---

name: inference description: "Routes VITS text-to-speech synthesis and voice-conversion tasks from checkpoints." disable-model-invocation: true metadata: disco-role: operating license: MIT

Inference

Use this route when you need to synthesize speech from a checkpoint or run voice conversion with a VITS model.

Use this route when

• You have a checkpoint and want generated audio.
• You need to choose synthesis settings such as noise scale, length scale, or speaker id.
• You need the LJ Speech single-speaker inference path or the VCTK multi-speaker path.
• You need voice conversion from a source audio file or spectrogram to a target speaker.
• You need to troubleshoot checkpoint mismatches, sample-rate mismatches, or speaker-id problems.

Do not use this route when

• You are preparing filelists or building the extension; use data-preparation.
• You are launching training or resuming checkpoints; use training.
• You only need a quick environment sanity check; use ../../scripts/check_install.py or ../../scripts/model_smoke.py.

Read first

• ../../references/workflows.md for the synthesis and voice-conversion command flow.
• ../../references/configuration.md for the config and file-layout differences.
• ../../references/api-reference.md for SynthesizerTrn.infer and voice_conversion.
• ../../references/troubleshooting.md for cross-cutting failures.
• references/troubleshooting.md in this sub-skill for checkpoint and speaker-id failures.

Bundled helpers

• ../../scripts/synthesize.py — run TTS or voice conversion from a checkpoint.
• ../../scripts/model_smoke.py — validate inference and voice-conversion wiring on synthetic inputs.
• ../../scripts/check_install.py — confirm the repo imports and the backend is ready.

Common workflow

1. Confirm the checkpoint matches the config family.
2. Build the monotonic-alignment extension and verify CUDA.
3. For TTS, provide text and speaker id when the config is multi-speaker.
4. For voice conversion, provide source audio and both source and target speaker ids.
5. Use model_smoke.py when you need to check the model path without a real checkpoint.

Ownership boundaries

• Include checkpoint-driven TTS, voice conversion, and audio-output planning here.
• Exclude dataset cleanup and training launch; route those to sibling sub-skills.