Bringing Back the Voice: A Look at Kokoro and the Reconstruction of af_Sky

2025-01-01

The world of text-to-speech (TTS) is constantly evolving, with new models emerging all the time. One such model is Kokoro, an Apache TTS model that has been making waves for its ability to produce high-quality voices with a relatively small amount of training data. This is particularly relevant in the case of af_Sky, a voice that was previously taken down due to copyright concerns. This article explores Kokoro’s capabilities and how it is being used to reconstruct af_Sky.

The article begins by introducing Kokoro, an Apache TTS model based on a lightweight version of the StyleTTS 2 architecture. Kokoro’s performance is impressive, achieving results comparable to or better than much larger models on specific voices. However, it is currently limited in its ability to handle entirely new voices due to a lack of training data.

The article then highlights the recent addition of af_sky to Kokoro’s roster of downloadable voices. This follows the release of af_nicole, another voice trained on a limited amount of data. Notably, af_nicole demonstrates the ability to incorporate unique speaking styles into a general-purpose TTS model without affecting existing voices.

The focus then shifts to af_Sky itself. This voice gained notoriety after it was taken down due to copyright issues. However, a small amount of training data for af_Sky remains available online, consisting of snippets from a previous blog post and scattered locations across the internet. This data, totaling about 3 minutes, is being used to train Kokoro in an attempt to reconstruct af_Sky.

The article acknowledges that this is not the first attempt to recreate af_Sky. Previously, an unofficial clone was created using ElevenLabs. While imperfect, this clone demonstrated the potential for reconstruction with limited data.

Kokoro’s rendition of af_Sky is presented as a more refined effort. The model is freely available and can be run locally. Users can experiment with the model through a hosted demo or by downloading the model weights and installing dependencies.

The article concludes by emphasizing the significance of this project. The reconstruction of af_Sky, even in a partial form, showcases the possibility of reviving voices with minimal training data. The article also hints at future improvements to Kokoro, suggesting the use of more comprehensive training data.

What Undercode Says:

This article sheds light on several interesting aspects of TTS technology. First, it highlights the efficiency of Kokoro, a model that can produce high-quality results with a relatively small footprint. This is a significant advantage, as large models can be computationally expensive to train and run.

Second, the article explores the challenges and opportunities associated with limited training data. The case of af_Sky demonstrates that it is possible to reconstruct a voice using a small amount of data, but the quality may not be perfect. This opens up possibilities for reviving voices that may not have a large amount of training data readily available.

Third, the article underscores the importance of open-source tools like Kokoro. By making the model freely available, Kokoro empowers users to experiment with TTS technology and contribute to its development.

Overall, this article provides a fascinating glimpse into the world of TTS and the potential for reconstructing voices with limited data. The success of Kokoro in reviving af_Sky, even in part, paves the way for further exploration in this area. It will be interesting to see how TTS technology continues to evolve and how it is used to create new and innovative voice experiences.