FireRedTTS3 – Xiaohongshu's Open-Source Unified Speech Generation and Editing Model

1. What is FireRedTTS3

FireRedTTS3 is an open-source unified speech generation and editing model developed by Xiaohongshu's FireRed team. Built upon the RedAE semantic-enhanced continuous representation and LLM-DiT architecture, this model achieves zero-shot voice cloning, natural language description-driven sound design, and precise semantic and acoustic editing for specified regions within a single framework. It demonstrates state-of-the-art performance on four public benchmark datasets including Seed-TTS-Eval and MiniMax-MLS-Test. The codebase and model weights are fully open-sourced, supporting localized deployment, and providing an end-to-end unified solution for speech content production.

Technical Positioning and Domain:
Belongs to the intersection of speech synthesis and speech editing, positioned as a foundational multi-language speech generation and editing model. Unlike traditional TTS models that focus exclusively on text-to-speech synthesis, FireRedTTS3 integrates three major tasks—voice cloning, sound design, and speech editing—into a single framework, covering the complete production pipeline from speech generation to post-production modification. This establishes a differentiated position among open-source projects in the same category.

Development Background:
Developed and open-sourced by Xiaohongshu's FireRed team. With continuous technical accumulation in speech generation, this team has fully open-sourced model weights, inference code, and interactive demos to reduce the usage threshold of speech generation technology and promote the development of speech application ecosystems in multi-language and multi-dialect scenarios.

Core Value:
Addresses three long-standing pain points in traditional speech synthesis systems:

1. Task fragmentation—cloning and editing typically require collaboration between different models
2. Limited dialect coverage—high-quality open-source solutions for Chinese dialect synthesis have been lacking
3. Post-production modification requiring full-sentence re-recording with high costs
   FireRedTTS3 enables end-to-end speech production and editing through unified modeling and instruction control, achieving all tasks with a single model.

Technical Features:
Injects semantic information during the tokenizer training phase via RedAE, balancing acoustic details with generation stability. The LLM-DiT architecture inherits the text understanding and instruction-following capabilities from Qwen3-1.7B, combined with a "plan-first, synthesize-later" strategy to achieve precise control over sound design and speech editing tasks.

2. Key Features

• Zero-shot voice cloning: Given a few seconds of reference audio, synthesize speech in 24 languages and 21 Chinese dialects using the cloned voice. No fine-tuning required for specific speakers - the model directly learns voice characteristics from reference audio and transfers them to target text, significantly reducing the cost of personalized speech synthesis.

• Natural language sound design: Generate entirely new voices through natural language descriptions without needing any reference audio. For example, describing "a warm, magnetic voice of a middle-aged man," the Instruct version parses instructions into structured acoustic plans, then maps them to RedAE representations for synthesis, providing a novel voice design pathway for virtual characters and audiobook creation.

• Semantic speech editing: Enables precise modifications to specific regions of existing speech, including word changes, insertions, deletions, or content replacements. Users specify edit locations and content via natural language instructions, while the model only modifies the target area, maintaining consistency in semantics, voice characteristics, and prosody across the rest of the audio, avoiding full-sentence re-recording.

• Acoustic speech editing: Adjust speech rate, pitch, or volume using fixed template instructions, e.g., "adjust the speed to 0.5x." Acoustic editing and semantic editing operate independently, supporting standalone use or combined operations. This is particularly suitable for scenarios requiring precise audio adjustments in video post-production and podcast editing.

• Multilingual and dialect coverage: Supports zero-shot cloning across 24 languages and 21 Chinese dialects, covering major international languages and common dialects like Sichuanese, Cantonese, and Northeastern Mandarin. In the 24-language evaluation of MiniMax-MLS-Test, 22 languages ranked in the top two, with dialect synthesis capabilities standing out among open-source models.

• Unified modeling across three tasks: Integrates voice cloning, sound design, and speech editing into a single model, eliminating the need for separate training or model switching for different tasks. This design reduces system complexity and avoids error propagation from cascaded models, offering practical advantages in deployment and maintenance.

• RedAE semantic-enhanced continuous representation: Introduces a semantic teacher model during tokenizer training for feature distillation, injecting semantic information into continuous latent space. Compared to discrete token schemes, this preserves richer acoustic details; compared to traditional continuous representations, it mitigates autoregressive error accumulation, balancing audio quality with generation stability.

