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Chatterbox Turbo is a 350M parameter open-source text-to-speech model that runs 6x faster than real-time on a GPU. It features paralinguistic tags, zero-shot cloning, and includes built-in PerTh watermarking for safety.
Chatterbox Turbo is a fast, expressive, open-source text-to-speech (TTS) model designed for developers and production environments. It is the only open-source TTS with built-in watermarking, offering up to 6× faster than real-time inference on a GPU, with paralinguistic prompting, zero-shot cloning, and an MIT license. It is built for real-time applications, voice assistants, and interactive media, providing trustworthy voice AI that is both open and accountable.
The model addresses the need for high-performance, secure, and flexible TTS in production. Traditional TTS models often force a trade-off between speed, expressiveness, and security. Chatterbox Turbo eliminates this compromise by combining faster-than-realtime inference with built-in authentication via PerTh watermarking, solving the problem of deploying voice AI that is both performant and responsible.
A key feature is its exceptional speed and efficiency. Chatterbox Turbo runs up to 6× faster than real-time on a single GPU with roughly 75ms latency, enabled by its lean 350M parameter size and alignment-informed generation. This makes it streaming-ready for low-latency applications like voice assistants and interactive agent loops.
The model excels at zero-shot voice cloning, requiring only about 5 seconds of reference audio to clone a voice without any training or fine-tuning. It includes easy voice conversion scripts, allowing developers to quickly generate speech in a target voice directly at inference time.
Chatterbox Turbo introduces unique paralinguistic prompting, a first for open-source models. It uses text-based tags like [sigh], [gasp], [cough], and [laugh] to generate natural vocal reactions performed naturally in the cloned voice with matching emotional tone, eliminating the need for post-processing or manual audio editing.
Every audio output is authenticated by default with Resemble AI's built-in PerTh (Perceptual Threshold) Watermarker. This psychoacoustic watermarking embeds data imperceptibly into the audio, providing traceability for detection, provenance, and incident response without compromising audio quality.
The overall approach combines a developer-first philosophy with production-ready robustness. It is designed to be fast, expressive, and secure out of the box, using a lean architecture for low latency and incorporating responsible AI practices directly into the generation process.
Publisher
A
admin
Launch Date2025-12-30
Platformapi
Pricingfree
Tech Stack
#Python#PyTorch
Domain Authority
Ahrefs DR73/100