Shoutout to @fluidinference for helping us out with this! They are the masters of ANE and boosted FluidVoice from 0->1
Fluid Inference is an Applied AI Research Lab building the future of ambient intelligence. We believe intelligence should be embedded everywhere: in your applications, woven into your hardware, responding to the moments that matter.
Smaller models, built for specific tasks, outperform large models at what they're designed to do. We ship open-source models that embed directly into applications and hardware. We work with companies to train task-specific models optimized for their use cases. Intelligence that runs where it matters, with SDKs that make deployment simple.
We'll build tools that let anyone create and personalize models, not just ML engineers. Vibe coders and developers will build custom intelligence. We'll work with more customers to train task-specific models that outperform the giants for their needs.
Models will live in your environment and evolve with it. They'll learn through embedded fine-tuning and memory, adapting to each user over time. Intelligence that senses context, anticipates needs, and becomes genuinely personal. Present everywhere.
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Shoutout to @fluidinference for helping us out with this! They are the masters of ANE and boosted FluidVoice from 0->1
Snaply now supports NVIDIA Nemotron 3.5 ASR Multilingual via @FluidInference — 40 language-locales, on-device, no cloud, free to use. @NVIDIAAIDev @NVIDIAAI #NemotronSpeech #VoiceAl
Audivize now supports NVIDIA Nemotron 3.5 ASR Multilingual via @fluidinference, adding support for 40 language-locales all on-device. Demo: audivizelabs.com Model: huggingface.co/nvidia/NVIDIA-… @NVIDIAAIDev @NVIDIAAI #NemotronSpeech #VoiceAl
still not as fast as Core ML (~120x on M4 Pro) @Alex_tra_memory github.com/FluidInference…
This is @nvidia’s parakeet realtime eou model that is a streaming speech recognition model that’s also performs end of utterance detection. Credit to @fluidinference for the FluidAudio library and the CoreML version of the model.
Custom vocabulary support is now available in FluidAudio from @fluidinference
There’s a project that Spokenly and Slipbox utilise, they’re in the showcase section. I’ve used both of these on mobile and am really happy with the concept of a free unlimited local voice to text model that takes advantage of the Apple Neural Engine github.com/FluidInference…
We use those and kudos to @fluidinference for powering us up!!
Today's a big day for Nemotron models. Along with Ultra, we also shipped Nemotron Speech 3.5 that now supports 40 Languages and it's insanely Fast and Ultra Low latency! I collaborated with @Alex_tra_memory, @fluidinference and @ALTIC_DEV to port the model to coreML to make Show more
Nemotron ASR Multilingual running on an iPhone 17 Pro in CoreML. Many thanks to @fluidinference for the CoreML model and to @NVIDIAAI @NVIDIAAIDev for the model itself.
Supertonic3 running on an iPhone 17 Pro using ANE on CoreML. It’s blazing fast with low RAM consumption and background capable. 2 mins worth of audio generated in 3 secs. Many thanks to @fluidinference for the port.
Nvidia parakeet v3 is an insane model as well. Found a Swift library to bundle it into your apps. It runs entirely on device! github.com/FluidInference…
Got my Clawdbot transcribing WhatsApp voice notes in ~200ms using FluidAudio + Parakeet TDT on CoreML. Fully local on a Mac Mini, no cloud APIs. Transcription latency is basically instant.
There is two transcription engine you can use, the first one is from apple - preinstalled on macOS Tahoe. Second one is parakeet from FluidAudio, accessible on all devices, but you need to trigger the download model.
3: Hex turns voice into text with a hotkey—press-and-hold to transcribe, or double-tap to lock and paste anywhere on macOS (Apple Silicon). Open-source, on-device options via WhisperKit/FluidAudio. github.com/kitlangton/Hex github.com/kitlangton/Hex
Claude Opus 4.6 doing the reasoning. Search, extraction, and speech are Osaurus tools. The voice is generated on-device via FluidAudio from @fluidinference. No audio API, no per-character pricing. github.com/osaurus-ai/osa…
FluidVoice 1.5.15 now supports @NVIDIAAI Nemotron 3.5 ASR Multilingual collaborating with @fluidinference - 40 language-locales, free forever, fully on-device, and ultra fast! 👉Download here —> altic.dev/fluid @NVIDIAAIDev @NVIDIAAI #NemotronSpeech #VoiceAI
Spokenly now supports NVIDIA Nemotron 3.5 ASR Streaming Multilingual via @fluidinference — 40 locales, on-device, free, no subscription. Privacy-first dictation, now global. App: spokenly.app Model: huggingface.co/nvidia/NVIDIA-… @NVIDIAAIDev #VoiceAI
murmur v0.3.0 is out 🚀 Free. Offline. Speech-to-Text for macOS. Hold a key, speak, release. what's new: - 3 tiers: Fast, Standard, Accurate - FluidAudio engine using Apple Neural Engine - 98+ languages - zero-downtime backend switching try now: anubhavitis.github.io/murmur/#install
Yep, noticed the same—FluidAudio + Parakeet on ANE is a sweet spot for low-latency + privacy. I built Hapi for that exact reason (staying fully local on M-series). Curious if you’ve benchmarked WER vs Whisper on your setup?
Parakeet is great. Transcribes at roughly 200x realtime on my M4 MacBook Pro. My current stack for local-only podcast transcription is Parakeet + FluidAudio for diarisation: github.com/HartreeWorks/s…
it seems like both Hex and Spokenly both use Parakeet (model from Nvidia) on FluidAudio ("a Swift SDK for fully local, low-latency audio AI on Apple devices") whats interesting is that FluidAudio an open source project that makes use of the M-series chips neural engine (ANE)
The CAM++ CoreML conversion I did myself, but the segmentation-3.0 model on CoreML is made possible thanks to the amazing FluidAudio project by FluidInference: github.com/FluidInference…
