As artificial intelligence continues to revolutionize how we communicate text-to-speech (TTS) technology has become an essential tool across industries – from customer service and content creation to accessibility and virtual assistants. In 2025, the TTS landscape is more advanced and competitive than ever, with leading text to speech AI models offering near-human voice synthesis, expressive speech control, and support for multiple languages and styles. But with so many tools on the market, how do you choose the one that truly fits your needs?
In this article, we’ll break down the best AI models for text to speech in 2025, comparing their features, voice quality, and ideal use cases to help you find the right solution for your product or service.
Understanding text to speech AI models
Text-to-speech (TTS) technology allows machines to convert written text into spoken audio that sounds natural and human-like. Whether you’re building an interactive assistant, narrating video content, or improving accessibility for users with visual impairments, TTS systems play a crucial in delivering high-quality voice experiences.
What is text to speech based on AI?
Text-to-speech technology uses deep learning AI models to analyze written input and generate human-like audio output. Advanced TTS engines can mimic tone, emotion, and natural cadence, making the output more engaging and realistic. In 2025, leading TTS models offer multilingual support, customizable voice styles, and high-quality synthesis suitable for podcasts, audiobooks, IVR systems, and more.
“Modern TTS models represent a significant leap in quality compared to traditional speech synthesizers. They effectively blur the once-obvious line between real and artificial speech. Today’s solutions not only enable the generation of speech in multiple languages, but also allow for the customization of emotions, accents, and speaking pace.
Each model is different, so it needs to be carefully chosen to match our specific expectations.”
Top text to speech (TTS) AI models
In 2025, many powerful tools can turn text into natural-sounding speech. Here’s a list of the best AI models for text to speech – each one stands out for its voice quality, speed, and features.
ElevenLabs
ElevenLabs is one of the most popular AI tools for text-to-speech (TTS) in 2025, offering natural and expressive voice generation. It supports real-time audio streaming, which means the speech can start playing almost immediately while the text is still being processed. This works well for apps like voice assistants or interactive platforms.
ElevenLabs also offers many realistic voices in different languages, with options to adjust the tone or create custom ones. Developers can easily connect to the service through its API and use it in their own apps. The TTS output is smooth, clear, and suitable for things like audiobooks, games, or customer service tools. It’s a flexible and reliable choice for anyone looking to turn text into speech.
OpenAI
OpenAI’s text-to-speech (TTS) API provides real-time streaming capabilities, allowing developers to generate and play audio as it’s being processed. The API supports multiple high-quality voices optimized for clarity and natural prosody, with the ability to control speaking style and intonation using structured prompts. This makes it well-suited for interactive applications like virtual agents, reading assistants, or dynamic voice responses.
The TTS endpoint integrates easily via REST, returning audio in standard formats such as MP3. Developers can choose between preset voices and fine-tune delivery through prompt engineering to achieve specific tones or emotions. With low-latency performance and flexible deployment, OpenAI’s TTS system is a reliable choice for real-time voice synthesis across various platforms.
Kokoro-82M
Kokoro-82M is an open-source text-to-speech (TTS) model developed by Hexgrad, designed for efficient and high-quality speech synthesis. With only 82 million parameters, it delivers performance comparable to larger models, making it suitable for deployment on resource-constrained devices.
Kokoro-82M supports multiple languages, including American and British English, French, Korean, Japanese, and Mandarin. It offers a variety of voicepacks, allowing users to select from different accents and styles. The model processes phoneme sequences generated via espeak-ng, and outputs 24kHz audio suitable for various applications. Its compact size and efficient architecture make it an excellent choice for developers seeking a balance between performance and resource utilization.
Genny by LOVO
Genny by LOVO is a text-to-speech (TTS) platform designed to deliver high-quality, expressive voice output. It supports asynchronous processing, meaning the system generates audio in the background and lets you check back when it's ready. This approach is useful for apps that don’t need real-time playback but require reliable and polished voice results.
Genny offers a wide range of voices across different languages and styles, allowing for flexible use in projects like e-learning, marketing, or media production. Developers can easily access these voices through the API and integrate them into their systems with minimal effort. It's a solid choice for teams looking to add lifelike voiceovers without the complexity of building custom TTS models.
