1. Abstract
Fun-Audio-Chat-8B is an open-sourced "large audio language model" from the FunAudioLLM team, aimed at more natural, low-latency voice interactions. It uses "dual-resolution speech representation" (5Hz shared backbone + 25Hz refined head) to reduce computational overhead, while preserving text LLM capabilities as much as possible through the Core-Cocktail training strategy. It covers tasks such as voice question answering, audio understanding, voice function calling, voice command following, and "voice emotional resonance". The model is bilingual in Chinese and English, and the license is Apache-2.0.
2. Core features
1. Dual-resolution speech characterization: Reduce costs with a shared backbone with lower frame rates and maintain voice quality with high frame rate heads.
2. Comprehensive ability coverage: voice question answering, audio understanding, voice function calling, voice command following, voice emotional resonance.
3. Clear reasoning form: Provide speech-to-text (S2T) and speech-to-speech (S2S) sample scripts to facilitate quick link verification.
4. Supporting components: It is recommended to download Fun-CosyVoice3-0.5B-2512 for speech synthesis capabilities.
3. Installation
- Clone the code and install dependencies:
git clone --recurse-submodules https://github.com/FunAudioLLM/Fun-Audio-Chat && cd Fun-Audio-Chat- Install
ffmpegand create apython=3.12environment. - 2. Install the framework version: install
torch==2.8.0,torchaudio==2.8.0, andrequirements.txtaccording to the repository example. - 3. Prepare video memory: The official reference is about 24GB video memory; The training resource requirements are higher.
4. Typical use cases
- Voice assistant/customer service: user voice input, model output text or direct output voice reply (S2S).
- Audio comprehension and voice Q&A: Q&A, summary, and key points extraction of the recorded content (driven by task prompts).
- Voice function call: Map the "spoken instruction" to the tool definition, which is suitable for voice control workflow and business actions.
- Voice style and emotional expression: Strengthen the consistency of emotion and tone in the "voice dialogue" scenario (need to combine the configuration of the synthesis side and data verification).
- Product demo: The warehouse provides a web demo (including server and front-end) for interaction verification and display.
5. Ecology and competing products
- Ecosystem: The project is based on the Transformers ecosystem and acknowledges/cites open source components or related work such as LlamaFactory, Moshi, and CosyVoice in the repository.
- Direction of competing products: The common differences of similar "speech-to-speech/voice dialogue" solutions are end-to-end degree, latency, controllability (function call/tool format), and support for multilingual and emotional styles; It is recommended to use your target scenario data (noise, accent, long audio, business terminology) for alignment evaluation before selection.
6. Limitations and precautions
- Computing power and engineering costs: The threshold for inference video memory is not low, and end-to-end voice links also involve encoding and decoding and real-time I/O.
- Voice content compliance and privacy: Real business needs to clarify recording authorization, storage policies and desensitization processes.
- Function call security: tool definitions and permissions must be strictly converged to avoid "voice commands" triggering overstepping actions.
- Effect fluctuations: Different microphones, noise, and accents will significantly affect the stability, so it is recommended to add pre-treatment and manual bottoming.
7. Project address
https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B
8. Frequently asked questions
Q: How much video memory does Fun-Audio-Chat-8B require for inference?
A: The official reference given is about 24GB of video memory; It really depends on precision, batch, audio length and concurrency.
Q: How does Fun-Audio-Chat-8B implement speech-to-text and speech-to-speech?
A: The repository provides sample scripts for infer_s2t.py (S2T) and infer_s2s.py (S2S), and you can run them by preparing the weights and environment as described.
Q: Why should I download Fun-CosyVoice3-0.5B-2512 for Fun-Audio-Chat-8B?
A: The sample flow uses a speech synthesis model to convert text/intermediate representations into final speech output.
Q: Does the Fun-Audio-Chat-8B support languages other than Chinese and English?
A: The disclosure description states that it supports English and Chinese; Scaling to more languages often requires additional data and training/fine-tuning validation.
Q: Can the Fun-Audio-Chat-8B be used as a production-grade voice assistant?
A: It can be used as a base for verification and secondary development, but it is recommended to complete monitoring, latency optimization, content security, permission control, and business data evaluation.
