
GPT-SoVITS
Few-shot voice cloning from one minute of audioOpen source
About GPT-SoVITS
GPT-SoVITS is an open-source TTS and voice-cloning toolkit with a local web UI covering the full pipeline, from preparing training audio to synthesis. Two official modes: synthesis from about 5 seconds of reference audio, or fine-tuning on about 1 minute of data for a closer voice, in Chinese, English, Japanese, Korean and Cantonese. MIT code by GitHub account RVC-Boss; 63,616 stars (2026-10-25). V5 hit main in 2026-10 with no release; code matching 9 CVEs from 2025 (command injection, unsafe deserialisation) remains as of 2026-10-19 (some load points depend on the PyTorch version in use).

What it is
GPT-SoVITS is an open-source text-to-speech (TTS) and voice-cloning project; the code, scripts for downloading pretrained models and a local Gradio-based web interface (WebUI) all live in the GitHub repo RVC-Boss/GPT-SoVITS. The repo description reads "1 min voice data can also be used to train a good TTS model! (few shot voice cloning)". The README lists two ways to use it: zero-shot synthesis, which generates speech in the same voice from just a reference clip of about 5 seconds; and few-shot fine-tuning, which fine-tunes the model on about 1 minute of training data in exchange for higher similarity and realism. Supported languages are Chinese, English, Japanese, Korean and Cantonese, and it can synthesise across languages, for example speaking an English sentence from Chinese reference audio.
The name has two parts, matching the project's two models: the GPT part generates an intermediate speech representation from text and reference audio, and the SoVITS part turns that back into sound. The README's acknowledgements list ar-vits, SoundStorm, VITS and others as theoretical sources. The official wiki (2026-10-07 version) gives parameter counts of 90M+77M for v1 and v2, 330M+77M for v3 through v5, 133M+77M for v2Pro and 152M+77M for v2ProPlus.
The repo was created by RVC-Boss on 2024-01-15 with the first version of the code. The account's public profile gives no name, location or bio. RVC-Boss is also the top account on the contributor list of another voice-changing project, RVC-Project/Retrieval-based-Voice-Conversion-WebUI (created 2023-03-27, MIT licence, 38,846 stars as of 2026-10-25). Chinese documentation includes a Chinese README, a Chinese changelog and a Chinese user manual on Yuque, plus a separate AutoDL cloud image and ModelScope download source for users in mainland China; there are also English, Japanese, Korean and Turkish READMEs, and an English guide on rentry linked from the README.
As of 2026-10-25 the repo had 63,616 stars and 6,721 forks, with 1,065 commits on main, 620 of them signed by RVC-Boss and the rest from about a hundred contributors; there were 800 open issues and 104 open PRs. The latest commit on main was on 2026-10-19, adding semantic streaming segmentation to API v2.
There are only four tagged GitHub releases: 20240821v2 (2024-08-21), 20250228v3 (2025-02-28), 20250422v4 (2025-04-22) and 20250606v2pro (2025-06-06). Later updates have gone straight into code branches: streaming inference arrived in 2025-11; in 2026-08 the automatic speech recognition model was swapped and single-GPU training improved; from 2026-10-05 CUDA Graph acceleration and the V5 model were added, and V5's code was merged into main on 2026-10-06, but as of 2026-10-25 no release tag had been made for it.
This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible.
Core capabilities
Two paths: zero-shot and few-shot. For a quick result, about 5 seconds of clean reference audio plus its transcript is enough; for a steadier, closer voice, fine-tune the two models through the WebUI's training flow. The official wiki (2026-10-07) says: "Few-shot fine-tuning based on v5 has not yet been studied or tested." The wiki also notes that if you do fine-tune, V2 and V2Pro(Plus) differ little in SoVITS voice similarity.
