🎤 YingMusic-Singer-Plus: Controllable Singing Voice Synthesis with Flexible Lyric Manipulation and Annotation-free Melody Guidance

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Chunbo Hao^1,2 · Junjie Zheng² · Guobin Ma¹ · Yuepeng Jiang¹ · Huakang Chen¹ · Wenjie Tian¹ · Gongyu Chen² · Zihao Chen² · Lei Xie¹

¹ Audio, Speech and Language Processing Group (ASLP@NPU), School of Computer Science, Northwestern Polytechnical University, China
² AI Lab, GiantNetwork, China

## 🎥 Demo Video YingMusic-Singer-Plus Demo, narration voiceover provided by [VoiceSculptor](https://github.com/ASLP-lab/VoiceSculptor). Click the badge below to jump to watch the demo video: | YouTube | Bilibili | |---------|----------| | [![YouTube](assets/YingMusic-Singer-Plus.png)](https://www.youtube.com/watch?v=ktjJFS5R3Dk) | [![Bilibili](assets/YingMusic-Singer-Plus.png)](https://www.bilibili.com/video/BV1zPDSBhEkz/) | ## 📖 Introduction **YingMusic-Singer-Plus** is a fully diffusion-based singing voice synthesis model that enables **melody-controllable singing voice editing with flexible lyric manipulation**, requiring no manual alignment or precise phoneme annotation. Given only three inputs — an optional timbre reference, a melody-providing singing clip, and modified lyrics — YingMusic-Singer-Plus synthesizes high-fidelity singing voices at **44.1 kHz** while faithfully preserving the original melody.

YingMusic-Singer-Plus Architecture

Overall architecture of YingMusic-Singer-Plus. Left: SFT training pipeline. Right: GRPO training pipeline.

