Open Source Text to Speech Software - Page 3
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Text to Speech Software
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TTS Voice Wizard
Speech to Text to Speech, sends text as OSC messages
Speech to Text to Speech. Song now playing. Sends text as OSC messages to VRChat to display on avatar. (STTTS) (Speech to TTS) (VRC STT System) Use TTS Voice Wizard's accessibility features to improve your VRChat experience (it works outside of VRChat too!) You can convert your Speech-to-Text and back to Speech through various Speech Recognition and Text-to-Speech methods. You can send what you say as OSC messages to VRChat to be displayed on your avatar using KillFrenzyAvatarText or VRChats Chatbox. The app can translate your speech from one language to over 20 other support languages. There are 100+ different voices with various customization options so you can pick a voice that best suits you. Display the current song you are listening to on Spotify or via your browser. Display tracker and controller battery life in conjunction with XSOverlay. Use in conjunction with HRtoVRChat_OSC to enable you to display your heartrate in VRChat's Chatbox. -
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Amica
Amica is an open source interface for interactive communication
Amica is an open source interface for interacting with fully animated 3D characters that combine voice chat, vision, and an emotion engine into a single experience. It lets you hold natural conversations with AI characters that can see, listen, and speak, while expressing emotional states through facial expressions and body language. Users can import VRM character models, adjust their appearance, tune the voice to match the character, and define behavior using different large language models and TTS backends. Under the hood, Amica leverages modern web and desktop technologies: three.js and three-vrm for 3D rendering, Transformers.js for running models in the browser, Whisper and Silero VAD for speech recognition and voice-activity detection, and a variety of LLM backends such as llama.cpp servers, ChatGPT-compatible APIs, Ollama, KoboldCpp, and others. It also integrates multiple text-to-speech providers, including ElevenLabs, OpenAI, Coqui, RVC, and AllTalkTTS. -
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LuxTTS
A high-quality rapid TTS voice cloning model
LuxTTS is an open-source text-to-speech (TTS) system focused on delivering high-quality, rapid voice synthesis and voice cloning that runs extremely fast and efficiently on consumer hardware. It implements a lightweight architecture based on ZipVoice and optimized sampling techniques so that it can generate speech at speeds up to roughly 150 times real-time on a single GPU and faster than real-time on CPU, all while producing audio at high fidelity with 48 kHz quality. The project supports zero-shot voice cloning, meaning it can adapt to a reference speaker’s voice with minimal example data, enabling realistic and personalized synthetic speech. Intended for developers, hobbyists, and creators, the repository includes installation instructions, usage examples, and Python APIs that make it feasible to integrate the model in local workflows, web demos, or production systems. Its design emphasizes efficiency and practicality, fitting within modest GPU memory footprints. -
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gTTS
Python library and CLI tool to interface with Google Translate
gTTS (Google Text-to-Speech) is a Python library and command-line tool that wraps the speech functionality of Google Translate. It lets you send text to the Google Translate TTS endpoint and receive spoken audio back as MP3 data, either written to a file, a file-like object, or standard output. The library is designed to handle long texts, using a speech-specific sentence tokenizer that keeps intonation and punctuation natural while splitting requests into acceptable chunks. It supports customizable text pre-processors, which can correct pronunciations, tweak formatting, or handle domain-specific vocabulary before sending it to the API. gTTS is primarily aimed at developers who want a quick way to add cloud-backed speech to scripts, apps, or pipelines without managing any model weights locally. A small CLI utility, gtts-cli, makes it easy to test or batch-generate MP3 files right from the shell. -
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CosyVoice
Multi-lingual large voice generation model, providing inference
CosyVoice is a multilingual large voice generation model that offers a full-stack solution for training, inference, and deployment of high-quality TTS systems. The model supports multiple languages, including Chinese, English, Japanese, Korean, and a range of Chinese dialects such as Cantonese, Sichuanese, Shanghainese, Tianjinese, and Wuhanese. It is designed for zero-shot voice cloning and cross-lingual or mix-lingual scenarios, so a single reference voice can be used to synthesize speech across languages and in code-switching contexts. CosyVoice 2.0 significantly improves on version 1.0 by boosting accuracy, stability, speed, and overall speech quality, making it more suitable for production environments. The repository contains training recipes, inference pipelines, deployment scripts, and integration examples, positioning it as a comprehensive toolkit rather than just a set of model weights. -
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StyleTTS 2
Towards Human-Level Text-to-Speech through Style Diffusion
