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TodayNovember 13Friday4 items
  1. inclusionAI Models34

    inclusionAI Releases Duo-1.0-distill-quant FP8 Weights for Full-Duplex Interaction

    inclusionAI released Duo-1.0-distill-quant, FP8-quantized four-step distilled Realization weights for the Duo 1.0 full-duplex multimodal world model. The weights overlay onto the inclusionAI/Duo-1.0-causal-base bundle, which supplies the shared Intent, codecs, speech models and Realization backbone, and require a runtime supporting the packaged ModelOpt FP8 format. The weights were initialized from MiniMax H3 and then trained by the Duo team.

  2. inclusionAI Models40

    inclusionAI Releases Duo-1.0-distill Four-Step Distilled Weights for Duo 1.0

    inclusionAI has released Duo-1.0-distill, the four-step distilled Realization weights for Duo 1.0, a full-duplex multimodal world model for real-time audio-visual interaction. The weights ship as a PyTorch Distributed Checkpoint overlay on the inclusionAI/Duo-1.0-causal-base bundle, using an Euler sampler with 4 steps and flow shift 5.0. The weights were initialized from MiniMax H3 and then trained by the Duo team.

  3. MarkTechPost62

    Fish Audio Releases Live 3, a Full-Duplex Voice Model That Reacts in 186 ms

    Fish Audio released Live 3, a full-duplex voice model for real-time voice agents that keeps listening while it speaks and learns turn-taking from real human conversations instead of hand-tuned pipeline rules. Fish Audio says it can decide to respond in as little as 186 ms for short reactions like "mm-hmm," though that figure is a floor for small reactions rather than time-to-first-audio for a full reply, and no benchmark scores have been published. Live 3 runs on the Fish Agent Platform (public beta), the Fish Audio API and on-premises for enterprises, priced at $0.035 per minute until December 31, 2026 versus a $0.07 list rate, and supports English only.

  4. MarkTechPost52

    Liquid AI Releases LiquidCodec, a 74M-Parameter Streaming Audio Codec

    Liquid AI released LiquidCodec, a roughly 74M-parameter streaming speech tokenizer that powers its LFM3-Audio-2B and LFM3-Omni-2B models, with weights published on Hugging Face. The codec compresses 24 kHz audio into 12 token streams at 12.5 Hz frames and runs on a single CPU thread, where Liquid AI reports about 361x real-time encoding and 87x decoding. Liquid AI says it beat X-Codec 2, NanoCodec and Mimi on ViSQOL and mel distance across all 8 test sets, though NanoCodec still led PESQ (WB) on 7 of 8 sets.

November 12Thursday
  1. The Decoder71

    ElevenLabs releases Scribe v3 and Scribe v3 Live speech-to-text models

    ElevenLabs released two speech-to-text models, Scribe v3 for pre-recorded audio and Scribe v3 Live for low-latency real-time use, both available through the ElevenAPI developer interface and the ElevenAgents platform. The company says Scribe v3 cuts word errors by about 25 percent versus its predecessor across 102 languages, and Scribe v3 Live uses conversational context to tell when a speaker has actually finished instead of relying on pause timers. Independent benchmarks from Artificial Analysis place Scribe v3 among the most accurate transcription models, trailing only an Alibaba preview model, but show it lagging on speed and priced at $3.33 per 1,000 minutes.

November 9Monday
  1. Zyphra Research78

    Zyphra releases ZONOS3-Mini, an Apache 2.0 zero-shot voice-cloning TTS model that runs on-device

    Zyphra released ZONOS3-Mini, a sparse MoE text-to-speech model with 100M active and 800M total parameters under Apache 2.0, trained on 112 NVIDIA H100s in 2.5 days. It reaches under 570 ms time-to-first-audio in the browser on a Pixel 9 and under 150 ms on an M3 MacBook Air, with runtimes for WebGPU, SGLang, vLLM and GGML. The model adds control over identity, accent, quality, mood and delivery, including word-level steering, and Zyphra reports it ranks first among open models on its new ZTTS2-Eval benchmark.