How the models evolved. Per the Chinese changelog: V2 was released on 2024-08-21, adding Korean and Cantonese and expanding the SoVITS base-model training data from about 2,000 hours to about 5,000; V3 was merged on 2025-02-11 with better voice similarity, and fine-tuning needed 14G of VRAM at launch, dropping to 12G after gradient checkpointing was added on 2025-02-12 and to 8G after LoRA training arrived on 2025-02-23; V4 was released in 2025-04, mainly fixing V3's metallic artefacts and outputting 48k audio natively; V2Pro and V2ProPlus were released on 2025-06-04, described officially as beating V4 at hardware cost close to V2; V5 was added from 2026-10-05, and the wiki says its inference is about 4 times as fast as V2, with a Turbo version using 4-step inference. The wiki's selection advice: take the V2Pro route when training audio is of average quality, and choose V4 or V5 for studio-grade material.
Self-reported evaluation scores. On the Chinese part of the public Seed-TTS test set, the wiki gives each version's word error rate (WER) and speaker similarity (SIM): v2 0.017/0.549, v4 0.013/0.735, v2ProPlus 0.016/0.737, v5 Turbo (4 steps) 0.016/0.782, v5dev 0.017/0.805, against 0.013/0.755 for real recordings as the reference. The project author ran these numbers with the official seed-tts-eval models. The wiki also explains that the real-recording row marks the precision ceiling of these measurements: SIM above the real recording or WER below it says nothing meaningful about voice similarity or stability, the English version suggests reading any SIM above 0.75 as 0.75, and it notes the scores are for reference only.
A complete data-processing toolchain. The WebUI integrates the steps needed before training: vocal and accompaniment separation (UVR5), automatic slicing of long audio, denoising, automatic speech recognition for labelling, and an interface for checking labels by hand. The Chinese README says the recognition models have been switched to multilingual options such as Fun-ASR-Nano, SenseVoice and FunASR (2026-08).
Inference speed and API. The English README at the repo root gives v2ProPlus real-time factors (RTF, lower is faster) of about 0.028 on an RTX 4060 Ti, about 0.014 on an RTX 4090 and about 0.526 on an Apple M4 CPU (English README as of 2026-10-25). Besides the WebUI, the repo ships a FastAPI-based api_v2.py, which by default listens only on port 9880 on the local 127.0.0.1; streaming output has been supported since 2025-11-28, and a 2026-10-18 commit added semantic streaming acceleration for V5 and V2ProPlus.
How it compares
Versus the hosted service ElevenLabs. ElevenLabs is a monthly-subscription cloud service; its pricing page as of 2026-10-25 shows a free tier with 10k credits a month; Starter at $6 a month, with instant voice cloning and a commercial licence; Creator at $22 a month, with professional voice cloning; and higher tiers at $99, $299 and $990. The page also carried a first-month discount ending on November 1. ElevenLabs needs no GPU of your own and offers multiple languages, dubbing, an API and commercial support; GPT-SoVITS costs nothing, but users must bring their own NVIDIA GPU, set up the environment themselves, and carry data security and compliance themselves.
Versus the open-source F5-TTS. F5-TTS (SWivid/F5-TTS, 15,449 stars as of 2026-10-25) also supports zero-shot cloning. Its code is MIT, but the README says the pretrained models are CC-BY-NC because of the licence of the Emilia training data, so the official weights can't be used commercially and commercial use requires retraining. The Hugging Face repo holding GPT-SoVITS's official base models, lj1995/GPT-SoVITS, is marked MIT.
Versus the open-source fish-speech. fish-speech (33,395 stars as of 2026-10-25) uses the Fish Audio Community License v1.0 (updated 2026-10-13): research and non-commercial use are free, individuals or organisations with annual revenue under $500k may make limited commercial use, and offering it as a hosted service requires a separate licence. GPT-SoVITS's root licence is MIT with no added conditions.
Who it's for / Not for
Good fit: individual creators with an NVIDIA GPU who want to do dubbing, audiobooks, game characters or video narration on their own machine; teams that need offline processing and can't upload voice samples to a third-party cloud; users working mainly in Chinese who need Chinese docs and community tutorials (the Yuque manual, AutoDL image and ModelScope download source all target Chinese users); and developers who want to build on the MIT code and embed TTS in their own tools, who can call the repo's api_v2.py directly.
