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Agent Skill · 云服务

azure-speech-to-text-rest-py(Azure speech TO text rest PY 文档)

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

azure-speech-to-text-rest-py 是云服务类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描已通过,按安装量在云服务类的 479 个 Skill 里排第 181。

总安装 1,464周安装 61GitHub Stars 35,705下载 488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
unknown

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:azure-speech-to-text-rest-py(Azure speech TO text rest PY 文档)
来源仓库:https://github.com/sickn33/antigravity-awesome-skills
仓库路径:skills/azure-speech-to-text-rest-py
安装命令:
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill azure-speech-to-text-rest-py
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill azure-speech-to-text-rest-py

这个 Skill 是做什么的

云服务类 Skill 对接各家云平台的接口,用来查询资源、估算成本和执行变更。

用于 Azure 语音转文本服务的 REST API 调用与短音频转录。适合处理最多 60 秒的音频文件,无需 SDK,仅通过 HTTP 请求实现。提供环境变量配置、认证方式说明和基础客户端示例。需先创建 Azure 语音资源并获取密钥与区域信息。

azure-speech-to-text-rest-py 属于云服务类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/azure-speech-to-text-rest-py

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill azu…;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

    它需要接触敏感数据,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

azure-speech-to-text-rest-py 怎么安装?

在终端执行 npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill azure-speech-…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

azure-speech-to-text-rest-py 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

azure-speech-to-text-rest-py 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

azure-speech-to-text-rest-py 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

azure-speech-to-text-rest-py 用的人多吗?

公开数据是累计安装 1,464 次,近一周安装 61 次,下载 488 次,GitHub 35,705 星。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。

azure-speech-to-text-rest-py 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 35,705 星,维护活跃度相对可查。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,想直接扩展它能力的人。
  • 需要处理云服务类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

云服务类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
azure-deploy

Azure 部署 · Codex、Claude

6,641,856764调用外部服务未明确通过
azure-diagnostics

Azure diagnostics 问题管理 · Codex、Claude

6,575,520764调用外部服务未明确通过
azure-aigateway

Azure aigateway 部署 · Codex、Claude

6,553,272764调用外部服务未明确有提醒
azure-ai

Azure AI 搜索 · Codex、Claude

6,514,128764调用外部服务未明确通过
azure-cloud-migrate

Azure Cloud migrate 部署 · Codex、Claude

6,506,784764调用外部服务未明确有提醒
azure-speech-to-text-rest-py

Azure speech TO text rest PY 文档 · 本页

1,46435,705接触敏感数据未明确通过

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

Azure Speech to Text REST API for Short Audio

Simple REST API for speech-to-text transcription of short audio files (up to 60 seconds). No SDK required - just HTTP requests.

Prerequisites

  1. Azure subscription - Create one free
  2. Speech resource - Create in Azure Portal
  3. Get credentials - After deployment, go to resource > Keys and Endpoint

Environment Variables

# Required
AZURE_SPEECH_KEY=<your-speech-resource-key>
AZURE_SPEECH_REGION=<region>  # e.g., eastus, westus2, westeurope

# Alternative: Use endpoint directly
AZURE_SPEECH_ENDPOINT=https://<region>.stt.speech.microsoft.com

Installation

pip install requests

Quick Start

import os
import requests

def transcribe_audio(audio_file_path: str, language: str = "en-US") -> dict:
    """Transcribe short audio file (max 60 seconds) using REST API."""
    region = os.environ["AZURE_SPEECH_REGION"]
    api_key = os.environ["AZURE_SPEECH_KEY"]

    url = f"https://{region}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1"

    headers = {
        "Ocp-Apim-Subscription-Key": api_key,
        "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
        "Accept": "application/json"
    }

    params = {
        "language": language,
        "format": "detailed"  # or "simple"
    }

    with open(audio_file_path, "rb") as audio_file:
        response = requests.post(url, headers=headers, params=params, data=audio_file)

    response.raise_for_status()
    return response.json()

# Usage
result = transcribe_audio("audio.wav", "en-US")
print(result["DisplayText"])

Audio Requirements

FormatCodecSample RateNotes
WAVPCM16 kHz, monoRecommended
OGGOPUS16 kHz, monoSmaller file size

Limitations:

  • Maximum 60 seconds of audio
  • For pronunciation assessment: maximum 30 seconds
  • No partial/interim results (final only)

Content-Type Headers

# WAV PCM 16kHz
"Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000"

# OGG OPUS
"Content-Type": "audio/ogg; codecs=opus"

Response Formats

Simple Format (default)

params = {"language": "en-US", "format": "simple"}
{
  "RecognitionStatus": "Success",
  "DisplayText": "Remind me to buy 5 pencils.",
  "Offset": "1236645672289",
  "Duration": "1236645672289"
}

Detailed Format

params = {"language": "en-US", "format": "detailed"}
{
  "RecognitionStatus": "Success",
  "Offset": "1236645672289",
  "Duration": "1236645672289",
  "NBest": [
    {
      "Confidence": 0.9052885,
      "Display": "What's the weather like?",
      "ITN": "what's the weather like",
      "Lexical": "what's the weather like",
      "MaskedITN": "what's the weather like"
    }
  ]
}

Chunked Transfer (Recommended)

For lower latency, stream audio in chunks:

import os
import requests

def transcribe_chunked(audio_file_path: str, language: str = "en-US") -> dict:
    """Stream audio in chunks for lower latency."""
    region = os.environ["AZURE_SPEECH_REGION"]
    api_key = os.environ["AZURE_SPEECH_KEY"]

    url = f"https://{region}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1"

    headers = {
        "Ocp-Apim-Subscription-Key": api_key,
        "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
        "Accept": "application/json",
        "Transfer-Encoding": "chunked",
        "Expect": "100-continue"
    }

    params = {"language": language, "format": "detailed"}

    def generate_chunks(file_path: str, chunk_size: int = 1024):
        with open(file_path, "rb") as f:
            while chunk := f.read(chunk_size):
                yield chunk

    response = requests.post(
        url,
        headers=headers,
        params=params,
        data=generate_chunks(audio_file_path)
    )

    response.raise_for_status()
    return response.json()

