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Matcha-TTS

shivammehta25/Matcha-TTS

Matcha-TTS is a fast TTS architecture with conditional flow matching

GitHub Website

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

48views this month

1.4k stars216 forksLast push Aug 31, 2026 Jupyter Notebook MIT

Decision brief

Matcha-TTS employs non-autoregressive probabilistic techniques with deep learning for fast TTS generation.

Good fit when

When you require rapid deployment of text-to-speech engines without autoregressive dependencies
For applications needing state-of-the-art ICASSP 2024 advancements in TTS technology

Avoid when

If your project requires real-time adaptability to user input that necessitates autoregressive methods
In scenarios where licensing flexibility is not a priority, favoring proprietary systems with dedicated support

Observed Jul 17, 2026 · Source: enrich:decision_facts

Matcha-TTS alternatives Trust report

Verify the decision

Maintenance and security

Full trust report

Maintenance: Active (19d since push); As of Sep 20, 2026
Provenance: Not a fork · Personal account; As of Sep 20, 2026
Security (OSV): 103 low (103 low); As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/shivammehta25/Matcha-TTS

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

[ICASSP 2024] Matcha-TTS offers a method for fast text-to-speech generation using deep learning and diffusion models with non-autoregressive probabilistic techniques.

Capability facts

Languages: jupyter notebook, python
Source: github.language+pyproject.toml · Sep 20, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 20, 2026)

conda create -n matcha-tts python=3.10 -y

Source link

README

Installation 1. Create an environment (suggested but optional) 2. Install Matcha TTS using pip or from source from source 3. Run CLI / gradio app / jupyter notebook ```bash

For agents

This page has a .md twin and JSON over the API.

/tools/shivammehta25-matcha-tts.md GET /api/graphcanon/tools/shivammehta25-matcha-tts GET /api/graphcanon/graph?tool=shivammehta25-matcha-tts