42 results tagged
ml
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A generative adversarial network that takes images and re-does them in various anime studios' styles. GPU enabled. Download the pre-trained model to get a jumpstart.
A framework that instantiates AI/ML models more or less automagickally - one line of code. Can read training data from URLs as well as locally. Has a GUI also but it's an optional install. Sits on top of PyTorch.
The description's kind of weird, but it seems to use a neural network to generate pictures of someone based upon a picture that you give it. "Generate your lover with your photo," whatever that means.
A face detector (not facial identification) deep learning system based upon OpenCV and Tensorflow. Optimized for CPU, not GPU operation but does have a tensorflow-gpu switch available. Can even identify faces that aren't edge-on or partially obscured.
Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
Stanford has open sourced a self-hosted, personal assistant system. Designed with privacy in mind. Speech recognition, analysis, task execution. They want to make so that it's easy and highly useful for everyone to integrate into their stuff. Can monitor things and filter for certain things. Aims for composability. Services (skills) are also open source and crowdsourced.
Github: https://github.com/stanford-oval
API: https://almond.stanford.edu/doc/my-api.md
Github: https://github.com/stanford-oval
API: https://almond.stanford.edu/doc/my-api.md
An application that uses AI and ML to intelligently strip the vocals out of music tracks. Written in Python, uses Tensorflow. The pre-trained model is included in the distribution. Use as a CLI tool or a library for your own code.
textacy is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, textacy focuses primarily on the tasks that come before and follow after. Abstracts away the boilerplate for the stuff you actually care about.
Quickstart: https://chartbeat-labs.github.io/textacy/getting_started/quickstart.html
Quickstart: https://chartbeat-labs.github.io/textacy/getting_started/quickstart.html
A remarkably streamlined and simple to use system for using AI and ML models to interact with data. Build interactive data analyses with just a very little code. The demo shows most of everything you need to be productive. Hot reloading; change some of the Python code in your research script, the display updates. Don't need to mess with HTML and Javascript, just a text editor and a web browser.
A F/OSS natural language translation system that seems to want to give Google Translate a run for its money. The corpuses used for training appear to be crowdsourced, and I think you can download the trained models on their own. Aims to be self-hosted.
Github: https://github.com/apertium
Installation docs: http://wiki.apertium.org/wiki/Installation
Github: https://github.com/apertium
Installation docs: http://wiki.apertium.org/wiki/Installation
An NLP deep learning toolkit for building training pipelines. Tries to minimize the effort for constructing the training and inference stages. Defines modular building blocks of neural network components, and a suite of NLP models. The end goal is to make building a neural network as easy as playing with Legos. Supports English and Chinese.
A three-stage deep learning system which can figure out how to imitate a person's voice from as little as five seconds of recorded speech. Speaks with the deepfaked voice in realtime. The sample is used to condition an existing TTS model to sound like someone.
Running it inside a Docker container: https://sean.lane.sh/posts/2019/07/Running-the-Real-Time-Voice-Cloning-project-in-Docker/
Running it inside a Docker container: https://sean.lane.sh/posts/2019/07/Running-the-Real-Time-Voice-Cloning-project-in-Docker/
A basic server which implements a neural network and a REST API. Give it some basic training data, and then chat with it. It will slowly learn how to communicate with you.
A deep learning NLP modeling framework based on PyTorch. Text classifiers, sequence taggers, joint intent-slot models.
An online book about designing human-centered AI products.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 45+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license.
I just wanted to let everyone know I have built out a chatbot to help answer mycroft related questions and will be adding a lot to it over the coming weeks allowing for it to answer questions about the project as well as eventually troubleshoot some issues.
OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
Github: https://github.com/OpenRefine/OpenRefine
Written in Java 8.
Github: https://github.com/OpenRefine/OpenRefine
Written in Java 8.