Skip to main content
HomeBlogArtificial Intelligence (AI)

7 Best Open Source Text-to-Speech (TTS) Engines

Explore 7 common free, open-source text-to-speech engines for your ML projects.
Mar 2024  · 7 min read

Working with artificial intelligence (AI) or machine learning (ML) with a need for a text-to-speech engine? In that case, you're going to need an open-source solution. Let's explore how text-to-speech (TTS) engines work and some of the best open-source options.

In this simple guide, I'll share more about TTS engines and list down some of the best options available.

What Is a Text-to-Speech (TTS) Engine?

Before we get started with the list, let's quickly define what a text-to-speech engine actually is.

A text-to-speech engine is a software that converts written text into spoken words. It utilizes natural language processing (NLP) to analyze and interpret written text and then uses a speech synthesizer to generate human-like speech.

TTS engines are commonly used in applications such as virtual assistants, navigation systems, and accessibility tools.

Interested in working with NLP? DataCamp’s Natural Language Processing in Python skill track will help you get your technical know-how up to speed.

What Are Open-Source Text-to-Speech (TTS) Engines?

Open source Text-to-Speech (TTS) engines are valuable tools for converting written text into spoken words, enabling applications in accessibility, automated voice responses, and virtual assistants, among others.

They are usually developed by a community of developers and released under an open-source license, allowing for anyone to use, modify, and distribute the software freely.

The 7 Best Open Source Text-to-Speech (TTS) Engines

Here are some well-known open-source TTS engines:

1. MaryTTS (Multimodal Interaction Architecture)

A flexible, modular architecture for building TTS systems, including a voice-building tool for generating new voices from recorded audio data.

Here's an overview diagram of the architecture behind this engine:

MaryTTS architecture

Source: MaryTTS GitHub

This architecture includes some basic components such as:

  • A markup language parser: A component that reads and interprets the markup language used in the text field.
  • A processor: A component that takes in the parsed text and performs any necessary actions, such as converting it to speech or generating visual output.
  • A synthesizer: A component responsible for producing the final output, whether it be audio or visual. It helps to add speech characteristics, such as intonation and inflection, to make the output sound more natural.

Pros: The MaryTTS architecture is highly customizable, allowing for developers to create their own parsers, processors, and synthesizers to fit their specific needs. This also allows for flexibility in integrating the software into different platforms and applications.

Cons: Due to its highly customizable nature, there may be a learning curve for developers who are unfamiliar with markup language and text-to-speech technology.

2. eSpeak

eSpeak TTSA compact open-source software speech synthesizer for English and other languages, eSpeak produces clear and intelligible speech across a wide range of languages. It's known for its simplicity and small footprint.

eSpeak can be run on various platforms, including Windows, Linux, macOS, and Android.

Pros: Easy to use, supports many languages and voices.

Cons: Limited features and customization options, and written in C.

Link: GitHub

3. Festival Speech Synthesis System

Developed by the University of Edinburgh, Festival offers a general framework for building speech synthesis systems as well as including examples of various modules. It's widely used for research and educational purposes.

The figure below shows the general utterance structure of Festival. It involves a tree shape with links between nodes showing a relation.

Festival utterance structure


Pros: Highly customizable, suitable for research purposes.

Cons: Difficult to use for beginners, requires some coding knowledge.

Link: GitHub

4. Mimic

mimic TTS

Source: Mimic

Developed by Mycroft AI, Mimic is capable of producing highly natural-sounding speech. It includes Mimic 1, based on the Festival Speech Synthesis System, and Mimic 2, which uses deep neural networks for voice synthesis.

Pros: Offers both traditional and modern voice synthesis methods and supports multiple languages.

Cons: Limited documentation.

Link: GitHub

5. Mozilla TTS

A deep learning-based TTS engine that aims to create more natural and human-like speech synthesis. It leverages modern neural network architectures, particularly sequence-to-sequence models.

Pros: Uses advanced technology for more natural speech and is free to use.

Cons: Limited language support.

