vLLM-Omni supports several autoregressive TTS models. They share a mostly common CLI shape (--text, --ref-audio, --ref-text, plus an output-path flag — --output-dir for most, --output for OmniVoice) and live together in this hub. Each model has its own subdirectory containing a single end2end.py script; this README is the single doc entry point.

For online serving, see examples/online_serving/text_to_speech/. For the full list of supported architectures across all modalities, see Supported Models.

Supported Models

Model	HuggingFace repo	Stages	Voice cloning	Streaming	Special modes	Sample rate
CosyVoice3	`FunAudioLLM/Fun-CosyVoice3-0.5B-2512`	2 (talker + code2wav)	✓	✓	—	24 kHz
Fish Speech S2 Pro	`fishaudio/s2-pro`	dual-AR	✓	✓	—	44.1 kHz
GLM-TTS	`zai-org/GLM-TTS`	2 (AR + DiT)	✓ (required)	✓	—	24 kHz
Ming-omni-tts	`inclusionAI/Ming-omni-tts-0.5B`	2 (AR + audio VAE)	✓	✓	style / IP / dialect / TTA / podcast	44.1 kHz
Ming-flash-omni-TTS	`Jonathan1909/Ming-flash-omni-2.0`	single (talker only)	— (caption-controlled)	—	style / IP / basic captions	44.1 kHz
MOSS-TTS-Nano	`OpenMOSS-Team/MOSS-TTS-Nano`	single (AR + codec)	✓ (required)	✓	voice_clone, continuation	48 kHz
OmniVoice	`k2-fsa/OmniVoice`	2 (gen + dec)	✓	—	voice design, language hint	24 kHz
Qwen3-TTS	`Qwen/Qwen3-TTS-12Hz-1.7B-{CustomVoice,VoiceDesign,Base}`	2 (talker + code2wav)	✓ (Base)	✓	3 task variants	24 kHz
VoxCPM2	`openbmb/VoxCPM2`	single (native AR)	✓	✓ (online)	continuation	48 kHz
IndexTTS-2	`IndexTeam/IndexTTS-2`	2 (AR talker + S2Mel DiT + BigVGAN)	✓ (required)	—	emotion control (`--emo-audio`, `--emo-text`, `--emo-vector`)	22.05 kHz
Voxtral TTS	`mistralai/Voxtral-4B-TTS-2603`	varies	✓	✓	voice presets	24 kHz

Common Quick Start

Most models share this invocation shape:

python examples/offline_inference/text_to_speech/<model>/end2end.py \
    --text "Hello, this is a test." \
    --ref-audio /path/to/reference.wav \
    --ref-text  "Transcript of the reference audio."

--ref-audio and --ref-text are optional (text-only synthesis works without them) and must be provided together for voice cloning. The exotic scripts — Qwen3-TTS, Voxtral TTS, CosyVoice3 — accept additional model-specific flags documented in their per-model section below. Qwen3-TTS in particular uses its own argparse surface (--query-type, --audio-path, etc.) and does not follow the common shape; see its section.

CosyVoice3

2-stage TTS pipeline (talker + code2wav) at 24 kHz.

Prerequisites

uv pip install -e .
# Includes soundfile, onnxruntime, x-transformers, einops via requirements.

Download the model snapshot:

from huggingface_hub import snapshot_download
snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512',
                  local_dir='pretrained_models/Fun-CosyVoice3-0.5B')

If your downloaded checkpoint lacks config.json, add it:

{
    "model_type": "cosyvoice3",
    "architectures": ["CosyVoice3Model"]
}

This is required because AutoConfig.register("cosyvoice3", CosyVoice3Config) only registers the class mapping; the loader still reads model_type from config.json to select the class.

