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This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken Chinese audio into written text. It functions as a toolkit for training, evaluating, and deploying speech-to-text models, utilizing a specialized pinyin-to-text converter that transforms phonetic sequences into Chinese characters using a probability graph model. The system is distinguished by its deployment flexibility, offering a dockerized recognition server that provides transcription capabilities as a remote API. It supports high-performance streaming through a gRPC speech-
jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
Haxl is a Haskell library and remote service request orchestrator designed for coordinating concurrent data fetching, request batching, and caching across multiple remote service providers. It functions as a framework for retrieving data from external databases and web services while minimizing network round trips. The project distinguishes itself through an applicative-based request batching system that groups multiple individual data requests into single calls to reduce network overhead. It employs an asynchronous parallel request scheduler to execute independent requests concurrently and u
This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a robust framework for managing application state by synchronizing data across browsers, mobile devices, and server-side runtimes. By treating local storage as the primary source of truth, it enables applications to remain functional without network connectivity, automatically reconciling changes with remote backends once a connection is restored. The database distinguishes itself through a modular architecture that supports cross-environment synchronization and high-performance d
DeepDanbooru is a deep learning tool for tagging anime-style images with Danbooru-style tags. It uses a pre-trained convolutional neural network to analyze images and predict tags identifying characters, attributes, and artwork details.
The main features of kichangkim/deepdanbooru are: Anime Image Taggers, Custom Model Training, Deep Learning Inference Engines, Model-Based Tag Predictions, Image Tag Training Pipelines, Training Dataset Preparation, Remote Data Fetching, SQLite Storage Adapters.
Open-source alternatives to kichangkim/deepdanbooru include: nl8590687/asrt_speechrecognition — This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken… dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… facebook/haxl — Haxl is a Haskell library and remote service request orchestrator designed for coordinating concurrent data fetching,… pubkey/rxdb — This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a… advimman/lama — Lama is an image restoration framework and deep learning model designed for image inpainting and object removal. It… caffe2/caffe2 — Caffe2 is a high-performance deep learning framework and C++ machine learning library. It serves as a modular system…