---
license: cc-by-4.0
pretty_name: 6k Diverse Reference Voices
size_categories:
- 1K<n<10K
task_categories:
- text-to-speech
- audio-classification
- feature-extraction
tags:
- voice
- reference-voice
- character-voice
- tts
- voice-acting
- moss
- voice-search
- speech
- audio
- casting
- synthetic
---

# 6k Diverse Reference Voices

**6,064 permissively licensed reference voices** for casting expressive voice-acting generations.

All voices in this collection are permissively usable: they were either **synthetically created** or
**extracted from the CC-BY part of Emilia**. Licensed under **CC-BY-4.0**.

Source / attribution: derived from `TTS-AGI/moss-reference-voices-consolidated` (CC-BY-4.0),
re-published under LAION with clarified metadata documentation. If you use this dataset,
please attribute both LAION and the original TTS-AGI source.

Every voice was auto-annotated by Gemini (name, tagline, language, accent, age/gender read,
register, timbre, distinctive features, emotional range, casting suggestions for 4 genres,
free-text tags, search text) and scored on **99 measured dimensions**: 57 VoiceNet voice-quality
axes, 40 Empathic-Insight emotion axes, plus **genuineness** and **burst-blend**. Each voice ships
with **three audio variants** and **per-variant DNSMOS**.

Live search UI: **https://projects.laion.ai/moss-reference-voice-search/** — code + reproducer under
[`search_tool/`](search_tool/) in this repo.

## Composition

Counts by `source` (= per-voice `source` field, also the cid prefix). Verified from `metadata.parquet`:

| `source` | count | what it is |
|---|---:|---|
| `emolia` | 3,000 | Emilia-derived voices, kept with traceable id (`emolia_c*`). |
| `char` | 1,336 | Synthetic character voices (`k<n>_age<n>_bg<n>`). |
| `refvoice` | 956 | Synthetic reinterpreted reference voices, opaque id. |
| `mediathek` | 472 | Synthetic German reinterpretations, opaque id. |
| `anime` | 300 | Synthetic anime-derived reinterpretations, English delivery, opaque id. |
| **total** | **6,064** | |

Language (Gemini-identified, 4,727/6,064 non-null): English 4,250 · German 473 · null 1,337 · rest 4.
Gender read: Male 4,262 · Female 1,675 · Androgynous 109 · Non-human 8 · edge cases 10.

## The three audio variants — and why

Each voice has three parallel renders of the same demo clip:

| variant | tar suffix | what it is |
|---|---|---|
| **orig** | `<cid>.orig.mp3` | Original demo clip. |
| **sidon** | `<cid>.sidon.mp3` | SIDON-denoised + loudness-normalized `orig`. Can introduce metallic ringing / over-smoothing on some clips. |
| **cbx** | `<cid>.cbx.mp3` | Chatterbox self-conversion of the `sidon` clip (artifact-cleanup pass, occasionally softens texture). |

Dataset-level mean DNSMOS-OVRL is tied (orig 3.343, sidon 3.346, cbx 3.344 — see
`annotations/dnsmos_stats.json`), but per-voice **~62% win with a processed variant**
(orig wins 2,317 = 38.2%, sidon 1,940 = 32.0%, cbx 1,807 = 29.8%).
Use the precomputed **`best_version`** field (argmax DNSMOS per voice) instead of defaulting
to one variant dataset-wide.

## File layout

```
data/voices-0000.tar … voices-0011.tar   # WebDataset shards, ~505-506 voices each, ~2 GB total
metadata.parquet                          # flat index, one row per voice (6064 x 30, see below)
annotations/
  dims.npy            # (6064, 99) float32 — 99-dim scores on ORIG audio
  dims_enh.npy        # (6064, 99) float32 — 99-dim scores on SIDON audio
  dim_catalog.json    # 99-dim schema: [{i, code, name, group, desc}]
  dnsmos.json         # {cid: {orig, sidon, cbx}} DNSMOS-OVRL per variant
  dnsmos_stats.json   # means + per-variant win counts / win_pct
search_tool/          # FastAPI search server + pipeline + demo page
README.md / LICENSE   # this file / CC-BY-4.0
```

