---
license: apache-2.0
language:
- es
tags:
- TTS
- PL-BERT
- barcelona-supercomputing-center
---




# PL-BERT-es-

## Overview

<details>
<summary>Click to expand</summary>

- [Model Description](#model-description)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Training Details](#training-details)
- [Citation](#citation)
- [Additional information](#additional-information)

</details>

---

## Model Description

**PL-BERT-es** is a phoneme-level masked language model trained on Spanish text with diverse regional accents. It is based on the [PL-BERT architecture](https://github.com/yl4579/PL-BERT), which learns phoneme representations via a BERT-style masked language modeling objective.

This model is designed to support **phoneme-based text-to-speech (TTS) systems**, including but not limited to [StyleTTS2](https://github.com/yl4579/StyleTTS2). Thanks to its Spanish-specific phoneme vocabulary and contextual embedding capabilities, it can serve as a phoneme encoder for any TTS architecture requiring phoneme-level features.

Features of our PL-BERT:
- It is trained **exclusively on Spanish** phonemized text.
- It uses a reduced **phoneme vocabulary of 178 tokens**.
- It uses a simple tokenizer for words.
- It includes a custom `token_maps.pkl` and adapted `util.py`.

---

## Intended Uses and Limitations

### Intended uses

- Integration into phoneme-based TTS pipelines such as StyleTTS2, Matxa-TTS, or custom diffusion-based synthesizers.
- Accent-aware synthesis and phoneme embedding extraction for Spanish.


### Limitations

- Not designed for general NLP tasks like classification or sentiment analysis.
- Only supports Spanish phoneme tokens.
- Some accents may be underrepresented in the training data.

---

## How to Get Started with the Model

Here is an example of how to use this model within the StyleTTS2 framework:

1. Clone the StyleTTS2 repository: https://github.com/yl4579/StyleTTS2
2. Inside the `Utils` directory, create a new folder, for example: `PLBERT_es`.
3. Copy the following files into that folder:
   - `config.yml` (training configuration)
   - `step_1000000.t7` (trained checkpoint)
   - `token_maps.pkl` (phoneme to ID mapping)
   - `util.py` (modified to fix position ID loading)

4. In your StyleTTS2 configuration file, update the `PLBERT_dir` entry to:

   `PLBERT_dir: Utils/PLBERT_es`

5. Update the import statement in your code to:

   `from Utils.PLBERT_es.util import load_plbert`

6. Use `espeak-ng` with the language code `es-419` to phonemize your Spanish text files for training and validation.

Note: Although this example uses StyleTTS2, the model is compatible with other TTS architectures that operate on phoneme sequences. You can use the contextualized phoneme embeddings from PL-BERT in any compatible synthesis system.

---

## Training Details

### Training data

The model was trained on a Spanish corpus phonemized using espeak-ng. It uses a consistent phoneme token set with boundary markers and masking tokens.

Tokenizer: custom (split using whitespaces)  
Phoneme masking strategy: word-level and phoneme-level masking and replacement  
Training steps: 1,000,000  
Precision: Mixed (fp16)

### Training configuration

Model parameters:

- Vocabulary size: 178  
- Hidden size: 768  
- Attention heads: 12  
- Intermediate size: 2048  
- Number of layers: 12  
- Max position embeddings: 512  
- Dropout: 0.1  

Other parameters:

- Batch size: 8  
- Max mel length: 512  
- Word mask probability: 0.15  
- Phoneme mask probability: 0.1  
- Replacement probability: 0.2  
- Token separator: space  
- Token mask: M  
- Word separator ID: 102


### Evaluation

The model has not been benchmarked via perplexity or extrinsic evaluation, but has been successfully integrated into TTS pipelines such as StyleTTS2, where it enables the synthesis of Spanish.

---


## Citation

If this code contributes to your research, please cite the work:

```
@misc{zevallos2025plbertes,
      title={PL-BERT-es},
      author={Rodolfo Zevallos, Jose Giraldo and Carme Armentano-Oller},
      organization={Barcelona Supercomputing Center},
      url={https://huggingface.co/langtech-veu/PL-BERT-es},
      year={2025}
}
```

## Additional Information


### Author

The [Language Technologies Laboratory](https://huggingface.co/BSC-LT) of the [Barcelona Supercomputing Center](https://www.bsc.es/) by [Rodolfo Zevallos](https://huggingface.co/rjzevallos).

### Contact
For further information, please send an email to <langtech@bsc.es>.

### Copyright
Copyright(c) 2025 by Language Technologies Laboratory, Barcelona Supercomputing Center.

### License

[Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)


### Funding
This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA.