A software developer with a keen interest in writing about technology, finance, and entrepreneurship. I've written for businesses in a variety of fields, including new technology, healthcare, programming, consumer applications, corporate computing, UI/UX, outsourcing, and education.
Text-to-Speech technology (more generally known as TTS) is an assistive technology that reads digital text aloud. With the rise in digital devices’ usage and the growing dependence upon speech recognition and similar technologies, TTS is gaining significance.
But, the applications of the technology don’t just stop there. With the aid of this technology, you can turn written emails into audio recordings. It can also enable the visually challenged folks to understand text information.
TTS (Text to Speech) works with almost every personal digital device, including computers, cellphones, and tablets. All kinds of text files can be read out loud, including Word and Pages documents. Even online web pages can be read aloud.
We will be looking at some of the best open-source Text to Speech libraries through this article. This will help us understand their characteristics and benefits more fully.
TTS: Text-to-Speech for all
TTS is a library for powerful multi-speaker Text to Speech creation. Built on the latest research, it was developed to accomplish the optimal trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for monitoring dataset quality and is already utilized in 20+ languages for companies and research projects. It can conduct High-performance Deep Learning models for Text2Speech tasks. It offers fast and efficient model training.
Coqui TTS is a library for powerful Text to Speech generation. Built on the latest research, it was developed to accomplish the optimal trade-off among ease-of-training, speed, and quality. The software contains pre-trained models and tools to monitor data quality; it is already used in 20+ languages for companies and research projects.
TensorFlowTTS delivers real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Megan, Multiband-Megan, FastSpeech, FastSpeech2 based on TensorFlow 2. With Tensorflow 2, we can speed up the training/inference process, optimizing further by employing fake-quantize aware and pruning, creating Text to Speech models that can be run quicker than real-time and deploying on mobile devices or embedded systems.
MaryTTS is an open-source, multilingual Text to Speechh Synthesis tool written in Java.
DFKI’s Language Technology Lab and the Institute of Phonetics at Saarland University worked together to develop this project.
It is now managed by the Multimodal Speech Processing Group in the Cluster of Excellence MMCI and DFKI.
As of version 5.2, MaryTTS supports German, British and American English, French, Italian, Luxembourgish, Russian, Sweden, Telugu, and Turkish; more languages are in progress.
MaryTTS comes with toolkits for fast adding support for new languages and for constructing unit selection and HMM-based synthesis voices.
pyttsx3 is a Text to Speech conversion library in Python. Unlike similar libraries, it works offline and is compatible with both Python 2 and 3. Python TTSSX3 module offers two voices – one female voice and one male voice. Sapi5 for Windows provides both female and male voices.
TransformerTTS is a Pytorch Implementation of Neural Speech Synthesis with Transformer Network. This model can be trained around 3 to 4 times faster than the well-known seq2seq model like tacotron, and the quality of synthesized speech is practically the same. The experiment proved that it took about 0.5 seconds per step.