flac-detective 1.6.0

Advanced FLAC authenticity analyzer - Detects MP3-to-FLAC transcodes with high precision

🎵 FLAC Detective

Find the fake FLACs in your music library.

Anyone can take an MP3, re-save it as FLAC, and it looks lossless — but the quality is already gone. FLAC Detective reads each file, spots the fingerprints a lossy codec 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".

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📊 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.

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🔍 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 11 heuristic rules built around that idea (cutoff frequency, MP3-bitrate signatures, compression artefacts) plus protection rules so genuine vinyl rips, cassette transfers and naturally quiet recordings aren't flagged. An optional 12th rule — a small CNN — sharpens borderline verdicts. 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.

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⚙️ 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)

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.

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🤖 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 →

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📚 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

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Licensed under the MIT License.