3 repositorios
Hosting speech-to-text inference services on local or cloud infrastructure for network access.
Distinct from Cloud or Self-Hosted Deployments: The candidates focus on specific application types like Matrix or Shiny, whereas this is for generic ASR server hosting.
Explore 3 awesome GitHub repositories matching devops & infrastructure · Recognition Server Deployments. Refine with filters or upvote what's useful.
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-
Provides the ability to host a speech-to-text service on local or cloud machines to accept HTTP requests.
PaddleX is a PaddlePaddle-based framework for building, deploying, and fine-tuning AI model pipelines, with pre-built support for computer vision, OCR, document analysis, and time series tasks. It offers a toolkit of ready-to-use pipelines for image classification, object detection, segmentation, and pose estimation, alongside an end-to-end OCR document analysis pipeline that extracts text, tables, formulas, and layout information. The platform also includes a dedicated time series forecasting pipeline for analyzing historical data to detect anomalies, classify patterns, and predict future val
Provides REST API deployment for formula recognition pipelines.
Julius is a high-performance, open-source speech recognition engine designed for large vocabulary continuous speech recognition. It functions as a comprehensive framework utilizing Hidden Markov Model-based acoustic modeling and N-gram language models to convert live or recorded audio into text. The engine is built to support real-time streaming and provides a network-accessible service that allows external applications to manage recognition sessions and receive transcription results through programmatic commands. The engine distinguishes itself through its modular architecture and support fo
Starts a network server allowing external clients to send control commands and receive real-time recognition results.