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  **Awaken: An Efficient Training Recipe for Full-Duplex Conversation That Retains Model Ability**
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- [![Blog](https://img.shields.io/badge/Blog-Project%20Page-orange)](https://github.com/OpenmartAI/awake-audio-chat/blob/main/webpage/index.html)
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- [![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/OpenmartAI/awake-audio-chat)
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  [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Model-yellow)](https://huggingface.co/openmartdev/awaken-full-duplex)
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  [![Base model](https://img.shields.io/badge/Base-Fun--Audio--Chat--8B-green)](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B)
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  on 100,132 records with replay of the interaction data.
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  3. **GRPO**: improves backchannel timing from the model's own samples in conversational windows.
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- See the [project page](https://github.com/OpenmartAI/awake-audio-chat/blob/main/webpage/index.html) for the full data composition and training details.
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  ## Model Details
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  ## Installation
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  ```bash
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- git clone https://github.com/OpenmartAI/awake-audio-chat
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- cd awake-audio-chat
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  # 1. torch + torchaudio for YOUR CUDA driver first (the default PyPI wheel targets the newest
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  # CUDA and will not load on older drivers), e.g. for CUDA 12.8:
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  - **Spoken QA**: our rows are means over 3 seeds; Freeze-Omni, Moshi and Fun-Audio-Chat-Duplex-8B
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  are reported scores from Table 7 of the Fun-Audio-Chat technical report.
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  Because our runs and the published results are scored differently, compare across systems with
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- care. Full methodology is on the [project page](https://github.com/OpenmartAI/awake-audio-chat/blob/main/webpage/index.html).
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  ## Inference Details
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  ## License
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- The code and model weights are licensed under the [Apache 2.0 License](https://github.com/OpenmartAI/awake-audio-chat/blob/main/LICENSE), the same license
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  as the base model [Fun-Audio-Chat-8B](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B).
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  ## Acknowledgments
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  The model architecture is derived from [Fun-Audio-Chat](https://github.com/FunAudioLLM/Fun-Audio-Chat)
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  and the speech codec from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (both Apache-2.0);
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  the text backbone is [Qwen3](https://github.com/QwenLM/Qwen3), and the ear follows
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- [Whisper](https://github.com/openai/whisper)'s encoder. See [`NOTICE`](https://github.com/OpenmartAI/awake-audio-chat/blob/main/NOTICE).
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  ## Contact
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- - 🐛 Submit an [Issue](https://github.com/OpenmartAI/awake-audio-chat/issues)
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- - 💡 Submit a [Pull Request](https://github.com/OpenmartAI/awake-audio-chat/pulls)
 
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  **Awaken: An Efficient Training Recipe for Full-Duplex Conversation That Retains Model Ability**
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+ [![Blog](https://img.shields.io/badge/Blog-Project%20Page-orange)](https://github.com/avera-labs/AwakenFullDuplex/blob/main/webpage/index.html)
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+ [![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/avera-labs/AwakenFullDuplex)
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  [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Model-yellow)](https://huggingface.co/openmartdev/awaken-full-duplex)
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  [![Base model](https://img.shields.io/badge/Base-Fun--Audio--Chat--8B-green)](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B)
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  on 100,132 records with replay of the interaction data.
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  3. **GRPO**: improves backchannel timing from the model's own samples in conversational windows.
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+ See the [project page](https://github.com/avera-labs/AwakenFullDuplex/blob/main/webpage/index.html) for the full data composition and training details.
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  ## Model Details
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  ## Installation
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  ```bash
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+ git clone https://github.com/avera-labs/AwakenFullDuplex
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+ cd AwakenFullDuplex
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  # 1. torch + torchaudio for YOUR CUDA driver first (the default PyPI wheel targets the newest
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  # CUDA and will not load on older drivers), e.g. for CUDA 12.8:
 
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  - **Spoken QA**: our rows are means over 3 seeds; Freeze-Omni, Moshi and Fun-Audio-Chat-Duplex-8B
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  are reported scores from Table 7 of the Fun-Audio-Chat technical report.
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+ | System | OpenBookQA ↑ |
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+ |--------|-------------:|
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+ | Fun-Audio-Chat-8B (half-duplex) | 83.52 |
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+ | Fun-Audio-Chat-Duplex-8B (reported) | – |
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+ | Freeze-Omni (reported) | 30.98 |
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+ | Moshi (reported) | 25.93 |
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+ | Awaken SFT | 76.2 |
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+ | **Awaken GRPO** | **75.6** |
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+
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  Because our runs and the published results are scored differently, compare across systems with
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+ care. Full methodology is on the [project page](https://github.com/avera-labs/AwakenFullDuplex/blob/main/webpage/index.html).
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  ## Inference Details
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  ## License
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+ The code and model weights are licensed under the [Apache 2.0 License](https://github.com/avera-labs/AwakenFullDuplex/blob/main/LICENSE), the same license
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  as the base model [Fun-Audio-Chat-8B](https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B).
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  ## Acknowledgments
 
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  The model architecture is derived from [Fun-Audio-Chat](https://github.com/FunAudioLLM/Fun-Audio-Chat)
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  and the speech codec from [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) (both Apache-2.0);
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  the text backbone is [Qwen3](https://github.com/QwenLM/Qwen3), and the ear follows
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+ [Whisper](https://github.com/openai/whisper)'s encoder. See [`NOTICE`](https://github.com/avera-labs/AwakenFullDuplex/blob/main/NOTICE).
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  ## Contact
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+ - 🐛 Submit an [Issue](https://github.com/avera-labs/AwakenFullDuplex/issues)
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+ - 💡 Submit a [Pull Request](https://github.com/avera-labs/AwakenFullDuplex/pulls)