Open Source Python Text to Speech Software - Page 4
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Python Text to Speech Software
View 205 business solutionsBrowse free open source Python Text to Speech Software and projects below. Use the toggles on the left to filter open source Python Text to Speech Software by OS, license, language, programming language, and project status.
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Mice MX OS speech to text Voice Control
Mice speech to text with MX Cinnamon OS ISO
Note about this image This image contains a system based on Linux MX, which was created to improve accessibility within the Linux environment. The distribution uses the Cinnamon desktop interface, which is configured to be operated using voice commands and outputs. The user interface and the control of your own devices and home automation systems can be customized and extended. The voice control program MiceStTM.py was developed to enable easy adaptation to other languages. However, only German settings are currently implemented. category: System commands comment: Screen grid trigger: Display screen (Ras.*|Grid)* terminal_command: /opt/micesttm/read-aloud/screen_grid.py & sleep 1 && xdotool search --name "screen grid" windowactivate intern_command: tts: Screen grid for the mouse click was selected. -
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MiniMax-MCP
Official MiniMax Model Context Protocol (MCP) server
MiniMax-MCP is the official Model Context Protocol (MCP) server for accessing MiniMax’s multimodal generative APIs from MCP-compatible clients. It acts as a bridge between tools like Claude Desktop, Cursor, Windsurf, OpenAI Agents, and the MiniMax platform, exposing capabilities such as text-to-speech, voice cloning, image generation, text-to-image, video generation, image-to-video, text-to-video, and music generation. The server is written in Python and distributed under the MIT license, with a pyproject.toml and uv-based workflow that makes installation and execution reproducible. Configuration is handled through JSON files that tell MCP clients how to launch the server (typically via uvx minimax-mcp) and which environment variables to use for the API key, host, and output directory. The README carefully explains region-specific API hosts for global and mainland users to avoid invalid-key errors, and documents both local stdio transport and SSE-based network transport modes. -
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Mocking Bird
Clone a voice in 5 seconds to generate arbitrary speech in real-time
MockingBird is an open-source voice cloning and real-time speech generation toolkit that lets you clone a speaker’s voice from a short audio sample (reportedly as little as 5 seconds) and then synthesize arbitrary speech in that voice. It builds on deep-learning based TTS / voice-cloning technology (in the lineage of projects such as Real-Time-Voice-Cloning), but extends it with support for Mandarin Chinese and multiple Chinese speech datasets — broadening its applicability beyond English. The codebase is implemented in Python (with PyTorch) and includes modules for encoder, synthesizer, vocoder, preprocessing, and inference, as well as demo scripts and a web-server interface for easier experimentation or deployment. MockingBird supports both using pretrained models and training your own synthesizer (with custom datasets), giving flexibility for voice-cloning or custom-voice synthesis depending on your needs. -
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OpenAI-Compatible Edge-TTS API
Free, high-quality text-to-speech API endpoint to replace OpenAI
OpenAI-Compatible Edge-TTS API is a local, OpenAI-compatible text-to-speech API that uses edge-tts—Microsoft Edge’s online TTS service—as the backend. The project emulates the /v1/audio/speech endpoint used by OpenAI, so any client that can talk to the OpenAI TTS API can be redirected to this service with minimal changes. It exposes parameters for input text, voice selection, audio format, and playback speed, mirroring the OpenAI interface while mapping popular OpenAI voice names to equivalent Edge voices. Because it relies on Edge’s TTS, the audio generation itself is free, and the project essentially acts as a smart proxy that handles formatting and streaming. The server supports Server-Sent Events (SSE) for streaming audio, enabling low-latency playback in chat UIs and other interactive tools. A Docker image is provided for one-command deployment, and environment variables can be used to configure default voice, language, response format, authentication, and logging options. -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. The project comes with configuration-driven training scripts, documentation, and examples that demonstrate how to set up pipelines for tasks. -
