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Audio & Voice AI

STYLETTS2

StyleTTS 2 is a Python and PyTorch text-to-speech model and research implementation that uses style diffusion and adversarial training with large speech language models to synthesize speech in a style suited to the input text.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

STYLETTS2 — 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 it when you need a repository-based speech-synthesis model with documented inference notebooks and training code, and can manage the Python environment, model files and any dataset or audio prerequisites.

Who it is for

  • Developers building a text-to-speech workflow around a self-hosted model.
  • Researchers or practitioners exploring speech synthesis, style diffusion or speaker adaptation.
  • Teams preparing speech data and compute for training or fine-tuning a model.

Documented uses

  • Generate speech from text with a pretrained single-speaker model.
  • Explore multispeaker inference using the LibriTTS notebook and reference audio.
  • Train a model using the documented LJSpeech, VCTK or LibriTTS workflows.
  • Fine-tune a multispeaker base model for new speakers.
Before you choose

Requirements & limits

The repository is public and lists the MIT license. The README notes that inference depends on a GPL-licensed package not included in the repository and discusses other packages with different licensing or quality trade-offs.

  • The README specifies Python 3.7 or later, installed requirements, and phonemizer plus espeak-ng for running the demo.
  • The LJSpeech pretrained model is specified at 24 kHz; LJSpeech training data must be upsampled to 24 kHz under the README instructions.
  • The LibriTTS inference demo requires downloading and extracting reference_audio.zip under the demo directory.
  • Training data lists use filename.wav|transcription|speaker; speaker labels are needed for multispeaker training.
  • The README says the second-stage DDP version is not working and the current version uses data parallelism.

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

Set up StyleTTS 2 for LJSpeech training

Prepare the repository, Python dependencies, and 24 kHz LJSpeech data for training.

Before you start

  • Python 3.7 or later
  • A LJSpeech dataset download
  • Linux package installation access if installing the optional demo dependencies

Steps

  1. Clone the repository with the command below and enter it with cd StyleTTS2.

    Documented command
    git clone https://github.com/yl4579/StyleTTS2.git

  2. Install the Python requirements using the command below.

    Documented command
    pip install -r requirements.txt

  3. Download and extract LJSpeech into the data folder, then upsample the data to 24 kHz.

  4. If you want to run the demo, install phonemizer with the command below and install espeak-ng with sudo apt-get install espeak-ng.

    Documented command
    pip install phonemizer

Expected outcome

The repository dependencies and LJSpeech training data are prepared; the README's next stage is the training procedure.

Guide limitations

  • The README specifies Python >= 3.7 but does not document hardware requirements.
  • LJSpeech must be upsampled to 24 kHz for the provided pretrained text aligner and pitch extractor.
  • The phonemizer and espeak-ng steps are optional and described for running the demo.
  • This recipe stops at setup and does not establish that training or inference has been run.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
STYLETTS2Generate speech from text with a pretrained single-speaker model.Linux, Self-hostedThe repository is public and lists the MIT license. The README notes that inference depends on a GPL-licensed package not included in the repository and discusses other packages with different licensing or quality trade-offs.
EdgedictDecode speech from a YouTube Live video into text.Cloud
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
yl4579/StyleTTS2 · Official homepage title · retained in its source language
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