Popular GitHub Repositories for Error Handling

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

Flexible and powerful framework for managing multiple AI agents and handling complex conversations

Distilled variant of Whisper for speech recognition. 6x faster, 50% smaller, within 1% word error rate.

Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model w/CPU ONNX and NVIDIA GPU PyTorch support, handling, and auto-stitching

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Developer friendly Natural Language Processing ?

Tools for handling speech data in machine learning projects.

Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)

Evaluate your speech-to-text system with similarity measures such as word error rate (WER)

A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Claude Code dashboard with usage stats, error analysis, and sharable feature

A fast, low-resource Natural Language Processing and Text Correction library written in Rust.

Use AI-powered code edits, explanations, code generation, error diagnosis, and chat in Visual Studio Code with the official OpenAI API.

High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

Failure archive for ChatGPT and similar models

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

3.41% and 17.11% error on CIFAR-10 and CIFAR-100

Tensor network based quantum software framework for the NISQ era

Python module for evaluating ASR hypotheses (e.g. word error rate, word recognition rate).