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PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target detection, image segmentation, text recognition, speech synthesis, etc. An end-to-end development kit that meets the needs of enterprises for low-cost development and rapid integration. The model library of Flying Paddle is an industrial-level model library tailored around the actual R&D process of domestic enterprises, serving enterprises in many fields such as energy, finance, industry, and agriculture. -
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Parallel WaveGAN
Unofficial Parallel WaveGAN
Parallel WaveGAN is an unofficial PyTorch implementation of several state-of-the-art non-autoregressive neural vocoders, centered on Parallel WaveGAN but also including MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN. Its main goal is to provide a real-time neural vocoder that can turn mel spectrograms into high-quality speech audio efficiently. The repository is designed to work hand-in-hand with ESPnet-TTS and NVIDIA Tacotron2-style front ends, so you can build complete TTS or singing voice synthesis pipelines. It includes a large collection of “Kaldi-style” recipes for many datasets such as LJSpeech, LibriTTS, VCTK, JSUT, CMU Arctic, and multiple singing voice corpora in Japanese, Mandarin, Korean, and more. The project provides pre-trained models, Colab demos, and example configurations, allowing researchers to quickly evaluate vocoder quality or adapt models to new datasets. -
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RealtimeTTS
Converts text to speech in realtime
RealtimeTTS is a low-latency text-to-speech library built for real-time applications such as voice chat with LLMs, assistants, and interactive tools. It is designed around a streaming model: you can feed it text incrementally (for example, as an LLM responds) and get audio output almost immediately, which keeps end-to-end latency very low. The library is engine-agnostic and plugs into a wide range of cloud and local TTS systems, including OpenAI, ElevenLabs, Azure, Coqui, Piper, StyleTTS2, Edge TTS, Google TTS, system TTS and others, so you can swap providers without rewriting your pipeline. It supports both internet-based engines and fully local engines, which lets you choose between privacy, cost, and quality trade-offs. RealtimeTTS also includes robustness features such as automatic fallbacks when a backend fails, so production systems can stay responsive even if one TTS provider is temporarily unavailable. -
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Sopro TTS
A lightweight text-to-speech model with zero-shot voice cloning
Sopro TTS is an open-source text-to-speech (TTS) project that implements a lightweight model capable of producing speech from text with zero-shot voice cloning, meaning it can mimic a speaker’s voice from only a few seconds of reference audio. Built with a 169 million-parameter architecture that uses dilated convolutions and cross-attention layers instead of large Transformer stacks, it achieves relatively fast real-time performance even on CPUs (about a 0.25 real-time factor measured on an M3 base). The model is designed to work with a small set of dependencies and to be accessible for developers who want offline TTS with customizable voice style, including options for streaming or non-streaming generation modes. Users can install it with standard Python tools, run a demo server locally, and experiment with CLI or Python API usage for producing synthetic speech. -
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SpeakLogPSU can speak chat messages with an individual voice if the NPC or player was configured or with a default one. You will never miss if someone talks to you. Voice cloning can be accomplished with Coqui in less than five minutes without GPU. The result is archived and can be used the next time in game. Some TTS projects already started to add tag support to speak text with emotions or sing it. If a game designer has that in mind with a good chat log she can voiced her game over night. reads the log and sends new chat text to piper. ~/.config/Epic/PSUnreal/Saved/Logs/Pongo_Donjo_chat.txt If a line number is set it can speak all the chat text and waits for new chat text. Python 3.8.10 download: https://www.planeshift.it/Download https://github.com/rhasspy/piper
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Speect is a multilingual TTS system. It offers a full text-to-speech system with various API's, as well as an environment for research and development of TTS systems and voices. It is written in ANSI C and uses a plug-in mechanism for extensions. Speect also includes an extensive set of Python bindings for quick implementation of new ideas, these bindings are derived from SWIG interface files and can easily be extended for other languages supported by SWIG. Speect is free and open source software. As a collection it is distributed under a MIT license.
