FLAC Detective — catch lossy files masquerading as FLAC

🎵 FLAC Detective

An MP3 renamed to .flac looks lossless, weighs lossless, and fools every player you own. It can't fool a spectrogram — and this reads it for you.

PyPI version PyPI Downloads CI Docs License: MIT

Find the fake FLACs in your music library.

A lossy codec throws away the top of the spectrum and never gives it back. FLAC Detective reads each file, spots the fingerprints that loss leaves behind, and tells you which files are real and which are fakes.

pip install flac-detective       # needs Python 3.10+
flac-detective /path/to/music    # scan a file or a whole folder

Every file gets a verdict, like a traffic light:

✅ AUTHENTIC      real lossless         → keep it
❓ WARNING        borderline            → give it a listen
⚠️  SUSPICIOUS     probably a transcode  → likely a fake
❌ FAKE_CERTAIN   definitely a fake     → replace it

The scan only reads your files — it never changes anything.

🟢 New to all this? → Start Here — the 5-minute beginner's guide No command line, no jargon. From "what is this?" to "I checked my music".

📊 See why a file was flagged

Add --format html and you get a single self-contained page: a triage table sorted worst-first, plus a spectrum plot for every flagged file. The MP3 "cliff" — a sharp drop well below the real ceiling — is right there for the eye, with the detected cutoff marked.

FLAC Detective HTML report — triage table and per-file spectrum cliffs

Three transcodes at different MP3 bitrates show the wall falling at different frequencies (96 kbps ~11 kHz, 128 kbps ~16 kHz, 160 kbps ~17.5 kHz); the authentic file runs full-range.

🔍 How it works

An MP3 re-saved as FLAC is lossless as a container, but the audio already passed through a lossy codec — and that leaves fingerprints. The clearest is the spectral cliff: MP3 discards everything above a bitrate-dependent frequency, so the spectrum falls off a wall where a real recording keeps going.

FLAC Detective scores each file with 10 heuristic rules built around that idea (cutoff frequency, MP3-bitrate signatures, silent-passage energy) plus protection rules so genuine vinyl rips, cassette transfers and naturally quiet recordings aren't flagged. An optional CNN sharpens borderline verdicts, and a frame-alignment rule reads the fingerprint MDCT quantisation leaves behind — the one signal that survives at 256–320 kbps, where the spectral cliff has nothing left to show. The rules sum to a 0–150 score:

Verdict Score What to do
✅ AUTHENTIC ≤ 30 keep it
❓ WARNING 31–54 borderline — check manually
⚠️ SUSPICIOUS 55–85 likely a transcode
❌ FAKE_CERTAIN ≥ 86 definitely transcoded

The guiding principle is "protect authentic files first": a false alarm on real music is worse than missing a borderline fake. Treat AUTHENTIC as "no evidence of transcoding", not a guarantee.

→ Every rule explained: Technical Details.

⚙️ Usage

flac-detective /path/to/music              # scan a folder
flac-detective                             # interactive (prompts for a path)

flac-detective /music --format csv  -o triage.csv   # spreadsheet, worst-first
flac-detective /music --format html -o report.html  # visual report (see above)
flac-detective /music --deep                        # catch high-bitrate AAC/Opus/Vorbis (slower)
flac-detective /music --advanced                    # show the plumbing: scores, cutoff, per-rule detail

By default the output is easy mode — a plain-language verdict and a recommended action per file ("Almost certainly a fake — the sound stops dead at ~16 kHz, the wall of a 128 kbps MP3. → Replace it."). Add --advanced for the scores, cutoff frequencies and per-rule reasoning. The desktop GUI has the same Advanced toggle.

Analyses FLAC, WAV, ALAC (.m4a) and APE (.ape) — codec-agnostic, and a lossy .m4a is correctly rejected (the real codec is probed, never trusted by extension).

→ Full guide & every flag: User Guide.

🖥️ Prefer a window to a command line?

pip install "flac-detective[gui]"
flac-detective-gui

A desktop app (PySide6): choose a folder or drag it in, watch the progress bar, then triage a sortable, colour-coded verdict table — click any file to see its spectrum with the detected cutoff marked and the reasons for its verdict. Export to HTML/CSV/JSON.

🎚️ It also catches fake hi-res

Beyond lossy-as-lossless, FLAC Detective flags files sold as high-resolution that aren't: 44.1/48 kHz upsampled to 96/192 kHz (a hard spectral cliff with digital silence above it), and 16-bit audio padded into a 24-bit container. This is a separate axis from the transcode verdict — reported as hires_verdict (GENUINE_HIRES / UPSAMPLED / PADDED_DEPTH / …) in the CSV report, the GUI and the Python API. A genuine 96 kHz recording that simply rolls off early reads GENUINE_HIRES, not a false alarm.

Install options & upgrading 
pip install flac-detective                 # base
pip install "flac-detective[ml]"           # + optional CNN (Rule 12)
pip install "flac-detective[gui]"          # + desktop GUI (flac-detective-gui)
docker pull ghcr.io/guillain-rdcde/flac_detective:latest   # or Docker (amd64 + arm64)

pip install does not upgrade an existing install — use -U to get the latest release:

pip install -U flac-detective
flac-detective --version
Use it from Python or beets

Python API:

from flac_detective import FLACAnalyzer

result = FLACAnalyzer().analyze_file("song.flac")
print(result["verdict"])   # AUTHENTIC, WARNING, SUSPICIOUS, or FAKE_CERTAIN

beets plugin — triage transcodes without leaving your workflow:

pip install "flac-detective[beets]"
# in config.yaml:  plugins: flacdetective

beet flacdetective                          # analyse & tag the whole library
beet ls flacdetective_verdict:FAKE_CERTAIN  # list the certain fakes

Stores flacdetective_verdict and flacdetective_score on each item; an optional auto: yes analyses files as they're imported.

🤖 The ML side: a case study worth reading

Rule 12's model went through a real R&D saga, written up as a learning resource: a false-positive audit over 11 234 real FLACs, four instructive dead-ends, a debunked "AUC 0.99" caught by cross-validation, and a twist where a "fundamental limit" turned out to be an artifact of listening in mono — fixed by going stereo. Real-world specificity on 11 234 authentic FLACs climbed from 80 % to 95 %.

📖 Read the ML detective story →

📚 More

Licensed under the MIT License.

Project links

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  • Released:
Newer release available (1.13.8)

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Avatar for GuillainM from gravatar.com GuillainM

Credits

Author: Guillain d'Erceville

GitHub Statistics

Data verified by PyPI on Aug 15, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.10

Provides Extra

ml beets gui dev docs

Tags

flac audio analysis transcode detection mp3 quality authenticity

Classifiers

Report project as malware

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