MUSION, a toolbox for music information retrieval and music signal processing.

A collection of MIR open-source tools, dedicated to efficient inference tasks.

Advantages

Minimal Package Dependencies

Designed to be lightweight with minimal package dependencies, ensuring easy installation and compatibility with various environments. This reduces the complexity of managing dependencies and helps avoid potential conflicts with other packages.

Accelerated Multi-file Parallel Processing

Significantly accelerating the analysis and processing of large datasets. By leveraging multi-threading and multi-processing techniques, and using ONNX TRT/CUDA for multi-GPU acceleration, MUSION can efficiently handle multiple files simultaneously, reducing the overall processing time.

Unified and Concise UI

Making it easy to use across different tools and applications. Whether you prefer using the Python interface, command-line interface (CLI), MUSION provides a consistent and intuitive experience for all users.

Available tools

beat beat tracking
Detect beat and downbeat in music

separate music source separation
Separate songs into "drums", "bass", "other", "vocals"

struct music structure analysis / music segmentation
Detect chorus part for pop songs

transcribe automatic music transcription
Transcribe piano, drums or vocal audio to BEAT-ALIGNED MIDI file.

Installation

1. Using PyPI

python -m pip install musion

2. Download ONNX model files from https://zenodo.org/records/13906170, and put them in the corresponding folders.

Example Usage

All the tools use the same procedure and method, only different at tool name.
Here is a example for the struct tool.

from musion.struct import Struct

struct = Struct()

# Select one of the two ways for input
## 1. Process a single file, given its file path
struct_res = struct(audio_path='dir/audio.wav')
## audio_path could also be a directory that contains audio files, or a list of audio paths.
struct_res = struct(audio_path='dir/')
struct_res = struct(audio_path=['audio1.wav', 'audio2.wav'])

## 2. Process a single audio data, given its pcm
from musion import MusionPCM
pcm = MusionPCM(samples, sample_rate)
struct_res = struct(pcm=pcm)

""" Optional Parameters """
# Save the result to a file
from musion import SaveConfig
save_cfg = SaveConfig(dir_path='dir/to/save/in', keys=['struct'])
# Where the keys can be obtained by
struct.result_keys
# Save the result by passing save_cfg, to the file: dir/to/save/in/audio.strcut
struct(audio_path='dir/audio.wav', save_cfg=save_cfg)

# Enable parallel processing when input contains multiple files, just set a proper number for num_workers
struct(audio_path='dir/', num_workers=5)
# Overwrite the existing result
struct(audio_path='dir/', overwrite=True)

Command-Line Interface

Almost the same parameters as above. Type '$ musion -h' for more details. Here's a comprehensive example.

$ musion separate test_wavs/ --save_dir results_dir/ --save_keys vocals.wav bass.wav --num_workers 5

Contributing

If you would like to participate in the development of Musion you are more than welcome to do so. Don't hesitate to throw us a pull request and we'll do our best to examine it quickly.