Project Introduction
An overview of the Chinese Vocal Synthesis Archive (Project CVSA)
Chinese Vocal Synth Archive (Project CVSA) is a dedicated platform for the collection, documentation, and preservation of information surrounding Chinese singing voice synthesis (SVS).
Background and Related Projects
While several platforms systematically organize data within the Chinese virtual singer community, each serves a distinct niche:
- Moegirlpedia (萌娘百科): A comprehensive wiki-style encyclopedia (MediaWiki) containing extensive records of Chinese virtual singer songs and voicebanks.
- VCPedia: Established by former Moegirlpedia editors, this site serves as a specialized information aggregator focused exclusively on Chinese SVS content via a traditional wiki format.
- VocaDB: A global collaborative database for Vocaloid, UTAU, and other synthesizers. While it hosts a vast majority of Chinese SVS works, it focuses primarily on structured metadata (artists, discography, and PVs).1
- TDD (天钿Daily): A data-driven discussion site that periodically crawls and analyzes VC-related statistics to highlight industry trends and dimensions.
Identifying the Gaps
Despite their strengths, existing platforms face specific limitations:
- Manual Overhead: Moegirlpedia, VCPedia, and VocaDB rely almost entirely on manual entry and human editing for song inclusion and updates.
- Content Depth: VocaDB excels at metadata but often lacks descriptive context, such as background stories or detailed producer insights.
- Scope: TDD focuses strictly on statistical trends and lacks qualitative or descriptive information about the works themselves.
Our Mission: Project CVSA
Project CVSA integrates the strengths of its predecessors while addressing these functional gaps. Our goal is to create a more efficient and descriptive archive by implementing:
- Fully Automated Discovery: Programmatic identification and creation of new song entries.
- Automated Metadata Extraction: High-efficiency harvesting of technical song data.
- Dynamic Statistics: Automated collection and tracking of song performance metrics.
- Hybrid Collaboration: While leveraging automation for data, we actively encourage community contributors to provide descriptive content and perform quality control.
- Resource Integration: Under appropriate licensing, we cite and aggregate data from existing reputable sources to ensure a comprehensive knowledge base.
This document is provided under the CC BY-NC-SA 4.0 license.