Alternatives
Products that do what Willow Inference Server: Optimized ASR/TTS/LLM for Willow/WebRTC/REST does
Hey HN! Willow Inference Server (WIS) is a focused and highly optimized language inference server implementation. Our goal is to "automagically" enable performant, cost-effective self-hosting of released state of the art/best of breed models to enable speech and language tasks: Primarily targeting CUDA (works on CPU too) with support for low-end (cheap) devices such as the Tesla P4, GTX 1060, and up. Don't worry - it screams on an RTX 4090 too! (See benchmarks on Github). Memory optimized - all three default Whisper (base, medium, large-v2) models loaded simultaneously with TTS support…
- 1WO
As the Home Assistant project says, it's the year of voice! I love Home Assistant and I've always thought the ESP BOX[0] hardware is cool. I finally got around to starting a project to use the ESP BOX hardware with Home Assistant and other platforms. Why? - It's actually "Alexa/Echo competitive". Wake word detection, voice activity detection, echo cancellation, automatic gain control, and high quality audio for $50 means with Willow and the support of Home Assistant there are no compromises on looks, quality, accuracy, speed, and cost. - It's cheap. With a touch LCD display, dual…
2023 · github.com
- 2KT
Kitten TTS is an open-source series of tiny and expressive text-to-speech models for on-device applications. We are excited to launch a preview of our smallest model, which is less than 25 MB. This model has 15M parameters. This release supports English text-to-speech applications in eight voices: four male and four female. The model is quantized to int8 + fp16, and it uses onnx for runtime. The model is designed to run literally anywhere eg. raspberry pi, low-end smartphones, wearables, browsers etc. No GPU required! We're releasing this to give early users a sense of the latency and voices…
2025 · github.com
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The fastest, most accurate dictation model in the world
Jul 2026 · willowvoice.com
- 4TN
Kitten TTS (https://github.com/KittenML/KittenTTS) is an open-source series of tiny and expressive text-to-speech models for on-device applications. We had a thread last year here: https://news.ycombinator.com/item?id=44807868. Today we're releasing three new models with 80M, 40M and 14M parameters. The largest model (80M) has the highest quality. The 14M variant reaches new SOTA in expressivity among similar sized models, despite being <25MB in size. This release is a major upgrade from the previous one and supports English text-to-speech applications in…
Mar 2026 · github.com
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I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
Feb 2026 · github.com
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First TTS model to support all 22 Indic languages + English
2024
- 7WM
We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com
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Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make…
2022 · github.com
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General Compute▲315AI models that run on an inference cloud optimized for speed
May 2026 · generalcompute.com
- 12TV
May 2026 · github.com
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- 14IP
The stack: two agents on separate boxes. The public one (nullclaw) is a 678 KB Zig binary using ~1 MB RAM, connected to an Ergo IRC server. Visitors talk to it via a gamja web client embedded in my site. The private one (ironclaw) handles email and scheduling, reachable only over Tailscale via Google's A2A protocol. Tiered inference: Haiku 4.5 for conversation (sub-second, cheap), Sonnet 4.6 for tool use (only when needed). Hard cap at $2/day. A2A passthrough: the private-side agent borrows the gateway's own inference pipeline, so there's one API key and one billing relationship…
Mar 2026 · georgelarson.me
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2022 · freesubtitles.ai
- 21MI
2022 · max.io
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- 23AT
A 3.16M-parameter INT4 transformer running entirely in the on-chip memory of a Xilinx Kria KV260. Zero DRAM in the token loop, 59,965 tok/s on the fabric, bit-exact. Chat with it live.
27d ago · mikeayles.com
- 24PS
Welcome to Project S.A.T.U.R.D.A.Y. This is a project that allows anyone to easily build their own self-hosted J.A.R.V.I.S-like voice assistant. In my mind vocal computing is the future of human-computer interaction and by open sourcing this code I hope to expedite us on that path. I have had a blast working on this so far and I'm excited to continue to build with it. It uses whisper.cpp [1], Coqui TTS [2] and OpenAI [3] to do speech-to-text, text-to-text and text-to-speech inference all 100% locally (except for text-to-text). In the future I plan to swap out OpenAI for llama.cpp [4]. It is…
2023 · github.com
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