Update README.md

README.md

@@ -127,7 +127,7 @@ configs:
         path: scp/scp-*
 ---
 
-# MuSSeD: Bilingual (English–Korean) Speech-Separation Mixtures
+# MuSSeD: Multilingual Speech Separation Dataset (English, Korean)
 
 A bilingual **speech-separation** corpus of synthetic multi-speaker mixtures built from
 English ([LibriTTS-R](https://www.openslr.org/141/)) and Korean read-speech sources ([AI-Hub Multi-Speaker Speech Synthesis corpus](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=542)).
@@ -136,7 +136,7 @@ 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
+- **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
@@ -151,9 +151,7 @@ This dataset is designed for **trustworthy evaluation**.
 ### 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. **No speaker appears in more than
-one split** — off-diagonal overlap is exactly 0, so a model can never be evaluated on a
-voice it saw during training.
+`EN_`/`KO_` tag) and cross-checked between splits.
 
 | Split | Unique speakers |
 |-------|----------------:|
@@ -162,8 +160,6 @@ voice it saw during training.
 | test  | 88  |
 | **total** | **514** (322 EN + 192 KO) |
 
-Pairwise shared speakers: `train∩dev = 0`, `train∩test = 0`, `dev∩test = 0`.
-
 ### 2. Representative splits (dev/test track train)
 
 The dev and test splits follow the **same distributions** as train for the properties
@@ -197,14 +193,14 @@ KO-only mixtures remain represented at every count:
 
 Across the 514 unique speakers, the male / female balance is:
 
-<img src="./figure/gender_ratio.png" width="60%">
+<img src="./figure/gender_ratio.png" width="65%">
 
 ---
 
 ## Dataset Structure
 
 ```
-Librittsr-KOR-n/
+MuSSeD-EnKo/
 ├── train/ {2spk,3spk,4spk}/
 │   ├── raw/   <mixture_id>.wav
 │   └── json/  <mixture_id>.json
@@ -214,12 +210,9 @@ Librittsr-KOR-n/
 ```
 
 **Filename convention.** A mixture filename is the concatenation of its source
-utterance ids, each of the form `EN_<spk>_...` or `KO_<spk>_...`. Example (4spk):
-
-```
-KO_5801_G1A2E7_HJH_001971_KO_5487_G1A4E7_KSH_004401_KO_5495_..._KO_5396_...wav
-                └ 4 sources → speakers KO_5801, KO_5487, KO_5495, KO_5396
-```
+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 schema (per mixture)
 
@@ -247,37 +240,22 @@ KO_5801_G1A2E7_HJH_001971_KO_5487_G1A4E7_KSH_004401_KO_5495_..._KO_5396_...wav
 
 ---
 
-## Per-split statistics
-
-| Split / spk | # mix | Hours | Avg len | EN+KO | No-overlap | Mean overlap (>0) |
-|-------------|------:|------:|--------:|------:|-----------:|------------------:|
-| train/2spk | 10000 | 18.60 | 6.69 s | 36.1% | 21.4% | 0.589 |
-| train/3spk | 10000 | 21.98 | 7.91 s | 51.2% | 18.0% | 0.621 |
-| train/4spk | 10000 | 24.78 | 8.92 s | 59.4% | 15.3% | 0.651 |
-| dev/2spk   |  1300 |  2.40 | 6.65 s | 33.0% | 23.8% | 0.572 |
-| dev/3spk   |  1300 |  2.87 | 7.94 s | 51.1% | 18.3% | 0.627 |
-| dev/4spk   |  1300 |  3.26 | 9.02 s | 59.5% | 15.9% | 0.646 |
-| test/2spk  |  1300 |  2.48 | 6.87 s | 35.0% | 22.2% | 0.587 |
-| test/3spk  |  1300 |  2.87 | 7.95 s | 51.0% | 18.2% | 0.636 |
-| test/4spk  |  1300 |  3.25 | 9.00 s | 55.0% | 15.7% | 0.656 |
-
----
-
 ## Usage
 
 ```python
-import json, glob, os, soundfile as sf
-
-root = "path/to/Librittsr-KOR-n"
-split, spk = "test", "2spk"
-
-for wav in sorted(glob.glob(f"{root}/{split}/{spk}/raw/*.wav")):
-    audio, sr = sf.read(wav) 
-    meta = json.load(open(wav.replace("/raw/", "/json/").replace(".wav", ".json")))
-    n_src   = meta["speaker_count"]
-    overlap = meta["overlapped_ratio"]
-    langs   = [s["language"] for s in meta["speakers"]]
-    # ... train / evaluate your separation model ...
+from datasets import load_dataset
+import json
+
+# config = "2spk" | "3spk" | "4spk"  (Kaldi-style file lists live in the "scp" config)
+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 ...
 ```
 
 ---
@@ -289,14 +267,11 @@ This dataset is derived from multiple sources with **different licenses**:
 - **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`. Please verify the
-Korean-source license and update this section (and the `license`/`license_name` fields)
-accordingly prior to distribution.
+Because of these mixed terms the top-level tag is set to `other`.
 
 ## Citation
 
-If you use this dataset, please cite LibriTTS-R and the Korean source corpus, and this
-release. *(Add BibTeX once the license/attribution is finalized.)*
+If you use this dataset, please cite LibriTTS-R, the Korean source corpus, and this release:
 ```bibtex
 @misc{bilingmousse2026,
   title  = {MOUSSE: Multilingual Overlapped Speech Separation with Predictive Uncertainty-Guided Sampling},