• LLM-DiT generation framework: Uses Qwen3-1.7B as the backbone, inheriting its text understanding and instruction-following capabilities. Input sequences are compressed through an Aggregator for autoregressive modeling, then undergo patch-level denoising generation via the DiT module under backbone conditions, achieving both temporal coherence and fine-grained acoustic control.

3. How to Use

1. Environment Setup: Clone the GitHub repository and install dependencies from requirements.txt. It is recommended to use a Linux system with an NVIDIA GPU, ensuring the video memory capacity meets the requirements for loading the Qwen3-1.7B model and the DiT module. Refer to the official documentation for specific hardware requirements.

2. Model Download: Download the pre-trained FireRedTeam/FireRedTTS3 model from the ModelScope platform to a local directory. The model includes Base and Instruct versions; select the appropriate weights based on your specific task, and ensure the directory structure matches the official repository.

3. Language Identification Configuration (Optional): To enable the Base version to automatically detect the language of input text, download the FastText language identification model separately. After configuration, the model will automatically detect the language and select suitable pronunciation rules. Otherwise, you must manually specify the language tag.

4. Zero-Shot Cloning: Load the Base version model and input a reference audio, reference text, and target text. The model extracts the voice characteristics from the reference audio and generates speech with the corresponding voice based on the target text. It is recommended that the reference audio duration be no less than 3 seconds, with clear content and no background noise.

5. Voice Design: Call the Instruct version's generate_voice_design interface, inputting only a natural language description and target text to generate a completely new voice. The more specific the description, the higher the match accuracy between the generated voice and the intended design. Attributes such as gender, age perception, and audio quality characteristics can be specified.

6. Semantic Editing: Call the generate_semantic_edit interface to specify insertions, deletions, or replacements using natural language instructions. For example, "Replace '你好' with '您好'". The model will locate the target section and execute the modification while keeping the rest unchanged.

7. Acoustic Editing: Call the generate_acoustic_edit interface and use fixed template instructions to adjust speaking rate, pitch, or volume. Template formats include adjust the speed to 0.5x, raise the pitch, with operation granularity controllable at a fine level.

8. Dialect Synthesis: Input dialect tags prefixed with ZH_ (e.g., ZH_Sichuan) and use reference audio from the same dialect for optimal results. Cross-dialect reference audio may lead to insufficient accent transfer; it is recommended to match reference samples by dialect.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For projects requiring unified voice cloning, voice design, and speech editing capabilities with emphasis on Chinese dialect support, FireRedTTS3 represents the most comprehensive open-source solution currently available. Its capabilities make it particularly suitable for production scenarios requiring flexible editing in audiobook creation, game voice acting, and similar applications. For multilingual cloning projects with minimal editing requirements and limited deployment resources, consider XTTS or the lightweight version of CosyVoice3.

Scenario-Based Selection: For dialogue-oriented interaction scenarios primarily using English and Chinese, ChatTTS offers more advantages with its lightweight deployment and real-time response capabilities. For content creation scenarios requiring localized Chinese dialect support, FireRedTTS3's 21-dialect coverage represents a rare capability among open-source models, making it the more appropriate choice.

6. Editor's Summary

FireRedTTS3's technical innovations manifest in two key aspects: First, RedAE introduces semantic distillation into the tokenizer training phase, resolving the contradiction between semantic alignment and error accumulation within a continuous representation framework. This design provides a novel approach for representation learning in speech generation models. Second, the LLM-DiT architecture combines large language models' instruction-following capabilities with diffusion Transformers' temporal modeling strengths, establishing a "comprehend-plan-synthesize" generation paradigm that enables precise control of acoustic properties through natural language instructions.

From a practical perspective, the three-task unified modeling directly reduces the complexity of speech content production systems - processes previously requiring multiple models for cloning, design, and editing can now be accomplished by a single model. The capability to support 21 Chinese dialects is relatively rare among open-source speech models, offering a low-cost tool for localized content creation such as dialect-based short videos and regional cultural programs. Performance on four public benchmark datasets further demonstrates its generation quality can withstand cross-model comparisons.

