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name: training description: "Choose and launch StyleTTS2 first-stage training, second-stage training, fine-tuning, and one-GPU accelerate fine-tuning safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

StyleTTS2 training router

Use this sub-skill when the task is to choose or launch a StyleTTS2 training workflow from a source checkout: first-stage pretraining, second-stage joint training, fine-tuning from a LibriTTS-style checkpoint, or the one-GPU accelerate fine-tuning variant.

For data-list schemas, 24 kHz audio preparation, config-field edits, asset paths, and OOD text files, route to ../data-and-config/ first. For pretrained synthesis demos and voice-use guidance, route to ../inference/.

Start here

1. Identify the stage and config file with references/training-workflows.md.

2. Build the launch command with the bundled safe helper scripts/build_training_command.py, not by copying source training scripts:

   python scripts/build_training_command.py --repo-root /path/to/StyleTTS2 --stage first --config Configs/config.yml
   

   The helper defaults to dry-run: it prints the exact command, checks the checkout/config/assets lightly, and does not train, download models, or write checkpoints. Add --run only after the user explicitly wants to start training.

3. Before resuming or switching from first-stage to second-stage/fine-tuning, read references/checkpoints-and-resume.md.

4. For launch failures, CUDA/backend problems, OOM/NaN behavior, DDP warnings, WavLM cache, or missing dependencies, use references/troubleshooting.md.

Stage routing

• First stage: --stage first; uses accelerate launch and writes periodic epoch_1st_*.pth plus the final first_stage_path under log_dir.
• Second stage: --stage second; use the plain Python DataParallel launcher because the repository documents DDP/accelerate as not working for this stage.
• Fine-tuning: --stage finetune; starts from a second-stage pretrained checkpoint such as a LibriTTS checkpoint and new speaker data.
• One-GPU fine-tuning: --stage finetune-accelerate; emits the documented memory-saving accelerate command with --mixed_precision fp16 --num_processes 1 by default.

Always expect CUDA for truthful training behavior. The source checkout is not a pip-installable package; activate an environment that can import the checkout modules, has CUDA-enabled PyTorch, and includes the runtime dependencies noted in the troubleshooting reference.