🎵 FLAC Detective

A lossy file renamed to .flac looks lossless, weighs lossless, and fools every player you own. It can't fool the arithmetic the encoder left behind — and this reads it for you.

MP3, AAC, Vorbis and — partially — Opus, through three independent kinds of evidence. A conviction needs two of them to agree.

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.

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 12 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:

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.

What the numbers describe. Every accuracy figure this project publishes was measured on direct transcodes — lossy audio re-encoded straight to FLAC, nothing in between. Transcodes that passed through a mastering chain before reaching a disc (levels, limiting, press — what wild compilations actually sell) are a different population: on 34 owner-attested wild MP3-sourced tracks, v1.11.4 signaled 8.8 %, v1.12.0 — after repairing four admission gates that had been calibrated on lab material — signaled 50.0 %, and v1.13.0 — after feeding the depth instrument to the cells the container check consults — signals 70.6 % and convicts its first two, both owner-attested fakes corroborated by independent evidence families. False convictions remain zero across every version and population, which is the half of the design that held throughout. Every number here was pre-registered before its measurement and scored on hash-verified bytes; the remaining gap is real, measured, and being worked on rather than blurred.

→ 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.

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 -U flac-detective
flac-detective --version

from flac_detective import FLACAnalyzer

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

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

🤖 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

• 📖 Full documentation site — getting started, user guide, technical details, API
• 🚀 Getting Started — install, first analysis, accuracy & file-safety notes
• 📋 Changelog · 🤝 Contributing · 🔒 Security
• 💬 Issues · Discussions

Licensed under the MIT License.