Auto-Tag Your Music Library — With Confidence Scores

“Tag my whole catalogue by mood, energy and genre” is one of those jobs that sounds like a weekend script and turns into a procurement cycle. The enterprise tagging tools — Cyanite is the one people name — do the job well, but the shape of the deal is the friction: you upload your audio, you sign up for a seat, and what comes back is a confident flat label (energetic, happy, techno) with no indication of how it was decided or how much to trust any single tag. For a side project, an internal catalogue tool, or a feature you’re still prototyping, that’s a lot of ceremony for some adjectives.

This post is a practical walkthrough of the self-serve alternative: tag tracks by name (no audio upload) in about 30 lines of Python against the FreqBlog Music API’s GET /tag — and, crucially, get a confidence and a provenance on every tag, so you know which ones are a measurement and which ones are a hint.

Why honesty is the feature

Audio tagging is not one problem — it’s a stack of problems with very different reliability. A track’s energy or danceability is a measurement off the waveform: reproducible, defensible, and the same every time. A one-word mood is a derivation from those measurements via a rule. A fine-grained mood like “aggressive: 0.71” is a model estimate from a research-grade classifier that was honest enough about its own limits that its authors deprecated it. Genre is a broad catalogue tag, not a taxonomy you should bet a recommender on.

Most taggers flatten all four into the same confident-looking string. FreqBlog’s /tag refuses to: every tag in the response carries a confidence and a provenance, so a measured high-energy and a model-estimated aggressive never look equally authoritative. That’s the differentiator — not “more tags,” but tags you can reason about.

The call — one track, in

GET /tag resolves a track from any of: track (plus optional artist), isrc, mbid, spotify_id, or track_id (a catalog itunes_track_id) — the same identifiers /lookup accepts. No Spotify account, no audio file.

import requests

API = "https://api.freqblog.com"
HEADERS = {"X-Api-Key": "fb_live_your_key_here"}

def tag(track, artist=None):
    params = {"track": track}
    if artist:
        params["artist"] = artist
    r = requests.get(f"{API}/tag", params=params, headers=HEADERS, timeout=30)
    r.raise_for_status()
    return r.json()

data = tag("Blinding Lights", "The Weeknd")
for t in data["tags"]:
    val = "" if t["value"] is None else f"  ({t['value']})"
    print(f"  {t['category']:16} {t['tag']:18} [{t['confidence']}]{val}")

For Blinding Lights that prints a handful of tags across the categories — a measured high-energy (1.0), very-danceable and electric-produced; a derived mood of tense; and a catalog genre of synthwave. The response envelope is { track, count, tags:[…], disclaimer }: track is the resolved stub (name, artist, ids), count is the number of tags, and disclaimer is a plain-English note about provenance you can surface in a tooltip.

The four confidence levels — what each one means

Every tag’s confidence + provenance tells you which bucket it came from. There are exactly four, and they’re ordered most-to-least authoritative:

So the rule for a consumer is simple and the API does the heavy lifting of being honest: trust the measured tags as fact, treat derived as a reasonable summary, and gate any product decision on a model-estimated mood by its value — it’s only emitted when the model’s probability clears 0.5, and the raw number is right there for you to threshold higher.

Tagging a whole library

The endpoint is one track per call, so “tag my library” is a loop — resolve each (title, artist) once, keep the tags you care about, and you’re done. A small, readable version that pulls just the high-confidence labels for a playlist:

library = [
    ("Blinding Lights", "The Weeknd"),
    ("Bohemian Rhapsody", "Queen"),
    ("One More Time", "Daft Punk"),
]

def trusted_tags(track, artist):
    """Measured + derived tags only — the ones safe to file on."""
    data = tag(track, artist)
    out = {}
    for t in data["tags"]:
        if t["confidence"] in ("measured", "derived", "catalog-genre"):
            out.setdefault(t["category"], t["tag"])
    return out

for title, artist in library:
    tags = trusted_tags(title, artist)
    print(f"{title} — {artist}")
    print(f"   {tags.get('energy')}, {tags.get('danceability')}, "
          f"mood={tags.get('mood')}, genre={tags.get('genre')}")

That’s the whole pattern: a function per track, a filter on confidence, and a dict you write to your own store. If you want the model-estimated mood axes too, drop the filter and read value — the data’s there, you just choose how much to trust it. Each successful call is one quota unit (it’s a /lookup projection — same data, same cost), and you’re only charged on a served 200; a miss (404) or a request with no/invalid identifier (422) costs nothing. The free tier’s 1,000 requests/month is enough to tag a real playlist before you ever need a paid plan.

What you can and can’t tag — the honest coverage

This is the part most “tag any track” pitches get wrong, so here it is plainly. There are two coverage stories and they’re different:

• The measured tags — the broad, reliable coverage. Energy, danceability, valence, acousticness and the derived mood come from our own analysis over the pre-analysed catalogue (over a million tracks), and any track not yet in it gets pulled in on demand the first time you look it up. You can tag these by name. Lead with this — it’s where /tag is dependable.
• The model-estimated mood axes — reachable when you supply the identifier. The AcousticBrainz SVM probabilities sit over 7.5M+ recordings, but they’re keyed by MusicBrainz ID. So you can reach that slice by passing an mbid or an isrc — when you supply the identifier — not by track name alone. Only a small fraction of AB rows are resolvable by name today.

How it compares to enterprise auto-tagging — honestly

• No upload, self-serve. You tag by name / ISRC / MBID against open-data analysis — there’s no audio to upload, no seat to provision, and you can be tagging a playlist 60 seconds after you get a free key.
• Confidence + provenance on every tag. This is the headline difference. A closed tagger gives you a label; /tag gives you a label plus where it came from and how much to trust it — so you can file the measured tags and gate the model-estimated ones.
• Open data, not a black box. The measured tags are our Essentia pipeline; the mood axes are AcousticBrainz; the genre is the broad catalogue tag. Nothing here is a proprietary score you can’t reason about — and we leave the known-bad fields (the ~80%-“electronic” AB genre column, the argmax mood verdict) out on purpose.
• Coverage, not magic. The measured tags need the track in the catalogue (or pulled in on demand); the model-estimated mood needs an MBID/ISRC. A track we can’t resolve simply comes back without those tags rather than with a confident guess — better an honest gap than a wrong label.

Further reading

• Spotify Audio Features Is Deprecated. Here’s What to Use Instead in 2026. — the full numeric feature set behind these tags
• The Spotify /recommendations Replacement — from tags to nearest tracks and a derived artist graph
• Migrating from Spotify Audio Features: a Field-by-Field Threshold Guide — what each audio feature measures, and how to set your own thresholds on it
• AcousticBrainz Alternative in 2026: The Honest Insider’s Guide — where the model-estimated mood axes come from