local-voice（本地语音）

Local Voice (FluidAudio TTS/STT)

Sub-second local voice AI for Apple Silicon Macs using FluidAudio's CoreML models.

Features

• TTS: Kokoro model with 54 voices, ~0.6-0.8s latency
• STT: Parakeet TDT v3, ~0.2-0.3s latency, 25 languages
• 100% local: No cloud, no cost, works offline
• Neural Engine: Runs on Apple's ANE for efficiency

Requirements

• macOS 14+ on Apple Silicon (M1/M2/M3/M4)
• Swift 5.9+
• espeak-ng (for TTS phoneme fallback)

Quick Setup

1. Install Dependencies

brew install espeak-ng

2. Build the Daemon

cd /path/to/skill/sources
swift build -c release

3. Install Binary and Framework

mkdir -p ~/clawd/bin
cp .build/release/StellaVoice ~/clawd/bin/
cp -R .build/arm64-apple-macosx/release/ESpeakNG.framework ~/clawd/bin/
install_name_tool -add_rpath @executable_path ~/clawd/bin/StellaVoice

4. Create LaunchAgent

cat > ~/Library/LaunchAgents/com.stella.tts.plist << 'EOF'
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.stella.tts</string>
    <key>ProgramArguments</key>
    <array>
        <string>$HOME/clawd/bin/StellaVoice</string>
    </array>
    <key>RunAtLoad</key>
    <true/>
    <key>KeepAlive</key>
    <true/>
    <key>StandardOutPath</key>
    <string>$HOME/.clawdbot/logs/stella-tts.log</string>
    <key>StandardErrorPath</key>
    <string>$HOME/.clawdbot/logs/stella-tts.err.log</string>
</dict>
</plist>
EOF

launchctl load ~/Library/LaunchAgents/com.stella.tts.plist

API Endpoints

The daemon listens on http://127.0.0.1:18790:

TTS - Text to Speech

# Simple text to WAV
curl -X POST http://127.0.0.1:18790/synthesize -d "Hello world" -o output.wav

# With speed control (0.5-2.0)
curl -X POST "http://127.0.0.1:18790/synthesize?speed=1.2" -d "Fast!" -o output.wav

# JSON endpoint
curl -X POST http://127.0.0.1:18790/synthesize/json \
  -H "Content-Type: application/json" \
  -d '{"text": "Hello", "speed": 1.0, "deEss": true}'

STT - Speech to Text

curl -X POST http://127.0.0.1:18790/transcribe \
  --data-binary @audio.wav \
  -H "Content-Type: audio/wav"
# Returns: {"text": "transcribed text"}

Health Check

curl http://127.0.0.1:18790/health
# Returns: ok

Voice Options

Default voice is af_sky. Change by modifying the source code.

Top Kokoro voices (American female):

• af_heart (A grade) - warm, natural
• af_bella (A-) - expressive
• af_sky (C-) - clear, light

All 54 voices: See references/VOICES.md

Expressiveness

Speed Control

• speed=0.8 → Calm, relaxed
• speed=1.0 → Natural pace
• speed=1.2 → Energetic, upbeat

Punctuation (automatic)

• ! → Excited tone
• ? → Rising intonation
• . → Neutral, falling
• ... → Pauses

SSML Tags

<phoneme ph="kəkˈɔɹO">Kokoro</phoneme>
<sub alias="Doctor">Dr.</sub>
<say-as interpret-as="date">2024-01-15</say-as>

Helper Script

See scripts/stella-tts.sh for a convenient wrapper:

scripts/stella-tts.sh "Hello world" output.wav
scripts/stella-tts.sh "Hello world" output.mp3  # Auto-converts

Integration Example

For voice assistants, update your voice proxy to use local endpoints:

// STT
const response = await fetch('http://127.0.0.1:18790/transcribe', {
    method: 'POST',
    headers: { 'Content-Type': 'audio/wav' },
    body: audioData
});
const { text } = await response.json();

// TTS
const audio = await fetch('http://127.0.0.1:18790/synthesize', {
    method: 'POST',
    body: textToSpeak
});

Troubleshooting

Library not loaded (ESpeakNG)

• Ensure ESpeakNG.framework is in the same directory as the binary
• Run install_name_tool -add_rpath @executable_path /path/to/binary

Slow first request

• First request loads models (~8-10s)
• Subsequent requests are sub-second

x86 vs ARM

• Must build and run on ARM64 native (not Rosetta)
• Check with uname -m (should show arm64)

Source Code

The daemon source is in sources/ directory. It's a Swift package using:

• FluidAudio (TTS + STT models)
• Hummingbird (HTTP server)

Rebuild after modifying:

cd sources && swift build -c release