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voice-stt-tts语音 stt tts

Agent Skill

用于辅助音频、音乐、语音转写、语音合成或声音素材处理。它适合让 Agent 生成配乐说明、整理音频流程、调用语音工具或处理播客和视频配音素材。使用时需要确认输入音频来源、输出格式、时长和模型限制;涉及人声克隆、版权音乐或公开发布时,应先核对授权和合规边界。

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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:voice-stt-tts(语音 stt tts)
来源仓库:https://github.com/aksenkin/voice-stt-tts
安装命令:
openclaw skills install voice-stt-tts
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 voice-stt-tts

简介

使用 Faster-Whisper 和 Edge TTS 的 OpenClaw 完整语音消息设置 (STT + TTS)

SKILL.md

name
voice-stt-tts
description
Full voice message setup (STT + TTS) for OpenClaw using faster-whisper and Edge TTS
homepage
https://docs.openclaw.ai/nodes/audio
metadata

Voice Messages (STT + TTS) for OpenClaw 🎙️

Complete voice message setup using faster-whisper for transcription and Edge TTS for voice replies.

What we configure

  • STT (Speech-to-Text) — transcribe voice messages via faster-whisper
  • TTS (Text-to-Speech) — voice replies via Edge TTS
  • 🎯 Result: voice → text → reply with voice

Installation

1. Create virtual environment (venv)

For Ubuntu create an isolated venv:

python3 -m venv ~/.openclaw/workspace/voice-messages

2. Install faster-whisper

Install packages in venv:

~/.openclaw/workspace/voice-messages/bin/pip install faster-whisper

What gets installed:

  • faster-whisper — Python library for transcription
  • Dependencies: ctranslate2, onnxruntime, huggingface-hub, av, numpy, and others.
  • Size: ~250 MB

Transcription Script

Path and content

File: ~/.openclaw/workspace/voice-messages/transcribe.py

#!/usr/bin/env python3
import argparse
from faster_whisper import WhisperModel


def transcribe(audio_path: str, model_name: str = "small", lang: str = "en", device: str = "cpu") -> str:
    model = WhisperModel(
        model_name,
        device=device,
        compute_type="int8" if device == "cpu" else "float16",
    )
    segments, _ = model.transcribe(audio_path, language=lang, vad_filter=True)
    text = " ".join(seg.text.strip() for seg in segments if seg.text and seg.text.strip()).strip()
    return text


def main():
    p = argparse.ArgumentParser()
    p.add_argument("--audio", required=True)
    p.add_argument("--model", default="small")
    p.add_argument("--lang", default="en")
    p.add_argument("--device", default="cpu", choices=["cpu", "cuda"])
    args = p.parse_args()

    text = transcribe(args.audio, args.model, args.lang, args.device)
    print(text if text else "")


if __name__ == "__main__":
    main()

What the script does:

  1. Accepts audio file path (--audio)
  2. Loads Whisper model (--model): small by default
  3. Sets language (--lang): en for English
  4. Transcribes with VAD filter (Voice Activity Detection)
  5. Outputs clean text to stdout

Make file executable:

chmod +x ~/.openclaw/workspace/voice-messages/transcribe.py

OpenClaw Configuration

1. Configure STT (tools.media.audio)

Add to ~/.openclaw/openclaw.json:

{
  "tools": {
    "media": {
      "audio": {
        "enabled": true,
        "maxBytes": 20971520,
        "models": [
          {
            "type": "cli",
            "command": "~/.openclaw/workspace/voice-messages/bin/python",
            "args": [
              "~/.openclaw/workspace/voice-messages/transcribe.py",
              "--audio",
              "{{MediaPath}}",
              "--lang",
              "en",
              "--model",
              "small"
            ],
            "timeoutSeconds": 120
          }
        ]
      }
    }
  }
}

Parameters:

ParameterValueDescription
enabledtrueEnable audio transcription
maxBytes20971520Max file size (20 MB)
type"cli"Model type: CLI command
commandPython pathPath to python in venv
argsargument arrayArguments for script
{{MediaPath}}placeholderReplaced with audio file path
timeoutSeconds120Transcription timeout (2 minutes)

2. Configure TTS (messages.tts)

Add to ~/.openclaw/openclaw.json:

{
  "messages": {
    "tts": {
      "auto": "inbound",
      "provider": "edge",
      "edge": {
        "voice": "en-US-JennyNeural",
        "lang": "en-US"
      }
    }
  }
}

Parameters:

ParameterValueDescription
auto"inbound"Key mode! — reply with voice only on incoming voice messages
provider"edge"TTS provider (free, no API key)
voice"en-US-JennyNeural"Voice (see available below)
lang"en-US"Locale (en-US for US english)

