readme fixes

about.md

@@ -1,7 +1,7 @@
 ## 📄 About
-Natural and efficient TTS in Catalan: using Matcha-TTS with the Catalan language.
+Natural and efficient TTS in Catalan: 🍵+🥑 .
 
-Here you'll be able to find all the information regarding our models Matxa 🍵 and alVoCat 🥑 , which have been trained with the use of deep learning. If you want specific information on how to train these model you can find it [here](https://huggingface.co/BSC-LT/matcha-tts-cat-multispeaker) and [here](https://huggingface.co/BSC-LT/vocos-mel-22khz-cat) respectively. The code we've used is also on Github [here](https://github.com/langtech-bsc/Matcha-TTS/tree/dev-cat).
+Here you'll be able to find all the information regarding our models 🍵 Matxa and 🥑 alVoCat, which have been trained with the use of deep learning. If you want specific information on how to train these model you can find it [here](https://huggingface.co/BSC-LT/matcha-tts-cat-multiaccent) and [here](https://huggingface.co/BSC-LT/vocos-mel-22khz-cat) respectively. The code we've used is also on Github [here](https://github.com/langtech-bsc/Matcha-TTS/tree/dev-cat).
 
 ## Table of Contents
 <details>
@@ -22,7 +22,7 @@ Here you'll be able to find all the information regarding our models Matxa 🍵
 
 The significance of open-source text-to-speech (TTS) technologies for minority languages cannot be overstated. These technologies democratize access to TTS solutions by providing a framework for communities to develop and adapt models according to their linguistic needs. This is why we have developed different open-source TTS solutions in Catalan, using an ensemble of technologies.
 
-Firstly, we created a [TTS model for central Catalan](https://huggingface.co/BSC-LT/matcha-tts-cat-multispeaker) by fine-tuning the Matcha-TTS English model. Matcha-TTS is a state-of-the-art model that employs deep learning, a form of AI, to train models that replicate human speech patterns, allowing it to generate lifelike synthetic voices from written text. After that, we fine-tuned this Catalan central model for three other Catalan dialects:
+Firstly, we created a [TTS model for central Catalan](https://huggingface.co/BSC-LT/matcha-tts-cat-multispeaker) by fine-tuning the Matcha-TTS English model. Matcha-TTS is a state-of-the-art model that employs deep learning, a form of AI, to train models that replicate human speech patterns, allowing it to generate lifelike synthetic voices from written text. After that, we fine-tuned this Catalan central model for four Catalan dialects, central plus three more:
 
 * Balear
 * North-Occidental
@@ -221,15 +221,15 @@ This version is tailored for the Catalan language, as it was trained only on Cat
 
 ## 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:
+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 (Matxa-base), which laid the groundwork for understanding the language's nuances. This first fine-tuning from English 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.
- 
+  
  * [Our version of the Festcat dataset.](https://huggingface.co/datasets/projecte-aina/festcat_trimmed_denoised)
+
+Then we further fine-tuned the single accent Catalan Matxa-based model with the soon to be published LaFrescat dataset that has 8.5 hours of recordings for four dialectal variants:
  
- This soon to be published dataset also included recordings of three different dialects:
+ * Central
  
  * Valencian
  
@@ -275,13 +275,19 @@ If this code contributes to your research, please cite the work:
 The Language Technologies Unit from Barcelona Supercomputing Center.
 
 ### Contact
-For further information, please send an email to <langtech@bsc.es>.
+For further information, please email <langtech@bsc.es>.
 
 ### Copyright
 Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
 
 ### License
-[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+The demo page and the inference scripts are under [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.en.html)
+
+The model weights are licensed under [Creative Commons Attribution Non-commercial 4.0](https://www.creativecommons.org/licenses/by-nc/4.0/). These models are free to use for non-commercial and research purposes. Commercial use is only possible through licensing by
+the voice artists. For further information, contact <langtech@bsc.es> and <lafrescaproduccions@gmail.com>. For more information see the [model page](https://huggingface.co/BSC-LT/matcha-tts-cat-multiaccent/).
 
 ### Funding
 This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/).
+
+Part of the training of the model was possible thanks to the compute time given by Galician Supercomputing Center CESGA
+([Centro de Supercomputación de Galicia](https://www.cesga.es/)), and also by [Barcelona Supercomputing Center](https://www.bsc.es/) in MareNostrum 5.