Confucius4-TTS - NetEase Youdao's Open-Source Multilingual Text-to-Speech Engine

What is Confucius4-TTS

Confucius4-TTS is a 1.3B-parameter multilingual text-to-speech engine open-sourced by NetEase Youdao. The model requires only a 3-second reference audio for zero-shotvoice cloning, with no reference text needed. It supports cross-lingual, accent-free synthesis in 14 languages including Chinese, English, Japanese, and Korean, and can transfer emotional prosody. The model can be deployed and trained locally, making it suitable for digital human dubbing, overseas localization, and multilingual content creation.

Main Features of Confucius4-TTS

Zero-Shot Ultra-Fast Cloning: Only 3 seconds of reference audio are needed to replicate any voice, with no reference text or transcription required and no prior training needed.
14-Language Cross-Lingual Accent-Free Synthesis: Supports Chinese, English, Japanese, Korean, German, French, Spanish, Indonesian, Italian, Thai, Portuguese, Russian, Malay, and Vietnamese. Voices can be seamlessly transferred across different languages, with authentic, accent-free pronunciation.
Emotional Prosody Transfer: Automatically extracts the emotional label of the reference audio through an audio prompt, accurately replicating intonation, prosody, and emotion, and supporting lossless cross-lingual transfer.
Local Offline Deployment: The full 54GB model weights are open-sourced under the Apache 2.0 license, allowing local inference, commercial use, and fine-tuning training.

Technical Principles of Confucius4-TTS

Speech Encoding Layer: The reference audio extracts semantic features through Wav2Vec2-BERT 2.0, and encodes speaker identity representation through ECAPA-TDNN.
Semantic Modeling Layer: A GPT-style large language model serves as the backbone, receiving text and speaker conditions to generate a semantic token sequence in the target language, uniformly modeling identity, language, and emotion.
Acoustic Generation Layer: A Flow Matching model converts semantic tokens into Mel spectrograms, replacing the traditional vocoder.
Waveform Synthesis Layer: The BigVGAN high-fidelity neural network vocoder restores the Mel spectrogram into the final audio waveform.
Overall Process: Reference audio → speech encoder extracts identity/semantic features → LLM generates semantic tokens → Flow Matching converts to Mel spectrogram → BigVGAN synthesizes waveform. No reference text is needed throughout the process, achieving true zero-shot end-to-end speech synthesis.

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How to Use Confucius4-TTS

Environment Preparation: Ensure Python 3.10 and CUDA 12.6 are installed locally, with an NVIDIA GPU with sufficient VRAM to support inference or training.
Get the Code: Clone the official Confucius4-TTS repository from GitHub to a local directory.
Create Environment: Create a new Python 3.10 virtual environment named confuciustts via conda and activate it.
Install Dependencies: Run the dependency installation command in the project directory to automatically configure all required Python libraries.
Prepare the Model: Download the official 54GB full weight files as well as pretrained components such as Wav2Vec2-BERT and Amphion MaskGCT, and place them in the specified directory.
Run Inference: Call the inference interface, pass in a 3-second reference audio, target text, and language code, and the model will generate synthesized speech with a cloned timbre and save it as an audio file.
Online Experience: Visit the official Gradio online demo page to directly upload reference audio and preview the synthesis results without local deployment.
Prepare Training Data: Organize the training set in TSV format according to the specification, including language code, audio path, normalized text, semantic token file, and reference audio path.
Train the T2S Model: Load the pretrained T2S weights and train the text-to-semantic-token generation module based on the prepared data.
Train the S2A Model: With T2S and the encoder frozen, separately train the Flow Matching model to convert semantic tokens into Mel spectrograms.
Obtain Open-Source Resources: Download the complete model weights, configuration files, and supporting toolchain through GitHub, HuggingFace, or the ModelScope platform.

Core Advantages of Confucius4-TTS

True Zero-Shot Cloning: Only 3 seconds of reference audio is needed to replicate any timbre, with no reference text or transcription required throughout, no prior training, and it is ready to use out of the box.
14 Languages with Cross-Lingual Accent-Free Speech: Supports 14 languages including Chinese, English, Japanese, and Korean. Any timbre can be seamlessly transferred across languages, with authentic and natural pronunciation, thoroughly solving industry pain points such as "Chinese accent".
Unified Transfer of Emotion and Prosody: The model can accurately replicate the intonation, prosody, and emotion of the reference audio through audio prompts, and supports lossless cross-lingual transfer.
Fully Open Source and Commercially Usable: The complete 54GB model weights + training code + toolchain are all open source under the Apache 2.0 license, allowing local offline deployment, free fine-tuning, and unrestricted commercial use.

Project Address of Confucius4-TTS

GitHub Repository：https://github.com/netease-youdao/Confucius4-TTS
HuggingFace Model Library：https://huggingface.co/netease-youdao/Confucius4-TTS
Online Experience Demo：https://confucius4-tts.youdao.com/gradio/

Comparison of Confucius4-TTS with Similar Competing Products

Comparison Dimension	Confucius4-TTS	CosyVoice
Reference Text Dependency	No reference text required; zero-shot cloning with 3 seconds of audio	Standard cloning usually requires reference text or transcription
Zero-Shot Threshold	Ready to use out of the box; any timbre requires no training	Supports cross-lingual cloning, but ease of use in text-free scenarios is slightly inferior
Cross-Lingual Accent	Accent-free transfer across 14 languages, with lower WER for low-resource languages	Strong cross-lingual capability, but accent purity in low-resource languages is slightly inferior
Emotion Transfer	Supports cross-lingual emotion/prosody transfer via audio prompts	Focuses on timbre cloning; audio-level emotion transfer is not a main feature
Open-Source License	Apache 2.0, 54GB full weights + toolchain, unrestricted commercial use	Open source, but some enhanced versions have usage restrictions
Technical Architecture	Speech Encoder + GPT-style LLM + Flow Matching	Self-developed large speech model, discrete token encoding and decoding
Low-Resource Language Robustness	Extremely low WER for Thai, Vietnamese, etc., with robust performance	Excellent for common languages, but the gap is obvious in niche language scenarios
Parameters and Deployment	1.3B parameters, inference on a single card, supports single-node training	0.5B/1.5B versions, flexible deployment but requires text alignment

Application Scenarios of Confucius4-TTS

Multilingual Content Creation: Quickly generate multilingual versions for short videos, podcasts, and audiobooks while maintaining the same timbre consistency, reducing dubbing costs
Digital Human Dubbing: Provide cross-language, emotionally expressive, consistent timbre for virtual streamers and AI digital humans, supporting seamless switching across 14 languages
Cross-Language Teaching: Language learning platforms can use the timbre of any native speaker to generate authentic foreign language pronunciation demonstrations, helping learners establish correct pronunciation perception
Localization for Overseas Business: In overseas scenarios such as gaming, e-commerce, and advertising, localized voice content can be quickly generated, avoiding the impact of Chinese-accented pronunciation on user experience
Emotional Voice Interaction: In scenarios such as intelligent customer service, companion AI, and accessibility assistance, natural and warm voice feedback can be output based on the emotional style of the reference audio