WaveNet
WaveNet, developed by DeepMind, is a neural network architecture that generates raw audio waveforms, producing highly natural-sounding speech. Unlike traditional text-to-speech systems that concatenate pre-recorded speech fragments, WaveNet models audio sample by sample, capturing the nuances of human speech, including intonation and rhythm
Integrated into Google Cloud's Text-to-Speech API, WaveNet offers over 90 voices across multiple languages and dialects, enabling developers to create applications with lifelike voice interactions. The API supports customization through Speech Synthesis Markup Language (SSML), allowing control over aspects like pitch, speaking rate, and pronunciation. This makes WaveNet suitable for various applications, including virtual assistants, accessibility tools, and content narration.
Azure
Azure AI Speech offers high-quality TTS with a wide range of natural-sounding voices in many languages. It supports both real-time synthesis and batch processing, making it suitable for everything from chatbots to audiobooks. The platform includes HD neural voices with emotional tone control and also allows businesses to create custom voice models.
Developers can integrate it using REST APIs or SDKs, with flexible tools for adjusting speech rate, pitch, and pronunciation. Azure’s TTS is a reliable choice for scalable and expressive voice applications, offering the flexibility and performance needed for use cases like virtual assistants, learning platforms, media production, and accessibility tools.
IBM
IBM Watson Text to Speech is a cloud-based service that converts text into natural-sounding audio using both standard and expressive neural voices. It supports multiple languages and lets developers fine-tune output using SSML to control pitch, speed, and pronunciation.
The API offers both REST and WebSocket options, with real-time audio streaming and support for formats like MP3 and WAV. Developers can also create custom voice models to match brand identity. It's a flexible and scalable solution for integrating TTS into virtual assistants, accessibility tools, or customer-facing applications.
Coqui TTS
Coqui TTS is an open-source, Python-based toolkit for advanced text-to-speech (TTS) and voice cloning applications. It supports a wide range of models, including Tacotron, Glow-TTS, FastSpeech, and VITS, along with vocoders like HiFi-GAN and WaveRNN. The toolkit offers multi-speaker and multilingual support, with pre-trained models available in over 1,100 languages.
Its modular design and command-line interface facilitate easy integration and customization, making it suitable for both research and production environments. Coqui TTS is licensed under the Mozilla Public License 2.0 and is actively maintained by the Coqui.ai team.
Choosing the right text to speech AI model for your needs
Not all AI voice models are created equal – the best one for you depends on what you're building and who will use it. Whether you're creating a mobile app or a voice assistant, it's important to match the features of the model to your specific needs.
Here are a few questions to help guide your choice:
- Do you need real-time results or is batch processing enough?
- Are you focused on one language, or do you need multilingual support?
- Is voice style or emotion important for your application?
- How accurate does the speech output need to be?
- Are you building a lightweight app or running on powerful servers?
Important factors to consider when choosing AI models
When comparing different TTS models, keep these technical and practical factors in mind:
- Voice quality – How natural and human-like is the speech?
- Language and accent support – Does it match your audience?
- Customization – Can you control style, tone, or create custom voices?
- Speed & latency – Especially important for real-time apps.
- Integration – Is there a well-documented API or SDK?
Best text to speech AI models by use case
Here’s a quick reference to help match models to common use cases:
| Use Case | Bext text to speech AI models |
| Voice Assistants | ElevenLabs, OpenAI, Azure TTS |
| Audiobooks / Storytelling | ElevenLabs, Google WaveNet, Genny |
| E-learning / Training content | Genny, Azure TTS |
| Live Captioning / Accessibility | OpenAI, Azure TTS (low latency) |
| Custom voice branding | Azure (custom voice), ElevenLabs |
| Offline / Embedded apps | Coqui TTS, Kokoro-82M |
FAQ: Text to speech AI
Which text to speech AI model is best for real-time applications like voice assistants?
For real-time voice generation, models like ElevenLabs, OpenAI, and Azure TTS are excellent choices. They support low-latency audio streaming, allowing speech playback to begin almost instantly as the text is being processed.
Can I create a custom text to speech AI voice for my brand?
Yes. Platforms like Azure and ElevenLabs allow you to build custom voice models. These can be trained to match a specific tone, accent, or speaking style, helping your product or brand sound unique and consistent.
Are there free or open-source alternatives to commercial text to speech AI APIs?
Absolutely. Coqui TTS and Kokoro-82M are great open-source options that you can run locally or on your own servers. They provide flexibility and cost control, though they may require more technical setup than cloud-based services.
