500 | 0.4793 |
| 0.5238 | 6.4242 | 1600 | 0.4771 |
| 0.5121 | 6.8244 | 1700 | 0.4769 |
| 0.5252 | 7.2281 | 1800 | 0.4755 |
| 0.5251 | 7.6283 | 1900 | 0.4758 |
| 0.5136 | 8.0320 | 2000 | 0.4721 |
| 0.5176 | 8.4322 | 2100 | 0.4729 |
| 0.5096 | 8.8324 | 2200 | 0.4746 |
| 0.5155 | 9.2361 | 2300 | 0.4729 |
| 0.5091 | 9.6363 | 2400 | 0.4699 |
| 0.5223 | 10.0400 | 2500 | 0.4707 |
| 0.5105 | 10.4402 | 2600 | 0.4695 |
| 0.5148 | 10.8404 | 2700 | 0.4689 |
| 0.5101 | 11.2441 | 2800 | 0.4694 |
| 0.5125 | 11.6443 | 2900 | 0.4690 |
| 0.5093 | 12.0480 | 3000 | 0.4686 |
| 0.5057 | 12.4482 | 3100 | 0.4671 |
| 0.5063 | 12.8484 | 3200 | 0.4693 |
| 0.5071 | 13.2521 | 3300 | 0.4669 |
| 0.5051 | 13.6523 | 3400 | 0.4685 |
| 0.5049 | 14.0560 | 3500 | 0.4660 |
| 0.5015 | 14.4562 | 3600 | 0.4679 |
| 0.5041 | 14.8564 | 3700 | 0.4663 |
| 0.5108 | 15.2601 | 3800 | 0.4678 |
| 0.5048 | 15.6603 | 3900 | 0.4680 |
| 0.508 | 16.0640 | 4000 | 0.4675 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0