StyleTTS2 is a state-of-the-art text-to-speech system that aims for human-level naturalness by combining style diffusion, adversarial training, and large speech language models. It extends the original StyleTTS idea by introducing a style diffusion model that can sample rich, realistic speaking styles conditioned on reference speech, allowing highly expressive and diverse prosody. The architecture uses a two-stage training process and leverages an auxiliary speech language model to guide generation toward more natural and coherent utterances. StyleTTS2 supports both single-speaker and multi-speaker configurations, with the ability to sample or transfer styles from reference audio, making it powerful for expressive TTS and character voices. The repository includes training scripts, configuration files, and pre-trained auxiliary modules such as a text aligner, pitch extractor, and PL-BERT-based linguistic encoder. -
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YandexStation
Management of Yandex Station and other smart home devices
YandexStation is a Home Assistant custom component that integrates Yandex-branded smart speakers and other devices with Alice into a unified smart home automation environment. It supports both local and cloud control, depending on the device type, with Yandex speakers often supporting both modes and third-party speakers typically limited to cloud control. The integration exposes playback and volume controls, as well as text-to-speech capabilities that send spoken messages in Alice’s voice directly to the speakers. It also lets you send arbitrary text commands as if you were talking to Alice, enabling scenarios such as “play my music,” launching routines, or querying information via Home Assistant automations. In local control mode, the component can read back what is currently playing, including album art, and supports seeking and track skipping, which is more limited in cloud-only mode. -
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MeloTTS
High-quality multi-lingual text-to-speech library by MyShell.ai
MeloTTS is an open-source text-to-speech (TTS) system that generates natural-sounding speech from text input. It utilizes advanced machine-learning models to produce high-quality audio outputs. -
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TTS WebUI
A single Gradio + React WebUI with extensions for ACE-Step
TTS-WebUI is a unified Gradio + React web interface that brings together a large ecosystem of text-to-speech, voice conversion, and audio generation models under a single UI. It supports a wide range of models such as Bark, MusicGen, Tortoise, RVC, StyleTTS2, ParlerTTS, CosyVoice, XTTSv2, Stable Audio, SeamlessM4T, and many others, exposing them as interchangeable backends for speech and music synthesis. The project provides an installer that sets up Conda, Python environments, and all necessary dependencies, so users can focus on experimenting with voices instead of managing tooling. It offers both a Gradio backend and an optional React frontend, which can be accessed on separate ports and even run inside Docker for more reproducible deployments. An extension system lets you enable extra models and tools, install community extensions from a catalog, and manage them via a dedicated GUI or CLI extension manager. -
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Bert-VITS2
VITS2 backbone with multilingual-bert
Bert-VITS2 is a neural text-to-speech project that combines a VITS2 backbone with a multilingual BERT front-end to produce high-quality speech in multiple languages. The core idea is to use BERT-style contextual embeddings for text encoding while relying on a refined VITS2 architecture for acoustic generation and vocoding. The repository includes everything needed to train, fine-tune, and run the model, from configuration files to preprocessing scripts, spectrogram utilities, and training entrypoints for multi-GPU and multi-node setups. It provides emotional modeling through “emo embeddings,” allowing voices to be conditioned on different affective states during synthesis. Releases include optimizations for Japanese and English alignment, expanded training data, spec caching and pre-generation tools, as well as ONNX export for more lightweight inference deployments. -
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ESPnet
End-to-end speech processing toolkit
ESPnet is a comprehensive end-to-end speech processing toolkit covering a wide spectrum of tasks, including automatic speech recognition (ASR), text-to-speech (TTS), speech translation (ST), speech enhancement, speaker diarization, and spoken language understanding. It uses PyTorch as its deep learning engine and adopts a Kaldi-style data processing pipeline for features, data formats, and experimental recipes. This combination allows researchers to leverage modern neural architectures while still benefiting from the robust data preparation practices developed in the speech community. ESPnet provides many ready-to-run recipes for popular academic benchmarks, making it straightforward to reproduce published results or serve as baselines for new research. The toolkit also hosts numerous pretrained models and example configs, ranging from Transformer and Conformer architectures to various attention-based encoder-decoder models. -
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EasyVoice
Open source text-to-speech tool, supports extra-long text
easyVoice is an open-source text-to-speech platform aimed at turning long-form text and novels into high-quality audio, with a strong focus on usability and scalability. It provides a web interface where users can paste or upload large texts and generate speech and subtitles in a single workflow, even for works exceeding 100,000 characters. The system supports multi-role voice acting, letting users assign different neural voices to different characters or narrative roles and configure parameters such as rate, pitch, and volume per role. It offers streaming playback so audio starts almost immediately, even for very long inputs, and automatically generates subtitle files suitable for video production or translation workflows. Under the hood, easyVoice uses a modern stack with Vue 3 and Element Plus on the front end, Node.js and Express on the back end, and TTS engines such as Microsoft Azure TTS and OpenAI-compatible APIs, orchestrated through ffmpeg. -
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GLM-TTS
