name: finetuning-data-prep
description: "Prepare and validate MOSS-TTS fine-tuning JSONL data and launch
supervised fine-tuning runs."
disable-model-invocation: true
metadata:
disco-role: operating
license: Apache 2.0
finetuning-data-prep
Use this sub-skill when the task is about preparing training JSONL, codec preprocessing, supervised fine-tuning launch planning, or first checkpoint smoke checks for MOSS-TTS Delay, Local, Local Transformer v1.5, Realtime, TTSD, SoundEffect v1, or VoiceGenerator workflows.
Use this for
- Choosing a raw JSONL schema for TTS, voice cloning, multi-speaker TTSD, SoundEffect v1, VoiceGenerator, or Realtime conversation fine-tuning.
- Validating raw or prepared JSONL before expensive codec preprocessing or
accelerate launch training.
- Running or modifying the repository fine-tuning helpers:
prepare_data.py, sft.py, run_train.sh, Accelerate DDP/FSDP/DeepSpeed configs, and sharded JSONL outputs.
- Planning batch size, gradient accumulation, mixed precision, loss weighting,
n_vq, and single-node or multi-node launch shapes.
- Diagnosing data-preparation and training-launch failures before handing generation/inference work to an inference sub-skill.
Route elsewhere
- SoundEffect v2 DiT fine-tuning, diffusion-specific manifests, or DiT checkpoint issues:
../soundeffect-v2/SKILL.md.
- llama.cpp conversion, quantization, backend evaluation, or GGUF runtime checks:
../llama-cpp-backend/SKILL.md.
- Full inference after training belongs to the owning inference sub-skill for the selected model family. This sub-skill only covers a quick post-finetune smoke check that verifies a checkpoint can generate a short WAV.
- Generic Hugging Face model-family selection and non-training prompts:
../hf-family-workflows/SKILL.md.
Operating sequence
- Pick the task id and model family from
references/data-formats.md.
- Validate the raw or prepared JSONL with
scripts/validate_training_jsonl.py before launching codec or model work.
- Preprocess raw audio into
audio_codes or conversation-turn codes using references/preprocessing.md.
- Launch training with
references/training-launches.md; use the bundled troubleshooting reference if ranks, shards, n_vq, memory, or reference-audio fields fail.
- After a checkpoint is saved, perform only a short smoke inference with the matching inference workflow, then route any deeper generation/evaluation request to the owning inference sub-skill.
Bundled references and tools
- JSONL schemas and task-specific required fields:
references/data-formats.md.
- Codec preprocessing, reference-code handling, sharding, and validator usage:
references/preprocessing.md.
accelerate, FSDP, DeepSpeed, run_train.sh, and hyperparameter launch patterns: references/training-launches.md.- Known failure modes and recovery actions:
references/troubleshooting.md.
- No-model JSONL checker:
scripts/validate_training_jsonl.py.