    Why it matters: Zyphra's compact MoE TTS model pairs zero-shot voice cloning with word-level delivery control while running in real time on phones and laptops.

October 20Tuesday
  1. Mistral AI82

    Mistral releases Voxtral 3 speech models and voice agent SDK

    Mistral released Voxtral 3, a family of speech-to-text and text-to-speech models plus a voice agent SDK built on LiveKit that streams listening, reasoning, tool calling and speaking in one pipeline. Voxtral Small Transcribe 3 covers 50+ languages with diarization and context biasing, Voxtral Mini TTS 3 supports 35+ languages and 50+ dialects, and the stack is available through Mistral's EU-hosted API or self-deployment. Pricing starts at $0.0035/min for batch transcription and $13 per 1M characters for TTS.

    Why it matters: Mistral's Voxtral 3 pairs multilingual speech models with a LiveKit-based voice agent SDK, giving teams a sovereign-deployable option for real-time voice agents.

October 19Monday
  1. inclusionAI Repositories58

    inclusionAI releases Ming-3.0-tts speech synthesis models in 0.5B dense and 7.9B MoE tiny variants

    inclusionAI released Ming-3.0-tts, an open-weight multilingual speech synthesis family with two models: Ming-3.0-tts-0.5b, a 0.5B dense model, and Ming-3.0-tts-tiny, a MoE model built on Ling V3 with 7.9B total and 1.3B active parameters. Both ship with inference code, a task cookbook and benchmark tables, and support voice cloning, text-based voice design, free-form natural-language delivery control, 14 inline paralinguistic tags, emotion and speed/pitch/volume control, plus Chinese, English, German, Spanish, French, Korean, Japanese, Sichuanese and Cantonese. On the October 2026 Artificial Analysis text-to-speech leaderboards, Ming-3.0-tts ranks 1st among open-weights models on Controlled Voice, 1st overall on Pronunciation Robustness at 93.2%, and 3rd among open-weights models on Provider Voice.

  2. inclusionAI Repositories58

    inclusionAI releases Ming-3.0-asr-tiny context-aware speech recognition model

    inclusionAI released Ming-3.0-asr-tiny, a speech recognition model that pairs the Ling-tiny-v3 language model with an audio encoder for Chinese and English transcription, dialect recognition and context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, Chuanyu and Shanghai, and hotwords or preceding dialogue can be supplied in the prompt to resolve names and technical terms. The repository ships Transformers and SGLang serving guides, and code and weights are released under the MIT License.

October 15Thursday
  1. inclusionAI Repositories67

    inclusionAI releases Ming-3.0-audio-flash and Ming-3.0-audio-tiny audio models

    inclusionAI released Ming-3.0-audio-flash, a 126B-parameter Mixture-of-Experts audio model with 6.5B activated parameters, alongside the compact Ming-3.0-audio-tiny at 9.3B parameters with 2.7B activated. The flash model covers Chinese and English speech recognition across dialects and noisy multi-speaker settings, long-audio understanding, non-speech sound and music perception, audio reasoning, and end-to-end spoken dialogue with optional synthesized speech output. It ships with vLLM-Omni, SGLang, and Transformers inference paths under the MIT License, with default Flash profiles using tensor parallelism across four 80 GB H800 GPUs at 128K context.

    Why it matters: The release details a 126B-parameter MoE audio model with 6.5B activated parameters, plus a 9.3B tiny variant, giving a concrete reference for open audio-language model sizing and deployment.

July 13Monday
  1. Vapi Blog58

    Vapi Humanness Index: Grok TTS scores 95 and MiniMax Speech 2.5 scores 93 against a real human at 100

    Vapi's Humanness Index, a blind crowdsourced leaderboard where listeners pick which of two cloned voices sounds more human, places xAI's Grok TTS at 95 and MiniMax Speech 2.5 at 93 against a real human recording scored at 100 after more than 11,000 votes. Every battle uses the same cloned voice and script across models, and the real human recording is included in the comparison set, so a 95 means listeners chose Grok TTS at nearly the same rate as the human on the same quote. Vapi argues naturalness is no longer the bottleneck for voice agents, since callers experience the voice rather than the reasoning behind it.