Quick start

python examples/offline_inference/text_to_speech/cosyvoice3/end2end.py \
    --model pretrained_models/Fun-CosyVoice3-0.5B \
    --tokenizer pretrained_models/Fun-CosyVoice3-0.5B/CosyVoice-BlankEN

Voice cloning

If --ref-audio is omitted, the script downloads the upstream zero_shot_prompt.wav from the CosyVoice repo into the current directory. To use your own clip, pass --ref-audio /path/to/reference.wav, and modify --prompt-text correspondingly.

python examples/offline_inference/text_to_speech/cosyvoice3/end2end.py \
    --model pretrained_models/Fun-CosyVoice3-0.5B \
    --tokenizer pretrained_models/Fun-CosyVoice3-0.5B/CosyVoice-BlankEN \
    --ref-audio /path/to/reference.wav \
    --prompt-text "You are a helpful assistant.<|endofprompt|>Trascript in your ref audio clip"

Streaming

Streaming is enabled by default via async_chunk: true in vllm_omni/deploy/cosyvoice3.yaml. Pass --no-async-chunk on vllm serve to switch to the legacy synchronous path.

Notes

Stage 0 (talker) emits speech tokens; stage 1 (code2wav) runs flow matching + HiFiGAN to synthesize waveform.
Deploy config auto-loads from vllm_omni/deploy/cosyvoice3.yaml based on HF model_type. Pass --deploy-config <path> to override.

GLM-TTS

2-stage TTS pipeline (AR + DiT flow-matching) at 24 kHz. Every request requires reference audio and its transcript for zero-shot voice cloning.

Quick start

python examples/offline_inference/text_to_speech/glm_tts/end2end.py \
    --model zai-org/GLM-TTS \
    --text "你好，这是语音合成测试。" \
    --ref-audio /path/to/reference.wav \
    --ref-text "这是参考音频的文本内容。" \
    --output-dir ./output

Architecture

Text → [Stage 0: AR] → Speech Tokens → [Stage 1: DiT + HiFT] → Audio (24 kHz)
        (Llama-based)    (32k vocab)      (Flow Matching)

Notes

--ref-audio and --ref-text are required together; GLM-TTS does not support text-only synthesis.
Reference audio should be 3-10 seconds.
First run may be slow due to lazy loading of WhisperVQ tokenizer and CampPlus ONNX speaker embedder.
Default sampling: temperature=1.0, top_k=25, top_p=0.8 (RAS method).
The --model path should point to the repository root (not llm/ subdirectory).

Fish Speech S2 Pro

4B dual-AR text-to-speech model from FishAudio with the DAC codec at 44.1 kHz.

Prerequisites

pip install fish-speech

Quick start

python examples/offline_inference/text_to_speech/fish_speech/end2end.py \
    --text "Hello, this is a test of the Fish Speech text to speech system."

Voice cloning

python examples/offline_inference/text_to_speech/fish_speech/end2end.py \
    --text "Hello, this is a cloned voice." \
    --ref-audio /path/to/reference.wav \
    --ref-text  "Transcript of the reference audio."

Streaming

python examples/offline_inference/text_to_speech/fish_speech/end2end.py \
    --text "Hello, this is a streaming test." \
    --streaming

Streaming requires async_chunk: true in the stage config.

Notes

Output: 44.1 kHz mono WAV.
DAC codec weights (codec.pth) are loaded lazily from the model directory.

Ming-omni-tts

Dense 0.5B two-stage TTS pipeline (AR + flow + audio VAE) at 44.1 kHz. The example covers style, IP voice, music-only generation, text-to-audio events, emotion, dialect, zero-shot cloning, podcast, speech+BGM, and speech+environment-sound cases.

Quick start

python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
    --case style \
    --deploy-config vllm_omni/deploy/ming_tts.yaml \
    --enforce-eager

Voice cloning

python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
    --case zero_shot \
    --ref-audio /path/to/reference.wav \
    --ref-text "在此奉劝大家别乱打美白针。" \
    --deploy-config vllm_omni/deploy/ming_tts.yaml \
    --enforce-eager

Streaming

python examples/offline_inference/text_to_speech/ming_tts/end2end.py \
    --case basic \
    --ref-audio /path/to/reference.wav \
    --streaming \
    --deploy-config vllm_omni/deploy/ming_tts.yaml \
    --enforce-eager

Notes

style, ip, bgm, and tta do not require reference audio.
Reference-audio cases use --ref-audio; zero_shot also requires --ref-text.
podcast uses multiple references via --ref-audio-paths.
Full case details live in ming_tts/README.md.

