A locally deployable speech synthesis project with reference-voice generation, emotion and pronunciation controls; use is governed by Bilibili’s custom model agreement.
IndexTTS is a speech synthesis project from IndexTeam at Bilibili. Current official materials describe IndexTTS 2.5, with reference-voice generation and controls for emotion, pronunciation and speaking speed. The repository also documents earlier versions, so the model identity is an important part of any setup or comparison.
The project provides a local WebUI and Python examples for technical users. Its free route is conditional on the Bilibili model use agreement, not a blanket permissive software license. Choose the version and intended use first, then evaluate whether its language coverage, dependencies and controls fit the production task.
Draft an authorized narration
Compare emotional delivery
Prepare pronunciation-sensitive content
Explore multilingual voice continuity
Official product image. Click to inspect the details.
Check the model and agreement
Prepare the environment
Generate a baseline sample
Review each adjustment
Example prompt or task: Using this authorized reference voice, generate the approved introduction in the selected language. Compare neutral and calm delivery, preserve the words, and review proper nouns and sentence transitions before export.
This is an editorial starting task. Official demonstrations illustrate capability; FindGoodAI has not measured voice similarity, latency or production throughput.
The agreement includes restrictions and separate-license requirements for certain large organizations. Read the original terms before selecting it for a project.
The Bilibili model agreement grants royalty-free use subject to its conditions. It includes a separate-license requirement where the user or affiliates exceed the stated thresholds of 100 million monthly active users or RMB 1 billion annual revenue, as defined in the agreement.
The terms also address redistribution and use for improving other AI models. Read the complete current agreement rather than treating this brief overview as the full permission set. Hardware, hosting and auxiliary resources remain separate costs; this listing does not promise an unlimited free hosted speech service.
There is qualifying royalty-free local use under the custom model agreement. It is not unrestricted use.
No. The current project uses the Bilibili Model Use License Agreement.
Chinese, English, Japanese, Spanish and Arabic.
No. Text-derived emotion control has specific component requirements.
Not necessarily. The model card describes segmentation and cross-segment prosody limits.
No. Use the repository and its links to identify project resources.
No. Permission for the recording and the intended voice use must be obtained separately.
Explore platforms, inputs and outputs, licensing, and access requirements.
The reviewed model card and repository provide installation, model downloads and local inference examples. Acceleration options can behave differently across hardware and operating systems, so establish a working baseline before adding optional runtime changes.
Inputs can include text, speaker reference audio and the selected emotion or pronunciation controls. Outputs are synthesized audio files. The model card notes that text-derived emotion control requires its corresponding component, and that some randomness choices can affect voice fidelity; keep the exact configuration with the output.
A local setup can process scripts and reference audio on your chosen machine, while model downloads and optional hosted interfaces have separate network and data-handling implications. Keep confidential scripts out of unreviewed third-party demos.
The model does not verify consent from the person in a reference recording. Use authorized material and preserve attribution or notices required by the relevant agreements when distributing models, derivatives or generated material.
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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