Qwen3-TTS VoiceDesign
Text-to-speech with Qwen3-TTS VoiceDesign. Design custom voices via natural language descriptions + seed-based timbre fixation. Includes OpenAI-compatible AP...
技能说明
name: qwen3-tts description: Text-to-speech with Qwen3-TTS VoiceDesign. Design custom voices via natural language descriptions + seed-based timbre fixation. Includes OpenAI-compatible API server, one-click setup, and batch seed exploration tools. Use when generating speech, designing voices, or adding TTS to OpenClaw.
Qwen3-TTS VoiceDesign
Text → Speech with natural language voice descriptions + seed-based timbre fixation.
Quick Start
# Generate speech (uses server defaults)
TTS_URL=http://your-server:8881 scripts/say.sh "Hello world!"
# Save to file
scripts/say.sh "Save this" output.mp3
# Batch compare seeds (voice exploration)
scripts/batch_seeds.sh "Hello world!" 42 123 201 456 789 /tmp/seeds
Environment Variables
All config via env vars — text is the only required argument:
| Variable | Default | Description |
|---|---|---|
TTS_URL | http://localhost:8881 | Server base URL (client side) |
TTS_SEED | 4096 | Random seed → controls timbre |
TTS_INSTRUCT | (generic female voice) | Voice description prompt |
TTS_MODEL_PATH | Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign | Model weights path |
TTS_PORT | 8881 | Server listen port |
TTS_HOST | 0.0.0.0 | Server bind address |
TTS_FORMAT | mp3 | Output format: mp3 / wav |
Server reads from .env file in its directory. Client scripts read from shell env.
Voice Description Example
30岁男性播音员,声音低沉磁性,
语速稳重从容,咬字清晰标准,
像新闻联播主播的专业感,又带一点温暖。
Tip: Once you've found your perfect voice (description + seed), set them as server defaults in
.env. Then client calls only need to passtext.
API
OpenAI-Compatible
curl -X POST $TTS_URL/v1/audio/speech \
-H "Content-Type: application/json" \
-d '{"input": "Hello!"}' -o speech.mp3
Custom (seed + instruct override)
curl -X POST $TTS_URL/tts \
-H "Content-Type: application/json" \
-d '{"text": "Hello!", "seed": 201, "instruct": "温柔女生"}' -o speech.mp3
GET (quick test)
curl "$TTS_URL/tts?text=Hello&seed=201" -o test.mp3
Seed Mechanics
Same (description + seed) → same timbre. Different seeds → completely different voices.
⚠️ Seeds are purely random — seed 42 and 43 can sound completely different. Finding a voice = opening blind boxes.
Workflow: fix description → batch 30-40 seeds → listen → shortlist 2-3 → compare across scenarios → pick.
Deploy Your Own
# One-click setup (Python 3.10+ and CUDA GPU required)
bash scripts/setup.sh ./my-tts
# Configure voice in .env
echo 'TTS_SEED=201' >> ./my-tts/.env
echo 'TTS_INSTRUCT=Your voice description here' >> ./my-tts/.env
# Start server
bash scripts/setup.sh start ./my-tts
Setup installs: qwen-tts, soundfile, pydub, uvicorn, fastapi, torch (CUDA).
Downloads VoiceDesign model (~3.5GB) via ModelScope (China) or HuggingFace.
Requirements: CUDA GPU with 4GB+ VRAM, Python 3.10+, ~4GB disk.
Scripts
| Script | Purpose |
|---|---|
scripts/say.sh | Generate speech — say.sh "text" [output.mp3] |
scripts/batch_seeds.sh | Compare seeds — batch_seeds.sh "text" seed1 seed2 ... |
scripts/tts_server.py | FastAPI server (fully env-configurable) |
scripts/setup.sh | One-click deploy (venv + deps + model download) |
OpenClaw Integration
In openclaw.json:
{
"env": { "OPENAI_TTS_BASE_URL": "http://<your-server>:8881/v1" },
"messages": {
"tts": {
"provider": "openai",
"openai": { "apiKey": "dummy", "model": "qwen3-tts", "voice": "default" },
"timeoutMs": 120000
}
}
}
Server Management
# Health check
curl -s $TTS_URL/health
# Start (foreground)
python tts_server.py
# Start (background, Linux/macOS)
nohup python tts_server.py > server.log 2>&1 &
# Auto-restart (Windows — scheduled task + guard script)
# Create tts_guard.bat:
# @echo off
# :loop
# python tts_server.py
# timeout /t 10
# goto loop
# Register: schtasks /create /tn "TTS-Guard" /tr "tts_guard.bat" /sc onlogon /rl highest
# Auto-restart (Linux — systemd)
# See setup.sh output for systemd unit template
# Stop
# Linux/macOS: kill $(lsof -ti:8881)
# Windows: for /f "tokens=5" %a in ('netstat -aon ^| findstr :8881') do taskkill /PID %a /F
Troubleshooting
- Connection refused → Server not running; start it
- 30s+ first request → Cold start (model loading ~60s); subsequent requests 10-15s
- Behind proxy → Set
NO_PROXY=<server_ip>on client side - Windows firewall →
netsh advfirewall firewall add rule name="TTS" dir=in action=allow protocol=TCP localport=8881 - No flash-attn on Windows → Expected; falls back to PyTorch SDPA (slower but works)
- PowerShell corrupts Chinese → Edit
.env/config via Python or SCP, not PowerShellSet-Content - Process dies on SSH disconnect → Use scheduled task (Windows) or systemd (Linux) instead of foreground
Voice Design Tips
Describe like casting a voice actor:
- Age/gender: "18岁女大学生" / "30岁男性播音员"
- Texture: "柔和温暖" / "清脆明亮" / "低沉磁性"
- Emotion: "轻柔细腻" / "活泼开朗"
- Accent: "南方口音软糯" / "台湾腔" / "东北大碴子味"
- Metaphor: "像棉花糖" / "像播音主持" (helps the model capture feeling)
⚠️ Timbre ≠ description. Description controls style/emotion; seed controls timbre. Don't put personality traits ("灵动俏皮") in description — that's the seed's job.
如何使用「Qwen3-TTS VoiceDesign」?
- 打开小龙虾AI(Web 或 iOS App)
- 点击上方「立即使用」按钮,或在对话框中输入任务描述
- 小龙虾AI 会自动匹配并调用「Qwen3-TTS VoiceDesign」技能完成任务
- 结果即时呈现,支持继续对话优化