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-shot voice cloning, cross-lingual synthesis, and fine-grained control. Use when: (1) User requests local TTS with high-quality Chinese/English voices. (2) Need voice cloning from reference audio. (3) Offline/inference TTS is required. (4) User wants natural-sounding speech with emotion/dialect control.

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:

bash

cd /Users/lhz/.openclaw/workspace/skills/cosyvoice3/scripts
bash install.sh

This will:

1. Create conda environment cosyvoice
2. Install PyTorch (CPU version for Apple Silicon)
3. Install CosyVoice dependencies
4. Download Fun-CosyVoice3-0.5B model (~2GB)

Usage

Quick Start - Basic TTS

重要：CosyVoice3 需要在参考文本中添加 <|endofprompt|> 标记！

bash

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:

bash

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

Advanced Features

Voice Cloning

Clone a voice from 3-10 seconds of reference audio:

python

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:

python

# 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:

python

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)

Troubleshooting

Model not found

If you get "model not found" errors, download models manually:

bash

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:

python

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

Resources

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