semvec/pypi-packages huggingface dataset:
d3net-spleeterweb 0.1.3
Unofficial Python package of D3Net implementation by Sony Research AI, used in Spleeter Web.
Requires Python: >=3.6

# D3Net (Music Source Separation) for Spleeter Web

**This is a modified version of the [official D3Net repo](https://github.com/sony/ai-research-code/blob/master/d3net/music-source-separation/) made to be compatible with [Spleeter Web](https://github.com/JeffreyCA/spleeter-web)!**

This is inference code for D3Net based music source separation.

## Quick Music Source Separation Demo by D3Net

From the Colab link below, you can try using D3Net to generate and listen to separated audio sources of your audio music file. Please give it a try!

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/ai-research-code/blob/master/d3net/music-source-separation/D3Net-MSS.ipynb)

## Getting started

## Prerequisites
* nnabla 
* librosa
* pydub
* numpy
* soundfile
* yaml

## Inference: Music source separation with pretrained model

Download the pre-trained D3Net model for Music Source Separation [here](https://nnabla.org/pretrained-models/ai-research-code/d3net/mss/d3net-mss.h5).

Run the below inference command for a sample audio file `test.wav` in current directory:
```python
 python ./separate.py -i ./test.wav -o output/ -m d3net-mss.h5 -c cudnn
 ```
Arguments:  
-i : Input files. (Any audio format files supported by FFMPEG.)  
-o : Output directory. (Output folder path to save separated instruments)  
-m : Model file. (Pre-trained model)  
-c : Context. (Extension modules : `cpu` or `cudnn`)

## Training: Train the music source separation model from scratch (**coming soon**)