TTS-AGI/moss-emolia-elise-hq-captioned

MOSS · Emolia + Elise + Inline-Bursts — HQ, captioned

A high-quality, richly captioned slice of the MOSS-local voice-acting corpus: expressive speech clips scored by a panel of acoustic detectors, filtered to the top by a composite reward, and captioned in the voice-acting format (a "how the voice sounds / how to perform it" description plus the script with inline vocal-burst tags). Audio is shipped both as flac (WebDataset tars) and as pre-computed MOSS-Audio-Tokenizer codes for direct TTS training.

6,263 clips drawn from three sources:

source_dataset	clips	what it is
emolia	3,768	emotion-bucketed expressive speech (Emolia)
mossinline	1,800	speech with inline vocal bursts (laughs, sighs, gasps…)
elise	695	Elise / DramaBox-style dramatic delivery

Every clip was scored on VoiceNet (57 perceptual voice dimensions), Empathic-Insight-Voice-Plus / EmoNet (40 emotions + Arousal / Valence / Authenticity), a genuineness score (felt vs. performed), a vocal-burst blend / naturalness score, and WER against the reference text; the composite score (range 0.3–16.0, mean ≈ 10.4) selects the high-quality tail.

Files

• data/data-000{0..3}.tar — WebDataset shards. Each member pair is <sample_key>.flac (audio) + <sample_key>.json (per-clip metadata). ~2,000 clips per shard.
• metadata.parquet — one row per clip with the full annotation set (scores + captions). Join to the audio via sample_key.
• tokenized_audio.parquet — the same clips as MOSS-Audio-Tokenizer codes, ready for MOSS-TTS training (no raw audio needed). Join via key (== sample_key).

metadata.parquet columns

column	meaning
sample_key / id	unique id, "<source>__<local-id>" (e.g. emolia__…); matches the tar member and the tokenized key
source_dataset	emolia / mossinline / elise
text	reference transcript
ext	audio extension (flac)
vn_* (57)	VoiceNet perceptual dimensions (warmth, roughness, tempo, register, resonance, …), ~0–6 scale
ei_* (43)	Empathic-Insight-Voice-Plus: 40 EmoNet emotions + ei_Arousal, ei_Valence, ei_Authenticity
genu	genuineness (felt vs. performed)
blend	vocal-burst blend / naturalness (0–10)
bude_caption	free-text BUD-E-Whisper caption
inline_burst	transcript with inline vocal-burst tags
procedural_caption	rule-based voice-acting caption: GENERAL: (how the voice sounds) + SCRIPT: (delivery cues + text)
voice_acting_caption	LLM-naturalised rewrite of the procedural caption (same structure, fluent wording)
score	composite selection reward (higher = higher quality)

tokenized_audio.parquet columns

column	meaning
key	id (matches sample_key)
target_codes	MOSS-Audio-Tokenizer codes for the target clip, int16 bytes, shape [target_frames, n_codebooks]
target_frames	number of code frames
ref_codes / ref_frames	optional reference-voice codes (often empty)
text	reference transcript
procedural_caption, voice_acting_caption	as above
source_dataset	emolia / mossinline / elise

Usage

Stream the audio + captions (WebDataset):

from datasets import load_dataset
ds = load_dataset("TTS-AGI/moss-emolia-elise-hq-captioned", split="train", streaming=True)
ex = next(iter(ds))
print(ex["json"]["voice_acting_caption"])
ex["flac"]["array"], ex["flac"]["sampling_rate"]

Load just the scores + captions:

ds = load_dataset("TTS-AGI/moss-emolia-elise-hq-captioned", "metadata", split="train")

Decode the MOSS tokens for training:

import numpy as np, pandas as pd
df = pd.read_parquet("tokenized_audio.parquet")
row = df.iloc[0]
codes = np.frombuffer(row["target_codes"], np.int16).reshape(row["target_frames"], -1)

Notes

• Captions and scores are model-generated (VoiceNet / EmoNet / genuineness / blend detectors + procedural templating + LLM rewrite) and are not manually verified.
• Part of the MOSS-local voice-acting data-generation effort. license: other — see the source datasets for provenance and terms.