Instructions to use baicai1145/GAME-MLX-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use baicai1145/GAME-MLX-medium with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download baicai1145/GAME-MLX-medium --local-dir GAME-MLX-medium
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
GAME-MLX-medium
GAME (Generative Adaptive MIDI Extractor) — MLX version of openvpi/GAME GAME-1.0-medium checkpoint, for Apple Silicon GPU inference.
| File | Description |
|---|---|
weights.fp16.safetensors |
fp16 weights (low memory) |
weights.safetensors |
fp32 weights (bit-aligned with torch) |
config.yaml |
architecture & features config (same as upstream) |
lang_map.json |
language map (en/ja/yue/zh) |
Parameters: 49,887,251 (49.89M) / 671 tensors. Key names preserved from the torch checkpoint (including model. Lightning prefix).
Usage
pip install gamemlx
gamemlx extract vocals.wav -m medium --language zh
import gamemlx
model, lang_map = gamemlx.load_model("medium")
gamemlx.extract(model, "vocals.wav", language="zh")
Alignment & performance
Verified on 20 test files against the official PyTorch implementation (CPU, 32-true, identical injected d3pm randomness): fp32 = 20/20 files with all onset/offset/pitch diffs 0.0000; fp16 = 19/20 (pitch diff ≤ 0.0011). MLX fp32 is ~9.4× faster than torch CPU (0.172s vs 1.609s avg per file), peak wired GPU memory 206MB (fp16: 111MB).
Also available on ModelScope: baicai1145/GAME-MLX-medium
License
MIT (following upstream openvpi/GAME).
Code: baicai-1145/game-mlx — pip install gamemlx
- Downloads last month
- 19
Quantized