#Sourcing Datasets for Multi-Stem Separation
Standard vocal/instrumental separation is simple to set up because the input data contains only two stems. For a multi-stem model (Vocals, Backing Vocals, Drums, Bass, Guitars, Synths), you need a dataset structured with parallel instrument files.
## 1. MoisesDB (The Ultimate Dataset)
 * **What it is:** Released by Moises.ai, **MoisesDB** is a massive open dataset designed specifically for advanced, multi-stem music demixing.
 * **Why it's perfect:** It features 12 detailed stems:
   * lead_vocals & backing_vocals
   * drums & bass
   * acoustic_guitar & electric_guitar
   * piano & synthesizer
   * strings & wind
   * percussion & other
 * **How to get it:** You can access the data and registration information through the official MoisesDB Github Page.
## 2. How to Organize Your Stems on your 2TB Drive
Your training folders should be organized as follows so the new MultiStemDataset in multi_stem_pipeline.py can read them automatically:
```text
/Volumes/Your_External_Drive/Multi_Stem_Training/
├── song_1/
│   ├── vocals.wav
│   ├── backing_vocals.wav
│   ├── drums.wav
│   ├── bass.wav
│   ├── guitar.wav
│   └── synth.wav
├── song_2/
│   ├── vocals.wav
│   ├── backing_vocals.wav
│   ├── drums.wav
│   ├── bass.wav
│   ├── guitar.wav
│   └── synth.wav

```
### Pro-Tip for Mixing Custom Stems:
If a song in your dataset contains multiple guitars (e.g. acoustic_guitar.wav and electric_guitar.wav), simply mix them together to create a single guitar.wav file before feeding it into your training setup!