Ming-flash-omni-TTS

Standalone talker-only deployment of Ming-flash-omni-2.0 at 44.1 kHz. Voice is controlled through caption fields (风格 / IP / 语速/基频/音量) rather than reference audio.

Prerequisites

The example calls into vllm_omni.model_executor.models.ming_flash_omni.prompt_utils for the default prompt and instruction builder; no extra pip install on top of the base vLLM-Omni install.

Quick start

python examples/offline_inference/text_to_speech/ming_flash_omni_tts/end2end.py --case style

Cases

# ASMR-style whisper (caption-driven)
python examples/offline_inference/text_to_speech/ming_flash_omni_tts/end2end.py --case style

# IP voice (preset character voice via caption)
python examples/offline_inference/text_to_speech/ming_flash_omni_tts/end2end.py --case ip

# Basic speed/pitch/volume control
python examples/offline_inference/text_to_speech/ming_flash_omni_tts/end2end.py --case basic

Override the default text per case with --text, write to a custom path with --output.

Notes

Talker-only deployment — for the multimodal Ming-flash-omni example, see examples/offline_inference/ming_flash_omni/.
Deploy config: vllm_omni/deploy/ming_flash_omni_tts.yaml (single GPU, enforce_eager, max_num_seqs: 1).
Decode defaults from the Ming cookbook: max_decode_steps=200, cfg=2.0, sigma=0.25, temperature=0.0, use_zero_spk_emb=True.

MOSS-TTS-Nano

Single-stage 0.1B AR LM + MOSS-Audio-Tokenizer-Nano codec at 48 kHz mono (mixed down from upstream stereo). ZH / EN / JA. Every request requires a reference clip via --ref-audio.

No built-in speaker presets. --ref-audio is required on every call. Default --mode voice_clone matches upstream's recommended workflow; --mode continuation is exposed for completeness but upstream's continuation-with-prompt path emits very short / near-silent output, so it is rarely useful in practice. Sample reference clips ship in the upstream repo under assets/audio/ (e.g. zh_1.wav, en_2.wav, jp_2.wav).

Quick start

# Fetch a sample reference clip (one-off, user-scoped cache).
REF_DIR="${XDG_CACHE_HOME:-$HOME/.cache}/moss-tts-nano"
mkdir -p "$REF_DIR"
[ -s "$REF_DIR/zh_1.wav" ] || \
    curl -L -o "$REF_DIR/zh_1.wav" https://raw.githubusercontent.com/OpenMOSS/MOSS-TTS-Nano/main/assets/audio/zh_1.wav

python examples/offline_inference/text_to_speech/moss_tts_nano/end2end.py \
    --text "你好，这是MOSS-TTS-Nano的语音合成演示。" \
    --ref-audio "$REF_DIR/zh_1.wav"

The first run downloads OpenMOSS-Team/MOSS-TTS-Nano and OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano from Hugging Face.

Reproducible runs

python examples/offline_inference/text_to_speech/moss_tts_nano/end2end.py \
    --text "Deterministic test." \
    --ref-audio "$REF_DIR/en_2.wav" \
    --seed 42

Notes

Output: 48 kHz mono WAV (the tokenizer is internally stereo at 48 kHz; the wrapper averages to mono before reaching the engine).
Deploy config: vllm_omni/deploy/moss_tts_nano.yaml (auto-loaded; override with --deploy-config).
Default --max-new-frames 375 ≈ 14 s of audio; raise for longer outputs.
--ref-text is rejected in voice_clone mode and required only with --mode continuation.
Run --help for the full sampling-knob surface (--audio-temperature, --audio-top-k, --audio-top-p, --text-temperature).

OmniVoice

Zero-shot multilingual TTS supporting 600+ languages, with three modes (auto / clone / design).

Prerequisites

huggingface-cli download k2-fsa/OmniVoice

Voice cloning requires transformers>=5.3.0. Auto and design modes work with transformers>=4.57.0.

Quick start (auto voice)

python examples/offline_inference/text_to_speech/omnivoice/end2end.py \
    --model k2-fsa/OmniVoice \
    --text "Hello, this is a test."

