Voice
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Inworld TTS‑1‑MAX
With 8.8 billion parameters and a Transformer‑based autoregressive architecture, it delivers near‑human speech quality, fine‑grained emotional control, and rich custom voices tailored to your brand or characters.
Inworld TTS‑1‑MAX is Inworld’s most powerful previous‑generation text‑to‑speech model, built for ultra‑realistic, expressive voice generation in high‑end media, enterprise, and immersive applications.
Instead of relying on fixed voice profiles, TTS‑1‑MAX API leverages in‑context learning to capture individual speaker characteristics from short audio prompts, enabling instant voice cloning and personality‑aware delivery. This capability lets creators reuse familiar voices across scenes, languages, and emotional tones without retraining underlying models.
Inworld TTS‑1‑MAX synthesizes high‑resolution 48 kHz audio with low latency, preserving nuance while keeping response times usable for many interactive workflows. Its autoregressive architecture sequences audio waveforms token‑wise, balancing fidelity with efficient inference that scales across cloud‑based deployments.
The model supports 11 major languages, giving developers a unified API for multilingual voice‑over, localization, and global conversational agents. Through audio markups and expressive prompts, TTS‑1‑MAX can render fine‑grained emotions, pacing, breaths, and non‑verbal vocalizations such as laughter, sighs, and gasps.
Compared to the smaller TTS‑1 (1.6B parameters), TTS‑1‑MAX uses over five times larger capacity to boost clarity, presence, and dynamic range at the cost of higher compute requirements. In practical terms, this means richer timbre and slower inference suitable for pre‑rendered scenes rather than ultra‑tight conversational loops.
TTS‑1‑MAX excels when broadcast‑quality narration is required, such as audiobooks, branded trailers, documentary‑style VO, and cinematic cutscenes. Its ability to render consistent, emotionally nuanced characters across long sequences reduces the need for manual ADR or large voice‑actor rosters.
Game studios use TTS‑1‑MAX to generate dynamic dialog and monologues for NPCs, enabling context‑aware responses that match scene mood and player choices. By combining voice cloning with markup‑driven expressions, teams can maintain a cohesive cast even as dialog breadth explodes.
Instead of relying on fixed voice profiles, TTS‑1‑MAX API leverages in‑context learning to capture individual speaker characteristics from short audio prompts, enabling instant voice cloning and personality‑aware delivery. This capability lets creators reuse familiar voices across scenes, languages, and emotional tones without retraining underlying models.
Inworld TTS‑1‑MAX synthesizes high‑resolution 48 kHz audio with low latency, preserving nuance while keeping response times usable for many interactive workflows. Its autoregressive architecture sequences audio waveforms token‑wise, balancing fidelity with efficient inference that scales across cloud‑based deployments.
The model supports 11 major languages, giving developers a unified API for multilingual voice‑over, localization, and global conversational agents. Through audio markups and expressive prompts, TTS‑1‑MAX can render fine‑grained emotions, pacing, breaths, and non‑verbal vocalizations such as laughter, sighs, and gasps.
Compared to the smaller TTS‑1 (1.6B parameters), TTS‑1‑MAX uses over five times larger capacity to boost clarity, presence, and dynamic range at the cost of higher compute requirements. In practical terms, this means richer timbre and slower inference suitable for pre‑rendered scenes rather than ultra‑tight conversational loops.
TTS‑1‑MAX excels when broadcast‑quality narration is required, such as audiobooks, branded trailers, documentary‑style VO, and cinematic cutscenes. Its ability to render consistent, emotionally nuanced characters across long sequences reduces the need for manual ADR or large voice‑actor rosters.
Game studios use TTS‑1‑MAX to generate dynamic dialog and monologues for NPCs, enabling context‑aware responses that match scene mood and player choices. By combining voice cloning with markup‑driven expressions, teams can maintain a cohesive cast even as dialog breadth explodes.
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