Poor fit: companies that need service-level agreements, invoices and commercial support, because the project has no company behind it and no paid support channel; setups that expose the service directly to the internet for many users, since the WebUI listens on every interface by default with no login, and the known vulnerabilities have no fix record; Mac-only users who want to train, since the README says models trained on a Mac GPU are noticeably worse and recommends CPU training instead; developers who want a one-line pip install to use it as a Python library, since the project isn't published on PyPI and the whole repo must be cloned; and projects that need stable version numbers for dependency pinning, since the latest release is still from 2025-06-06.
Getting started
Listen online first. The project provides an online v2ProPlus demo Space on Hugging Face (lj1995/GPT-SoVITS-ProPlus); upload reference audio to try synthesis without installing anything.
Windows users can use the bundled package. The README offers an unzip-and-run archive; double-click go-webui.bat to start the WebUI. Note that as of 2026-10-25 the package link in the README still points to the v3lora version of 2025-02-28, while the body of the 20250606v2pro release page gives download links for a newer v2pro-20250604 package and a separate build for 50-series GPUs.
Linux and macOS use conda. The README's steps: create a Python 3.10 environment, run install.sh, and use flags to choose the hardware (CU126, CU128, ROCM, MPS or CPU) and the model download source (Hugging Face, an HF mirror or ModelScope). The test environments listed in the README cover Python 3.9 to 3.12, PyTorch 2.2.2 to a 2.8 dev build, and CUDA 11.8 to 12.8.
Docker is also an option. The README points to the image xxxxrt666/gpt-sovits maintained by a community contributor, pulled 97,685 times as of 2026-10-25 and last updated on 2026-06-17; the README itself warns that the image lags the code, and V5 users must build their own. To try V5, the README suggests cloning the cuda_graph_accel_v5 branch.
Training follows the WebUI's tab order: vocal separation, slicing, denoising, automatic recognition, manual label checking, then fine-tuning the two models in turn, and finally switching to the inference tab to synthesise. Detailed illustrated instructions are in the Chinese manual on Yuque and the English guide on rentry.
Pricing & license
The software itself is free, with no paid edition, subscription or cloud service. The cost is mainly hardware: inference can run on consumer GPUs, and CPU works too but much more slowly (the English README gives a v2ProPlus real-time factor of about 0.526 on an M4 CPU). VRAM needed for V3 fine-tuning: 14G at launch, about 12G after gradient checkpointing was added on 2025-02-12, and about 8G after LoRA support on 2025-02-23; for the V2Pro series now recommended, the wiki only says it uses "slightly more VRAM than V2".
Licence. The root LICENSE has been MIT since the first commit on 2024-01-15 (copyright line Copyright (c) 2024 RVC-Boss); across the full commit history the file has never been modified, and there are no other licence files at the repo root. The official pretrained models are in the Hugging Face repo lj1995/GPT-SoVITS, whose model card is marked MIT.
The repo also includes third-party code under its own licences: NVIDIA BigVGAN, brought in with V3 on 2025-02-11, is MIT, and its bundled incl_licenses directory contains upstream licences including MIT, Apache-2.0 and BSD-3-Clause; the AP-BWE audio super-resolution code brought in on 2025-02-27 is MIT. Several other code groups declare Apache-2.0 only in their file headers, with no separate licence text in the repo: the Chinese text front end from PaddleSpeech (tone_sandhi, zh_normalization, g2pw, copyright PaddlePaddle Authors, distributed with the repo since 2024-01); the ERes2Net speaker-model code from Alibaba DAMO Academy's 3D-Speaker (brought in on 2025-06-04, used by the V2Pro series to extract speaker embeddings); and optimizer and activation module code in the AR module, copyright Xiaomi. Redistribution needs to keep these licence texts and copyright notices as well.
