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Step-Audio is a unified, open-source framework aimed at building intelligent speech systems that combine both comprehension and generation: it integrates large language models (LLMs) with speech input/output to handle not only semantic understanding but also rich vocal characteristics like tone, style, dialect, emotion, and prosody. The design moves beyond traditional separate-component pipelines (ASR → text model → TTS), instead offering a multimodal model that ingests speech or audio and produces speech accordingly, enabling natural dialogue, voice cloning, and expressive speech synthesis. Through its architecture, Step-Audio supports multilingual interaction, dialects, emotional tones (joy, sadness, etc.), and even more creative speech styles (like rap or singing), while allowing dynamic control over speech characteristics. It also provides a “generative data engine,” which can produce synthetic speech data (cloning voices, varying style) to support TTS training.
Features
- Unified multimodal speech-language model for both understanding (ASR / semantic parsing) and generation (speech synthesis / voice cloning)
- Support for multilingual input/output and multiple dialects, with control over style, emotion, prosody, and vocal tone
- Generative data engine that can synthesize speech data for TTS training, reducing reliance on manual voice data collection
- Instruction-driven fine-control system enabling dynamic adjustments (dialects, emotion, speed, style) for speech generation
- Suitable for building speech chatbots, voice assistants, interactive dialogue systems, or expressive TTS applications
- Fully open-source, enabling inspection, customization, and integration with downstream applications
Project Samples
