mohammadhossein/PMG

A Persian music dataset with audio clips and rich metadata (artist, genre/tags, instruments, happiness/energy/popularity, tempo, etc.).

Splits Overview

Split	Rows	Total Duration	Mean Dur (s)	Median Dur (s)
supervised	67,796	373.83 h (373:49:46)	19.85 s	19.80 s
unsupervised	102,215	566.17 h (566:10:18)	19.94 s	20.00 s

Split Descriptions

• supervised: Supervised split with rich metadata suitable for text-to-audio or conditioned training.
• unsupervised: Unsupervised split focusing on raw audio; metadata may be sparse or partially available.

Data Fields

Below are the columns detected across splits, with guessed descriptions. Types may say varies if they differ by split.

Column	Type	Description
Caption	string	Natural-language caption/description of the audio.
artist	string	Performer or creator name.
audio	dict	Original full mix (accompaniment + vocals)
duration	float64	Audio duration (seconds).
start_time	float64	Start time (seconds) within a larger source.
end_time	float64	End time (seconds) within a larger source.
energy	string	Energy score for the clip.
happiness	varies (float64, int64)	Happiness/valence score.
happiness.1	string	Alternative happiness/valence score (duplicate column in source).
instrument_audio	dict	Instrument-only stem (no vocals)
instruments	string	List of instruments present (string or list).
key	string	Musical key (e.g., C# minor).
popularity	varies (float64, int64)	Spotify popularity metric (0–100).
song	string	Song/track title.
spotify	string	Spotify track/ID/URL or boolean flag.
spotify_genre	string	Spotify-derived genre for the track.
tag	string	Tag/label assigned to the track/clip.
tempo	string	Beats per minute (BPM/tempo).
vocal_audio	dict	Vocal-only stem (no accompaniment)

Audio

• Primary column: audio_path (🤗 Datasets [Audio]),

  • instrument_audio (🤗 Datasets [Audio]),

  • vocal_audio (🤗 Datasets [Audio])

• Mono @ 32000 Hz

• Durations per split are summarized above (Total/Mean/Median/Min/Max).

Licensing

Citation

If you use this dataset, please cite appropriately:

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