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---
language:
- es
license: mit
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
pipeline_tag: text-to-speech
base_model: microsoft/speecht5_tts
model-index:
- name: speech T5 ES - Peter Gelderbloem
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. -->
# speec T5 ES - Peter Gelderbloem
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Vox Populi ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4400
## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6709 | 0.14 | 100 | 0.6107 |
| 0.624 | 0.28 | 200 | 0.5773 |
| 0.5921 | 0.43 | 300 | 0.5397 |
| 0.5504 | 0.57 | 400 | 0.4941 |
| 0.5289 | 0.71 | 500 | 0.4807 |
| 0.5236 | 0.85 | 600 | 0.4732 |
| 0.5194 | 0.99 | 700 | 0.4672 |
| 0.5095 | 1.13 | 800 | 0.4650 |
| 0.508 | 1.28 | 900 | 0.4608 |
| 0.5029 | 1.42 | 1000 | 0.4580 |
| 0.5029 | 1.56 | 1100 | 0.4569 |
| 0.4952 | 1.7 | 1200 | 0.4540 |
| 0.4939 | 1.84 | 1300 | 0.4540 |
| 0.4924 | 1.99 | 1400 | 0.4523 |
| 0.4902 | 2.13 | 1500 | 0.4519 |
| 0.492 | 2.27 | 1600 | 0.4491 |
| 0.4896 | 2.41 | 1700 | 0.4497 |
| 0.4874 | 2.55 | 1800 | 0.4482 |
| 0.4889 | 2.7 | 1900 | 0.4473 |
| 0.4888 | 2.84 | 2000 | 0.4471 |
| 0.4902 | 2.98 | 2100 | 0.4461 |
| 0.4818 | 3.12 | 2200 | 0.4457 |
| 0.4854 | 3.26 | 2300 | 0.4451 |
| 0.4886 | 3.4 | 2400 | 0.4429 |
| 0.4799 | 3.55 | 2500 | 0.4429 |
| 0.4806 | 3.69 | 2600 | 0.4429 |
| 0.4777 | 3.83 | 2700 | 0.4416 |
| 0.4787 | 3.98 | 2800 | 0.4423 |
| 0.4823 | 4.12 | 2900 | 0.4419 |
| 0.4822 | 4.26 | 3000 | 0.4409 |
| 0.4796 | 4.4 | 3100 | 0.4404 |
| 0.4805 | 4.54 | 3200 | 0.4418 |
| 0.4795 | 4.69 | 3300 | 0.4409 |
| 0.4784 | 4.83 | 3400 | 0.4411 |
| 0.4812 | 4.97 | 3500 | 0.4406 |
| 0.478 | 5.11 | 3600 | 0.4399 |
| 0.4766 | 5.25 | 3700 | 0.4396 |
| 0.4821 | 5.39 | 3800 | 0.4407 |
| 0.48 | 5.54 | 3900 | 0.4396 |
| 0.4795 | 5.68 | 4000 | 0.4400 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1 |