Voice cloning

python examples/offline_inference/text_to_speech/omnivoice/end2end.py \
    --model k2-fsa/OmniVoice \
    --text "Hello, this is a test." \
    --ref-audio ref.wav \
    --ref-text  "This is the reference transcription."

Voice design

python examples/offline_inference/text_to_speech/omnivoice/end2end.py \
    --model k2-fsa/OmniVoice \
    --text "Hello, this is a test." \
    --instruct "female, low pitch, british accent"

Language hint

python examples/offline_inference/text_to_speech/omnivoice/end2end.py \
    --model k2-fsa/OmniVoice \
    --text "你好，这是一个测试。" \
    --lang zh

Seed for Reproducibility

python examples/offline_inference/text_to_speech/omnivoice/end2end.py \
    --model k2-fsa/OmniVoice \
    --text "Hello, this is a test." \
    --seed 42

Notes

Stage 0 (Generator): Qwen3-0.6B with 32-step iterative unmasking.
Stage 1 (Decoder): HiggsAudioV2 RVQ + DAC at 24 kHz.

Qwen3-TTS

3-task-variant TTS with 24 kHz output. Has its own argparse surface (this script does not follow the common --text / --ref-audio shape).

Prerequisites

For ROCm builds, replace onnxruntime with onnxruntime-rocm:

pip uninstall onnxruntime
pip install onnxruntime-rocm

Task variants

CustomVoice: predefined speaker (speaker ID) with optional style instruction.
VoiceDesign: text + descriptive instruction designs a new voice.
Base: voice cloning from reference audio + transcript.

# Single sample
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-type CustomVoice
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-type VoiceDesign
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-type Base

# Base with a custom reference audio (Qwen3-TTS uses --audio-path, not --ref-audio):
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
    --query-type Base --audio-path /path/to/reference.wav

# Base variant has an additional mode flag:
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-type Base --mode-tag icl       # default
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-type Base --mode-tag xvec_only # x_vector_only_mode

# Batch (multiple prompts in one run)
python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py --query-type CustomVoice --use-batch-sample

Streaming

python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
    --query-type CustomVoice \
    --streaming \
    --output-dir /tmp/out_stream

Streaming requires async_chunk: true in the stage config.

Word Timestamps

Generate a WAV offline and a JSON sidecar with word-level timestamps from Qwen/Qwen3-ForcedAligner-0.6B:

python examples/offline_inference/text_to_speech/qwen3_tts/word_timestamps.py \
    --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice \
    --forced-aligner Qwen/Qwen3-ForcedAligner-0.6B \
    --text "Hello world." \
    --output-dir /tmp/qwen3_tts_timestamps

The script writes qwen3_tts_word_timestamps.wav and qwen3_tts_word_timestamps.json. On machines without a local CUDA toolkit, set VLLM_USE_FLASHINFER_SAMPLER=0 to avoid FlashInfer sampler JIT.

Batched decoding

The Code2Wav stage supports batched decoding through the SpeechTokenizer. Pass multiple prompts via --txt-prompts and set --batch-size accordingly. To raise max_num_seqs on either stage, point --stage-configs-path at a stage configs YAML with the desired values (see vllm_omni/model_executor/stage_configs/ for templates):

python examples/offline_inference/text_to_speech/qwen3_tts/end2end.py \
    --query-type CustomVoice \
    --txt-prompts examples/offline_inference/text_to_speech/qwen3_tts/benchmark_prompts.txt \
    --batch-size 4 \
    --stage-configs-path /path/to/qwen3_tts_batched.yaml

--batch-size must match a CUDA-graph capture size (1, 2, 4, 8, 16…).

Notes

Run --help for the full argument surface.
See qwen3_tts/end2end.py for the prompt-length-estimation logic the Talker uses.

VoxCPM2

Single-stage native AR TTS at 48 kHz. Pipeline: feat_encoder → MiniCPM4 → FSQ → residual_lm → LocDiT → AudioVAE.

Prerequisites

pip install voxcpm
# or, for a local source checkout:
export VLLM_OMNI_VOXCPM_CODE_PATH=/path/to/voxcpm

Quick start

python examples/offline_inference/text_to_speech/voxcpm2/end2end.py \
    --model openbmb/VoxCPM2 \
    --text "Hello, this is a VoxCPM2 demo."

