Nvidia releases open Nemotron Transcribe speech-to-text model

🚀 RELEASES & LAUNCHES

• Nvidia releases open Nemotron Transcribe speech-to-text model covering 40 languages
  Nvidia released Nemotron Transcribe, an open speech-to-text model that transcribes recordings in up to 40 languages.

  • Aimed at developers building meeting, medical, and batch transcription tools; it works on recordings, not live audio, leaving real-time use to rivals like ElevenLabs.
  • The release includes Nemotron 3.5 ASR, a 600M-parameter multilingual model supporting 40 language-locales (19 transcription-ready, 13 broad-coverage, 8 adaptation-ready) with automatic language detection.
  • Built on a Cache-Aware FastConformer-RNNT architecture with native punctuation and capitalization, configurable chunk sizes from 80ms to 1120ms, and an English-only 0.6B sibling model trained on 250,000+ hours of en-US speech.
  • Released under the OpenMDW 1.1 license on Hugging Face and via NVIDIA NIM, with fine-tuning tools and self-hosting support.
  • Daily.co benchmarks it as the lowest-latency STT model tested, with self-hosted costs around $0.01–$0.05 per agent-hour versus $0.10–$1.00 for typical transcription APIs.
  Sources 📄 Source: huggingface.co · 📎 Coverage: techcrunch.com · 👁 via TechCrunch AI, Hugging Face Blog

• Indie dev trains Apex-3, a 21B MoE (3.36B active) from scratch on 70B tokens
  An independent developer trained Apex-3, a 21B-parameter MoE model with 3.36B active parameters, entirely from scratch on 70B tokens and released the checkpoints.

  • Aimed at local/open-weight LLM enthusiasts and researchers who want to continue pretraining from a released checkpoint.
  • Apex-3 is a 20.99B-parameter MoE with 3.36B active per token, using 64 routed experts plus 1 shared expert (top-4 routing) and a SuperBPE tokenizer.
  • Trained from scratch (no external pretrained weights) on 70B tokens with Muon and DiLoCo on GH200 GPUs; SuperBPE's ~27% token efficiency makes this roughly equivalent to ~100B tokens.
  • A stable checkpoint was released on Hugging Face (YOON1v/Apex-3-59B-stable) so anyone can continue training; code and training notes are on GitHub.
  • The post focuses on emergent abilities observed in training logs rather than final SFT benchmark scores, offering rare public data on from-scratch MoE training at hobbyist scale.
  Sources 📄 Source: huggingface.co · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

• AuroraIMG2-50M-Preview: tiny open text-to-image model runs on a 2016 laptop CPU
  AuroraAI Research released AuroraIMG2-50M-Preview, an Apache 2.0 text-to-image model whose denoiser is only 50M parameters and runs on CPU.

  • Aimed at local/hobbyist users who want text-to-image generation without a GPU.
  • The denoiser is 50M parameters; with a custom text encoder and the pretrained Flux VAE, total end-to-end inference parameters are around 90M.
  • Runs in 27 seconds at 50 steps or 11 seconds at 20 steps on a 2016 Intel i5-7200U laptop CPU.
  • Licensed under Apache 2.0, with samples and a quick start included in the Hugging Face model card.
  • Shows tiny CPU-only diffusion models are viable for local image generation.
  Sources 📄 Source: huggingface.co · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA