Confucius4-TTS - NetEase Youdao's open-source multilingual speech synthesis engine - AiBoss

What is Confucius4-TTS?

Confucius4-TTS is an open-source 1.3B parameter multilingual speech synthesis engine from NetEase Youdao. The model can clone voice timbres with only 3 seconds of reference audio, requiring no reference text. It supports cross-language, 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 voice-over, overseas localization, and multilingual content creation.

Main functions of Confucius4-TTS

Zero-sample rapid cloningIt can replicate any timbre with just 3 seconds of reference audio, without the need for reference text or transcription, and without prior training.
14-language cross-language accent-free synthesisSupports Chinese, English, Japanese, Korean, German, French, Spanish, Indonesian, Italian, Thai, Portuguese, Russian, Malay, and Vietnamese. The voice can be seamlessly transferred between different languages, and the pronunciation is authentic and accent-free.
Emotional rhythm transferIt automatically extracts emotional tags from reference audio through audio prompt, accurately replicating intonation, rhythm, and emotion, and supports lossless transfer across languages.
Local offline deploymentThe 54GB full model weights are open source under the Apache 2.0 license, allowing for local inference, commercial use, and fine-tuning training.

The technical principles of Confucius4-TTS

Speech coding layerThe reference audio was used to extract semantic features using Wav2Vec2-BERT 2.0, and the speaker identity was encoded using ECAPA-TDNN.
Semantic modeling layerThe GPT-style large language model serves as the backbone, receiving text and speaker conditions to generate a semantic token sequence for the target language, unifying the modeling of identity, language, and emotion.
Sound student layerFlow Matching converts semantic tokens into Mel spectra, replacing traditional vocoders.
Waveform synthesis layerThe BigVGAN high-fidelity neural network vocoder restores the Mel spectrum to the final audio waveform.
Overall processReference audio → Speech encoder extracts identity/semantic features → LLM generates semantic token → Flow Matching converts to Mel spectrum → BigVGAN synthesizes waveform, all without reference text, achieving true zero-sample end-to-end speech synthesis.

How to use Confucius4-TTS

Environmental preparationEnsure that Python 3.10 and CUDA 12.6 are installed locally, and that an NVIDIA GPU with sufficient video memory is available to support inference or training.
Get codeClone the official Confucius4-TTS repository from GitHub to your local directory.
Creating an environmentCreate a new Python 3.10 virtual environment named confuciustts using conda and activate it.
Install dependencies: Execute the dependency installation command in the project directory to automatically configure all the necessary Python libraries.
Prepare the modelDownload the official 54GB full weight file and pre-trained components such as Wav2Vec2-BERT and Amphion MaskGCT, and place them in the specified directory.
Execution reasoningCall the inference interface, pass in 3 seconds of reference audio, target text and language code, and the model can generate synthesized speech with cloned timbre and save it as an audio file.
Online experienceVisit the official Gradio online demo page to upload reference audio and listen to the synthesized results directly without local deployment.
Prepare training dataThe training set is organized in TSV format according to specifications, including language code, audio path, normalized text, semantic token file and reference audio path.
Training T2S modelLoad pre-trained T2S weights and train the text-to-semantic token generation module based on the prepared data.
Training the S2A modelWith T2S and encoder frozen, train the Flow Matching model separately to convert semantic tokens into Mel spectra.
Obtain open source resourcesDownload the complete model weights, configuration files, and accompanying toolchain via GitHub, HuggingFace, or ModelScope.

Confucius4-TTS's core advantages

True zero-sample cloningIt can replicate any timbre with just 3 seconds of reference audio, without any reference text or transcription, and without prior training. It is ready to use out of the box.
14 languages, cross-language, accent-freeSupports 14 languages including Chinese, English, Japanese, and Korean. Any voice tone can be seamlessly transferred across languages, with authentic and natural pronunciation, completely solving industry pain points such as "Chinese accent".
Emotion and rhythm are transferred in a unified mannerThe model can accurately replicate the intonation, rhythm, and emotion of reference audio through audio prompt, and supports lossless transfer across languages.
Fully open source and commercially usableThe complete 54GB model weights, training code, and toolchain are all open source, licensed under the Apache 2.0 license, and can be deployed offline locally, fine-tuned freely, and used commercially without restrictions.

Confucius4-TTS project address

GitHub repositoryhttps://github.com/netease-youdao/Confucius4-TTS
HuggingFace model libraryhttps://huggingface.co/netease-youdao/Confucius4-TTS
Experience the demo onlinehttps://confucius4-tts.youdao.com/gradio/

Comparison of Confucius4-TTS with similar competing products

Comparison Dimensions	Confucius4-TTS	CosyVoice
Reference text dependency	Zero-sample cloning with just 3 seconds of audio, no reference text required.	Standard cloning typically requires a reference text or transcription.
Zero sample threshold	Ready to use right out of the box, any tone, no training required.	It supports cross-language cloning, but its ease of use is slightly inferior in text-free scenarios.
Cross-language accents	14 languages have no accent transfer, and less commonly spoken languages have lower WER.	Strong cross-language ability, but slightly less pure accent in less commonly spoken languages.
Emotional transfer	Supports cross-language transfer of audio prompt emotion/prosody.	Focusing on timbre cloning, audio-level emotional transfer is not the main focus.
Open source license	Apache 2.0, 54GB full authority + toolchain, unrestricted for commercial use.	Open source, some enhanced versions have usage restrictions.
Technical Architecture	Speech Encoder + GPT-style LLM + Flow Matching	Self-developed large-scale speech model, discrete token encoding and decoding
Robustness of Minor Languages	Thai, Vietnamese, and other languages have extremely low WER scores, demonstrating stable performance.	Excellent performance in common languages, but significant gap in performance in niche language scenarios.
Parameters and Deployment	1.3B parameters, single-card inference capability, supports single-node training	Versions 0.5B/1.5B offer flexible deployment but require text alignment.

Application scenarios of Confucius4-TTS

Multilingual content creationQuickly generate multilingual versions of short videos, podcasts, and audiobooks, maintaining consistent voice quality and reducing dubbing costs.
Digital human voiceoverProvides consistent, emotionally resonant voices for virtual anchors and AI digital humans, supporting seamless switching between 14 languages.
Cross-language teachingThe language learning platform can generate authentic foreign language pronunciation examples using any native speaker's voice, helping learners establish correct pronunciation awareness.
Localization of overseas businessFor overseas applications such as gaming, e-commerce, and advertising, localized voice content can be generated quickly, avoiding the negative impact of Chinese accents on the user experience.
Emotional voice interactionIn scenarios such as intelligent customer service, companion AI, and accessibility assistance, it can output natural and warm voice feedback based on the emotional style of reference audio.