File size: 4,890 Bytes
ef872a9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | ---
pretty_name: MOSS · Emolia + Elise + Inline-Bursts (HQ, captioned)
license: other
language:
- de
- en
- multilingual
task_categories:
- text-to-speech
- audio-classification
- automatic-speech-recognition
size_categories:
- 1K<n<10K
tags:
- audio
- speech
- voice-acting
- expressive-tts
- emotion
- vocal-bursts
- moss-tts
- voicenet
- emonet
configs:
- config_name: default
data_files:
- split: train
path: data/*.tar
- config_name: metadata
data_files:
- split: train
path: metadata.parquet
- config_name: tokenized
data_files:
- split: train
path: tokenized_audio.parquet
---
# 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):
```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.
|