--- pretty_name: MoodWave Music Tagging (Hindi / Nepali / English) license: other license_name: moodwave-research-use-only license_link: LICENSE.md language: - hi - ne - en language_creators: - found annotations_creators: - expert-generated - machine-generated multilinguality: - multilingual source_datasets: - original task_categories: - audio-classification task_ids: - audio-language-identification size_categories: - 1K **Status: work in progress.** Collection is ongoing and genre labels currently > cover the English portion only. See [Current state](#current-state) before > using this — the numbers below are live, not targets. ## Current state | | Count | |---|---| | Total tracks | 20,939 | | With a genre label | 20,710 | | Unlabelled (awaiting the labeling pass) | 229 | | Total audio | 1,763 hours | ### By language | Language | Tracks | |---|---| | `hi` | 10,202 | | `ne` | 4,906 | | `en` | 4,672 | | `instrumental` | 335 | | `pa` | 290 | | `other` | 208 | | `unlabelled` | 155 | | `new` | 118 | | `ur` | 40 | | `bho` | 12 | | `mai` | 1 | ### By genre | Genre | Tier | Tracks | |---|---|---| | Pop | A | 6,185 | | Ghazal | B | 1,630 | | Bhajan/Devotional | B | 1,384 | | Country | A | 1,286 | | Adhunik | B | 1,222 | | Hindustani Classical | B | 1,157 | | Folk | A | 1,124 | | Bhangra/Punjabi Folk | B | 1,072 | | Nepali Lok Geet | B | 994 | | Jazz | A | 870 | | Lok Dohori | B | 783 | | Hip-Hop | A | 550 | | Reggae | A | 481 | | Classical | A | 457 | | Rock | A | 450 | | Electronic | A | 331 | | R&B | A | 293 | | Tamang Selo | B | 292 | | Sufi/Qawwali | B | 89 | | Deuda | B | 60 | ## Why the taxonomy looks unusual **"Bollywood" is not a genre here.** It describes who commissioned the music, not what it sounds like — filmi spans Hindustani classical, folk, rock, qawwali and EDM. A Hindi film song therefore carries `language=hi`, `is_filmi=true` **and** a real musical genre. Encoding Bollywood as a genre teaches a model *"Hindi language ⇒ Bollywood"*, which then mislabels every Hindi rock song. This follows Apple Music (Primary `Bollywood` + Secondary `Soundtrack`), Spotify (`bollywood` is a child of `pop`) and MusicBrainz (no `bollywood` genre at all, only `filmi`). ### Genres **Tier A — language-agnostic sound:** Pop, Rock, Hip-Hop, Electronic, Folk, Jazz, R&B, Classical, Country, Reggae **Tier B — regional traditions with a distinct acoustic signature:** Hindustani Classical, Sufi/Qawwali, Bhajan/Devotional, Ghazal, Bhangra/Punjabi Folk, Lok Dohori, Tamang Selo, Deuda, Nepali Lok Geet, Adhunik Blues folds into R&B and Metal into Rock. `Classical` means the Western art-music tradition; Indian classical is `Hindustani Classical` (which also absorbs Carnatic). ### Two boundary rules 1. **Newa Dapha and Gunla Bajan are `Nepali Lok Geet`, not `Bhajan/Devotional`.** The Devotional class is defined by *sound* — harmonium + dholak/tabla + manjira — not by religious function. Newar traditions use dhimay/khin drums and asta-dhatu cymbals, and no harmonium. 2. **`genre=Deuda` never implies `language=ne`.** Deuda is performed in the Kumaon Division of Uttarakhand, India as well as in far-western Nepal. ## Schema | Column | Type | Notes | |---|---|---| | `track_id` | string | `yt_<11-char YouTube ID>`, or `lg_<11 hex>` for legacy entries | | `youtube_id` | string | Empty for legacy entries | | `audio_sha256` | string | Content hash of the encoded audio; the dedup key | | `title` | string | Original upload title, verbatim (may be Devanagari) | | `title_romanized` | string | **Song name** with promotional furniture stripped | | `artist` / `artist_id` | string | Populated only when confidently resolved (see below) | | `composer` | string | Music director, for filmi | | `genre` / `genre_tier` | string | 20 classes, tier `A` or `B`. Empty = unlabelled | | `tradition` | string | Sub-label: `salaijo`, `jhyaure`, `teej`, `newa_dapha`, … | | `language` | string | `hi` `ne` `en` `pa` `bho` `new` `mai` `ur` `other` `instrumental` | | `origin_region` | string | `NP` `IN` `PK` `other` | | `is_filmi` / `film_title` / `film_industry` | bool / string | Film provenance | | `release_year` / `decade` | int16 / string | `-1` when unknown | | `mood` / `mood_source` | string | 6 moods | | `vibes` | list\ | 12 vibes | | `vocal_gender` | string | `male` `female` `duet` `chorus` `instrumental` | | `is_live` `is_instrumental` `is_remix` `is_cover` | bool | | | `duration_s` | float32 | | | `source_channel_id` / `source_playlist_id` | string | Provenance | | `label_source` / `label_confidence` | string / float32 | Which signals agreed | | `taxonomy_version` | string | Must match `taxonomy/genres.json` | | `split` | string | `train` / `unlabelled` | ### A note on `artist` `artist` is populated **only when confidently resolved**, and is otherwise empty. Both title orderings occur on the source channels: ``` Nepathya - Tadhikha <- Artist - Track Hariyo Danda Mathi - Dharma Raj Thapa <- Track - Artist ``` Nothing in the string settles which is which, so the split is only made when there is real evidence (a known single-artist channel, or comma-separated names on exactly one side). `title_romanized` always carries the full cleaned name. ## Usage ```python from datasets import load_dataset ds = load_dataset("anujpaude1/moodwave-music-tagging", split="train") print(ds[0]["title_romanized"], "|", ds[0]["language"], "|", ds[0]["genre"]) # Labelled subset only labelled = ds.filter(lambda r: r["genre"] != "") # Nepali folk dohori = ds.filter(lambda r: r["genre"] == "Lok Dohori") ``` Query without downloading, via the SQL Console or DuckDB: ```sql SELECT language, genre, count(*) AS n FROM 'hf://datasets/anujpaude1/moodwave-music-tagging/data/manifest/train-*.parquet' WHERE genre != '' GROUP BY 1, 2 ORDER BY n DESC; ``` **Windows users:** pin `datasets<4.0`. Version 4.0 moved audio decoding to `torchcodec`, which has no Windows build. The metadata in this repo is unaffected, but the audio repo below is. ## Getting the audio No audio is distributed here. To reconstruct it, resolve `youtube_id` yourself: ```bash yt-dlp --js-runtimes node -f bestaudio \ --extract-audio --audio-format best \ -o "yt_%(id)s.%(ext)s" \ --sleep-requests 3 --sleep-interval 6 --max-sleep-interval 15 \ "https://www.youtube.com/watch?v=" ``` The reference pipeline transcodes to **Opus 48k mono 24 kHz** full-length (not clipped). 24 kHz rather than 16 kHz so that MERT and CLAP remain usable; discogs-effnet resamples to 16 kHz and reads nothing above 8 kHz, so the encoding is transparent to it. Legacy rows (`lg_` prefix) have no YouTube ID — they came from an earlier collection and are identified by content hash only. ## How the data was collected 1. **Enumerate** official label and artist channels with `yt-dlp --flat-playlist`. 2. **Filter** — reject anything outside 60–900 s, plus compilations and non-music. Label channels are not song channels: they also publish comedy series, trailers, teasers, interviews, BTS and episode promos, all of which sit inside the duration window. Duration alone let **19% non-music** through on the first pass. 3. **Download and transcode** the full song, discarding the source immediately. 4. **Label** — retrieval first (iTunes, Last.fm, MusicBrainz, channel provenance), with an LLM used only to reconcile that evidence onto the fixed taxonomy. A label is accepted only on agreement between 2+ independent sources. *(Not yet run for Hindi/Nepali.)* English genre labels come from a hand-curated ground-truth set of an earlier collection, joined by normalised filename at a 95% match rate. ## Limitations - **Labels are incomplete.** Genre currently covers the English portion only. - **No artist-disjoint splits yet.** The corpus is built from single-artist playlists, so a random split leaks artist identity and inflates accuracy (Flexer 2007: 75.7% → 58.5% under an artist filter). Do not evaluate on a random split. - **Sampling bias** toward what labels upload and promote; the Hindi catalogue skews heavily post-2000. - **Mood labels are weak.** Audio-only Hindi mood classification tops out around 58% F-measure in the literature; lyrics are needed to do better. - `Romantic` is a lyrical category with no position in valence–arousal space and will collapse into Calm and Sad under an audio-only model. It also appears on the vibe axis. ## Licence Annotations: **non-commercial research use** (see `LICENSE.md`). The underlying compositions and recordings remain the property of their rights holders. This repository distributes no audio. For takedown requests, open a discussion on this repository.