We helped i2Cat to build a comprehensive and diverse voice dataset to train a state-of-the-art text-to-speech (TTS) model for the Catalan language.
i2Cat faced a significant challenge in their quest to develop a state-of-the-art text-to-speech (TTS) model for the Catalan language. The cornerstone of any successful TTS model is a robust and diverse training dataset, particularly one that encompasses a wide range of voices varying in age, gender, and possibly other demographic factors. However, compiling such a dataset presented a considerable hurdle. I2Cat needed to gather and process a vast array of Catalan speech samples that accurately represented the diverse demographic profile of the Catalan-speaking population. This diversity was crucial for ensuring that the resulting TTS model could deliver natural-sounding, inclusive, and accessible speech synthesis across all segments of the Catalan-speaking community.
To address the challenge of developing a robust Text-to-Speech (TTS) model for the Catalan language, our solution centered on creating an extensive and diverse voice dataset. This dataset was meticulously compiled to include a wide range of voices, carefully selected to represent the rich diversity of the Catalan-speaking population. We focused on gathering voice samples across various age groups, genders, and regional dialects, ensuring that each demographic was adequately represented.
Leveraging state-of-the-art recording techniques and data collection strategies, we were able to amass a substantial volume of high-quality voice data. This comprehensive dataset became the foundation upon which we built our TTS model. It allowed us to train the model with a depth and breadth of speech patterns, enabling it to produce natural and authentic-sounding Catalan speech. The resultant TTS model is not just technologically advanced but also culturally inclusive, capable of delivering realistic and accessible voice outputs that resonate with all segments of the Catalan community.
As a result of the extensive and diverse voice dataset we created, I2Cat successfully trained a highly sophisticated and accurate Text-to-Speech (TTS) model for the Catalan language. This model, enriched by the broad range of collected speech samples, demonstrated remarkable proficiency in generating natural-sounding Catalan speech, effectively capturing the nuances and variations specific to the language.
The advanced training enabled the TTS model to accurately mimic human-like intonation and pronunciation, adapting seamlessly to different contexts and user needs. It showed exceptional performance, particularly in understanding and replicating regional dialects and accents, making it a highly inclusive tool that resonates with all segments of the Catalan-speaking population.
