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---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: dgo-tts-training-data-a-speecht5
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. -->
# dgo-tts-training-data-a-speecht5
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4943
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 4000
- training_steps: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:--------:|:-----:|:---------------:|
| 0.6491 | 5.3763 | 1000 | 0.6131 |
| 0.5683 | 10.7527 | 2000 | 0.5488 |
| 0.543 | 16.1290 | 3000 | 0.5120 |
| 0.5203 | 21.5054 | 4000 | 0.5201 |
| 0.5149 | 26.8817 | 5000 | 0.5136 |
| 0.4962 | 32.2581 | 6000 | 0.4971 |
| 0.488 | 37.6344 | 7000 | 0.4969 |
| 0.4742 | 43.0108 | 8000 | 0.4880 |
| 0.4746 | 48.3871 | 9000 | 0.4941 |
| 0.4634 | 53.7634 | 10000 | 0.4886 |
| 0.4491 | 59.1398 | 11000 | 0.4912 |
| 0.4416 | 64.5161 | 12000 | 0.4854 |
| 0.4388 | 69.8925 | 13000 | 0.4894 |
| 0.4263 | 75.2688 | 14000 | 0.4911 |
| 0.425 | 80.6452 | 15000 | 0.4853 |
| 0.4179 | 86.0215 | 16000 | 0.4862 |
| 0.4205 | 91.3978 | 17000 | 0.4882 |
| 0.4087 | 96.7742 | 18000 | 0.4869 |
| 0.4079 | 102.1505 | 19000 | 0.4898 |
| 0.4142 | 107.5269 | 20000 | 0.4892 |
| 0.4132 | 112.9032 | 21000 | 0.4937 |
| 0.4126 | 118.2796 | 22000 | 0.4908 |
| 0.4091 | 123.6559 | 23000 | 0.4901 |
| 0.4078 | 129.0323 | 24000 | 0.4886 |
| 0.4167 | 134.4086 | 25000 | 0.4926 |
| 0.4052 | 139.7849 | 26000 | 0.4906 |
| 0.4057 | 145.1613 | 27000 | 0.4905 |
| 0.408 | 150.5376 | 28000 | 0.4919 |
| 0.4054 | 155.9140 | 29000 | 0.4896 |
| 0.4096 | 161.2903 | 30000 | 0.4920 |
| 0.4087 | 166.6667 | 31000 | 0.4922 |
| 0.3987 | 172.0430 | 32000 | 0.4911 |
| 0.4006 | 177.4194 | 33000 | 0.4934 |
| 0.4017 | 182.7957 | 34000 | 0.4936 |
| 0.4 | 188.1720 | 35000 | 0.4923 |
| 0.401 | 193.5484 | 36000 | 0.4941 |
| 0.3984 | 198.9247 | 37000 | 0.4946 |
| 0.401 | 204.3011 | 38000 | 0.4923 |
| 0.4012 | 209.6774 | 39000 | 0.4945 |
| 0.396 | 215.0538 | 40000 | 0.4943 |
### Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.1
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