Link: GitHub

6. Tacotron 2 (by NVIDIA)

Although not an engine per se, Tacotron 2 is a neural network model architecture for generating natural speech. Open-source implementations of Tacotron 2 are available, and it has inspired many developments in speech synthesis technology.

This system allows users to synthesize speech using raw transcripts without any additional prosody information.

Pros: Developed by NVIDIA, good to be used as a neural network model.

Cons: Requires some technical knowledge to implement.

Although this engine can be quite technically difficult to master, you can always get familiar with related neural network models through online resources. One such place would be our neural networks guide or our tutorial on neural networks.

Link: GitHub

7. ESPnet-TTS

Part of the ESPnet project, this TTS engine is designed for end-to-end speech processing, including both speech recognition and synthesis. It uses modern deep-learning techniques to generate speech.

Pros: Modern and flexible, supports multiple languages.

Cons: Requires some technical knowledge to implement.

Link: GitHub

Open-Source TTS Engines Compared

TTS System




Use Cases


Modular architecture with customizable components

Highly customizable, flexible integration

Learning curve for developers

Ideal for developers and researchers creating customized TTS applications, especially in educational and accessibility-focused projects.


Compact open-source software synthesizer

Simple, supports many languages

Limited features and customization, written in C

Suitable for applications requiring a wide range of language support and minimal system resources, such as embedded systems or assistive technology.

Festival Speech Synthesis System

General framework with examples of modules

Highly customizable, suitable for research

Difficult for beginners, requires coding knowledge

Best for academic research and development projects with a need for deep customization and experimental setups.


Traditional and neural network-based voice synthesis

Natural-sounding speech, supports multiple languages

Limited documentation

Well-suited for projects requiring high-quality voice synthesis, like virtual assistants or multimedia applications.

Mozilla TTS

Deep learning-based, sequence-to-sequence models

Advanced technology for more natural speech, free to use

Limited language support

Ideal for open-source projects and developers interested in leveraging cutting-edge deep learning techniques for natural-sounding TTS.

Tacotron 2 (NVIDIA)

Neural network model for natural speech generation

Good as a neural network model

Technical knowledge required

Perfect for research and development in neural network-based speech synthesis, offering a foundation for innovative TTS applications.


End-to-end speech processing with deep-learning

Modern and flexible, supports multiple languages

Technical knowledge required

Aimed at developers and researchers working on advanced speech synthesis and recognition projects, especially those requiring multi-language support.

Applications of TTS Engines

Here are some ways the above TTS engines can be used:

1. Virtual assistants

Through the use of text-to-speech engines like the ones mentioned above, virtual assistants can be made. These virtual assistants can be similar to enterprise voice assistants such as Siri and Alexa.

Some of them can even be used for accessibility assistance for users with visual impairments, allowing them to hear written text instead of reading it.

2. Automatic voice responses with AI voice

TTS engines are also used in automated response systems, such as phone or chatbot assistants. These engines can read out responses based on specific prompts and interactions, providing a more human-like experience for users.

3. Video/image voiceover

Text-to-speech technology can also generate voiceovers for videos or images, allowing for more dynamic and engaging content.

For example, the eSpeak engine can be used to add voiceovers to videos in different languages, making them more accessible and appealing to a wider audience.

This is especially useful for applications in marketing, e-learning, and entertainment industries.

Challenges of Using Open-Source TTS Engines

Using an open-source option can be cost-effective and offers more flexibility for customization. However, there are some challenges that come with using these engines:

1. Limited language support

Many open-source TTS engines have limited language support compared to commercial solutions.

This limitation may be a barrier for users who need TTS in less commonly used languages.

2. Customization and implementation

Most open-source TTS engines require some coding knowledge to customize and implement. This makes it hard for regular business stakeholders to use them without technical support.

This may be a challenge for individuals or organizations without technical expertise.

3. Cost considerations

While open-source engines are free to use, they may require additional resources and time for customization and implementation.

Additionally, an engineer or analyst with the relevant know-how of TTS engines has to be hired or trained.

