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Model Overview
chatterbox-turbo is a 350M parameter text-to-speech model created by Resemble AI that prioritizes speed and efficiency without compromising audio quality. It represents the latest advancement in the chatterbox family, which also includes chatterbox-multilingual for 23+ languages and chatterbox-pro for expressive synthesis. The model reduces computational requirements and VRAM usage while maintaining high-fidelity output. A key engineering achievement involves distilling the speech-token-to-mel decoder, cutting generation steps from 10 to just one, making this model ideal for applications requiring low-latency voice synthesis.
Model inputs and outputs
The model accepts text inputs along with optional reference audio for voice cloning and various generation parameters. It outputs audio files in WAV format. The synthesis process can be controlled through temperature, sampling parameters, and optional seed values for reproducibility. Reference audio clips must exceed 5 seconds for effective voice cloning, or you can select from 20 pre-made voices.
Inputs
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Text: The content to synthesize (maximum 500 characters), supporting paralinguistic tags like [cough], [laugh], [chuckle], [clear throat], [sigh], [groan], [sniff], [gasp], and [sush]
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Voice: Pre-made voice selection from options including Andy, Abigail, Aaron, Brian, Chloe, Dylan, and others
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Reference Audio: Optional audio file for voice cloning (requires minimum 5-second duration)
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Temperature: Controls randomness in generation, ranging from 0.05 to 2.0 (default 0.8)
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Top P: Nucleus sampling threshold between 0.5 and 1.0 (default 0.95)
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Top K: Vocabulary limitation parameter between 1 and 2000 (default 1000)
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Repetition Penalty: Reduces token repetition with values from 1 to 2 (default 1.2)
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Seed: Optional integer for reproducible results
Outputs
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Audio File: Generated speech synthesis in WAV format with embedded Perth watermarking for responsible AI tracking
Capabilities
The model generates natural-sounding speech from text input with several distinctive features. It natively supports paralinguistic tags, allowing insertion of vocal expressions like coughs, laughs, and chuckles directly into your text without separate processing steps. Voice cloning works through zero-shot learning, requiring only a brief audio sample to replicate a speaker’s characteristics. The model handles English text with particular fluency and produces output suitable for both narration and interactive voice agent applications. Generation parameters like temperature and sampling methods give you fine-grained control over output variety and consistency. Every generated audio file includes imperceptible watermarks that persist through compression and editing, supporting content authenticity verification.
What can I use it for?
Build voice agents with minimal latency, making it suitable for real-time conversational applications and interactive media requiring rapid response times. Create narration for videos, audiobooks, and multimedia content where natural-sounding speech matters. Develop customer service systems that require fast audio generation without sacrificing voice quality. Implement voice cloning workflows for creative projects, localization, or character voices in games and animations. The model’s efficiency makes it cost-effective for high-volume audio production scenarios. Compare performance against speech-02-turbo if you need emotional expression across multiple languages, or consider resemble-enhance for post-processing audio quality.
Things to try
Experiment with paralinguistic tags to add character and authenticity to dialogue. Insert [chuckle] or [laugh] into conversational text to create more engaging voice agent interactions. Use lower temperature values (around 0.5) when you need consistent, predictable speech output, or increase it toward 2.0 for more creative variations. Test voice cloning by providing different 5-10 second audio samples to understand how speaker characteristics transfer. Adjust the repetition penalty when synthesizing lists or repeated content to avoid monotonous patterns. Combine multiple paralinguistic tags in a single prompt to create complex vocal expressions, such as alternating between [sigh], [groan], and speech for emotional depth.