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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… See the full description on the dataset page: https://huggingface.co/datasets/TTS-AGI/moss-emolia-elise-hq-captioned.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face
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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`clipswhat it is
emolia3,768emotion-bucketed expressive speech (Emolia)
mossinline1,800speech with inline vocal bursts (laughs, sighs, gasps…)
elise695Elise / 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

columnmeaning
sample_key / idunique id, "<source>__<local-id>" (e.g. emolia__…); matches the tar member and the tokenized key
source_datasetemolia / mossinline / elise
textreference transcript
extaudio 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
genugenuineness (felt vs. performed)
blendvocal-burst blend / naturalness (0–10)
bude_captionfree-text BUD-E-Whisper caption
inline_bursttranscript with inline vocal-burst tags
procedural_captionrule-based voice-acting caption: GENERAL: (how the voice sounds) + SCRIPT: (delivery cues + text)
voice_acting_captionLLM-naturalised rewrite of the procedural caption (same structure, fluent wording)
scorecomposite selection reward (higher = higher quality)

tokenized_audio.parquet columns

columnmeaning
keyid (matches sample_key)
target_codesMOSS-Audio-Tokenizer codes for the target clip, int16 bytes, shape [target_frames, n_codebooks]
target_framesnumber of code frames
ref_codes / ref_framesoptional reference-voice codes (often empty)
textreference transcript
procedural_caption, voice_acting_captionas above
source_datasetemolia / mossinline / elise

Usage

Stream the audio + captions (WebDataset):

python
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:

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

Decode the MOSS tokens for training:

python
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.