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Steel TTS
A cross-platform wrapper for common text-to-speech engines in Python
Steel is a cross-platform package for using common text-to-speech (speech synthesis) engines in Python. Steel currently supports the following TTS software: - Microsoft Speech API 5 (SAPI5) - eSpeak - NS Speech Synthesis - FreeTTS Documentation: http://sourceforge.net/p/steeltts/wiki/ Bug Tracker: http://sourceforge.net/p/steeltts/tickets/ If you are interested in contributing to the Steel TTS codebase, or would like to make a feature-request, please contact the lead developer, Jasper Danielson, at jrd4@rice.edu. -
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Tacotron-2
DeepMind's Tacotron-2 Tensorflow implementation
Tacotron-2 is a TensorFlow implementation of DeepMind’s Tacotron-2 end-to-end text-to-speech architecture, which predicts mel spectrograms from raw text and then feeds them to a neural vocoder such as WaveNet. It reproduces the original paper’s hyperparameters exactly via paper_hparams.py, while also offering a tuned hparams.py with extra improvements that often yield better audio quality in practice. The repository is structured as a full training pipeline: dataset preparation, preprocessing into spectrograms, Tacotron training, WaveNet (or Griffin-Lim) vocoder training, and final waveform synthesis. It includes directory layouts and logging directories for multiple datasets such as LJSpeech and M-AILABS en_US/en_UK, making it easier to adapt to new English corpora. Separate log trees track mel-spectrograms, attention plots, evaluation audio, and vocoder outputs, so you can inspect how alignment and audio quality evolve over time. -
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TensorFlowTTS
Real-Time State-of-the-art Speech Synthesis for Tensorflow 2
TensorFlowTTS is a state-of-the-art, open-source speech synthesis library built on TensorFlow 2. It offers a variety of architectures for text-to-speech, including classic and modern models such as Tacotron‑2, FastSpeech / FastSpeech2, and neural vocoders like MelGAN and Multiband‑MelGAN. Because it’s based on TensorFlow 2, it can leverage optimizations such as fake-quantization aware training and pruning — which allow models to run faster than real time and to be deployable on mobile or embedded platforms. The library supports multiple languages (English, French, Korean, Chinese, German, etc.) and is relatively easy to adapt to new languages. With integrated vocoder + mel-spectrogram generation pipelines, pre-trained models, and fairly flexible architecture, TensorFlowTTS is a great off-the-shelf and extensible TTS engine for applications ranging from voice assistants to content generation or accessibility tools. -
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Text To Speech Unlimited
Chuyển đổi văn bản thành giọng nói không giới hạn
Chuyển đổi văn bản thành giọng nói không giới hạn số lượng từ và có thể điều chỉnh tốc độ đọc, giọng đọc -
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Transformer TTS
Implementation of a Transformer based neural network
TransformerTTS is an implementation of a non-autoregressive Transformer-based neural network for text-to-speech, built with TensorFlow 2. It takes inspiration from architectures like FastSpeech, FastSpeech 2, FastPitch, and Transformer TTS, and extends them with its own aligner and forward models. The system separates alignment learning and acoustic modeling: an autoregressive Transformer is used as an aligner to extract phoneme-to-frame durations, while a non-autoregressive “ForwardTransformer” generates mel-spectrograms conditioned on text and durations. This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs. -
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Txt-2-Mp3 6.3 Mark 2 [I.S.A]
Txt-2-Mp3 6.3 Mark 2 [Improved.Simplified.Alternative]
'Txt2Mp3' an desktop application developed using python 3.6.8 and other add-on libaries. Can convert texts into audio (.mp3) files using gTTS (Google Text-to-speech) api module library. Compatible only for windows OS. -
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VITS
Conditional Variational Autoencoder with Adversarial Learning
VITS is a foundational research implementation of “VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech,” a well-known neural TTS architecture. Unlike traditional two-stage systems that separately train an acoustic model and a vocoder, VITS trains an end-to-end model that maps text directly to waveform using a conditional variational autoencoder combined with normalizing flows and adversarial training. This architecture enables parallel generation (fast inference) while achieving speech quality that rivals or surpasses many two-stage systems. The repository provides training and inference pipelines for common datasets such as LJ Speech (single-speaker) and VCTK (multi-speaker), including filelists, configs, and preprocessing scripts. It also includes monotonic alignment search code and g2p preprocessing, which are crucial components for aligning text and speech in an end-to-end setup. -
