Luel-ai/luel-multilingual-tts-samples

Multilingual TTS Samples (Luel)

License: All Rights Reserved. Proprietary. Access only for authorized parties; no redistribution or use without permission. See LICENSE.

A multilingual text-to-speech / read-speech dataset of short scripted utterances across 7 languages. Each sample is a single-speaker recording of a written prompt, paired with rich speaker and recording metadata. Useful for TTS training and evaluation, ASR adaptation, dialect/accent studies, and read-speech benchmarking.

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Quick Stats

Metric	Value
Languages	7 (English, Hindi, Japanese, Arabic, Korean, Hebrew, Spanish)
Total samples	685
Total duration	2h 20m (8,428 s)
Total words	13,707
Mean duration	12.3 s
Audio format	WAV
Speaker metadata	gender, dialect, mother tongue, birth place, year of birth, language proficiencies

Per-Language Breakdown

Language	Code	Samples	Duration	Words
Hindi	hi	211	48m 31s	5,782
English	en	199	31m 48s	4,299
Japanese	ja	163	38m 55s	1,597
Arabic	ar	51	10m 22s	1,054
Korean	ko	40	7m 05s	486
Hebrew	he	11	1m 54s	231
Spanish	es	10	1m 53s	258

Full per-language stats (gender breakdown, per-dialect counts) are in stats.json.

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Structure

audio/
  ar/  ar_00001.wav … ar_00051.wav
  en/  en_00001.wav … en_00199.wav
  es/  es_00001.wav … es_00010.wav
  he/  he_00001.wav … he_00011.wav
  hi/  hi_00001.wav … hi_00211.wav
  ja/  ja_00001.wav … ja_00163.wav
  ko/  ko_00001.wav … ko_00040.wav
metadata.csv
stats.json
LICENSE
README.md

The dataset follows the Hugging Face audiofolder layout: metadata.csv carries one row per audio file, with file_name referencing the WAV at audio/{lang_code}/{id}.wav.

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Loading

from datasets import load_dataset

ds = load_dataset(
    "Luel-ai/luel-multilingual-tts-samples",
    split="train",
)
print(ds[0])

Filter by language:

en = ds.filter(lambda x: x["language_code"] == "en")

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Metadata Schema

Each row in metadata.csv contains:

Field	Type	Description
file_name	string	Relative path to audio (audio/{code}/{id}.wav)
id	string	Sample id ({lang_code}_{NNNNN})
language	string	Spoken language (e.g. English)
language_code	string	ISO 639-1 code (en, hi, ja, ar, ko, he, es)
script	string	Text the speaker read
type_of_script	string	Prompt type (e.g. monologues)
duration_seconds	float	Audio duration in seconds
gender	string	Speaker gender
ethnicity	string	Speaker self-reported ethnicity (may be empty)
birth_place	string	Speaker country of birth
mother_tongue	string	Speaker native language
dialect	string	Speaker dialect / regional variety
year_of_birth	int	Speaker year of birth
years_at_birth_place	int	Years lived at birth place
languages_data	json	Spoken languages with proficiency levels (JSON-encoded)
recording_environment	string	Environment label (home, etc.)
os	string	Recording OS
device	string	Device type
browser	string	Browser used

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Intended Uses

• TTS / voice cloning training and evaluation
• Multilingual ASR fine-tuning, especially for low-resource accents and dialects
• Speaker / dialect classification benchmarks
• Read-speech / prosody research

Out-of-Scope Uses

• Identifying or re-contacting speakers
• Any use prohibited by the LICENSE

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Access

This repository is gated. You must accept the conditions to access files and content. For access requests, contact Luel (https://luel.ai).