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espnet/mixdata_svs_visinger2_spkemb_lang_pretrained
mixdata_svs_visinger2_spkemb_lang_pretrained is a text-to-audio model from espnet. Use it for the text-to-audio task on the model card, and read the license before you ship it in a product. It is set up for espnet. The card lists the license as mit.
A role-playing singing dialogue system that converts speech input into character-based singing output.
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From the Hugging Face model README
A role-playing singing dialogue system that converts speech input into character-based singing output.
<div align="center"> </div>SingingSDS is an innovative role-playing singing dialogue system that seamlessly converts natural speech input into character-based singing output. The system integrates automatic speech recognition (ASR), large language models (LLM), and singing voice synthesis (SVS) to create an immersive conversational singing experience. It is a cascaded SDS that responds through singing rather than speaking, fostering more affective, memorable, and pleasurable interactions in character-based roleplay and interactive entertainment scenarios. SingingSDS employs a modular ASR-LLM-SVS pipeline and supports a wide range of configurations across character personas, ASR and LLM backends, SVS models, melody sources, and voice profiles.
<div align="center"> <img src="https://huggingface.co/espnet/SingingSDS/resolve/main/assets/demo.png" alt="SingingSDS Interface" style="max-width: 100%; height: auto;"/> <p><em>SingingSDS Web Interface: Interactive singing dialogue system with character visualization, audio I/O, evaluation metrics, and flexible configuration options.</em></p> </div>conda create -n singingsds python=3.11
conda activate singingsds
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
pip install -r requirements.txt
First install uv:
# On macOS/Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
# On Windows:
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
# Or via pip:
pip install uv
Then install dependencies:
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt
pip install -r requirements.txt
python -m venv singingsds_env
# On Windows:
singingsds_env\Scripts\activate
# On macOS/Linux:
source singingsds_env/bin/activate
pip install -r requirements.txt
python cli.py \
--query_audio tests/audio/hello.wav \
--config_path config/cli/yaoyin_default.yaml \
--output_audio outputs/yaoyin_hello.wav \
--eval_results_csv outputs/yaoyin_test.csv
Run minimal inference without evaluation.
python cli.py \
--query_audio tests/audio/hello.wav \
--config_path config/cli/yaoyin_default_infer_only.yaml \
--output_audio outputs/yaoyin_hello.wav
--query_audio: Input audio file path (required)--config_path: Configuration file path (default: config/cli/yaoyin_default.yaml)--output_audio: Output audio file path (required)Start the web interface:
python app.py
Then visit the displayed address in your browser to use the graphical interface.
💡 Tip: You can also try our HuggingFace demo for a quick test without local installation!
The system supports multiple preset characters:
timbre2timbre1| Model | Description |
|---|---|
openai/whisper-large-v3-turbo | Latest Whisper model with turbo optimization |
openai/whisper-large-v3 | Large Whisper v3 model |
openai/whisper-medium | Medium-sized Whisper model |
openai/whisper-small | Small Whisper model |
funasr/paraformer-zh | Paraformer for Chinese ASR |
| Model | Description |
|---|---|
gemini-2.5-flash | Google Gemini 2.5 Flash |
google/gemma-2-2b | Google Gemma 2B model |
meta-llama/Llama-3.2-3B-Instruct | Meta Llama 3.2 3B Instruct |
meta-llama/Llama-3.1-8B-Instruct | Meta Llama 3.1 8B Instruct |
Qwen/Qwen3-8B | Qwen3 8B model |
Qwen/Qwen3-30B-A3B | Qwen3 30B A3B model |
MiniMaxAI/MiniMax-Text-01 | MiniMax Text model |
| Model | Language Support |
|---|---|
espnet/visinger2-zh-jp-multisinger-svs | Bilingual (Chinese & Japanese) |
espnet/aceopencpop_svs_visinger2_40singer_pretrain | Chinese |
SingingSDS/
├── app.py, cli.py # Entry points (demo app & CLI)
├── pipeline.py # Main orchestration pipeline
├── interface.py # Gradio interface
├── characters/ # Virtual character definitions
├── modules/ # Core modules
│ ├── asr/ # ASR models (Whisper, Paraformer)
│ ├── llm/ # LLMs (Gemini, LLaMA, etc.)
│ ├── svs/ # Singing voice synthesis (ESPnet)
│ └── utils/ # G2P, text normalization, resources
├── config/ # YAML configuration files
├── data/ # Dataset metadata and length info
├── data_handlers/ # Parsers for KiSing, Touhou, etc.
├── evaluation/ # Evaluation metrics
├── resources/ # Singer embeddings, phoneme dicts, MIDI
├── assets/ # Character visuals
├── tests/ # Unit tests and sample audios
└── README.md, requirements.txt
We welcome contributions! Please feel free to submit issues and pull requests.
The Yaoyin (遥音) character assets, including character_yaoyin.png created by illustrator Zihe Zhou, are commissioned exclusively for the SingingSDS project. Screenshots of the system that include these assets, such as demo.png, are also covered under this license. The assets may be used only for direct derivatives of SingingSDS, such as project-related posts, usage videos, or other content directly depicting the project. Any other use requires express permission from the illustrator, and these assets may not be used for training, fine-tuning, or improving any artificial intelligence or machine learning models. For full license details, see assets/character_yaoyin.LICENSE.
All source code in this repository is licensed under the MIT License. This license applies only to the code. Character assets remain under their separate license and restrictions, as described in the Character Assets section.
The models used in SingingSDS are subject to their respective licenses and terms of use. Users must comply with each model’s official license, which can be found at the respective model’s official repository or website.
If you find our work helpful or inspiring, please feel free to cite it:
@article{singingsds2024,
title={SingingSDS: A Singing-Capable Spoken Dialogue System for Conversational Roleplay Applications},
author={Author list will be added later},
journal={arXiv preprint arXiv:2511.20972},
year={2024}
}