Risks & limits
Security vulnerabilities. GitHub Security Lab disclosed 9 vulnerabilities on 2025-06-27 (CVE-2025-49833 to CVE-2025-49841), tested against 20250228v3. Four are command injection: the slicing, denoising, speech-recognition and labelling entry points in webui.py splice user input directly into shell commands; the other five are unsafe deserialisation in the UVR5 separation module, the inference UI and the model-weight handling code, where loading a maliciously crafted model file is enough to run arbitrary code. The NVD record for CVE-2025-49833 carries a CVSS 4.0 score of 8.9 (high) from GitHub as the numbering authority. Per issue #2194, the researcher asked for a vulnerability-report contact on 2025-03-15, followed up twice on 05-06 and 06-30, and on 07-08 commented that, having had no reply, the report had been published. As of commit b4570b8 on main on 2026-10-19, webui.py still runs spliced commands with shell=True, and inference_webui.py and process_ckpt.py still load weights with explicit weights_only=False; the three UVR5 load points (two in vr.py, one in bsroformer.py) don't set weights_only explicitly, and the PyTorch docs say the default changed to weights_only=True from 2.6, so the risk at these three points depends on the version in use, and older versions in the README's test table such as 2.5.1 and 2.2.2 are still affected. The main WebUI and the inference WebUI bind 0.0.0.0, and Gradio is launched with no login authentication; setting the environment variable is_share to True also creates a public Gradio link, which the changelog says is meant for settings such as Colab. The /set_gpt_weights and /set_sovits_weights endpoints in api_v2.py accept any weight path, and the backend likewise loads them with weights_only=False; it binds only 127.0.0.1 by default, and the -a flag makes it listen externally. The repo has no SECURITY.md, and its GitHub security advisories page is empty.
Technical checks. The code has no licence keys, activation or use-case detection, and anyone who downloads it can use every feature. Several Gradio interfaces set analytics_enabled=False, turning off Gradio's built-in usage statistics. The WebUI and README also offer no speaker-consent verification or watermarking of synthesised audio.
Legal framing. The project's own licence is only the MIT LICENSE at the root; third-party code distributed with the repo carries its own licences. The top of both the main WebUI and the inference UI shows a statement, which the English UI renders as: "This software is open source under the MIT license. The author does not have any control over the software. Users who use the software and distribute the sounds exported by the software are solely responsible." It is followed by: "If you do not agree with this clause, you cannot use or reference any codes and files within the software package. See the root directory Agreement-LICENSE for details." The wording of the second sentence is stricter than the MIT text, and the LICENSE file itself contains no such clause. Cloning someone else's voice also touches voice rights, portrait rights and local rules on deep-synthesis content, and under the statement those responsibilities fall entirely on the user.
Training data. The wiki gives only the duration of the base-model training data: about 2,000 hours for v1, about 5,000 hours in total for v2's SoVITS part (about 2,500 hours for the GPT part), and about 7,000 hours from v3 on, while the V5 notes say the diffusion model used a 5,000-hour training set. The README's to-do list has the item "Try expand training dataset (2k hours -> 10k hours)." checked as done, which doesn't match the wiki's figures.
Maintenance and versions. Of the 1,065 commits on main, 620 come from RVC-Boss alone; there are 800 open issues; the latest release is still from 2025-06-06, and V5 exists only as branches and commits, with no version tag.
Our verdict
GPT-SoVITS's defining traits: the MIT licence covers both the code and the official base models; the WebUI chains data processing, training and inference into one flow; there is a Chinese README, a Yuque manual, an AutoDL image and a ModelScope download source; and in the author's own tests V5's zero-shot speaker similarity is 0.782/0.805 (0.737 for v2ProPlus), while the wiki notes that scores above real recordings have no practical meaning and are for reference only.
Its limits: code matching the 9 vulnerabilities disclosed in 2025 can still be found as of 2026-10-19, and the WebUI listens on every interface by default with no login; the latest release is still from 2025-06-06, and V5 is available only from branches; the wiki gives only the duration of the base-model training data; and the project has no company behind it and no paid support channel.
