snips-nlu-rs
ort
snips-nlu-rs | ort | |
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2 | 7 | |
337 | 596 | |
0.0% | 14.3% | |
10.0 | 9.4 | |
over 1 year ago | 7 days ago | |
Rust | Rust | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
snips-nlu-rs
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Ask HN: Offline, Embeddable Speech Recognition?
I am in shop for a speech recon library that works offline and can fit on a phone (Android).
I used to use Snips AI (https://snips.ai/), which worked well until it was acquired by Sonos. Now the portal is down and I can't modify the model anymore.
Looking for something ideally written in C or that can target C for portability. Free/libre and copyleft preferable to avoid the acquisition trap again.
Tapping into the vast pools of knowledge of HN; could you please suggest alternatives, preferably ones you have experience with?
Thank you.
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Getting long build times because of build script in dependency. Any work arounds?
Hey guys, I'm trying to write something that parses intents. I came across this: snips-nlu.
ort
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AI Inference now available in Supabase Edge Functions
To solve this, we built a native extension in Edge Runtime that enables using ONNX runtime via the Rust interface. This was made possible thanks to an excellent Rust wrapper called Ort:
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AI Inference Now Available in Supabase Edge Functions
hey hn, supabase ceo here
As the post points out, this comes in 2 parts:
1. Embeddings models for RAG workloads (specifically pgvector). Available today.
2. Large Language Models for GenAI workloads. This will be progressively rolled out as we get our hands on more GPUs.
We've always had a focus on architectures that can run anywhere (especially important for local dev and self-hosting). In that light, we've found that the Ollama[0] tooling is really unbeatable. I heard one of our engineers explain it like "docker for models" which I think is apt.
To support models that work best with GPUs, we're running them with Fly GPUs - pretty much this: https://fly.io/blog/scaling-llm-ollama (and then we stitch a native API around it). The plan is that you will be able to "BYO" model server and point the Edge Runtime towards it using simple env vars / config.
We've also made improvements for CPU models. We built a native extension in Edge Runtime that enables using ONNX runtime via the Rust interface. This was made possible thanks to an excellent Rust wrapper, Ort[1]. We have the models stored on disk, so there is no downloading, cold-boot, etc.
The thing I most like about this set up is that you can now use Edge Functions like background workers for your Postgres database, offloading heavy compute for generating embeddings. For example, you can trigger the worker when a user inserts some text, and then the worker will asynchronously create the embedding and store it back into your database.
I'll be around if there are any questions.
[0] ollama.com
[1] Ort: https://github.com/pykeio/ort
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Moving from Typescript and Langchain to Rust and Loops
In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.
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How to create YOLOv8-based object detection web service using Python, Julia, Node.js, JavaScript, Go and Rust
ort - ONNX runtime library.
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Do you use Rust in your professional career?
Our main model in Rust is a deep neural network, using ONNX via the ort rust bindings. The application is some particular applications of process automation.
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onnxruntime
You could try ort https://github.com/pykeio/ort It looks like it's in active development and supports GPU inference
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
I would't try to distribute your ml models with the typical frameworks, especially not with python. Have you looked in to ONNX?For example: https://github.com/pykeio/ort
What are some alternatives?
rustling-ontology - Ontology for rustling
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
yolov8_onnx_go - YOLOv8 Inference using Go
Porcupine - On-device wake word detection powered by deep learning
onnxruntime-php - Run ONNX models in PHP
yolov8_onnx_javascript - YOLOv8 inference using Javascript
SpeechLoop - Many ASRs under one roof. With Benchmarking... answering the question. What is the best ASR for my dataset?
langchainjs - 🦜🔗 Build context-aware reasoning applications 🦜🔗
yolov8_onnx_julia - YOLOv8 inference using Julia
yolov8_pytorch_python - YOLOv8 inference using Ultralytics API