Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
CosyVoice3 macOS
Local text-to-speech using Alibaba's CosyVoice3 on macOS Apple Silicon. Supports Chinese, English, Japanese, Korean, and 18+ Chinese dialects. Provides zero-...
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Local text-to-speech using Alibaba's CosyVoice3 on macOS Apple Silicon. Supports Chinese, English, Japanese, Korean, and 18+ Chinese dialects. Provides zero-...
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- Tencent SkillHub
- What's included
- SKILL.md, scripts/download_models.py, scripts/install.sh, scripts/tts.py
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Release facts
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- Tencent SkillHub
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- Version
- 1.0.0
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Documentation
CosyVoice3 TTS
Local text-to-speech using Alibaba's CosyVoice3 on macOS Apple Silicon.
Overview
CosyVoice3 is an advanced TTS system based on large language models, supporting: 9 languages: Chinese, English, Japanese, Korean, German, Spanish, French, Italian, Russian 18+ Chinese dialects: Cantonese, Sichuan, Dongbei, Shanghai, etc. Zero-shot voice cloning: Clone any voice from 3-10 seconds of audio Cross-lingual synthesis: Speak Chinese with English voice or vice versa Fine-grained control: Emotions, speed, volume via text tags
Prerequisites
macOS with Apple Silicon (M1/M2/M3) Python 3.10 Conda installed ~5GB disk space for models
Installation
Run the installation script: cd /Users/lhz/.openclaw/workspace/skills/cosyvoice3/scripts bash install.sh This will: Create conda environment cosyvoice Install PyTorch (CPU version for Apple Silicon) Install CosyVoice dependencies Download Fun-CosyVoice3-0.5B model (~2GB)
Quick Start - Basic TTS
重要：CosyVoice3 需要在参考文本中添加 <|endofprompt|> 标记！ cd /Users/lhz/.openclaw/workspace/cosyvoice3-repo export PATH="$HOME/miniconda3/bin:$PATH" conda activate cosyvoice python -c " import sys sys.path.append('third_party/Matcha-TTS') from cosyvoice.cli.cosyvoice import AutoModel import torchaudio cosyvoice = AutoModel(model_dir='pretrained_models/Fun-CosyVoice3-0.5B') for i, j in enumerate(cosyvoice.inference_zero_shot( '你好，这是CosyVoice3语音合成测试。', '希望你以后能够做的比我还好呦。<|endofprompt|>', # 注意这个标记！ 'asset/zero_shot_prompt.wav' )): torchaudio.save('output.wav', j['tts_speech'], cosyvoice.sample_rate) print('Generated: output.wav') "
Using the TTS Script
Generate speech from text: cd /Users/lhz/.openclaw/workspace/skills/cosyvoice3/scripts conda activate cosyvoice # Basic TTS with default voice python tts.py "你好，这是一个测试。" # With custom reference audio for voice cloning python tts.py "你好，这是克隆的声音。" --reference /path/to/reference.wav # Cross-lingual (English text with Chinese voice) python tts.py "Hello, this is cross-lingual synthesis." --reference asset/zero_shot_prompt.wav --lang en # With speed control python tts.py "这是一段快速的语音。" --speed 1.5 # Save to specific path python tts.py "你好。" --output ~/Desktop/greeting.wav
Available Assets
Reference audio files in cosyvoice3-repo/asset/: zero_shot_prompt.wav - Default Chinese female voice cross_lingual_prompt.wav - English prompt for cross-lingual
Voice Cloning
Clone a voice from 3-10 seconds of reference audio: from cosyvoice.cli.cosyvoice import AutoModel import torchaudio cosyvoice = AutoModel(model_dir='pretrained_models/Fun-CosyVoice3-0.5B') # Clone voice and generate for i, j in enumerate(cosyvoice.inference_zero_shot( '这是克隆后的声音在说话。', 'Reference text transcription', '/path/to/reference.wav' )): torchaudio.save('cloned.wav', j['tts_speech'], cosyvoice.sample_rate)
Fine-Grained Control
Control prosody with special tags: # Add laughter "他突然[laughter]笑了起来[laughter]。" # Add breathing "他说完这句话[breath]，深吸一口气。" # Strong emphasis "这是<strong>非常重要</strong>的。" # Combined "在面对挑战时，他展现了非凡的<strong>勇气</strong>与<strong>智慧</strong>[breath]。"
Dialect Support
Use instruct mode for dialects: cosyvoice = AutoModel(model_dir='pretrained_models/CosyVoice-300M-Instruct') for i, j in enumerate(cosyvoice.inference_instruct( '你好，这是测试语音。', '中文男', '用四川话说这句话<|endofprompt|>' )): torchaudio.save('sichuan.wav', j['tts_speech'], cosyvoice.sample_rate)
Model not found
If you get "model not found" errors, download models manually: cd /Users/lhz/.openclaw/workspace/cosyvoice3-repo export PATH="$HOME/miniconda3/bin:$PATH" conda activate cosyvoice python -c " from modelscope import snapshot_download snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B') "
Memory issues
For long text, split into sentences: text = "很长的文本..." sentences = text.split('。') for sent in sentences: if sent.strip(): # Process each sentence
Audio format
Reference audio requirements: Format: WAV, MP3 Sample rate: 16kHz+ (automatically resampled) Duration: 3-10 seconds optimal Content: Clear speech, minimal background noise
Scripts
install.sh - Installation script for macOS tts.py - Main TTS script with CLI interface download_models.py - Download pretrained models
References
CosyVoice GitHub Fun-CosyVoice3 Demo
Model Files
Located in cosyvoice3-repo/pretrained_models/: Fun-CosyVoice3-0.5B/ - Main model (recommended) CosyVoice2-0.5B/ - Previous version CosyVoice-300M/ - Lighter model CosyVoice-300M-SFT/ - SFT version CosyVoice-300M-Instruct/ - Instruct version
Notes
First inference takes ~30 seconds (model warmup) Subsequent inferences are faster Apple Silicon uses CPU mode (no CUDA) RTF (real-time factor) ~0.3-0.5 on M-series chips Model files are cached locally after first download
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
3 Scripts1 Docs
- SKILL.md
- scripts/download_models.py
- scripts/install.sh
- scripts/tts.py