Datasets:

Shadow0482
/

ic-tts

license: apache-2.0
language:
  - en
task_categories:
  - text-to-speech
tags:
  - synthetic
  - tts
  - voice-cloning
  - omnivoice
pretty_name: ic-tts

ic-tts

Synthetic speech dataset: LLM-written text, spoken by OmniVoice (voice cloning from reference audio).


Utterances	395
Total duration	1.00 h
Mean / min / max length	9.1s / 3.5s / 15.2s
Sample rate	24000 Hz mono, FLAC
Language	English
Reference voices	1

Pipeline

Text: Qwen/Qwen2.5-0.5B-Instruct prompted with random topic/style/length combos, cleaned and de-duplicated -> texts.json.
Audio: OmniVoice (0.6B, GGUF f16) through audio.cpp v0.9.1, zero-shot voice clone, 2x T4 in parallel.
Basic filtering: clips that are too short or whose duration is implausible for the text length are dropped.

Files

audio/ - audio clips
metadata.jsonl - one row per clip: id, text, path, duration (seconds), sample_rate, ref_voice (file_name duplicates path for the HF audio viewer)
texts.json - all generated texts (including any not synthesized)

Usage

from datasets import load_dataset
ds = load_dataset("Shadow0482/ic-tts", split="train")
print(ds[0]["text"], ds[0]["duration"])

Notes and limitations

Fully synthetic. Text can contain LLM errors or odd phrasing; speech can contain TTS artifacts. No manual verification was done.
The cloned voice(s) come from reference recordings. Only use voices you have the right/consent to clone, and check the licenses of the models used (Qwen, OmniVoice) before commercial use.