Controllable & emotion-expressive zero-shot TTS
GLM-TTS is an advanced text-to-speech synthesis system built on large language model technologies that focuses on producing high-quality, expressive, and controllable spoken output, including features like emotion modulation and zero-shot voice cloning. It uses a two-stage architecture where a generative LLM first converts text into intermediate speech token sequences and then a Flow-based neural model converts those tokens into natural audio waveforms, enabling rich prosody and voice character even for unseen speakers. The system introduces a multi-reward reinforcement learning framework that jointly optimizes for voice similarity, emotional expressiveness, pronunciation, and intelligibility, yielding output that can rival commercial options in naturalness and expressiveness. GLM-TTS also supports phoneme-level control and hybrid text + phoneme input, giving developers precise control over pronunciation critical for multilingual or polyphone-rich languages. -
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Read Aloud
An awesome browser extension that reads aloud webpage content
Read Aloud is a browser extension for Chrome, Firefox, and other Chromium-based browsers that converts webpage text to audio using text-to-speech technology. It is designed to work on a wide variety of sites, including news, blogs, online textbooks, course materials, fanfiction, and more. The extension targets users who prefer listening over reading, as well as people with dyslexia, other learning disabilities, or eye strain, and children learning to read. Read Aloud lets users choose from multiple voices: built-in browser voices, plus premium cloud voices from providers such as Google Wavenet, Amazon Polly, IBM Watson, and Microsoft. It offers simple controls through a toolbar button, right-click menu, and keyboard shortcuts for play, pause, stop, rewind, and fast forward. Users can customize voice, speed, pitch, and text highlighting behavior directly from the extension’s settings panel. -
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Supertonic
Lightning-fast, on-device TTS, running natively via ONNX
Supertonic is a lightning-fast, on-device text-to-speech system built around ONNX Runtime for maximum speed and portability. It focuses on running entirely locally, eliminating the need for cloud APIs and providing low latency and strong privacy guarantees, even on constrained devices like Raspberry Pi boards and e-readers. The core model is highly compact at around 66 million parameters, yet benchmarks show it can generate speech up to 167× faster than real time on modern consumer hardware and significantly outpace popular cloud TTS APIs in throughput and real-time factor. Supertonic is designed to handle real-world text gracefully, including numbers, dates, currency symbols, abbreviations, and technical units, without requiring heavy pre-processing or custom text normalization. The repository provides complete reference implementations across many programming ecosystems—Python, Node.js, browser (WebGPU/WASM), Java, C++, C#, Go, Swift, iOS, Rust, and Flutter. -
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TTS
Deep learning for text to speech
TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed, and quality. TTS comes with pre-trained models, tools for measuring dataset quality, and is already used in 20+ languages for products and research projects. Released models in PyTorch, Tensorflow and TFLite. Tools to curate Text2Speech datasets underdataset_analysis. Demo server for model testing. Notebooks for extensive model benchmarking. Modular (but not too much) code base enabling easy testing for new ideas. Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). Speaker Encoder to compute speaker embeddings efficiently. Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN). If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option. -
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edge-TTS-record
Tool that can record speech synthesis
edge-TTS-record is a Windows-based tool that records speech synthesized by the Microsoft Edge browser’s online TTS voices and saves the result as .wav audio files. The idea is simple but effective: since Edge’s online TTS voices (such as “Xiaoxiao” or “Yunyang” for Chinese) are often high-quality, this tool provides a way to “capture” them offline for later use. Users can type or paste text, preview the speech, and then trigger the recorder; the system automatically captures the audio output from the browser and writes it to a WAV file. The tool includes a small GUI (built with Aardio) and aims to be plug-and-play — after downloading the .exe you can immediately start using it without deep configuration. It is cloud-based in the sense that it relies on Edge’s online TTS service, so internet connection is required; but once recorded, the audio is local. -
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Amphion
Toolkit for audio, music, and speech generation
Amphion is a toolkit from OpenMMLab dedicated to audio, music, and speech generation, aimed at both reproducible research and helping newcomers get started in generative audio. It provides standardized implementations and recipes for classic and state-of-the-art generative models in audio, including TTS, music generation, and voice conversion. A distinctive feature of Amphion is its emphasis on visualization: it offers interactive visualizations of model architectures and generation processes, making it easier to understand how complex generative audio models work. The toolkit is organized with example experiments (“egs”) and visualization demos that guide users through training, evaluation, and inspection of models. Built on the broader OpenMMLab ecosystem, Amphion follows modular design patterns and configuration systems similar to other OpenMMLab projects, easing adoption for users who are already familiar with that stack. -
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Audiblez
