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name: soundeffect-v2 description: "Operate MOSS-SoundEffect v2 DiT/DAC/Qwen3 inference, demo, fine-tuning metadata, and checkpoint export workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

soundeffect-v2

Use this sub-skill for MOSS-SoundEffect v2.0 workflows: the separate text-to-audio diffusion package built from a DiT backbone, Flow Matching scheduler behavior, DAC VAE audio representation, and Qwen3 text encoder. It covers isolated environment setup, pipeline inference, audio saving, Gradio launch parameters, fine-tuning metadata validation, and export of fine-tuned DiT checkpoints into a loadable Hugging Face-style directory.

Best-fit requests

• Generate environmental or sound-effect audio with MossSoundEffectPipeline.
• Choose seconds, diffusion steps, CFG scale, sigma shift, dtype, seed, device, and output path for SoundEffect v2 inference.
• Recover from TorchDynamo/Triton compile failures or first-call compilation issues in the v2 pipeline.
• Launch or adapt the SoundEffect v2 Gradio demo using SOUNDEFFECT_MODEL_DIR and device settings.
• Prepare JSONL metadata for SoundEffect v2 DiT fine-tuning with required audio and prompt fields.
• Validate SoundEffect v2 metadata without importing torch or loading a model.
• Fine-tune the v2 DiT from an existing Hugging Face model directory or repo id, then export the resulting checkpoint for pipeline loading.

Route elsewhere

• MOSS-SoundEffect v1 or any Delay-family autoregressive sound-effect workflow: ../hf-family-workflows/SKILL.md.
• Delay-family fine-tuning data preparation, audio_codes, or generic MOSS-TTS training data: ../finetuning-data-prep/SKILL.md.
• Generic MOSS-TTS voice cloning, continuation, multilingual TTS, voice generation, or dialogue TTS: ../hf-family-workflows/SKILL.md.

Operating map

1. Start with references/pipeline-and-finetune.md for setup, inference, Gradio, fine-tuning, and export workflows.
2. Use references/api-reference.md for exact package pins, pipeline signatures, CLI-style variables, metadata schema, training/export arguments, and outputs.
3. Use references/troubleshooting.md when dependency conflicts, model cache/downloads, compile errors, CUDA memory, long runs, metadata errors, export failures, or Gradio launch issues appear.
4. Use scripts/validate_soundeffect_metadata.py before launching training. It is stdlib-only and checks JSONL structure plus optional audio-path existence.

Quick safeguards

• Create a clean Python 3.12+ environment for this package. Do not reuse the top-level MOSS-TTS environment.
• Install PyTorch CUDA 12.8 wheels through the PyTorch index when using the torch-cu128 extra.
• For inference, prefer CUDA with torch.bfloat16; CPU fallback exists but can be too slow for practical generation.
• Set TORCHDYNAMO_DISABLE=1 before Python if TorchDynamo, Triton, or CUDA Graph compilation fails.
• Keep seconds within the model limit, normally 30 seconds, and keep num_inference_steps bounded for smoke tests.
• Validate fine-tuning metadata before launching accelerate; each JSONL row must include non-empty audio and prompt strings.
• During export, provide a valid SOURCE_HF_DIR containing the frozen vae, text_encoder, tokenizer, and scheduler subdirectories.