Porcupine
mxnet
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Porcupine | mxnet | |
---|---|---|
31 | 4 | |
3,412 | 20,644 | |
1.4% | - | |
9.1 | 4.1 | |
2 days ago | 6 months ago | |
Python | C++ | |
Apache License 2.0 | 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.
Porcupine
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Speech Recognition in Unity: Adding Voice Input
Download pre-trained models: "Porcupine" from Porcupine Wake Word and Video Player Context from Rhino Speech-to-Intent repositories - You can also train a custom models on Picovoice Console.
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Speech Recognition with SwiftUI
Below are some useful resources: Open-source code Picovoice Platform SDK Picovoice website
In order to initialize the voice AI, we’ll need both Porcupine (.ppn) and Rhino (.rhn) model files. Picovoice has made several pre-trained Porcupine and pre-trained Rhino models available on the Picovoice GitHub repositories. For this Barista app, we’re going to use the trigger phrase Hey Barista and the Coffee Maker context.
- Voice Assistant app in Haskell
- Ask HN: Offline, Embeddable Speech Recognition?
- How to get high-quality, low-cost Speech-to-Text transcription?
- Researchers find Amazon uses Alexa voice data to target you with ads
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Offline voice recognition on RPi Pico
I asked them to support Pi Pico, but it seems my petition would need more support from the community.
I know about PicoVoice ! They support a Cortex M4 Arduino board : Nano 33 BLE Sense. But that board is out of stock for over an year. We can do pretty cool stuff with it like this.
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Is it possible to self host a voice assistant?
Consider looking at https://github.com/mozilla/DeepSpeech , you can get pre-compiled versions of it and there are versions that will run on a Raspberry Pi, and yes... it's all local, but your mileage may vary. And there is also https://picovoice.ai/ they run stuff locally on the machine, but again each use a constrained local language model/syntax. The other real question, as https://www.reddit.com/user/eduncan911/ correctly states is the use of the wake-word... most systems process all sound, ie. are listening all the time, a number of the Alexa or Google Assistants or similar approaches embed a smaller model or use hardware/neural networks to recognize the wake-word before passing on sound to further syntax processing, so think of most of these devices as always listening and processing and you'd be right, so factor that into power usage etc.
mxnet
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List of AI-Models
Click to Learn more...
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Introduction to deep learning hardware in the cloud
Build – Choose a machine learning framework (such as TensorFlow, PyTorch, Apache MXNet, etc.)
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just released my Clojure AI book
Clojure and Python also have bindings to the Apache MXNet library. Is there a reason why you didn't use them in some of your projects?
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Can Apple's M1 help you train models faster and cheaper than Nvidia's V100?
> But you still lose something, e.g. if you use half precision on V100 you get virtually double speed, if you do on a 1080 / 2080 you get... nothing because it's not supported.
That's not true. FP16 is supported and can be fast on 2080, although some frameworks fail to see the speed-up. I filed a bug report about this a year ago: https://github.com/apache/incubator-mxnet/issues/17665
What consumer GPUs lack is ECC and fast FP64.
What are some alternatives?
snowboy - Future versions with model training module will be maintained through a forked version here: https://github.com/seasalt-ai/snowboy
mycroft-precise - A lightweight, simple-to-use, RNN wake word listener
Caffe - Caffe: a fast open framework for deep learning.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Caffe2
mlpack - mlpack: a fast, header-only C++ machine learning library
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!