Generate audiobooks from e-books
Audiblez is a tool for generating high-quality .m4b audiobooks directly from .epub e-books using the Kokoro-82M neural text-to-speech model. It focuses on making audiobook creation easy and fast: from a single command, the tool splits an e-book into chapters, synthesizes audio for each section, and then merges the results into a structured audiobook with chapter-based WAV files and a final .m4b container. The Kokoro-82M model it uses is compact (82M parameters) yet natural sounding, trained on under 100 hours of audio, and supports multiple languages, including English (US/UK), Spanish, French, Hindi, Italian, Japanese, Brazilian Portuguese, and Mandarin Chinese. Audiblez can run entirely from the command line via a PyPI package or through a simple cross-platform GUI built on wxPython, giving both advanced users and non-technical users an accessible workflow. -
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ChatTTS webUI & API
A simple native web interface that uses ChatTTS to synthesize text
ChatTTS-ui is a local web interface and API wrapper around the ChatTTS speech synthesis system, designed to make advanced TTS models easy to use from a browser. It runs a small backend server (Python + Torch + ffmpeg) and exposes a simple webpage where you can type text, adjust parameters, and generate audio. The project supports Chinese, English, and mixed text with digits and control symbols, making it suitable for bilingual content and numerically heavy text like announcements or prompts. From version 0.96 onward, ffmpeg installation is required for deployment, and previous CSV/PT voice tables are no longer valid, so users instead work with updated “voice value” parameters. For convenience, there is a prepackaged Windows build: you download a release archive, extract it, and double-click app.exe to start the web UI, which opens on localhost:9966. -
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FastKoko
Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
FastKoko is a self-hosted text-to-speech server built around the Kokoro-82M model and exposed through a FastAPI backend. It is designed to be easy to deploy via Docker, with separate CPU and GPU images so that users can choose between pure CPU inference and NVIDIA GPU acceleration. The project exposes an OpenAI-compatible speech endpoint, which means existing code that talks to the OpenAI audio API can often be pointed at a Kokoro-FastAPI instance with minimal changes. It supports multiple languages and voicepacks and allows phoneme based generation for more accurate pronunciation and prosody. The server also offers per-word timestamped captions, which makes it useful for creating subtitles or aligning audio with text. A built in web UI, API documentation, and debug endpoints for monitoring system status help users explore voices, test requests, and integrate the service into larger systems. -
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models. -
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Speech-AI-Forge
Speech-AI-Forge is a project developed around TTS generation model
Speech-AI-Forge is a full-stack project built around modern text-to-speech generation models, providing both an API server and a Gradio-based web UI for interactive use. At its core, it acts as a hub that wires together multiple speech-related capabilities, including TTS, speech-to-text and LLM-based control flows, behind a consistent interface. The system is designed to be deployed in several ways: you can try it online via hosted demos, spin it up in a one-click Colab environment, run it in Docker containers, or set it up locally with its environment preparation scripts. It is model-agnostic and advertises support for a variety of TTS and speech models such as ChatTTS, CosyVoice, Fish-Speech, FireredTTS and others, as well as Whisper-based ASR, giving you a flexible playground for experimenting with different speech stacks. The project also integrates with general-purpose LLMs (for example GPT- or LLaMA-style models), which can be used to pre-process text, manage conversations. -
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ekho
Chinese text-to-speech engine
ekho is a project with relatively sparse documentation, but from the repository it appears to be a small-scale tool for audio processing and playback, possibly with features for speech synthesis or manipulation. The repo includes scripts and configuration files suggesting interactions with media/audio handling libraries. Because of limited README detail, it seems targeted at users comfortable reading and modifying code, rather than end users expecting polished UIs. The code structure implies that Ekho may support hooking into audio input/output streams, perhaps for tasks like audio capture, playback, transformation, or simple voice-based operations. It might serve as a lightweight base or utility for building custom audio-related workflows, such as streaming, playback orchestration, or combining audio modules. Given the limited explicit features, Ekho would be best suited for developers or hobbyists who want a flexible foundation to add their own logic for TTS. -
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Dragonfire
The open-source virtual assistant for Ubuntu based Linux distributions
Dragonfire is the open-source virtual assistant project for Ubuntu-based Linux distributions. Her main objective is to serve as a command and control interface to the helmet user. So that you will be able to give orders just by using your voice commands and your eye movements. That makes the helmet handsfree. We are planning to ship Dragonfire as a preinstalled software package on DragonOS Linux Distribution. DragonOS will be a Linux distribution specially designed for the helmet. It will contain various software packages for controlling the helmet. It will be the first of its kind. Dragonfire uses Mozilla DeepSpeech to understand your voice commands and Festival Speech Synthesis System to handle text-to-speech tasks.
