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Tts

Tts is a Python deep-learning library for text-to-speech generation. It provides released speech models, tools for training and fine-tuning models, speaker-encoding components, and utilities for analyzing and curating datasets.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Tts — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Tts when speech generation or model development needs to happen in a Python-based workflow, rather than through a hosted end-user interface.

Who it is for

  • Python developers adding speech synthesis to software.
  • Researchers and model builders training or fine-tuning speech models.
  • Teams evaluating pretrained models for multilingual or multi-speaker synthesis.

Documented uses

  • Convert text into audio using a released model and the Python API.
  • Generate speech using the documented multi-speaker, multilingual XTTS v2 example.
  • Train or fine-tune speech-synthesis models.
  • Analyze and curate text-to-speech datasets.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MPL-2.0 license. The README documents PyPI, local repository and Docker installation paths.

  • The README recommends Python 3.9 to below 3.12 and reports testing on Ubuntu 18.04.
  • PyPI installation is presented as the simplest path for released-model synthesis; local installation is intended for coding or training.
  • The XTTS v2 example requires both a speaker audio file and a language parameter.
  • The project documents a CPU Docker image and points to separate Docker guidance for GPU support.
  • Model families and capabilities vary by implementation; check the relevant model documentation for the intended workflow.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Coqui TTS locally for coding or training

Set up a local editable installation for development or model training.

Before you start

  • A Python environment; the README specifies Python 3.9 or later and earlier than 3.12.

Steps

  1. Clone the repository with the command below.

    Documented command
    git clone https://github.com/coqui-ai/TTS

  2. Install the editable package and the listed optional dependencies with the command below.

    Documented command
    pip install -e .[all,dev,notebooks]

  3. For Ubuntu or Debian, optionally run the command below followed by the command below.

    Documented command
    make system-deps
    Documented command
    make install

Expected outcome

The repository is installed locally in editable mode with the selected extras.

Guide limitations

  • The README describes the system-dependency commands as intended for Ubuntu or Debian.
  • The recipe does not document a model-training workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
coqui-ai/TTS · Official homepage title · retained in its source language
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