v0.1.0

Voice Actions v0.1.0

Voice-to-voice processing pipeline: Audio → ASR (Qwen3) → WASM chain → TTS (Qwen3) → MP3 Output

Features

• Speech-to-text using Qwen3-ASR (supports 30+ languages)
• Text-to-speech using Qwen3-TTS (named speakers: Vivian, Ryan, etc.)
• Chainable WASM processing modules (echo, LLM, custom)
• MP3 output (192 kbps via libmp3lame)
• Multi-platform: Linux x86_64/ARM64 (libtorch), macOS Apple Silicon (MLX Metal GPU)
• Self-contained: all shared libraries bundled (WasmEdge, libopus, libmp3lame)

Release Archives

Archive	Platform	Backend
voice-actions-linux-x86_64.tar.gz	Linux x86_64	libtorch CPU
voice-actions-linux-aarch64.tar.gz	Linux ARM64	libtorch CPU
voice-actions-macos-aarch64.zip	macOS Apple Silicon	MLX Metal GPU

Quick Start

# Extract
tar xzf voice-actions-linux-x86_64.tar.gz
cd voice-actions-linux-x86_64

# Download models
pip install huggingface_hub transformers
huggingface-cli download Qwen/Qwen3-ASR-0.6B --local-dir models/Qwen3-ASR-0.6B
huggingface-cli download Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local-dir models/Qwen3-TTS-12Hz-0.6B-CustomVoice

# Generate tokenizer.json
python3 -c "
from transformers import AutoTokenizer
for model in ['Qwen3-ASR-0.6B', 'Qwen3-TTS-12Hz-0.6B-CustomVoice']:
    path = f'models/{model}'
    tok = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
    tok.backend_tokenizer.save(f'{path}/tokenizer.json')
"

# Run with bundled echo module
./voice-actions \
  --input recording.mp3 \
  --output response.mp3 \
  --asr-model ./models/Qwen3-ASR-0.6B \
  --tts-model ./models/Qwen3-TTS-12Hz-0.6B-CustomVoice \
  --wasm echo.wasm