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raditotev/bg-audiobooks-tts

Bulgarian Audiobook Speech Dataset A high-quality Bulgarian speech dataset derived from audiobooks narrated by Plamen Sivov, suitable for text-to-speech (TTS) and automatic speech recognition (ASR) tasks. Dataset Summary Property Value Language Bulgarian (bg) Total Duration 15.2 hours Total Clips 10,627 Speaker Plamen Sivov (single speaker) Source YouTube audiobooks Sample Rate 24,000 Hz Audio Format WAV, mono, 16-bit PCM Clip Duration… See the full description on the dataset page: https://huggingface.co/datasets/raditotev/bg-audiobooks-tts.

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Dataset Card

Bulgarian Audiobook Speech Dataset

A high-quality Bulgarian speech dataset derived from audiobooks narrated by Plamen Sivov, suitable for text-to-speech (TTS) and automatic speech recognition (ASR) tasks.

Dataset Summary

Property	Value
Language	Bulgarian (bg)
Total Duration	15.2 hours
Total Clips	10,627
Speaker	Plamen Sivov (single speaker)
Source	YouTube audiobooks
Sample Rate	24,000 Hz
Audio Format	WAV, mono, 16-bit PCM
Clip Duration	3–15 seconds
License	CC0-1.0

Dataset Structure

Each sample contains:

Field	Type	Description
`file_name`	string	Path to audio file
`text`	string	Raw transcription (Whisper large-v3)
`text_normalized`	string	TTS-ready text (numbers/dates/abbr expanded)
`duration`	float	Audio duration in seconds
`speaker_id`	string	Speaker identifier
`speaker_name`	string	Speaker full name
`language`	string	Language code (bg)
`source_file`	string	Original audiobook file
`book_title`	string	Book/chapter title
`segment_start`	float	Start time in source file (seconds)
`segment_end`	float	End time in source file (seconds)
`rms_energy`	float	Audio energy (loudness indicator)
`sample_rate`	int	Audio sample rate (24000)
`clip_id`	int	Unique clip identifier

Usage

python

from datasets import load_dataset

# Load dataset
ds = load_dataset("raditotev/bg-audiobooks-tts")

# Access a sample
sample = ds["train"][0]
print(sample["text"])
print(sample["text_normalized"])
print(f"Duration: {sample['duration']:.1f}s")

# Play audio (in notebook)
from IPython.display import Audio
Audio(sample["file_name"], rate=24000)

Use Cases

—Text-to-Speech (TTS): Fine-tune models like F5-TTS, XTTS-v2, or VITS for Bulgarian
—Automatic Speech Recognition (ASR): Fine-tune Whisper or other ASR models for Bulgarian
—Voice Cloning: Single-speaker dataset ideal for voice adaptation
—Bulgarian NLP: Text normalization research, prosody analysis

Dataset Creation

Source

Audio from Plamen Sivov's YouTube channel, used with explicit permission from the author.

Processing Pipeline

1.Audio downloaded with yt-dlp
2.Converted to 24kHz mono WAV
3.Transcribed with OpenAI Whisper large-v3 (MLX)
4.Segmented into 3–15 second clips
5.Filtered for quality (minimum energy, text length, Cyrillic ratio)
6.Text normalized for TTS (numbers, dates, abbreviations expanded to words)

Quality Notes

—Transcriptions generated by Whisper large-v3 — expect ~95%+ accuracy for Bulgarian
—Some clips may contain minor transcription errors
—text_normalized field provides TTS-ready text with all non-verbal elements expanded

Citation

bibtex

@misc{bg_audiobooks_tts_2026,
  title={Bulgarian Audiobook Speech Dataset},
  author={Radi Totev/ Plamen Sivov},
  year={2026},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/datasets/raditotev/bg-audiobooks-tts}}
}

Acknowledgments

—Plamen Sivov for creating and sharing the audiobook recordings
—OpenAI Whisper for transcription
—Apple MLX for efficient on-device processing

License

This dataset is released under CC0-1.0. Attribution should be given to Plamen Sivov as the original narrator.