tiny-voca dataset

A synthetic American English speech corpus with two voices, F2 (female) and M2 (male), made to train the tiny-voca text-to-speech model. Every sentence is read by both voices.

Clips	17,371
Duration	19.1 hours
Voices	F2, M2
Audio	24 kHz mono, 16-bit FLAC, stored inside the Parquet files
Language	English (US)
License	CC BY 4.0

Usage

from datasets import load_dataset

batch1 = load_dataset("n4ze3m/tiny-voca", "batch1")
batch2 = load_dataset("n4ze3m/tiny-voca", "batch2")

row = batch1["train"][0]
print(row["voice_id"], row["text"])
audio = row["audio"]["array"]

Contents

Config	Split	Clips	Hours	Text
batch1	train	8,226		LibriTTS-R transcripts and LLM-written sentences
batch1	validation	718		same
batch1	test	596		same
batch2	train	7,831	7.6	Mozilla Common Voice sentences and project templates

The three batch1 splits total 11.5 hours. Splits are made by sentence: both readings of a sentence are always in the same split. batch2 adds training data only, so the validation and test sentences are the same for every model trained on this corpus.

How it was made

1. The two voices were created from text descriptions with Qwen3-TTS 1.7B VoiceDesign. They are not recordings or imitations of real people. The reference clips are in references/, and the descriptions and seeds are in voices.json.
2. Each sentence was synthesized in both voices with Qwen3-TTS 1.7B Base, using the reference clips as the voice prompt.
3. Each clip was transcribed with Whisper small.en. Clips above 15% word error rate, or with implausible duration, speaking rate or silence, were generated a second time. Clips that failed twice were left out: 270 of 9,810 in batch1 and 327 of 8,158 in batch2. They are listed in generation/batch1/review.jsonl and generation/batch2/review.jsonl.
4. Accepted clips were peak-normalized to 0.95.

The generation scripts and their settings are in generation/.

The automatic screening catches wrong or missing words. It does not measure naturalness, and the corpus has not been checked by listeners clip by clip.

Text sources

The origin, source_id, source_license and source_url columns record the source of every row.

Config	Source	Sentences	License of the text
batch1	LibriTTS-R transcripts (book sentences)		CC BY 4.0
batch1	Written for this project with an LLM (DeepSeek), mostly conversational
batch2	Mozilla Common Voice sentence collector	2,700	CC0
batch2	Mozilla Common Voice Wikipedia sentences	779	CC0
batch2	Templates written for this project: numbers, dates, money, names, questions	600

batch1 has 4,905 sentences in total. A quarter of the batch2 sentences are questions and a tenth are exclamations, which batch1 was short of.

Columns

Column	Description
id	Clip ID, <text_id>_<voice_id>
text_id, voice_id	Sentence and voice
text	The sentence that was read
split	train, validation or test
category, origin	Kind of sentence and where it came from
source_id, source_license, source_url	Source attribution
audio	24 kHz audio
duration_seconds, sample_rate	Audio length and rate
asr_transcript, normalized_wer	Whisper transcript and its word error rate against text
attempt, seed, batch_id	Generation attempt and random seed
teacher_model, reference_audio	Model and voice prompt used

batch1 also has requested_style (a style hint, not a verified label) and qc_reasons (always empty for accepted clips).

Annotations

annotations/ holds the phoneme-level annotations used for training: phonemes, stress, and the duration of each phoneme in 10.7 ms frames, from forced alignment with facebook/wav2vec2-lv-60-espeak-cv-ft. Clips that failed the alignment checks are listed with a reason in the *.rejected.jsonl files.

Directory	Covers	Phonemes from	Accepted clips
annotations/v3	batch1	eSpeak NG	9,490 of 9,540
annotations/v4	batch1 and batch2	dizione	17,326 of 17,371

Limitations

• The speech is synthetic. It carries the pronunciation habits and errors of the model that generated it.
• Two voices, one accent, read speech only.
• Digits, abbreviations and symbols are rare: most numbers in the text are written out in words.

License

CC BY 4.0. Please credit "tiny-voca dataset, Muhammed Nazeem" and link to this page.

@misc{nazeem2026tinyvocadata,
  author = {Nazeem, Muhammed},
  title  = {tiny-voca dataset: synthetic two-voice American English speech},
  year   = {2026},
  url    = {https://huggingface.co/datasets/n4ze3m/tiny-voca}
}

Author: Muhammed Nazeem (ORCID 0009-0002-9991-0217).

The LibriTTS-R transcripts are used under CC BY 4.0: Koizumi et al., "LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus", Interspeech 2023, https://www.openslr.org/141/. The Common Voice sentences are CC0, from https://github.com/common-voice/common-voice. The audio was generated with Qwen3-TTS (Apache-2.0). See LICENSE.md.