---
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