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local-stt-workflow本地 stt 工作流程

Agent Skill

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。

总安装

4,039

周安装

170

GitHub Stars

公开资料未说明

下载量

1,414
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

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03

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能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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按下载量换算1,336

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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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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