---

name: vits description: "Routes VITS text-to-speech workflows for data preparation, training, synthesis, and voice conversion." disable-model-invocation: true metadata: disco-role: operating license: MIT

VITS

Use this skill for the VITS repository when the task is about text-to-speech data preparation, single- or multi-speaker training, inference, or voice conversion.

Start here

• Read references/repo-provenance.md if you want to check whether this skill still matches the current checkout.
• Read references/workflows.md for the end-to-end workflow map and the bundled helper scripts.
• Read references/configuration.md for the LJ Speech and VCTK config variants, filelist layouts, and key hyperparameters.
• Read references/api-reference.md when you need verified class, function, or method signatures.
• Read references/troubleshooting.md when an import, backend, data-format, or runtime step fails.

Required setup

• Use a Python environment with the package dependencies installed.
• Install or verify the speech-text dependencies used by this repo: PyTorch with CUDA support for training and GPU inference, librosa, phonemizer, Unidecode, scipy, tensorboard, and Cython.
• Provide espeak or espeak-ng if you need english_cleaners or english_cleaners2.
• Build the monotonic-alignment extension before importing models or running any model smoke checks.

Root routes

data-preparation

Use this route for custom dataset preprocessing, filelist cleaning, dataset layout checks, espeak/phonemizer issues, and the monotonic-alignment build helper.

training

Use this route for LJ Speech or VCTK training, checkpoint resume, DDP launch planning, config selection, and training diagnostics.

inference

Use this route for text-to-speech synthesis, checkpoint-driven sample generation, and voice conversion.

Shared helper scripts

• scripts/check_install.py — run this first to confirm the repo imports, CUDA is visible, and optional speech dependencies are present.
• scripts/build_monotonic_align.py — build the extension into the importable nested package layout expected by monotonic_align.
• scripts/model_smoke.py — run a tiny GPU forward, inference, or voice-conversion smoke check with synthetic tensors.
• scripts/preprocess_text.py — clean filelists for custom datasets.
• scripts/launch_training.py — print or launch the correct single- or multi-speaker training command with a safe MASTER_PORT.
• scripts/synthesize.py — synthesize speech or run voice conversion from a checkpoint and input text or audio.

Using the routes

• Start with data-preparation if the problem is about filelists, text cleaners, or preparing custom corpora.
• Start with training if the problem is about configs, distributed launch, resume logic, or losses.
• Start with inference if the problem is about checkpoints, sample generation, or voice conversion outputs.
• Use scripts/model_smoke.py before any longer run when you need a quick environment or model check.

What this skill does not do

• It does not replace the original repository checkout with generated content.
• It does not rely on the original checkout remaining at a fixed path.
• It does not cover unrelated speech frameworks or generic audio packages unless they are needed to use VITS itself.