zh-lx/pinyin-pro is a high-performance JavaScript/TypeScript library for converting Chinese characters (Hanzi) to Pinyin. It is used both in browsers and Node.js environments, supporting a wide range of output formats (tone marks, tone numbers, initials, finals, etc.) and advanced features like surname detection, tone sandhi, polyphonic character handling, word segmentation, and fuzzy text matching. The library is organized as a monorepo with three packages: the core `pinyin-pro` library, a `@pinyin-pro/data` package containing large curated dictionaries (modern, complete, traditional, buddhist), and documentation sites in Chinese and English. Architecturally, the core employs a middleware pipeline to process input text through sequential transformations (non-Chinese handling, pattern extraction, tone type conversion, etc.). It uses an Aho-Corasick automaton for efficient phrase matching against its multi-tier dictionaries (single characters to multi-word phrases). Multiple tokenization strategies (reverse max match, max probability, min tokenization) are implemented for word segmentation. The library has been heavily optimized for performance, achieving ~99.8% accuracy in benchmarks and competitive speed with native Rust-based alternatives. Key entry points are the `pinyin()` function for basic conversion, and additional functions for matching (`match`), segmentation (`segment`), HTML ruby annotation (`html`), polyphonic listing (`polyphonic`), custom dictionary management (`customPinyin`, `addDict`), and format conversion (`convert`). External dependencies are minimal: the library runs entirely on its built-in dictionaries and does not require any runtime dependencies. The codebase is well-tested (vitest) with a dedicated CI pipeline (GitHub Actions) for quality assurance, bundle size comparison, and automated release.
This repository provides a Claude Code Skill for reducing AIGC detection rates in Chinese academic writing. The Skill is available in two versions: a compact SKILL.md (~414 lines) for efficient daily use and a full SKILL_full.md (~554 lines) with detailed examples for maximum effectiveness. It does not contain executable code but offers a comprehensive set of rewriting rules based on 16 identified AI writing patterns across content, language, and style layers. The core methodology involves pattern recognition, constrained rewriting (7 hard constraints), a 4-step SOP (risk scanning → rewriting → rhythm check → noise preservation), and a 6-dimension quality scoring system. The Skill transforms input text by breaking AI predictability while preserving academic meaning and injecting human-like variability through techniques like theoretical repositioning, asymmetric restructuring, and judicious noise retention.
ChatTTS is a generative speech model application designed specifically for dialogue scenarios, functioning as a Text-to-Speech (TTS) library with integrated serving infrastructure. The core logic resides in the ChatTTS package, where `ChatTTS/core.py` defines the main inference interface. The architecture combines a GPT-based language model (`ChatTTS/model/gpt.py`) for token generation, a DVAE (`ChatTTS/model/dvae.py`) for audio compression, and Vocos as a vocoder. The system supports multiple speakers, prosodic control (laughter, pauses), and text normalization for Chinese and English. Deployment is handled through multiple entry points: a FastAPI-based API server (`examples/api/main.py`), a Gradio WebUI (`examples/web/webui.py`), and CLI tools (`examples/cmd/run.py`). A significant engineering highlight is the integration of vLLM (`ChatTTS/model/velocity/`) to optimize inference throughput for the GPT component. The repository includes robust asset management (`ChatTTS/utils/dl.py`) for model weights with SHA256 verification and ONNX export capabilities (`examples/onnx/exporter.py`) for cross-platform deployment. While the underlying model is research-grade (trained on 100k+ hours of audio), the repository structure prioritizes application delivery over experimental research. It provides a stable library interface with clear separation between model logic, audio processing (`tools/audio/`), and serving layers. The codebase demonstrates high craftsmanship in handling stochastic generation reproducibility (`tools/seeder/ctx.py`) and speaker embedding management (`ChatTTS/model/speaker.py`), making it a production-ready TTS solution rather than a raw research notebook collection.
This repository is a curated collection of Chinese vocabulary lists for standardized tests (HSK 3.0, BCT, YCT), enriched with pinyin, zhuyin, part-of-speech tags, frequency data, and English definitions. It includes Python and JavaScript scripts for data cleaning, format conversion (TXT/TSV/XLSX), POS tag correction using SUBTLEX frequency corpus, and pinyin tone conversion. The data is organized by test level and version (2021 and 2025 HSK standards), with multiple output formats suitable for flashcard apps (Anki, Pleco) and language learning tools. The repository is primarily a data asset with supporting ETL scripts, not a production application. It demonstrates solid data engineering practices for language resource curation, though it suffers from some file duplication and hardcoded paths.