This repository is a mobile SDK for real-time interactive AI avatars, primarily targeting Android and iOS platforms. It provides on-device inference for speech recognition (via WeNet) and face/body segmentation (via ncnn-based U-Net models), enabling low-latency lip-sync and avatar animation driven by audio input. The SDK is structured as a C++ core with platform-specific wrappers (Java for Android, Objective-C for iOS), and includes bundled third-party libraries (ncnn, OpenCV, FFmpeg, ONNX Runtime) for computer vision, audio processing, and neural network inference. In production, the system ingests 16kHz PCM audio from a microphone, extracts MFCC features, runs streaming speech recognition, and simultaneously processes the audio through a U-Net model to generate facial animation frames. These frames are rendered using OpenGL ES (Android) or Metal (iOS) with alpha blending for compositing onto a background. The SDK also includes model download, encryption (AES-128-CBC), and resource management utilities. The architecture is monolithic: all core logic lives in a single C++ module (duix-sdk), with platform-specific integration code in the app directories. The demo app in Android provides a fully functional test harness. While the SDK is feature-complete and demonstrates significant engineering effort in mobile AI inference, the codebase exhibits several quality issues: duplicated third-party headers across architectures, hardcoded encryption keys, outdated library versions (OpenCV 4.6.0, ncnn 2022/2023), and minimal inline documentation. The high star count and fork activity suggest a strong community, but the code itself is functional rather than exemplar. The most reusable components are the reference-counted memory allocator (dh_mem.h) and the AES stream decryption wrapper (gaes_stream.h), which can be extracted for general-purpose use.
This repository serves as the companion code for the O'Reilly book 'Practical MLOps', providing a collection of examples spanning the MLOps lifecycle. It is a hybrid repository with a dominant data research character, containing Jupyter notebooks for exploratory data analysis (e.g., COVID-19 trends, sugar intake correlations), AutoML experiments (Ludwig text classification), and model interoperability scripts (ONNX conversion). It also includes application-like components such as a Flask-based model serving container, CLI tools for CSV linting, and cloud deployment scripts for AWS, Azure, and GCP. The code is organized by book chapters and demonstrates practical MLOps patterns, though many examples are simplified for educational purposes rather than production readiness. The most reusable asset is the modularized CSV linter, which provides a well-structured CLI for data quality checks.
DART (Detect Anything in Real Time) is a training-free framework that converts SAM3 into a real-time multi-class open-vocabulary detector. In practice, the codebase is an inference-optimization and deployment framework: it exports SAM3 backbone components to ONNX/TensorRT, applies FP16/INT8 quantization, builds optimized TRT engines, and runs pipelined video inference with CUDA graphs and torch.compile. It also includes an LLM-driven interactive agent (Qwen via OpenAI-compatible API) that performs iterative segmentation through tool calls (segment_phrase, examine_each_mask, select_masks_and_return), plus video dataset handling, grounding/mask export utilities, and COCO-style evaluation scripts. The repository is a hybrid: fundamentally an application/framework for real-time detection, it carries strong research-code characteristics — calibration caches (calib/*.json), ad-hoc experiment scripts with hardcoded checkpoints (sam3.pt, backbone.onnx), and iterative debugging comments ('Previous test showed...'). The architecture centers on ONNX export of SAM3 blocks (EfficientViT backbone, text encoder, prompt encoder, mask predictor, pixel decoder), TRT engine building, and a quantization layer (dartf/quant/) with experimental plugins tightly coupled to specific ONNX graph patterns. A media production pipeline (dartf/media/) builds explainer videos with automated slide rendering and music mixing. The codebase shows signs of being an experimental workspace rather than a maintained library: hardcoded paths in evaluation configs, version-sensitive TensorRT API usage, a fragile ONNX export pipeline depending on onnxsim subprocess calls, and potential drift between ONNX-exported blocks and real backbone behavior. The 'training-free' claim is partially contradicted by the presence of distillation training code, and the package name dart-detect vs module sam3 mismatch may cause import confusion. Despite these issues, several well-isolated components — an abstract prediction-file evaluator, a TensorRT layer analysis script, and an LLM agent client — could be extracted for reuse in other vision or agent projects.
This repository is a deprecated ONNX Model Zoo containing thousands of pre-trained models (mostly computer vision, some NLP and generative AI) exported to the ONNX format from PyTorch (timm, torchvision) and other frameworks. It also contains turnkeyml benchmark artifacts (turnkey_stats.yaml) that record export success, model metadata, ONNX operator counts, and system environment for each model across opsets 16–18. The primary purpose was to provide a centralized collection of ready-to-use ONNX models for inference and to validate cross-version ONNX export compatibility. However, the repository is explicitly deprecated (LFS disabled July 2025) and users are redirected to Hugging Face. The codebase consists almost entirely of binary model files and YAML metadata; the only reusable software components are a few preprocessing scripts (e.g., imagenet_preprocess.py, face_preprocess.py) in the validated/ directories. There is no application logic, no API, no training pipelines—this is purely a model distribution and benchmark data repository. Reproducibility is low due to hardcoded paths, frozen env snapshots that are out of date, and reliance on external model sources whose weights may have changed.
Bert-VITS2 is a multilingual text-to-speech system based on VITS2 (Variational Inference Text-to-Speech with improved Transformer backbone). It supports Chinese, Japanese, and English synthesis with optional emotion conditioning via pre-trained models (WavLM, wav2vec2-emotion, CLAP). The codebase provides a complete pipeline: data preprocessing (resampling, text normalization, BERT feature extraction), multi-speaker training with distributed support, inference through a Gradio web UI or FastAPI server (hiyoriUI.py), and ONNX export for deployment. The architecture uses separate per-language BERT models for text encoding, a flow-based decoder, stochastic duration predictor, and multi-scale discriminators (including a WavLM-based discriminator). The repository includes numerous versioned subdirectories (oldVersion/, onnx_modules/) for backward compatibility with model versions 1.0.1 through 2.4, resulting in significant code duplication and maintenance overhead. The README explicitly states the project is no longer maintained and recommends the successor project Fish-Speech.