## 📄 About
Natural and efficient TTS in Catalan: using Matcha-TTS with the Catalan language.
Here you'll be able to find all the information regarding our model, which has been trained with the use of deep learning. If you want specific information on how to train the model you can find it [here](https://huggingface.co/BSC-LT/matcha-tts-cat-multispeaker). The code we've used is also on Github [here](https://github.com/langtech-bsc/Matcha-TTS/tree/dev-cat).
## Table of Contents
Click to expand
- [General Model Description](#general-model-description)
- [Adaptation to Catalan](#adaptation-to-catalan)
- [Intended Uses and Limitations](#intended-uses-and-limitations)
- [Samples](#samples)
- [Citation](#citation)
- [Additional Information](#additional-information)
## General Model Description
**Matcha-TTS** is a non-autorregressive encoder-decoder model designed for fast acoustic modelling in TTS.
The encoder part processes input sequences of phonemes and, together with a phoneme duration predictor, outputs averaged acoustic features. And the decoder,
which is essentially a U-Net backbone based on the Transfomer architecture, predicts the refined spectrogram.
The model is trained with optimal-transport conditional flow matching.
This yields an ODE-based decoder capable of generating high output quality in fewer synthesis steps.
**Vocos** is a fast neural vocoder designed to synthesize audio waveforms from acoustic features.
Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain.
Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.
The goal of this model is to provide an alternative to hifi-gan that is faster and compatible with the acoustic output of several TTS models.
This version is tailored for the Catalan language, as it was trained only on Catalan speech datasets.
## Adaptation to Catalan
The original Matcha-TTS model excels in English, but to bring its capabilities to Catalan, a multi-step process was undertaken. Firstly, we fine-tuned the model from English to Catalan central, which laid the groundwork for understanding the language's nuances. This first fine-tuning was done using two datasets:
* [Our version of the openslr-slr69 dataset.](https://huggingface.co/datasets/projecte-aina/openslr-slr69-ca-trimmed-denoised)
* A studio-recorded dataset of central catalan, which will soon be published.
This soon to be published dataset also included recordings of three different dialects:
* Valencian
* Occidental
* Balear
With a male and a female speaker for each dialect.
Then, through fine-tuning for these specific Catalan dialects, the model adapted to regional variations in pronunciation and cadence. This meticulous approach ensures that the model reflects the linguistic richness and cultural diversity within the Catalan-speaking community, offering seamless communication in previously underserved dialects.
In addition to training the Matcha-TTS model for Catalan, integrating the eSpeak phonemizer played a crucial role in enhancing the naturalness and accuracy of generated speech. A TTS (Text-to-Speech) system comprises several components, each contributing to the overall quality of synthesized speech. The first component involves text preprocessing, where the input text undergoes normalization and linguistic analysis to identify words, punctuation, and linguistic features. Next, the text is converted into phonemes, the smallest units of sound in a language, through a process called phonemization. This step is where the eSpeak phonemizer shines, as it accurately converts Catalan text into phonetic representations, capturing the subtle nuances of pronunciation specific to Catalan. You can find the espeak version we used [here](https://github.com/projecte-aina/espeak-ng/tree/dev-ca).
After phonemization, the phonemes are passed to the synthesis component, where they are transformed into audible speech. Here, the Matcha-TTS model takes center stage, generating high-quality speech output based on the phonetic input. The model's training, fine-tuning, and adaptation to Catalan ensure that the synthesized speech retains the natural rhythm, intonation, and pronunciation patterns of the language, thereby enhancing the overall user experience.
Finally, the synthesized speech undergoes post-processing, where prosodic features such as pitch, duration, and emphasis are applied to further refine the output and make it sound more natural and expressive. By integrating the eSpeak phonemizer into the TTS pipeline and adapting it for Catalan, alongside training the Matcha-TTS model for the language, we have created a comprehensive and effective system for generating high-quality Catalan speech. This combination of advanced techniques and meticulous attention to linguistic detail is instrumental in bridging language barriers and facilitating communication for Catalan speakers worldwide.
## Intended Uses and Limitations
This model is intended to serve as an acoustic feature generator for multispeaker text-to-speech systems for the Catalan language.
It has been finetuned using a Catalan phonemizer, therefore if the model is used for other languages it may will not produce intelligible samples after mapping
its output into a speech waveform.
The quality of the samples can vary depending on the speaker.
This may be due to the sensitivity of the model in learning specific frequencies and also due to the quality of samples for each speaker.
## Samples
* Female samples