Emolia-HQ

Emolia-HQ is a high-quality, speaker-paired subset of the LAION Emolia dataset. Each sample includes a target utterance and a reference utterance from the same speaker, enabling speaker-conditioned tasks such as voice conversion, expressive TTS, and speaker-aware emotion recognition.

Source

Derived from laion/Emolia by:

1. Quality filtering: Only samples with dnsmos >= 3.0 are retained.
2. Speaker pairing: Each target sample is matched with a reference audio from the same speaker (different utterance), forming a "quadruplet". Samples where no same-speaker reference exists are included as pairs (target only).
3. Metadata enrichment: speaker_id and language_id fields are extracted from the key and injected into each sample's JSON metadata.

Data Format

The dataset is stored as WebDataset .tar files, organized by language:

emolia_hq/
  DE/   # German  (243 tars, ~130 GB)
  EN/   # English (2,380 tars, ~2,476 GB)
  FR/   # French  (298 tars, ~187 GB)
  JA/   # Japanese (96 tars, ~163 GB)
  KO/   # Korean  (246 tars, ~79 GB)
  ZH/   # Chinese (929 tars, ~1,681 GB)

Each sample within a tar file is grouped by a shared base key:

Quadruplet (target + same-speaker reference)

File	Description
<key>.mp3	Target audio
<key>.json	Target metadata
<key>.ref.mp3	Reference audio (same speaker, different utterance)
<key>.ref.json	Reference metadata

Pair (no reference found)

File	Description
<key>.mp3	Target audio
<key>.json	Target metadata

JSON Metadata Fields

Field	Description
id	Unique utterance ID
text	Transcription
duration	Audio duration in seconds
dnsmos	DNS-MOS quality score (all >= 3.0)
speaker	Original speaker ID
speaker_id	Extracted speaker ID (e.g., DE_B00000_S00010)
language_id	Extracted language code (e.g., DE)
language	Language code lowercase
emotion_caption	Natural language description of the emotional content
emotion_annotation	Dictionary of 50+ emotion/prosody scores
characters_per_second	Speaking rate
wavelm_timbre_embedding	128-dim speaker timbre embedding

Statistics

Language	Tars	Size
DE (German)	243	~130 GB
EN (English)	2,380	~2,476 GB
FR (French)	298	~187 GB
JA (Japanese)	96	~163 GB
KO (Korean)	246	~79 GB
ZH (Chinese)	929	~1,681 GB
Total	4,192	~4,716 GB

~97% of samples include a same-speaker reference audio (quadruplets). The remaining ~3% are pairs where the speaker only appeared once across the entire dataset.

Usage

import webdataset as wds

dataset = wds.WebDataset("emolia_hq/EN/EN-B000000_standard_hq.tar")

for sample in dataset:
    key = sample["__key__"]
    target_audio = sample["mp3"]          # bytes
    target_meta = sample["json"]          # bytes -> json.loads()
    ref_audio = sample.get("ref.mp3")     # bytes or None
    ref_meta = sample.get("ref.json")     # bytes or None

License

Same as the source Emolia dataset. See laion/Emolia for details.