Token导航 LogoToken导航

Agent Skill · 开发

local-stt-workflow(本地 stt 工作流程)

local-stt-workflow 用于辅助测试设计、自动化测试和回归验证,适合在 OpenClaw 中需要补充测试、分析失败日志或验证功能改动时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

local-stt-workflow 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 7,137。

总安装 4,039周安装 170GitHub Stars 公开资料未说明下载 1,414核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.2

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:local-stt-workflow(本地 stt 工作流程)
来源仓库:https://github.com/mozi1924/local-stt-workflow
安装命令:
openclaw skills install local-stt-workflow
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install local-stt-workflow

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

验证本地语音转文本服务配置与接口兼容性,支持调试与回归测试。模拟 OpenAI STT 协议,便于集成现有 AI 应用无需改造代码。输出标准 JSON 响应格式,兼容主流语音处理流水线。需确保本地服务端口可达且音频文件格式符合输入规范。

local-stt-workflow 属于开发类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install local-stt-workflow;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

local-stt-workflow 怎么安装?

在终端执行 openclaw skills install local-stt-workflow。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

local-stt-workflow 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

local-stt-workflow 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

local-stt-workflow 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

local-stt-workflow 用的人多吗?

公开数据是累计安装 4,039 次,近一周安装 170 次,下载 1,414 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

local-stt-workflow 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
local-stt-workflow

本地 stt 工作流程 · 本页

4,039—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
local-stt-workflow
description
Local speech-to-text workflow for an OpenAI-compatible STT server, typically on http://127.0.0.1:8000/v1. Use when configuring, testing, debugging, or validating audio transcription with /v1/audio/transcriptions or /v1/audio/translations, especially for OpenClaw audio pipelines, multipart upload compatibility, model registration, streaming SSE behavior, response_format handling, local model-path fallback, and “did the request reach the server or not?” investigations.

Local STT Workflow

Use this skill to debug the full transcription path, not just the model.

Default assumption: the local STT server lives at http://127.0.0.1:8000/v1.

Current local model-path fallback worth remembering: if the server did not pull a model by name, it may be loading directly from a local path such as ./models/Qwen3-ASR-0.6B-bf16.

When exact route shape matters, the local OpenAPI document is available at:

  • http://localhost:8000/openapi.json

Use this OpenAPI doc as a schema/reference source to compare this local mlx-audio server against OpenAI’s API. Do not treat it as a health check.

Workflow

1. Verify the server before blaming OpenClaw

Check the basics first:

curl http://127.0.0.1:8000/health
curl http://127.0.0.1:8000/v1/models

Confirm that the intended STT model exists, usually qwen3-asr.

If the model does not appear by pulled registry name, do not assume STT is broken — this server may be running a local-path model such as ./models/Qwen3-ASR-0.6B-bf16.

If the server is task-gated, ensure STT is enabled:

MLX_AUDIO_SERVER_TASKS=stt uv run python server.py

If the model is missing, register it before testing clients — but first check whether the server is intentionally loading from a local path and verify the exact accepted model IDs through /v1/models or http://localhost:8000/openapi.json.

2. Prove the raw STT endpoint works

Always isolate the server from the client stack.

Minimal direct transcription test:

curl -X POST http://127.0.0.1:8000/v1/audio/transcriptions \
  -F file=@sample.wav \
  -F model=qwen3-asr \
  -F response_format=json

Useful richer test:

curl -X POST http://127.0.0.1:8000/v1/audio/transcriptions \
  -F file=@sample.wav \
  -F model=qwen3-asr \
  -F response_format=verbose_json \
  -F 'timestamp_granularities[]=segment' \
  -F 'timestamp_granularities[]=word'

If direct curl works but OpenClaw does not, the bug is probably in the message ingestion or routing layer, not the STT backend.

3. Distinguish server failure from routing failure

Use this rule hard:

  • Direct curl fails → fix the local STT server first
  • Direct curl works, but OpenClaw shows no transcript → inspect OpenClaw audio pipeline / attachment routing
  • OpenClaw sends requests, but fields are wrong → inspect request shape compatibility

This distinction saves a shitload of time.

4. Check the request shape

This server is designed around OpenAI-style multipart form upload.

Expected core fields for /v1/audio/transcriptions from the current local OpenAPI schema:

  • required: file, model
  • optional: language, verbose, max_tokens, chunk_duration, frame_threshold, stream, context, prefill_step_size, text

This means the local server is not exposing the same form shape as OpenAI Whisper-style docs. Do not blindly assume response_format, prompt, or timestamp_granularities[] exist just because OpenAI supports them.

If a client is suspected of sending the wrong shape, inspect traffic with a temporary dump proxy or server logs.

5. Use the reference doc when exact fields matter

Read references/stt-api.md when you need exact behavior for:

  • response_format=json|text|verbose_json|srt|vtt
  • stream=true SSE events
  • timestamp_granularities[]
  • include[]
  • translation endpoint semantics
  • error envelope shape
  • current compatibility limits

Do not guess field support from generic OpenAI docs when this local server may intentionally differ.

Current notable mismatch: the local schema exposes context and text, plus chunking/prefill controls like chunk_duration, frame_threshold, and prefill_step_size, which are not the usual OpenAI STT field set.

6. OpenClaw-specific debugging pattern

When OpenClaw STT appears broken:

  1. Confirm tools.media.audio is configured, not messages.stt
  2. Confirm base URL points at http://127.0.0.1:8000/v1
  3. Confirm the chosen model exists in /v1/models
  4. Send the exact inbound audio file directly to /v1/audio/transcriptions
  5. Inspect gateway logs for any sign of transcription dispatch
  6. If there is no /audio/transcriptions request at all, the problem is upstream of STT

If OpenClaw never hits the server, stop tweaking model params. That would be cargo-cult debugging.

7. Preferred test ladder

Use this order:

  1. GET /health
  2. GET /v1/models
  3. direct curl transcription with the same audio file
  4. compare request fields against http://localhost:8000/openapi.json
  5. OpenAI client compatibility test
  6. OpenClaw integration test
  7. dump-proxy / log inspection only if still ambiguous

8. Common conclusions

Niche input container bug

Typical signs:

  • direct upload of a less-common container like .m4a returns 500
  • server logs mention unsupported format handling during temp write or normalization
  • converting the same source audio to mp3 or wav makes transcription succeed immediately

Conclusion: treat this as an input-container compatibility bug, not an ASR-quality failure. For now, transcode niche formats to mp3 or wav before testing recognition quality.

Server good, client bad

Typical signs:

  • manual curl returns { "text": ... }
  • OpenClaw logs show no transcription request
  • changing model/language does nothing

Conclusion: fix routing, not inference.

Multipart mismatch

Typical signs:

  • server is up
  • model exists
  • client gets 400 errors
  • direct curl works but app client does not

Conclusion: compare multipart field names and values.

Feature mismatch

Typical signs:

  • client expects diarization, logprobs, or richer streaming fields
  • local server only implements a smaller compatible subset

Conclusion: align expectations with references/stt-api.md.

Resources

references/

  • references/stt-api.md — exact local API behavior, schema, response formats, SSE events, limits, and compatibility notes

平台分布

OpenClaw

94.48%
按下载量换算1,336

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills