In recent research, the Institute for Natural Language Processing (IMS) at the University of Stuttgart, Germany, introduced ToucanTTS, marking a significant advancement in the field of text-to-speech (TTS) technology. With support for speech synthesis in more than 7,000 languages, this new toolset is capable of completely transforming the field of multilingual TTS systems.
ToucanTTS is an advanced TTS toolbox through which modern speech synthesis models can be taught, trained and used. Since PyTorch and Python are the only programming languages used in its development, it is highly functional and powerful, yet accessible and suitable for beginners. The toolkit stands out especially for its extensive language support, which meets the needs of a wide range of international audiences.
ToucanTTS is the most multilingual TTS model available and is distinguished by its ability to synthesize speech in more than 7,000 languages. It facilitates multi-speaker speech synthesis, allowing users to imitate the rhythm, accent and intonation of multiple speakers. This functionality is especially useful for applications that require stylistic diversity and voice customization.
Human-in-The-Loop editing functionality has been included in the toolkit, which is particularly useful for literary studies and poetry reading tasks. With the use of this feature, users can customize the synthesized speech to suit their own requirements and tastes. ToucanTTS has offered interactive demos for a variety of applications, such as voice design, style cloning, multilingual speech synthesis, and reading human-edited poetry. These examples show the versatility and robustness of the toolset, accelerating users' understanding and utilization of its capabilities.
ToucanTTS has been built on the FastSpeech 2 architecture at its core, with certain improvements, including a normalization flow-based PostNet inspired by PortaSpeech. This design guarantees high-quality, natural-sounding speech synthesis. An autonomous aligner trained with connectionist temporal classification (CTC) and spectrogram reconstruction has also been included in the toolkit for various uses.
The use of articulatory representations of phonemes as input is one of the most unique features of ToucanTTS. This method greatly improves the quality and usability of speech synthesis for low-resource languages by allowing the system to take advantage of multilingual data.
In conclusion, ToucanTTS is a notable advancement in text-to-speech technology. Its user-friendly design and wide range of language support make it highly beneficial for educators, researchers, and developers. The features of ToucanTTS and its open source nature ensure that it will be essential to advancing and democratizing speech synthesis technology.
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Tanya Malhotra is a final year student of University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with specialization in artificial intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with a burning interest in acquiring new skills, leading groups and managing work in an organized manner.
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