Voice cloning

Pass a reference audio for isolated cloning, or both --ref-audio + --ref-text for prompt continuation:

python examples/offline_inference/text_to_speech/voxcpm2/end2end.py \
    --text "Hello, this is a voice clone demo." \
    --ref-audio /path/to/reference.wav \
    --ref-text  "Transcript of the reference audio."

Streaming

Streaming is exposed through the online OpenAI Speech API (stream=true). See examples/online_serving/text_to_speech/voxcpm2/gradio_demo.py for an AudioWorklet-based gapless streaming player; the offline end2end.py script does not expose a streaming path.

Notes

Output: 48 kHz mono WAV.
Deploy config: vllm_omni/deploy/voxcpm2.yaml (auto-loaded by HF model_type).

IndexTTS-2

2-stage TTS pipeline (GPT AR talker + S2Mel CFM DiT + BigVGAN vocoder) at 22.05 kHz. Every request requires reference audio for zero-shot voice cloning. Supports emotion conditioning via audio, text, or 8-dim vector.

Quick start

python examples/offline_inference/text_to_speech/indextts2/end2end.py \
    --model IndexTeam/IndexTTS-2 \
    --text "你好，这是一个语音合成测试。" \
    --ref-audio /path/to/reference.wav

Emotion control

# Emotion from reference audio
python examples/offline_inference/text_to_speech/indextts2/end2end.py \
    --model IndexTeam/IndexTTS-2 \
    --text "今天天气真好！" \
    --ref-audio /path/to/ref.wav \
    --emo-audio /path/to/happy.wav

# Emotion from 8-dim vector (happy angry sad afraid disgusted melancholy surprised calm)
python examples/offline_inference/text_to_speech/indextts2/end2end.py \
    --model IndexTeam/IndexTTS-2 \
    --text "今天天气真好！" \
    --ref-audio /path/to/ref.wav \
    --emo-vector 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

# Emotion from text description
python examples/offline_inference/text_to_speech/indextts2/end2end.py \
    --model IndexTeam/IndexTTS-2 \
    --text "今天天气真好！" \
    --ref-audio /path/to/ref.wav \
    --emo-text "happy and excited"

Notes

--ref-audio is required — IndexTTS-2 does not support text-only synthesis.
Stage 0 (AR Talker): GPT-2 generates mel codes from text + reference audio.
Stage 1 (S2Mel + BigVGAN): CFM DiT converts mel codes to waveform at 22.05 kHz.
Deploy config: vllm_omni/deploy/indextts2.yaml. Stage 1 runs with enforce_eager: true (DiT has dynamic shapes).

Voxtral TTS

Voxtral-4B-TTS (Mistral). Has its own argparse surface; uses voice presets and the mistral_common SpeechRequest protocol.

Prerequisites

Latest mistral_common with SpeechRequest support:

pip install -e /path/to/mistral-common  # or upgrade from PyPI when available

Quick start (voice preset)

python examples/offline_inference/text_to_speech/voxtral_tts/end2end.py \
    --write-audio --voice cheerful_female \
    --model mistralai/Voxtral-4B-TTS-2603 \
    --text "That eerie silence after the first storm was just the calm before another round of chaos, wasn't it?"

Voice cloning (capability gated upstream)

python examples/offline_inference/text_to_speech/voxtral_tts/end2end.py \
    --write-audio \
    --model mistralai/Voxtral-4B-TTS-2603 \
    --text "This is a test message." \
    --ref-audio path/to/reference_audio.wav

Streaming + concurrency

python examples/offline_inference/text_to_speech/voxtral_tts/end2end.py \
    --num-prompts 32 --concurrency 8 --streaming --write-audio --voice neutral_female \
    --model mistralai/Voxtral-4B-TTS-2603 \
    --text "..."

Available voice presets are listed on the HF model card (mistralai/Voxtral-4B-TTS-2603).

Notes

--num-prompts N replicates the prompt for performance measurement.
--concurrency M requires --streaming and must evenly divide --num-prompts.
Run --help for the full argument surface.

