Chatterbox TTS: Open-Source Voice Cloning That Beat ElevenLabs in Blind Testing

Most “open-source alternative to ElevenLabs” claims don't survive a real side-by-side comparison. Chatterbox's does. In a blind evaluation run through Podonos, listeners heard paired samples from Chatterbox and ElevenLabs with no labels, context, or indication of which model made each clip—and preferred Chatterbox's output 63.75% of the time. That result, combined with a permissive MIT license that supports commercial use, is why Chatterbox has become one of the year's most-discussed open-weight TTS releases.

What Is Chatterbox?

Chatterbox is Resemble AI's open-source text-to-speech model family, built by a three-person team and released under the MIT license. It handles two jobs particularly well: zero-shot voice cloning from a short reference clip and emotion exaggeration control—a parameter that lets you move a voice from flat and restrained to dramatically expressive. Resemble AI describes the latter as a first for open-source TTS.

The Chatterbox family has expanded beyond the original English model into three variants, each optimized for a different priority:

• Chatterbox (original) — the base English model, tuned for quality and emotional range.
• Chatterbox Multilingual (V3) — a 0.5B-parameter model covering more than 20 languages. It is designed to preserve a cloned voice's timbre, accent, and rhythm when the same speaker changes languages instead of drifting toward a generic accent.
• Chatterbox Turbo — a speed-optimized 350M-parameter variant with roughly 75 ms latency and generation about six times faster than real time, aimed at voice assistants and other applications where response time matters more than maximum expressiveness.

Under the Hood: Cloning, Emotion, and Watermarking

Zero-shot cloning. Chatterbox can reproduce a target voice from as little as 5–10 seconds of reference audio, with no fine-tuning or separate training run. Provide a clip, and the model can immediately carry that voice into new text.

Two tunable parameters. Output is shaped mainly by two settings: exaggeration, which controls emotional intensity, and cfg_weight, which balances adherence to the reference voice against the input text. The default value of 0.5 for both works well in many cases; increasing exaggeration produces the dramatic delivery most associated with Chatterbox.

Paralinguistic tags. Chatterbox Turbo introduces text-based tags for sighs, gasps, coughs, and similar reactions. The model performs them in the cloned voice with a matching emotional tone instead of requiring a separately recorded sound effect to be added afterward.

Built-in watermarking. Every generated clip includes Resemble AI's PerTh (Perceptual Threshold) watermark at generation time. It is inaudible to listeners but designed to survive compression and basic editing, helping generated audio remain traceable to its source. That is a meaningful trust feature when deploying voice cloning in products where provenance matters.

Getting Started with Chatterbox

1. Set up the environment. Install Chatterbox through pip. The project is developed and tested on Python 3.11, and the official setup recommends a conda environment to keep dependency versions consistent.
2. Clone the repository. Pull resemble-ai/chatterbox from GitHub, or load a checkpoint directly from Hugging Face if you do not need a local repository clone.
3. Pick the right variant. Use the original Chatterbox for expressive English narration, Chatterbox Multilingual when a cloned voice must remain consistent across languages, or Chatterbox Turbo when latency is the priority.
4. Try it before integrating. Resemble AI hosts a Gradio demo on Hugging Face Spaces, where you can test voice cloning and the exaggeration parameter in a browser before writing integration code.
5. Scale beyond self-hosting if needed. Resemble AI also offers a hosted TTS service based on the same model family for teams that need managed scaling instead of operating their own GPUs.

Tips for Better Chatterbox Output

• Match the reference clip to the target language. With Chatterbox Multilingual, ensure the reference audio's language tag matches the language being generated. Otherwise, the output may inherit the reference clip's accent instead of using the target language's natural pronunciation. Setting cfg_weight to 0 can help when an exact match is unavailable.
• Start with the default exaggeration setting. The default value of 0.5 is tuned to work across many prompts. Treat higher values as a deliberate creative choice for dramatic delivery rather than the automatic starting point.
• Use Turbo for interactive applications. If a user is waiting for a response—such as from a voice agent, assistant, or live demo—Turbo's roughly 75 ms latency may matter more than the marginal quality gain of a larger model.
• Account for the watermark in compliance planning. Because every output includes a PerTh watermark by default, mention it in product documentation and policies concerning disclosure of AI-generated audio.
• Provide clean 7–20 second reference clips. Resemble AI used this range in its benchmark testing, making it a reliable target for zero-shot cloning quality even though the model can work with shorter samples.

Chatterbox vs. ElevenLabs vs. Other Open Models

Chatterbox's main differentiator is not only that it competes with a closed commercial leader on perceived quality. It combines that result with an MIT license, built-in watermarking, and full self-hosting—capabilities that the ElevenLabs API does not offer together.

Frequently Asked Questions

Is Chatterbox free for commercial use?

Yes. Chatterbox is released under the MIT license, one of the most permissive open-source licenses. It permits commercial use, modification, and distribution, subject to the license's notice and copyright conditions.

How does Chatterbox compare with ElevenLabs in actual quality?

In a blind evaluation conducted through Podonos using identical text and reference clips, with no model labels shown to evaluators, listeners preferred Chatterbox's output 63.75% of the time over ElevenLabs.

What's the difference between Chatterbox, Chatterbox Multilingual, and Chatterbox Turbo?

The original Chatterbox is the base English model with strong emotional range. Chatterbox Multilingual extends voice cloning to more than 20 languages while preserving speaker identity. Chatterbox Turbo prioritizes speed, with roughly 75 ms latency for real-time use cases.

Does Chatterbox watermark its audio output?

Yes. Every generation automatically includes Resemble AI's PerTh watermark, embedded so that it is inaudible to listeners but detectable for provenance and authenticity checks.

How much reference audio does Chatterbox need to clone a voice?

It can work with as little as 5–10 seconds of reference audio, with no separate training or fine-tuning step required for zero-shot cloning. A clean clip in the 7–20 second range is a practical target for more reliable results.

Try a Comparable Voice Cloning Feature

Chatterbox itself is available through Resemble AI's open-source implementation and official demo. If you want to explore browser-based voice cloning, try Voice Clone as a comparable feature. It is not Chatterbox and does not run the Chatterbox model, but it lets you test a similar voice-cloning workflow before deciding how to deploy Chatterbox.

Only clone voices you own or have explicit permission to use.

Explore Voice Clone →