协助使用FastAPI开发Python网页API,整理输入验证、错误处理和异步调用,并通过代码组织与缓存兼顾实现的可读性和响应性能。
- 设计API路由与输入验证
- 检查异步数据库与API调用
- 整理错误处理和日志
将VoiceStudio接入智能体或自动化流程,使用已保存的声音生成语音、转写录音、制作字幕、配音和旁白,并检查处理状态与音频文件。
Generate local speech with saved or designed voices, transcribe audio, and automate dubbing or narration workflows through VoiceStudio's REST API or MCP server. Use when the user wants to work with VoiceStudio audio or connect it to an agent or automation.
本技能通过VoiceStudio生成语音、转写录音、制作字幕、配音和长篇旁白。先检查已保存的声音和支持的引擎,再通过REST API或供智能体调用工具的MCP进行操作。完成后检查处理状态、音频文件格式与时长,并返回可用的成果文件。
适合使用同一种声音制作旁白,或将录音转成文字和字幕。配音时需要保留说话人、发言时间安排与背景音,或希望在现有自动化流程中加入音频处理时,也可以使用。
需要正在运行的VoiceStudio后端及支持相应处理的模型。受保护的环境还需使用认证凭据。语言支持和声音设计功能取决于引擎。下载模型、云端翻译和外部连接需要用户选择;声音克隆仅限已获使用授权的声音。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月21日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
Use the user's running VoiceStudio backend. Prefer local processing; cloud translation, model downloads, remote-backend connections, and external integrations require the user's choice. Installed models can run offline; do not promise that every configured workflow is offline.
The default backend is http://localhost:3900; honor the user's configured address. Electron's renderer and development proxy are separate services, not the public backend API.
GET /health.GET /openapi.json, and voices/engines with GET /v1/audio/voices. Do not invent profile IDs or infer installed models from a catalog listing.bun install, bun run setup:api, then bun run dev from the repository root). Do not install a second backend or overwrite an existing checkout.Save this JSON to speech-request.json, replacing voice with a discovered profile ID when the user selected a voice:
{"model":"tts-1","voice":"alloy","input":"Every voice has a story. Let's tell yours.","response_format":"wav"}
curl --fail-with-body --show-error http://localhost:3900/v1/audio/speech \
-H 'Content-Type: application/json' --data-binary @speech-request.json \
--output speech.wav
tts-1 and tts-1-hd are aliases, not quality guarantees. OpenAI voice names are compatibility aliases, not those providers' actual voices. Prefer a discovered saved voice for repeatable narration. Check the installed engine's language and voice-design capabilities before promising a result.
Check the HTTP status and decode/probe the output before calling it audio: an error response can be written to the output path. Report the saved path and actual duration/format. Never fabricate a successful generation, transcript, or job completion.
curl --fail-with-body --show-error http://localhost:3900/v1/audio/transcriptions \
-F file=@clip.wav -F model=whisper-1 -F response_format=verbose_json
Use json or text for plain transcripts, verbose_json for timestamps, and srt/vtt for subtitles when supported by the running schema. Check transcription-model readiness before long recordings; explain a missing model and obtain download authorization rather than silently installing it.
Use the installed OpenAPI schema to discover profile creation, reference uploads, generation, dubbing, and long-form job operations. Their native endpoints are broader than the OpenAI compatibility API and evolve independently.
The running backend mounts an MCP endpoint at http://localhost:3900/mcp. Use the client's supported HTTP transport and discover its tools at runtime. Reuse an existing connection rather than spawning another service. For stdio-only clients, consult the project's MCP guide for its shim.
For n8n, calling agents, containers, or other hosts, make the backend address reachable from that environment: container localhost refers to the container. Keep authentication and explicit remote-routing choices intact. An integration-directory listing does not mean the integration is connected.
On errors, read the response body, distinguish unavailable backend, missing model, unsupported capability, authentication, and busy hardware. Fix the reported condition; do not switch to a hosted provider or download a model without authorization.
The Electron app includes voice cloning and design, saved profiles and gallery, stories/audiobooks, dubbing projects, transcription, tools, and integrations. Prefer its existing project and voice identifiers over creating duplicates. For desktop automation, inspect the actual UI and current Settings shortcuts; do not assume shortcut bindings or microphone permissions are the same on every host.
Before dictation, verify an installed speech-to-text model and a working input device. Distinguish recording, paused recording, transcription processing, and completed text. Copying a transcript and inserting it into another app are different operations; confirm the intended target before typing and preserve clipboard content where supported. Never report text delivered just because transcription completed.
For dubbing, inspect missing/failed segments and timing overflow before export. Preserve music and non-speech audio when requested; do not silently replace the whole soundtrack. Preserve the source and make the output a separate artifact. Translation style prompts belong to the selected translation operation, and remote agent translation requires the user's configured provider choice.
Use the integrations directory as discovery and setup guidance, not proof of a working connector. Verify actual credentials, endpoint reachability, and supported operations before describing a connection as ready. Container deployments need persistent data volumes and a backend URL reachable by the calling client.
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协助使用FastAPI开发Python网页API,整理输入验证、错误处理和异步调用,并通过代码组织与缓存兼顾实现的可读性和响应性能。
协助配置使用Vite的网页应用开发环境并生成生产文件,涵盖框架集成、环境变量、测试配置和代码拆分,帮助整理设置并改善加载性能。
协助维护VoiceStudio,整理缺陷报告、审查提交的变更、调查自动检查失败,并准备发布,推进Electron应用和Python后端的修复、测试与分发前验证。