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name: inference description: "Pretrained StyleTTS2 inference, asset checks, phonemizer readiness, and long-form synthesis troubleshooting for LJSpeech and LibriTTS." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference

Use this sub-skill for pretrained StyleTTS2 demos and troubleshooting only. It covers the LJSpeech single-speaker path, the LibriTTS multi-speaker path, reference-audio style extraction, diffusion/style controls, and safe asset checks.

Route elsewhere

• Training or fine-tuning -> the training sub-skill.
• Data-list prep or config edits -> the data-and-config sub-skill.

Start here

Run scripts/check_inference_assets.py before trying to synthesize. It inspects expected checkpoints, bundled helper assets, optional phonemizer/espeak readiness, and LibriTTS reference-audio availability without downloading or generating audio.

Bundled references

• references/inference-workflows.md for distilled LJSpeech and LibriTTS synthesis procedures and control parameters.
• references/model-assets.md for required checkpoints, reference audio, and public download locations.
• references/troubleshooting.md for phonemizer, asset, backend, and voice-permission issues.

Cautions

• Treat generated speech as synthesized unless you have permission and license to use the voice.
• The README notes a GPL-licensed fork and an MIT-licensed PyPI package as external alternatives, but they are not this repository's core workflow.
• Older GPUs can produce high-pitched background noise; prefer a newer CUDA GPU or CPU inference if you hit that issue.

Output contract

The notebook workflows produce 24 kHz waveforms, usually returned as NumPy arrays that can be played with IPython.display.Audio.