OpenClaw

voice-stt-tts

Full voice message setup (STT + TTS) for OpenClaw using faster-whisper and Edge TTS

aksenkin0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install aksenkin/voice-stt-tts

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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:

Accepts audio file path (--audio)
Lo...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/aksenkin/voice-stt-tts
Version: —
Commit: 750d1ec1b9f25d251b1744805d13a6565a77a786
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# 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:

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

### 2. Install faster-whisper

Install packages in venv:

```bash
~/.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`

```python
#!/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. Lo...

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