Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
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...
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Text-to-speech with Qwen3-TTS VoiceDesign. Design custom voices via natural language descriptions + seed-based timbre fixation. Includes OpenAI-compatible AP...
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Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/batch_seeds.sh, scripts/say.sh, scripts/setup.sh, scripts/tts_server.py
Validation
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- Review SKILL.md after the package is downloaded.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- xiaoyaner0201
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Documentation
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: VariableDefaultDescriptionTTS_URLhttp://localhost:8881Server base URL (client side)TTS_SEED4096Random seed → controls timbreTTS_INSTRUCT(generic female voice)Voice description promptTTS_MODEL_PATHQwen/Qwen3-TTS-12Hz-1.7B-VoiceDesignModel weights pathTTS_PORT8881Server listen portTTS_HOST0.0.0.0Server bind addressTTS_FORMATmp3Output 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 pass text.
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
ScriptPurposescripts/say.shGenerate speech — say.sh "text" [output.mp3]scripts/batch_seeds.shCompare seeds — batch_seeds.sh "text" seed1 seed2 ...scripts/tts_server.pyFastAPI server (fully env-configurable)scripts/setup.shOne-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 PowerShell Set-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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts1 Docs
- SKILL.md
- scripts/batch_seeds.sh
- scripts/say.sh
- scripts/setup.sh
- scripts/tts_server.py