Datasets:
mp3 audioduration (s) 3.07 30 | json dict | ref.mp3 audioduration (s) 3.07 29.7 ⌀ | ref.json dict | __key__ stringlengths 22 24 | __url__ stringclasses 3 values |
|---|---|---|---|---|---|
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} | EN_B00031_S00008_W000000 | hf://datasets/TTS-AGI/emolia-hq@56453f267fc2cd5a1a47f23ad87846c7dcd6f611/EN/EN-B000318_standard_hq.tar | ||
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"speaker": "EN_B00031_S00038",
"speaker_id": "EN_B00031_S00038",
"text": " It's generally the writer's choice whether to use a full stop or a semicolon.",
"wav": "EN_B00031/EN_B00031_S00038/mp3/EN_B00031_S00038_W000027.mp3",
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} | EN_B00031_S00038_W000003 | hf://datasets/TTS-AGI/emolia-hq@56453f267fc2cd5a1a47f23ad87846c7dcd6f611/EN/EN-B000318_standard_hq.tar |
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in Data Studio
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:
- Quality filtering: Only samples with
dnsmos >= 3.0are retained. - 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).
- Metadata enrichment:
speaker_idandlanguage_idfields 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.
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