Datasets:
license: other
license_name: mixed-see-license-section
task_categories:
- audio-to-audio
language:
- en
- ko
tags:
- speech-separation
- source-separation
- cocktail-party
- bilingual
- english
- korean
- speech
pretty_name: MuSSeD Multilingual Speech Separation Dataset
size_categories:
- 10K<n<100K
dataset_info:
- config_name: 2spk
features:
- name: audio
dtype: audio
- name: file_id
dtype: string
- name: speaker_type
dtype: string
- name: meta
dtype: string
splits:
- name: train
num_bytes: 2161230124
num_examples: 10000
- name: dev
num_bytes: 281341657.3
num_examples: 1300
- name: test
num_bytes: 288195834.9
num_examples: 1300
download_size: 2704697738
dataset_size: 2730767616.2000003
- config_name: 3spk
features:
- name: audio
dtype: audio
- name: file_id
dtype: string
- name: speaker_type
dtype: string
- name: meta
dtype: string
splits:
- name: train
num_bytes: 2608317239
num_examples: 10000
- name: dev
num_bytes: 337024379.8
num_examples: 1300
- name: test
num_bytes: 331952820.8
num_examples: 1300
download_size: 3196579806
dataset_size: 3277294439.6000004
- config_name: 4spk
features:
- name: audio
dtype: audio
- name: file_id
dtype: string
- name: speaker_type
dtype: string
- name: meta
dtype: string
splits:
- name: train
num_bytes: 2958162977
num_examples: 10000
- name: dev
num_bytes: 378898175.9
num_examples: 1300
- name: test
num_bytes: 375917477.9
num_examples: 1300
download_size: 3610134728
dataset_size: 3712978630.8
configs:
- config_name: 2spk
data_files:
- split: train
path: 2spk/train-*
- split: dev
path: 2spk/dev-*
- split: test
path: 2spk/test-*
- config_name: 3spk
data_files:
- split: train
path: 3spk/train-*
- split: dev
path: 3spk/dev-*
- split: test
path: 3spk/test-*
- config_name: 4spk
data_files:
- split: train
path: 4spk/train-*
- split: dev
path: 4spk/dev-*
- split: test
path: 4spk/test-*
MuSSeD: Multilingual Speech Separation Dataset (English, Korean)
A bilingual speech-separation corpus of synthetic multi-speaker mixtures built from English (LibriTTS-R) and Korean read-speech sources (AI-Hub Multi-Speaker Speech Synthesis corpus). Each mixture concatenates/overlaps 2, 3, or 4 single-speaker utterances, and every mixture ships with rich per-speaker JSON metadata (speaker id, gender, language, timing, transcription, overlap ratio, source utterance id).
- Audio: 16 kHz, mono, 16-bit PCM WAV
- Mixtures: 37,800 total — 30,000 train / 3,900 dev / 3,900 test
- Speaker counts: 2spk, 3spk, 4spk
- Total duration: ≈ 82.5 h (train ≈ 65.4 h, dev ≈ 8.5 h, test ≈ 8.6 h)
- Languages: English (
ENG) + Korean (KOR), including bilingual mixtures - Speakers: 514 speakers
Reliability & Verification
MuSSeD is built for leakage-free, representative evaluation: splits are speaker-disjoint, dev/test mirror the train distribution, overlap spans the full difficulty range, and every mixture ships with complete per-speaker ground truth — each property is summarized below.
1. Speaker-disjoint splits
Speaker identity is parsed directly from the mixture filename (the token after each
EN_/KO_ tag) and cross-checked between splits.
| Split | Unique speakers |
|---|---|
| train | 326 |
| dev | 100 |
| test | 88 |
| total | 514 (322 EN + 192 KO) |
2. Representative splits
The dev and test splits follow the same distributions as train for the properties that matter for separation difficulty — bilingual share and overlap share are nearly identical across splits, so validation/test numbers are representative of the training distribution.
3. Controlled, full-range overlap
Overlap is not concentrated at one difficulty. Mixtures span the full range from clean (no overlap) to fully overlapped, with the harder end growing as speaker count increases (2-4 spk), which is the expected and desired behaviour.
4. Metadata
Every mixture has a paired JSON with per-speaker ground truth, enabling exact, reproducible analysis of language / gender / overlap / timing.
Composition
The share of bilingual mixtures grows with speaker count, while English-only / Korean-only mixtures remain represented at every count:
Across the 514 unique speakers, the male / female balance is:
Dataset Structure
MuSSeD-EnKo/
├── train/ {2spk,3spk,4spk}/
│ ├── raw/ <mixture_id>.wav
│ └── json/ <mixture_id>.json
├── dev/ {2spk,3spk,4spk}/ {raw,json}/
└── test/ {2spk,3spk,4spk}/ {raw,json}/
Filename convention. A mixture filename is the concatenation of its source
utterance ids, each of the form EN_<spk>_... or KO_<spk>_... — e.g. a 4spk file
KO_5801_..._KO_5487_..._KO_5495_..._KO_5396_....wav encodes speakers KO_5801,
KO_5487, KO_5495, KO_5396.
JSON
{
"mixture_id": "EN_260_123286_000027_000001_EN_4992_41806_000003_000002",
"speaker_count": 2,
"overlapped_ratio": 0.0,
"total_length_sec": 14.44,
"speakers": [
{
"speaker_id": "260",
"speaker_name": "Brad Bush",
"gender": "Male",
"language": "ENG",
"length_sec": 2.48,
"start_time_sec": 0.0,
"end_time_sec": 2.48,
"transcription": "I had rather not disturb him while he is quiet.",
"original_file_id": "/test-clean/260/123286/260_123286_000027_000001"
}
]
}
Usage
from datasets import load_dataset
import json
# config = "2spk" | "3spk" | "4spk"
ds = load_dataset("syshim/MuSSeD-EnKo", "2spk", split="test")
ex = ds[0]
audio = ex["audio"] # {"array": np.ndarray, "sampling_rate": 16000}
meta = json.loads(ex["meta"]) # per-mixture ground truth
n_src = meta["speaker_count"]
overlap = meta["overlapped_ratio"]
langs = [s["language"] for s in meta["speakers"]]
# ... train / evaluate your separation model ...
License
This dataset is derived from multiple sources:
- English audio originates from LibriTTS-R (CC BY 4.0).
- Korean audio originates from a Multi-speaker Speech Synthesis Data by National Information Society Agency (NIA)
Because of these mixed terms the top-level tag is set to other.
Citation
If you use this dataset, please cite LibriTTS-R, the Korean source corpus, and this release:
@misc{mousse2026,
title = {MOUSSE: MULTILINGUALOVERLAPPEDSPEECHSEPARATIONWITHPREDICTIVE UNCERTAINTYESTIMATION},
year = {2026}
}