## ✨ Key Features - **Annotation-free**: No manual lyric-MIDI alignment required at inference - **Flexible lyric manipulation**: Supports 6 editing types — partial/full changes, insertion, deletion, translation (CN↔EN), and code-switching - **Strong melody preservation**: CKA-based melody alignment loss + GRPO-based optimization - **Bilingual**: Unified IPA tokenizer for both Chinese and English - **High fidelity**: 44.1 kHz stereo output via Stable Audio 2 VAE ## 🚀 Quick Start ### Option 1: Install from Scratch ```bash # We strongly recommend uv for faster dependency resolution. uv venv --python 3.10 source .venv/bin/activate # On Windows: .venv\Scripts\activate uv pip install -r requirements.txt # If you are in CN, use the USTC mirror for faster downloads: uv pip install -r requirements.txt -i https://mirrors.ustc.edu.cn/pypi/simple # Alternatively, conda is also supported: conda create -n YingMusic-Singer-Plus python=3.10 conda activate YingMusic-Singer-Plus pip install uv uv pip install -r requirements.txt # If you are in CN: uv pip install -r requirements.txt -i https://mirrors.ustc.edu.cn/pypi/simple ``` ### Option 2: Pre-built Environment **Conda** 1. Download and install **Miniconda** from https://repo.anaconda.com/miniconda/ for your platform. Verify with `conda --version`. 2. Download the pre-built environment package for your setup from the table below. 3. Navigate to your Conda `envs/` directory and create a folder named `YingMusic-Singer-Plus`. 4. Move the downloaded package into that folder and extract it: ```bash tar -xvf ``` **uv** 1. Install **uv** via `pip install uv` or follow the [official instructions](https://docs.astral.sh/uv/getting-started/installation/). 2. Download the pre-built environment package for your setup from the table below. 3. Extract the package and activate the environment: ```bash tar -xvf source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` | CPU Architecture | GPU | OS | Type | Download | |------------------|--------|---------|-------|--------------| | AMD64 | NVIDIA | Linux | Conda | Coming soon | | AMD64 | NVIDIA | Linux | uv | Coming soon | | AMD64 | NVIDIA | Windows | uv | Coming soon | ### Option 3: Docker Build the image: ```bash docker build -t YingMusic-Singer-Plus . ``` ## 🎵 Inference ### Option 1: Online Demo (HuggingFace Space) Visit https://huggingface.co/spaces/ASLP-lab/YingMusic-Singer-Plus to try the model instantly in your browser. ### Option 2: Local Gradio App (same as online demo) ```bash python app_local.py ``` ### Option 3: Command-line Inference ```bash python infer.py \ --ref_audio examples/hf_space/melody_control/melody_control_ZH_02_timbre.wav \ --melody_audio examples/hf_space/melody_control/melody_control_ZH_02_melody.wav \ --ref_text "就让你|在别人怀里|快乐" \ --target_text "Missing you in my mind|missing you in my heart" \ --output output/melody_control_zh_missing_you.wav ``` Enable vocal separation and accompaniment mixing: ```bash python infer.py \ --ref_audio examples/hf_space/lyric_edit/SingEdit_EN_01.wav \ --melody_audio examples/hf_space/lyric_edit/SingEdit_EN_01.wav \ --ref_text "can you tell my heart is speaking|my eyes will give you clues" \ --target_text "can you spot the moon is grinning|my lips will show you hints" \ --separate_vocals \ --mix_accompaniment \ --output output/lyric_edit_en_moon_grinning.wav ``` ### Option 4: Batch Inference > **Note**: All audio fed to the model must be pure vocal tracks (no accompaniment). If your inputs contain accompaniment, run vocal separation first using `src/third_party/MusicSourceSeparationTraining/inference_api.py`. The input JSONL file should contain one JSON object per line, formatted as follows: ```json { "id": "lyric_edit_en_moon_grinning", "melody_ref_path": "examples/hf_space/lyric_edit/SingEdit_EN_01.wav", "gen_text": "can you spot the moon is grinning|my lips will show you hints", "timbre_ref_path": "examples/hf_space/lyric_edit/SingEdit_EN_01.wav", "timbre_ref_text": "can you tell my heart is speaking|my eyes will give you clues" } ``` ```bash python batch_infer.py \ --input_type jsonl \ --input_path /path/to/input.jsonl \ --output_dir /path/to/output \ --ckpt_path /path/to/ckpts \ --num_gpus 4 ``` Multi-process inference on **LyricEditBench (melody control)** — the test set will be downloaded automatically: ```bash python inference_mp.py \ --input_type lyric_edit_bench_melody_control \ --output_dir path/to/LyricEditBench_melody_control \ --ckpt_path ASLP-lab/YingMusic-Singer-Plus \ --num_gpus 8 ``` Multi-process inference on **LyricEditBench (singing edit)**: ```bash python inference_mp.py \ --input_type lyric_edit_bench_sing_edit \ --output_dir path/to/LyricEditBench_sing_edit \ --ckpt_path ASLP-lab/YingMusic-Singer-Plus \ --num_gpus 8 ``` ## 🏗️ Model Architecture YingMusic-Singer-Plus consists of four core components: | Component | Description | |-----------|-------------| | **VAE** | Stable Audio 2 encoder/decoder; downsamples stereo 44.1 kHz audio by 2048× | | **Melody Extractor** | Encoder of a pretrained MIDI extraction model (SOME); captures disentangled melody information | | **IPA Tokenizer** | Converts Chinese & English lyrics into a unified phoneme sequence with sentence-level alignment | | **DiT-based CFM** | Conditional flow matching backbone following F5-TTS (22 layers, 16 heads, hidden dim 1024) | **Total parameters**: ~727.3M (453.6M CFM + 156.1M VAE + 117.6M Melody Extractor) ## 📊 LyricEditBench We introduce **LyricEditBench**, the first benchmark for melody-preserving lyric modification evaluation, built on [GTSinger](https://github.com/GTSinger/GTSinger). The dataset is available on HuggingFace at https://huggingface.co/datasets/ASLP-lab/LyricEditBench. ### Results

Table 2: Comparison with Baseline Model on LyricEditBench across Task Types in Table 1 and Languages. Metrics (M): P: PER, S: SIM, F: F0-CORR, V: VS are detailed in Section 3. Best results are Bold.

## 🙏 Acknowledgements This work builds upon the following open-source projects: - [F5-TTS](https://github.com/SWivid/F5-TTS) — DiT-based CFM backbone - [Stable Audio 2](https://github.com/Stability-AI/stable-audio-tools) — VAE architecture - [SOME](https://github.com/openvpi/SOME) — Melody Extractor - [DiffRhythm](https://github.com/ASLP-lab/DiffRhythm) — Sentence-level alignment strategy - [GTSinger](https://github.com/GTSinger/GTSinger) — Benchmark base corpus - [Emilia](https://huggingface.co/datasets/amphion/Emilia-Dataset) — TTS pretraining data ## 📄 License The code and model weights in this project are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), **except** for the following: The VAE model weights and inference code (in `src/YingMusic-Singer/utils/stable-audio-tools`) are derived from [Stable Audio Open](https://huggingface.co/stabilityai/stable-audio-open-1.0) by Stability AI, and are licensed under the [Stability AI Community License](./LICENSE-STABILITY). ## ✉️ Contact Us If you are interested in leaving a message to our work, feel free to email cbhao@mail.nwpu.edu.cn or lxie@nwpu.edu.cn You’re welcome to join our WeChat group for technical discussions, updates.

## Star History