The model is suitable for speech algorithm researchers as a reference implementation for unified modeling, and serves as a production tool for audiobook content creators, game and film voice acting teams, and intelligent customer service developers. With full open-sourcing and local deployment support, it also caters to enterprise users with data privacy concerns.

Notably, hardware requirements and resource consumption of the Instruct version remain constraints for practical deployment. The dialect performance dependency on reference audio implies users need data curation expertise. Overall, FireRedTTS3 achieves a balanced trade-off between functional coverage, multilingual dialect capabilities, and generation quality. Its editing capabilities and dialect support form distinct competitive advantages, with future development contingent on ecosystem maturity and validation through broader application scenarios.

7. Application Scenarios

• Multilingual Audiobook and Podcast Production: Clone a voice using just a few seconds of reference audio, then rapidly generate audiobooks or podcasts in 24 languages without hiring multilingual voice actors. Creators can maintain consistent vocal characteristics across all language versions, establishing a unified auditory brand and significantly reducing production costs and time cycles for multilingual content.

• Game and Virtual Character Voice Acting: Design custom character voices through natural language descriptions, such as "ethereal female voice" or "deep mechanical tone," without requiring reference audio. Post-production editing allows precise modification of individual words in existing lines, avoiding full re-recording for single-word errors and improving iteration efficiency.

• Video and Podcast Post-Production Editing: Directly replace erroneous words, adjust speaking rate, or delete redundant segments in final audio outputs without returning to the recording studio. Semantic editing and acoustic editing can be combined, such as adjusting a segment's speed to 0.8x while replacing sensitive terms, drastically simplifying post-production workflows.

• Brand-Specific Intelligent Customer Service: Perform zero-shot cloning of enterprise-specific customer service voices, maintaining consistent brand audio identity while supporting multilingual services. Companies can deploy the same voice across different language customer service scenarios, enhancing brand voice recognition and reducing costs for multilingual support teams.

• Dialect Content Creation: Synthesize localized content using 21 Chinese dialects, including dialect short videos, regional cultural programs, and dialect-voiced film clips. Creators can batch-produce high-quality dialect content without needing to hire dialect voice actors, significantly lowering barriers to dialect-based content creation.

8. FAQ

Q: What languages and dialects does FireRedTTS3 support?
A: It supports zero-shot voice cloning for 24 languages and 21 Chinese dialects. The language coverage includes major international languages, while dialects encompass common Chinese variants such as Sichuanese, Cantonese, and Northeastern Mandarin. For specific lists, please refer to the README file in the GitHub repository.

Q: What are the differences between the Base version and the Instruct version?
A: The Base version focuses on zero-shot voice cloning and can directly perform multilingual and multidialect speech synthesis after loading. The Instruct version adds capabilities for sound design and voice editing on top of the Base version, requiring additional generation interfaces. It also has a larger model size and resource consumption.

Q: What hardware configuration is required?
A: The original text does not specify explicit hardware requirements. Based on the model architecture, the Qwen3-1.7B Backbone combined with the DiT module requires significant GPU memory. It is recommended to use an NVIDIA GPU with 16GB or more VRAM for inference. For detailed specifications, please consult the documentation in the official repository.

Q: Is commercial use supported?
A: The model weights and code are fully open-sourced, but specific commercial usage terms must comply with the LICENSE file in the GitHub repository. Before commercial deployment, carefully review the license content to confirm compliance with requirements such as attribution and citation.

Q: How to achieve optimal dialect cloning results?
A: It is recommended to use reference audio in the same dialect and input the corresponding ZH_ prefix dialect tag (e.g., ZH_Sichuan). Cross-dialect reference audio may result in insufficient accent transfer, affecting the authenticity of the generated speech in the target dialect.

Q: What specific operations does voice editing support?
A: Semantic editing supports inserting, deleting, or replacing specified content through natural language instructions. Acoustic editing allows adjusting speaking rate, pitch, and volume using fixed template commands. Both editing methods can be used independently or in combination, and only affect designated audio segments.

9. Project Links

• GitHub Repository: https://github.com/FireRedTeam/FireRedTTS3
• ModelScope Model Library: https://modelscope.cn/models/FireRedTeam/FireRedTTS3