SoulX-Singer

Singing voice synthesis (SVS) and conversion (SVC) at 24 kHz. Script: soulxsinger/end2end.py. Deploy: vllm_omni/deploy/soulxsinger_svs.yaml or soulxsinger_svc.yaml.

Prerequisites

Download DiT and preprocess weights, then set up separate SVS / SVC view directories. Copy soulxsinger/utils/phoneme/phone_set.json from upstream SoulX-Singer into the model weights dir as phoneme/phone_set.json — HuggingFace does not ship it.

# 1. DiT weights
export BASE=path/to/SoulX-Singer
export PREPROCESS=path/to/SoulX-Singer-Preprocess
export SVC_DIR=path/to/SoulX-Singer-svc

huggingface-cli download Soul-AILab/SoulX-Singer --local-dir "$BASE"

# 2. Preprocess weights (required)
huggingface-cli download Soul-AILab/SoulX-Singer-Preprocess --local-dir "$PREPROCESS"
export SOULX_PREPROCESS_WEIGHTS_DIR="$PREPROCESS"

# 3. SVS / SVC view directories
mkdir -p "$SVC_DIR"
cp $BASE/{config.yaml,README.md,assets} $SVC_DIR
mv $BASE/model-svc.pt $SVC_DIR/model-svc.pt

cat > "$BASE/config.json" <<'EOF'
{
  "model_type": "soulxsinger",
  "architectures": ["SoulXSingerPipeline"],
  "max_num_seqs": 1
}
EOF

cat > "$SVC_DIR/config.json" <<'EOF'
{
  "model_type": "soulxsinger",
  "architectures": ["SoulXSingerSVCPipeline"],
  "max_num_seqs": 1
}
EOF

config.yaml hyper-parameters live under $BASE; each view's config.json architectures field is the single source of truth for SVS vs SVC. Point --model at the matching directory ($BASE for SVS, $SVC_DIR for SVC). Deploy YAML is chosen automatically from config.json; optional --svs / --svc only assert the mode matches.

Online preprocess is the default: pass --prompt-audio and --target-audio, and the worker runs vocal separation, F0, and (for SVS) lyrics/MIDI before DiT. Install only what your run needs:

pip install "BS-RoFormer"   # vocal sep + F0 on GPU — SVS and SVC

Mandarin SVS also needs FunASR and Chinese G2P; ffmpeg must be on PATH:

# install optional dependencies:
pip install -e ".[soulx-svs]"

English SVS adds NeMo ASR and NLTK data; pass --language English:

pip install "nemo_toolkit[asr]==2.6.1" lhotse==1.32.2
python -c "import nltk; nltk.download('cmudict'); nltk.download('averaged_perceptron_tagger_eng')"

Precomputed metadata is the alternative: pass both --prompt-metadata-path and --target-metadata-path and skip online ASR/ROSVOT — none of the packages above are required. JSON can be produced by integrated preprocess on a prior run, or by upstream SoulX-Singer preprocess/ scripts if you prefer to run that outside vLLM-Omni.

Quick start

# SVS — default demo audio: tests/assets/soulxsinger/zh_prompt.mp3 + music.mp3
python examples/offline_inference/text_to_speech/soulxsinger/end2end.py \
    --model "$BASE" \
    --preprocess-weights-dir "$PREPROCESS" \
    --control score \
    --num-inference-steps 32 \
    -o output.wav

python examples/offline_inference/text_to_speech/soulxsinger/end2end.py \
    --model "$SVC_DIR" \
    --preprocess-weights-dir "$PREPROCESS" \
    --svc \
    --num-inference-steps 32 \
    -o output_svc.wav

SOULX_PREPROCESS_WEIGHTS_DIR makes --preprocess-weights-dir optional. Long SVS targets are handled in one request. See end2end.py --help for --pitch-shift, --vocal-sep, --auto-shift, and language/control options.

Notes

Output: 24 kHz mono WAV; batch only.
Defaults match upstream: --guidance-scale 3.0, --seed 42, --auto-shift on.
SVS --control: score or melody. MIDI / lyric QC: upstream midi_editor only.