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character-rvc-training-cover

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为用户指定角色发现与核验语音来源，整理授权录音和分组留出集，在固定 RVC v2/48k/F0 环境训练与选择模型、校验 added 索引，再分声部转换歌曲并混音交付；莫斯提马中文若舞仅为历史示例。

templates/singing-cover/character-rvc/SKILL.md

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Last checked: Nov 11, 2026

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Repository stars (not skill ratings): 9
Repository last updated: Sep 18, 2026
License: MIT

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LICENSEasset · 1,088 bytes
LICENSES.mddoc · 1,875 bytes
README.mddoc · 5,301 bytes
SKILL.mdcore · 5,615 bytes
examples/index-inputs.spec.jsonasset · 471 bytes
model-catalog/dependencies.jsonasset · 3,049 bytes
profile.jsonasset · 3,428 bytes
references/deployment.mddoc · 6,958 bytes
references/discovery.mddoc · 9,853 bytes
references/processing.mddoc · 10,750 bytes
references/repair.mddoc · 7,937 bytes
references/training.mddoc · 12,242 bytes
references/validation.mddoc · 6,973 bytes
scripts/build_filelist.pyruns · 5,642 bytes
scripts/discover.pyruns · 13,380 bytes
scripts/run_guard.pyruns · 5,023 bytes
sources.mddoc · 4,798 bytes
tests/test_discover.pyruns · 11,575 bytes
tests/test_helpers.pyruns · 7,101 bytes

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# 指定角色 RVC 训练与翻唱

## 1. 先发现来源，再补齐缺口

读 [README](README.md)、[profile](profile.json)、[sources](sources.md) 和[来源发现协议](references/discovery.md)。尊重用户指定角色、作品、语言和 CV；不要用历史莫斯提马或另一角色替换。先用 Agent 原生网页搜索官方/可信角色资料与语音，核对身份、语言和配音，再用 `scripts/discover.py` 的 yt-dlp 内建元数据搜索兜底；只有仍无可用来源才问用户补充，不能早早把搜集工作全部交回用户。

发现脚本接口：`--character --series --language --actor(可选) --provider bilibili|youtube|both --output 新JSON`，另支持重复 `--alias`、`--limit 1..10`、`--timeout 10..180`、`--inspect-url` 和 `--yt-dlp`。只搜元数据不下载，返回的是候选，不自动认定为官方或已获授权；它不是全网官方来源验证器。URL-only flat 候选用 inspect 补详情，搜索错误与真正无结果分别处理，不能把 412 或工具缺失当作角色无语音。读取来源页的逐项判断字段；身份匹配和权利许可是不同判断。已知角色仍可能有中日配等不同声线，不能仅以角色名配对。

建立新 job ID。权利状态保持 `unknown` / `pending-review`，直到取得本次具体依据；不要用一组 `true` 值代替授权书或继承旧用途字段。若身份明确而权限未明，可完成发现和计划，停在素材取得/训练之前，不因“先搜”越过权利边界。

## 2. 固定环境与输入

按[部署](references/deployment.md)固定官方 commit `4338f12c3c28c80b3ac015e2d0df66c41592746d`，使用原拼写 `requirments_cu118_py312.txt` 与 HF 格式 `assets/hubert_base/`，不能换成旧 `.pt`。读取 [model-catalog/dependencies.json](model-catalog/dependencies.json)，核对字节与可信来源；训练不需要 DDSP 权重。环境、下载和 GPU 分配均独立记录。用户指定远程主机时使用其已配置连接部署和取回，本机可只负责控制；不要猜主机、认证或 GPU 编号，也不要在 Mac 上把这条 CUDA 训练路线说成 MPS 已适配。