FLM- Audio, our 7B spoken dialog chatbot with native full-duplexity

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We’re excited to share RoboBrain-Audio（FLM-Audio）, a new 7B spoken dialog chatbot with native full-duplexity. FLM-Audio achieves superior response qualities and chatting experiences while requiring significantly less training data. Key innovations: Natural Monologue Abandons word-level timestamps and innovatively proposes the “Natural Monologue ” mechanism Preserves the inherent advantages of LLMs in generating coherence and instruction-following. effectively addresses the context-dependent pronunciation issues of certain words (especially numbers). Dual Training Paradigm Training spans two major stages, four sub-stages, simulating ASR, TTS, and interactive dialog tasks. Post-Training stage equips the model with the basic abilities of “listening” and “speaking”. Supervised Fine-Tuning (SFT) stage then shapes its dialogue and full-duplex interaction capabilities. Resource Links： https://arxiv.org/abs/2509.02521 https://huggingface.co/CofeAI/FLM-Audio GitHub - cofe-ai/flm-audio: FLM-Audio is a audio-language subversion of RoboEgo/FLM-Ego -- an omnimo The model is now open-sourced, and we look forward to your use and feedback.

Visit product View on Hacker NewsSep 19, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, context · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting

88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training, active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, audio · Missing: web3, crypto, cryptocurrency

12%12% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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