Therefore, in some cases, commercial solutions may be more cost-effective in the long run.

4. Support and documentation

Having limited resources and being community-driven, open-source projects may not always have extensive support and documentation available.

ESPnet documentation

Source: ESPnet Documentation

This can make it challenging for users to troubleshoot issues or learn how to use the engine effectively.

However, as these engines continue to gain popularity and more developers contribute to them, this challenge may diminish over time.

5. Security and performance

Since open-source engines are developed and maintained by a community, there may be concerns about security and performance.

However, these risks can be mitigated through proper vetting and monitoring of the engine's code and updates.

Additionally, choosing reliable and reputable open-source projects can help alleviate these concerns.

Choosing The Best Engine for TTS Integration

Let's now discuss how to go about selecting the right engine for your text-to-speech model.

Here are some factors to consider:

1. Purpose and use case

Start by identifying your specific use case and the purpose of using TTS. Understand what features and customization options are necessary for your project, and then choose an engine accordingly.

2. Language support

If you require support for a particular language or multiple languages, make sure to select an engine that offers such capabilities.

language support

In that case, going for the eSpeak engine may be a better option for you.

3. Cost and budget

Consider your budget and resources before selecting an engine. While open-source options may be cost-effective in the long run, they may require additional resources for customization and implementation.

4. Technical expertise

Evaluate the skill level of your team or yourself when working with TTS engines. If you do not have technical expertise, consider opting for a commercial solution that offers user-friendly interfaces and support.

5. Performance and quality

Ensure that the engine you choose provides high-quality, natural-sounding speech output. You may also want to test different engines to see which one best matches your desired level of performance.

Final Thoughts

Text-to-speech technology has come a long way in providing more natural and human-like speech output. With numerous open-source options available, it’s now more accessible and cost-effective to integrate TTS into various applications.

However, you'll also have to expect some limitations and challenges that come with using open-source engines before making a decision. I hope this guide has provided a greater understanding of TTS engines and helped you in selecting the best one for your needs.

Looking for ways to do this process in reverse? Check out our Spoken Language Processing in Python course.

Photo of Austin Chia
Austin Chia

I'm Austin, a blogger and tech writer with years of experience both as a data scientist and a data analyst in healthcare. Starting my tech journey with a background in biology, I now help others make the same transition through my tech blog. My passion for technology has led me to my writing contributions to dozens of SaaS companies, inspiring others and sharing my experiences.


You’re invited! Join us for Radar: AI Edition

Join us for two days of events sharing best practices from thought leaders in the AI space
DataCamp Team's photo

DataCamp Team

2 min

The Art of Prompt Engineering with Alex Banks, Founder and Educator, Sunday Signal

Alex and Adel cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and much more.
Adel Nehme's photo

Adel Nehme

44 min

The Future of Programming with Kyle Daigle, COO at GitHub

Adel and Kyle explore Kyle’s journey into development and AI, how he became the COO at GitHub, GitHub’s approach to AI, the impact of CoPilot on software development and much more.
Adel Nehme's photo

Adel Nehme

48 min

A Comprehensive Guide to Working with the Mistral Large Model

A detailed tutorial on the functionalities, comparisons, and practical applications of the Mistral Large Model.
Josep Ferrer's photo

Josep Ferrer

12 min

Serving an LLM Application as an API Endpoint using FastAPI in Python

Unlock the power of Large Language Models (LLMs) in your applications with our latest blog on "Serving LLM Application as an API Endpoint Using FastAPI in Python." LLMs like GPT, Claude, and LLaMA are revolutionizing chatbots, content creation, and many more use-cases. Discover how APIs act as crucial bridges, enabling seamless integration of sophisticated language understanding and generation features into your projects.
Moez Ali's photo

Moez Ali

How to Improve RAG Performance: 5 Key Techniques with Examples

Explore different approaches to enhance RAG systems: Chunking, Reranking, and Query Transformations.
Eugenia Anello's photo

Eugenia Anello

See MoreSee More