### WebDataset shards (`data/*.tar`)

Members for one voice are **contiguous**:

```
<cid>.orig.mp3     # original demo clip
<cid>.sidon.mp3    # SIDON-denoised + loudness-normalized
<cid>.cbx.mp3      # Chatterbox self-conversion of sidon
<cid>.json         # full per-voice record (see below)
```

The `<cid>.json` record = full voice entry (name, tagline, gender, age, language, accent,
register, timbre_profile, distinctive_features, emotional_range, casting
{classic_fantasy, sci_fi, mystery_horror, contemporary}, tags, search_text,
legacy `scores`, `source`) **plus**:

```json
{
  "dnsmos": {"orig": 3.44, "sidon": 3.40, "cbx": 3.29},
  "best_version": "orig",
  "dims_raw": [99 floats, order = annotations/dim_catalog.json, scored on orig],
  "dims_enh": [99 floats, same order, scored on sidon]
}
```

```python
import webdataset as wds
ds = wds.WebDataset("hf://datasets/LAION/6k-diverse-reference-voices/data/voices-{0000..0011}.tar").decode()
for sample in ds:
    cid = sample["__key__"]
    rec = sample["json"]  # dict with dnsmos, best_version, dims_raw, dims_enh
    print(cid, rec["name"], rec["best_version"])
```

### `metadata.parquet` — flat index (1 row / voice)

Columns (30 total):

- Identity / casting: `cid, name, gender, age, language, accent, tagline, tags (list<string>), source, shard`
- Quality per variant: `dnsmos_orig, dnsmos_sidon, dnsmos_cbx` (float, DNSMOS-OVRL 0–5, higher = better), `best_version` (`orig|sidon|cbx` = argmax DNSMOS)
- Key 99-dim values with both scorings (`dim_*` = on orig, `dim_*_enh` = on sidon):
  `dim_GEND (+_enh)` perceived gender (higher = more masculine),
  `dim_AGEV (+_enh)` perceived age,
  `dim_GENU (+_enh)` genuineness (sounds like real human recording),
  `dim_BLEND (+_enh)` vocal-burst blend quality,
  `dim_BKGN (+_enh)` background-noise level,
  `dim_VALN (+_enh)` / `dim_AROU (+_enh)` emotional valence / arousal,
  `dim_WARM (+_enh)` vocal warmth.

```python
import pandas as pd
df = pd.read_parquet("metadata.parquet")
loud_masculine = df[(df.dim_GEND > 4) & (df.dnsmos_orig > 3.4)]
```

Row order of `metadata.parquet` == row order of `annotations/dims.npy` / `dims_enh.npy`.

### 99-dim scores (`annotations/`)

- `dim_catalog.json`: list of 99 `{i, code, name, group, desc}`, `i` = column index into the `.npy` files.
  Groups: `emonet` 40 (indices 0–39, higher = stronger emotion),
  `voicenet` 57 (indices 40–96, timbre/prosody/register/style),
  `quality` 2 (index 97 `GENU` genuineness, 98 `BLEND` vocal-burst blend).
- `dims.npy`: float32 `(6064, 99)`, scored on **orig** audio. `dims_enh.npy`: same shape, scored on **sidon** audio.
  No NaNs. Observed range approx −3.9 … 12.2 (raw regressor outputs, not clipped to 0–6).
- Per-tar `dims_raw` == corresponding `dims.npy` row; `dims_enh` == `dims_enh.npy` row.
- `dnsmos.json`: `{cid: {"orig": float, "sidon": float, "cbx": float}}` for all 6,064 voices.

## License

**CC-BY-4.0.** You may share and adapt with attribution to LAION and the original
`TTS-AGI/moss-reference-voices-consolidated` source.

## Search

See [`search_tool/`](search_tool/) for the FastAPI server (BM25 / sentence-embedding /
VoiceCLAP text→audio similarity, optional AND-filters over any of the 99 dims), the pipeline
scripts (SIDON, Chatterbox self-conversion, DNSMOS, 99-dim scoring, VoiceCLAP, assembly),
and the live-demo reproducer.