Open Source Voice Cloning
Voice cloning is the rare software category where the license on the code is the least of your legal questions. Reproducing a specific person's voice needs their consent and rights to the recordings, no matter how permissive the tool is, and the model that generates the audio often carries its own separate license that the code license does not cover. Running open source cloning locally does not settle the ethics, but it does keep the two things you most want to control in your own hands: the reference audio you train on never leaves your machine, and the generated speech is produced on hardware you own rather than a vendor's cloud.
GPT-SoVITS-WebUI
Few-shot voice cloning and text-to-speech WebUI that can fine-tune from 1 minute of voice data
OpenVoice
Instant voice cloning model with tone color cloning, style control, and cross-lingual speech generation
RVC WebUI
VITS-based voice conversion web UI for training and running voice models from short audio samples
Chatterbox
Open-source text-to-speech models with multilingual voice cloning and built-in watermarking
F5-TTS
Text-to-speech system for fluent, faithful speech generation with flow matching
Coqui TTS
Advanced text-to-speech library with pretrained models in 1100+ languages and voice cloning tools
Our picks
Pick by the job: instant cloning, a fine-tuned voice, real-time conversion, or multilingual narration.
Best for few-shot voice cloning: GPT-SoVITS-WebUI GPT-SoVITS-WebUI is built for cloning when you have little audio: zero-shot text-to-speech from a five-second sample, or fine-tuning from about a minute for better similarity. It does cross-lingual inference across English, Japanese, Korean, Cantonese, and Chinese, and bundles dataset tools for separation, segmentation, ASR, and labeling. It runs as a Python WebUI or via Docker.
Best for instant cloning with permissive licensing: OpenVoice OpenVoice clones tone color from a short reference clip and generates speech in multiple languages and accents, including zero-shot cross-lingual output. It exposes controls for emotion, accent, rhythm, and pauses, and its newer release adds native support for six languages. It is MIT licensed and stated as free for commercial and research use.
Best for voice conversion and real-time changing: RVC WebUI RVC WebUI converts one voice into another rather than reading text aloud, training from about ten minutes of clean speech. It includes a real-time voice changer at roughly 170 ms latency, or 90 ms with ASIO, plus top1 retrieval to reduce timbre leakage and UVR5 vocal separation.
Best for multilingual narration with watermarking: Chatterbox Chatterbox turns text into speech for cloning, multilingual narration, and low-latency agents, covering twenty-three listed languages and cloning from a reference clip. Every generated file carries Resemble AI's PerTh watermark, designed to survive compression and editing, which helps when provenance matters. It installs via pip or runs from source offline.
What to weigh before cloning a voice
Consent comes before quality in this category, because the software license does not grant you the right to a person's voice. Confirm you have written permission covering cloning, reuse, distribution, and revocation before you feed anyone's recordings into these tools. Then read the model license, which can differ from the code license: F5-TTS, for example, ships its code under MIT while its pretrained models are CC-BY-NC, meaning non-commercial. Chatterbox takes the risk seriously in the other direction, embedding a PerTh perceptual watermark in every generated file, designed to survive MP3 compression and editing.
Next, decide between instant cloning and a trained voice, because they solve different problems. Zero-shot systems clone from seconds of reference audio and are convenient for demos and low-volume work: OpenVoice clones tone color from a short clip and can generate cross-lingual speech, and GPT-SoVITS runs zero-shot text-to-speech from a five-second sample. A fine-tuned voice takes more audio and compute but holds up better across long narration or a recurring character; GPT-SoVITS can fine-tune from about a minute, and RVC WebUI trains a conversion model from roughly ten minutes of clean speech. Note that RVC does voice conversion and a real-time voice changer rather than reading text aloud.
Finally, match deployment to your latency and privacy needs, and keep an exit path. Batch rendering on a GPU server suits long scripts, while real-time use, such as RVC's low-latency voice changer, demands tighter limits and predictable GPU availability. Whatever you pick, keep prompts, speaker samples, generated audio, embeddings, and checkpoints in ordinary files rather than hidden application state, so replacing the engine later is a copy rather than a rebuild.
Related categories
Frequently asked questions
Is it legal to clone a voice for commercial use?+
The software license is only part of the answer. You also need rights to the voice, the recordings used for reference or training, and the generated output in your jurisdiction, plus written consent covering cloning, reuse, distribution, and revocation. Model licenses matter too: F5-TTS releases its code under MIT but its pretrained models under CC-BY-NC, which is non-commercial. Owning a recording does not by itself grant the right to synthesize that person's voice.
How much audio does a good cloned voice need?+
It depends on the approach. Zero-shot systems imitate from very little: OpenVoice clones from a short reference clip and GPT-SoVITS runs from a five-second sample, which is fine for tests and demos. A more consistent custom voice needs more; GPT-SoVITS fine-tunes from about a minute, and RVC WebUI trains from roughly ten minutes of clean speech. Clean, consistent audio beats a larger pile of noisy calls, echo, or overlapping speakers.
What is the difference between voice cloning, text-to-speech, and voice conversion?+
Text-to-speech turns text into speech with a generic voice, which is F5-TTS and much of Coqui TTS. Voice cloning reproduces a specific speaker's identity from reference audio, the focus of OpenVoice and GPT-SoVITS. Voice conversion transforms one voice into another, including in real time, which is what RVC WebUI does through its VITS-based changer. Deciding which of the three you actually need narrows the tools quickly.
Which tool works for real-time voice changing?+
RVC WebUI is the one built for it, with a real-time voice changer reporting around 170 ms end-to-end latency, or 90 ms with ASIO hardware support. Real-time use needs low-latency capture, stable speaker conditioning, and predictable GPU availability, not just a fast model. Many cloning tools are stronger at batch generation than live conversion, so test end-to-end latency under load before relying on it for live agents or games.
Do these tools handle multiple languages and accents?+
Coverage varies widely. GPT-SoVITS does cross-lingual inference across English, Japanese, Korean, Cantonese, and Chinese; OpenVoice natively supports six languages; and Chatterbox lists twenty-three. Coqui TTS ships pretrained models for over 1100 languages. A cloned voice can keep its identity in one language and drift in another if the speaker samples lack the needed phonemes or prosody, so test the exact languages and accents you need.
Can I tell whether a clip was AI-generated or trace it back?+
Some tools build this in. Chatterbox embeds Resemble AI's PerTh perceptual watermark in every generated audio file, designed to stay imperceptible while surviving MP3 compression and common editing. Most others do not watermark by default, so if provenance matters for your workflow, keep your own metadata linking each clip to its model, prompt, and job, and consider human review before publishing anything that represents a real person or brand.
What if the cloning project I rely on stops being maintained?+
Your exposure depends on how portable your data is. Keep original recordings, cleaned segments, transcripts, prompts, generated samples, and any speaker embeddings or model checkpoints in documented formats, and record dependency versions or containerize the environment so results reproduce later. Coqui TTS itself began as a fork that revived an unmaintained project, which shows work can continue, but a well-kept dataset lets you retrain on another engine if one fades.