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Dataset types for training

Type 1 (MUSDB)

Different folders. Each folder contains all needed stems in format
<stem_name>.wav (or flac in latest releases).

The structure is the same as in MUSDBHQ18.

Example:

--- Song 1:
------ vocals.wav
------ bass.wav
------ drums.wav
------ other.wav
--- Song 2:
------ vocals.wav
------ bass.wav
------ drums.wav
------ other.wav
--- Song 3:
...........

Type 2 (Stems)

Each folder represents a single stem name.
The folder contains audio files consisting only of that stem.

Example:

--- vocals:
------ vocals_1.wav
------ vocals_2.wav
------ vocals_3.wav
------ vocals_4.wav
------ ...
--- bass:
------ bass_1.wav
------ bass_2.wav
------ bass_3.wav
------ bass_4.wav
------ ...
...........

Type 3 (CSV file)

You can provide a CSV file (or a list of CSV files) with the following structure:

instrum,path
vocals,/path/to/dataset/vocals_1.wav
vocals,/path/to/dataset2/vocals_v2.wav
vocals,/path/to/dataset3/vocals_some.wav
...
drums,/path/to/dataset/drums_good.wav
...

Type 4 (MUSDB Aligned)

The same structure as Type 1, but during training all instruments are loaded from the same position of the song.

Type 5 (Precomputed Chunks)

The same structure as Type 1, but all tracks are pre-split into chunks with 50% overlap.

Type 6 (MUSDB Aligned + Explicit Mixture)

An extension of Type 4, designed for scenarios where the mixture is treated as a separate signal, rather than always being reconstructed as sum(stems).

Structure is the same as Type 1 / Type 4, but it is recommended that each song folder contains mixture.wav.

Example:

--- Song 1:
------ vocals.wav
------ bass.wav
------ drums.wav
------ other.wav
------ mixture.wav

Key properties:

  • All stems are loaded aligned from the same song position
  • mixture.wav is loaded explicitly if present
  • If mixture.wav is missing, mixture is computed as sum of stems
  • Supports precomputed random chunks (same logic as Type 4)

Typical use cases:

  • Teacher–student or distillation training
  • Consistency losses
  • Training with real mixes not equal to sum of stems

Type 7 (Class-Balanced Aligned Dataset)

A class-balanced aligned dataset designed to reduce class frequency bias and improve learning of rare instruments.

Structure is the same as Type 6:

--- Song 1:
------ flute.wav
------ violin.wav
------ mixture.wav

How it works:

  1. A random instrument (class) is selected
  2. A random track containing this instrument is chosen
  3. An aligned chunk is loaded from that track
  4. The dataset returns which stems are actually present

Class frequency filtering:

  • For each instrument, the ratio of tracks where it appears is computed
  • Instruments appearing in more than max_class_presence_ratio (default: 0.4) of tracks are excluded
  • Prevents dominant classes (e.g. vocals) from overwhelming training

Returned values:

  • Stems tensor
  • Mixture tensor (from mixture.wav if available, otherwise sum of stems)
  • active_stem_ids: indices of instruments present in the current sample

Typical use cases:

  • Training on sparse multi-instrument datasets
  • Improving performance on rare instruments
  • Conditional or multi-head source separation models

Dataset for validation

  • The validation dataset must be the same structure as type 1 datasets (regardless of what type of dataset you're using for training), but also each folder must include mixture.wav for each song. mixture.wav - is the sum of all stems for song.

Example:

--- Song 1:
------ vocals.wav  
------ bass.wav 
------ drums.wav
------ other.wav
------ mixture.wav
--- Song 2:
------ vocals.wav  
------ bass.wav 
------ drums.wav
------ other.wav
------ mixture.wav
--- Song 3:
...........
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