Authentication Options

Option 1: Subscription Key (Simple)

headers = {
    "Ocp-Apim-Subscription-Key": os.environ["AZURE_SPEECH_KEY"]
}

Option 2: Bearer Token

import requests
import os

def get_access_token() -> str:
    """Get access token from the token endpoint."""
    region = os.environ["AZURE_SPEECH_REGION"]
    api_key = os.environ["AZURE_SPEECH_KEY"]

    token_url = f"https://{region}.api.cognitive.microsoft.com/sts/v1.0/issueToken"

    response = requests.post(
        token_url,
        headers={
            "Ocp-Apim-Subscription-Key": api_key,
            "Content-Type": "application/x-www-form-urlencoded",
            "Content-Length": "0"
        }
    )
    response.raise_for_status()
    return response.text

# Use token in requests (valid for 10 minutes)
token = get_access_token()
headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
    "Accept": "application/json"
}

Query Parameters

ParameterRequiredValuesDescription
languageYesen-US, de-DE, etc.Language of speech
formatNosimple, detailedResult format (default: simple)
profanityNomasked, removed, rawProfanity handling (default: masked)

Recognition Status Values

StatusDescription
SuccessRecognition succeeded
NoMatchSpeech detected but no words matched
InitialSilenceTimeoutOnly silence detected
BabbleTimeoutOnly noise detected
ErrorInternal service error

Profanity Handling

# Mask profanity with asterisks (default)
params = {"language": "en-US", "profanity": "masked"}

# Remove profanity entirely
params = {"language": "en-US", "profanity": "removed"}

# Include profanity as-is
params = {"language": "en-US", "profanity": "raw"}

Error Handling

import requests

def transcribe_with_error_handling(audio_path: str, language: str = "en-US") -> dict | None:
    """Transcribe with proper error handling."""
    region = os.environ["AZURE_SPEECH_REGION"]
    api_key = os.environ["AZURE_SPEECH_KEY"]

    url = f"https://{region}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1"

    try:
        with open(audio_path, "rb") as audio_file:
            response = requests.post(
                url,
                headers={
                    "Ocp-Apim-Subscription-Key": api_key,
                    "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
                    "Accept": "application/json"
                },
                params={"language": language, "format": "detailed"},
                data=audio_file
            )

        if response.status_code == 200:
            result = response.json()
            if result.get("RecognitionStatus") == "Success":
                return result
            else:
                print(f"Recognition failed: {result.get('RecognitionStatus')}")
                return None
        elif response.status_code == 400:
            print(f"Bad request: Check language code or audio format")
        elif response.status_code == 401:
            print(f"Unauthorized: Check API key or token")
        elif response.status_code == 403:
            print(f"Forbidden: Missing authorization header")
        else:
            print(f"Error {response.status_code}: {response.text}")

        return None

    except requests.exceptions.RequestException as e:
        print(f"Request failed: {e}")
        return None

Async Version

import os
import aiohttp
import asyncio

async def transcribe_async(audio_file_path: str, language: str = "en-US") -> dict:
    """Async version using aiohttp."""
    region = os.environ["AZURE_SPEECH_REGION"]
    api_key = os.environ["AZURE_SPEECH_KEY"]

    url = f"https://{region}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1"

    headers = {
        "Ocp-Apim-Subscription-Key": api_key,
        "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
        "Accept": "application/json"
    }

    params = {"language": language, "format": "detailed"}

    async with aiohttp.ClientSession() as session:
        with open(audio_file_path, "rb") as f:
            audio_data = f.read()

        async with session.post(url, headers=headers, params=params, data=audio_data) as response:
            response.raise_for_status()
            return await response.json()

# Usage
result = asyncio.run(transcribe_async("audio.wav", "en-US"))
print(result["DisplayText"])

Supported Languages

Common language codes (see full list):

CodeLanguage
en-USEnglish (US)
en-GBEnglish (UK)
de-DEGerman
fr-FRFrench
es-ESSpanish (Spain)
es-MXSpanish (Mexico)
zh-CNChinese (Mandarin)
ja-JPJapanese
ko-KRKorean
pt-BRPortuguese (Brazil)

Best Practices

  1. Use WAV PCM 16kHz mono for best compatibility
  2. Enable chunked transfer for lower latency
  3. Cache access tokens for 9 minutes (valid for 10)
  4. Specify the correct language for accurate recognition
  5. Use detailed format when you need confidence scores
  6. Handle all RecognitionStatus values in production code

When NOT to Use This API

Use the Speech SDK or Batch Transcription API instead when you need:

  • Audio longer than 60 seconds
  • Real-time streaming transcription
  • Partial/interim results
  • Speech translation
  • Custom speech models
  • Batch transcription of many files

Reference Files

FileContents
references/pronunciation-assessment.mdPronunciation assessment parameters and scoring

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

适合场景

01

Azure 资源规划

02

云服务升级

03

基础设施检查

04

企业云环境自动化

能力概览

能力 1

整理 Azure 服务操作流程

能力 2

提示 CLI/MCP 前置条件

能力 3

辅助云资源检查和规划

能力 4

保留官方服务来源线索

平台分布

Codex

38.37%
按下载量换算187

Claude

30.58%
按下载量换算149

Cursor

18.74%
按下载量换算91

Gemini CLI

9.66%
按下载量换算47

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。

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