3. Full configuration example

{
  "tools": {
    "media": {
      "audio": {
        "enabled": true,
        "maxBytes": 20971520,
        "models": [
          {
            "type": "cli",
            "command": "~/.openclaw/workspace/voice-messages/bin/python",
            "args": [
              "~/.openclaw/workspace/voice-messages/transcribe.py",
              "--audio",
              "{{MediaPath}}",
              "--lang",
              "en",
              "--model",
              "small"
            ],
            "timeoutSeconds": 120
          }
        ]
      }
    },
  },
  "messages": {
    "tts": {
      "auto": "inbound",
      "provider": "edge",
      "edge": {
        "voice": "en-US-JennyNeural",
        "lang": "en-US"
      }
    },
    "ackReactionScope": "group-mentions"
  }
}

Apply Changes

Restart Gateway

# Method 1: via openclaw CLI
openclaw gateway restart

# Method 2: via systemd
systemctl --user restart openclaw-gateway

# Check status
systemctl --user status openclaw-gateway
# Should show: active (running)

Testing

Test STT (transcription)

Action: Send a voice message to your Telegram bot

Expected result:

[Audio] User text: [Telegram ...] <media:audio> Transcript: <transcribed text>

Example response:

[Audio] User text: [Telegram kd (@someuser) id:12345678 +5s ...] <media:audio> Transcript: Hello. How are you?

Test TTS (voice replies)

Action: After successful transcription, bot should send a voice reply

Expected result:

  • Voice file arrives in Telegram
  • Voice note (round bubble)

Expected behavior:

  • Incoming voice → bot replies with voice
  • Text messages → bot replies with text (this is normal!)

Available Edge TTS Voices

Female voices

VoiceIDUsage example
Jennyen-US-JennyNeural← current
Anaen-US-AnaNeuralSofter

Male voices

VoiceIDUsage example
Dmitryen-US-RogerNeuralMore bass

How to change voice:

cat ~/.openclaw/openclaw.json | \
  jq '.messages.tts.edge.voice = "en-US-MichelleNeural"' > ~/.openclaw/openclaw.json.tmp
mv ~/.openclaw/openclaw.json.tmp ~/.openclaw/openclaw.json
systemctl --user restart openclaw-gateway

Additional Edge TTS Parameters

Adjusting speed, pitch, volume

{
  "messages": {
    "tts": {
      "edge": {
        "voice": "en-US-JennyNeural",
        "lang": "en-US",
        "rate": "+10%",      // Speed: -50% to +100%
        "pitch": "-5%",     // Pitch: -50% to +50%
        "volume": "+5%"     // Volume: -100% to +100%
      }
    }
  }
}

Troubleshooting

Problem: Voice not transcribed

Logs show:

[ERROR] Transcription failed

Possible causes:

  1. File too large — > 20 MB
   # Solution: Increase maxBytes in config
   maxBytes: 52428800  # 50 MB
  1. Timeout — transcription took > 2 minutes
   # Solution: Increase timeoutSeconds
   timeoutSeconds: 180  # 3 minutes
  1. Model not downloaded — first run
   # Solution: Wait while it downloads (1-2 minutes)
   # Models are cached in ~/.cache/huggingface/

Problem: No voice reply

Possible causes:

  1. Reply too short (< 10 characters)

- TTS skips very short replies - Solution: this is expected behavior

  1. auto: "inbound" but text message

- TTS in inbound mode replies with voice only on voice messages - Text messages get text replies — this is correct!

  1. Edge TTS unavailable
   # Check
   curl -s "https://speech.platform.bing.com/consumer/api/v1/tts" | head -c 100
   # If error — temporarily unavailable

Performance

Transcription time (Raspberry Pi 4/ARM)

Whisper ModelEst. timeQuality
tiny~5-10 secLow
base~10-20 secMedium
small~20-40 secHigh ← current
medium~40-80 secVery high
large~80-160 secMaximum

Recommendation: For Raspberry Pi use small or base. medium/large will be very slow.

Where Whisper models are stored

~/.cache/huggingface/

Models download automatically on first run.

Done! 🎉

After completing these steps:

  1. ✅ faster-whisper installed in venv
  2. transcribe.py script created
  3. ✅ OpenClaw configured (STT + TTS)
  4. ✅ Gateway restarted
  5. ✅ Voice messages working

Now your Telegram bot:

  • 🎙️ Accepts voice → transcribes via faster-whisper
  • 🎤 Replies with voice → generates via Edge TTS
  • 💬 Accepts text → replies with text (as usual)

Useful links:

  • OpenClaw docs: https://docs.openclaw.ai
  • TTS docs: https://docs.openclaw.ai/tts
  • Audio docs: https://docs.openclaw.ai/nodes/audio
  • Install skills: npx clawhub search voice

*Created: 2026-03-01 for OpenClaw 2026.2.26*

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