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VideoChat
Real-time voice interactive digital human
VideoChat is a real-time voice-interactive “digital human” system that combines automatic speech recognition, large language models, text-to-speech, and talking-head generation into a single conversational pipeline. It supports both pure end-to-end voice solutions based on multimodal large language models (GLM-4-Voice feeding directly into talking-head generation) and a more traditional cascaded pipeline using ASR → LLM → TTS → talking head. It is built as a Gradio Python demo, exposing a web interface where users can talk to an animated avatar that lip-syncs to synthesized speech while responding intelligently. The system is customizable: you can define your own avatar appearance and voice, and it supports voice cloning so you can generate a new voice from a short 3–10 second reference sample. The tech stack integrates FunASR for speech recognition, Qwen for language understanding, multiple TTS engines like GPT-SoVITS, CosyVoice, or edge-tts, and MuseTalk for talking-head generation. -
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Voice Conference Manager uses VoiceXML and CCXML to control speech recognition, text to speech, and voice biometrics for a telephone conference service. Say the names or numbers of people and VCM places them into the call. Can be hosted on public servers
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WaveRNN
WaveRNN Vocoder + TTS
WaveRNN is a PyTorch implementation of DeepMind’s WaveRNN vocoder, bundled with a Tacotron-style TTS front end to form a complete text-to-speech stack. As a vocoder, WaveRNN models raw audio with a compact recurrent neural network that can generate high-quality waveforms more efficiently than many traditional autoregressive models. The repository includes scripts and code for preprocessing datasets such as LJSpeech, training Tacotron to produce mel spectrograms, training WaveRNN on those spectrograms (with optional GTA data), and finally generating audio. A quick_start.py script allows users to immediately synthesize example sentences from a pretrained model and inspect both generated audio and attention plots. For custom TTS, the project guides you through training Tacotron, forcing GTA spectrogram export when desired, training WaveRNN with or without GTA, and then running joint generation. -
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WhisperSpeech
An Open Source text-to-speech system built by inverting Whisper
WhisperSpeech is an open-source text-to-speech system created by “inverting” OpenAI’s Whisper, reusing its strengths as a semantic audio model to generate speech instead of only transcribing it. The project aims to be for speech what Stable Diffusion is for images: powerful, hackable, and safe for commercial use, with code under Apache-2.0/MIT and models trained only on properly licensed data. Its architecture follows a token-based, multi-stage pipeline inspired by AudioLM and SPEAR-TTS: Whisper is used to produce semantic tokens, EnCodec compresses the waveform into acoustic tokens, and Vocos reconstructs high-fidelity audio from those tokens. The repository includes notebooks and scripts for inference, long-form synthesis, and finetuning, as well as pre-trained models and converted datasets hosted on Hugging Face. Performance optimizations like torch.compile, KV-caching, and architectural tweaks allow the main model to reach up to 12× real-time speed on a consumer RTX 4090. -
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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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shuyuan
Reading book source
shuyuan is a project oriented around reading and knowledge consumption, especially targeting large-scale text content such as books, articles, or educational material. The name suggests “academy” or “study hall,” and the tool aims to help users ingest, organize, and manage reading content — possibly offering features like text parsing, annotation, metadata generation, translation, or storage for later reference. The repository is set up to support document ingestion, indexing, and maybe some AI-aided summarization or lookup functions, which helps users convert large text corpora into a structured, searchable knowledge base. For learners, researchers, or avid readers, Shuyuan offers a way to bridge from plain text files or eBooks into a manageable, interactive resource — one where notes, references, and reading progress can be tracked. It likely supports different input formats (text, HTML, PDF), and may integrate optional translation or text normalization tools. -
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uListen is a TTS(Text To Speech) application. It can TALK you the web pages, chm files, pdf files and word files and plain text files.