Why it stands out
GPT-SoVITS is a voice-cloning and text-to-speech toolkit: zero-shot synthesis from about five seconds of reference audio, or few-shot fine-tuning on about one minute of data for a closer match. It has two stages. The GPT stage is an autoregressive model that turns text and the reference audio into semantic tokens; the SoVITS stage is a VITS-based acoustic model that turns those tokens into speech. In v1, v2 and v2Pro, SoVITS produces the waveform end to end; v3 and v4 replace it with a diffusion model and hand the result to a separate vocoder (the open BigVGANv2 in v3, the author's own vocoder in v4). Training and inference are packaged in a WebUI a non-engineer can operate.
RVC-Boss, the top contributor to the voice-conversion project Retrieval-based-Voice-Conversion-WebUI, created the repository in January 2024 and has kept shipping since, through v3, v4 and v2Pro; V5 was merged into the main branch in October 2026.
It clones a voice from about five seconds of reference audio, or fine-tunes on about one minute, and packages training and inference in a WebUI.
Sources
Official
- GitHub - RVC-Boss/GPT-SoVITS: 1 min voice data can also be used to train a good TTS model! (few shot voice cloning) · Accessed Oct 25, 2026
- GPT-SoVITS LICENSE (MIT) · Accessed Oct 25, 2026
- History for LICENSE - RVC-Boss/GPT-SoVITS · Accessed Oct 25, 2026
- GPT-SoVITS README (in Chinese) · Accessed Oct 25, 2026
- GPT-SoVITS changelog, Changelog_CN (in Chinese) · Accessed Oct 25, 2026
- Releases - RVC-Boss/GPT-SoVITS · Accessed Oct 25, 2026
- Release 20250606v2pro - RVC-Boss/GPT-SoVITS · Accessed Oct 25, 2026
- GPT-SoVITS features (Latest, including v5) - wiki · Accessed Oct 25, 2026
- Commit 3b95794: Integrate v5 training and inference updates with single-GPU training support
- GPT_SoVITS/BigVGAN (LICENSE and incl_licenses) · Accessed Oct 25, 2026
- Issue #2194: Vulnerability report contact person - RVC-Boss/GPT-SoVITS · Accessed Oct 25, 2026
- webui.py at b4570b8 · Accessed Oct 25, 2026
- GPT_SoVITS/inference_webui.py at b4570b8 · Accessed Oct 25, 2026
- GPT_SoVITS/process_ckpt.py at b4570b8 · Accessed Oct 25, 2026
- api_v2.py at b4570b8 · Accessed Oct 25, 2026
- RVC-Boss - GitHub profile · Accessed Oct 25, 2026
- GPT_SoVITS/text/tone_sandhi.py at b4570b8 (PaddlePaddle Authors, Apache-2.0 header) · Accessed Oct 25, 2026
- GPT_SoVITS/eres2net/ERes2NetV2.py at b4570b8 (3D-Speaker, Apache-2.0 header) · Accessed Oct 25, 2026
Security advisories
- NVD - CVE-2025-49833 · Accessed Oct 25, 2026
Media & third party
- lj1995/GPT-SoVITS - Hugging Face (pretrained models) · Accessed Oct 25, 2026
- GPT-SoVITS-ProPlus - Hugging Face Space · Accessed Oct 25, 2026
- GPT-SoVITS guide on Yuque (in Chinese) · Accessed Oct 25, 2026
- GHSL-2025-045_GHSL-2025-048: Command injection in GPT-SoVITS · Accessed Oct 25, 2026
- GHSL-2025-049_GHSL-2025-053: Remote Code Execution (RCE) in GPT-SoVITS · Accessed Oct 25, 2026
Related products
- RVC-Project/Retrieval-based-Voice-Conversion-WebUI · Accessed Oct 25, 2026
- Serialization semantics - PyTorch 2.14 documentation · Accessed Oct 25, 2026
- ElevenLabs Pricing · Accessed Oct 25, 2026
- SWivid/F5-TTS · Accessed Oct 25, 2026
- fish-speech LICENSE (Fish Audio Community License) · Accessed Oct 25, 2026
Data
- xxxxrt666/gpt-sovits - Docker Hub · Accessed Oct 25, 2026