Mikkeli Matlock
d7782bb9d9
Profiled hot spots on a 4-min track and cut the worst offenders: - Remove BPM: librosa.beat.beat_track ran on every load (~3.7s) for a number no better than tapping by hand. Dropped from AudioFile + the metadata panel. - LUFS short-term: replace 474 per-window pyloudnorm.integrated_loudness calls with one K-weighting pass (reusing pyloudnorm's own filter coefficients) + a vectorised sliding mean-square. This is true *ungated* EBU R128 short-term (the old loop wrongly gated each 3s window). Integrated + LRA still use pyloudnorm's gated calls. ~3.8s -> ~1.9s. - PSR: reuse LUFS's short-term series (memoised on the AudioFile) + vectorised sample-peak. ~3.0s -> ~0.2s. - True Peak: oversample the whole signal once, then an O(N) running max over windows instead of per-window resample_poly. Bit-identical to the old loop (max|diff| 0.0000 dB). ~2.1s -> ~1.1s. - Crest Factor: peaks via the same O(N) running max (last per-window loop gone). lufs+psr+true_peak: ~9.2s -> ~3.2s, plus ~3.7s of BPM removed from every load. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
uj-mastering-master
Custom mastering toolkit providing visual metrics for evaluating audio masterings. Developed with Claude Code assistance.
Features
Current
- PyQt5 GUI: drag-and-drop or file-dialog ingest of
.mp3,.wav,.flac - Switchable metrics via a dropdown, all sharing one analysis cache:
- RMS Power — 10 s rolling window with adaptive colour scale
- Waveform — min/max envelope, fixed ±1.1 scale
- LUFS — BS.1770 short-term (3 s) + integrated + loudness range (LRA)
- Crest Factor — peak-to-RMS spread over time
- PSR — peak-to-short-term-loudness ratio ("is it still breathing?")
- True Peak — 4× oversampled dBTP, catches inter-sample peaks
- Spectrogram — log-frequency STFT power heatmap over time
- Always-labelled axis extremes: every plot forces its exact min/max onto the ticks, so you can read the true range even on a log axis (e.g. the spectrogram's 22 kHz top, which otherwise falls between decade ticks)
- Native sample rate: audio is loaded without resampling, so the full band (up to the file's own nyquist, e.g. ~22 kHz for 44.1 kHz files) is analysed
- BPM detection via librosa
- CJK-safe font system with custom fonts loaded from
fonts/(gitignored), system fallbacks, and a live font selector - Background analysis thread so the UI stays responsive; metric switches compute off the GUI thread and cache, so re-selecting a metric is instant
- Embedded matplotlib canvas with auto-regenerated plots on font change
Roadmap
See CLAUDE.md for the full development roadmap. Near-term: dynamic range (DR meter), plot-style controls, interactive axis controls.
Quick start
This project uses uv. With uv installed:
uv sync
uv run ujm
uv run ujm is the only supported entry point — it boots the GUI.
Logging flags
uv run ujm --log-level DEBUG # ERROR | WARN | INFO | DEBUG | TRACE
uv run ujm --log-file # also write audio_analysis.log
Fonts
Drop .ttf / .otf / .ttc files into fonts/ to get them in the font
selector. The directory is gitignored to avoid bundling licensed font data.
See CJK_FONTS.md for details.
Dependencies
librosa, numpy, matplotlib, mutagen, pyloudnorm, PyQt5 — all pinned
through uv.lock. Python 3.10+.
Architecture
| Module | Responsibility |
|---|---|
main.py |
MainWindow + the ujm entry point |
analysis_results_manager.py |
Background QThread worker, result + metric-data cache |
master_core.py |
AudioFile: native-rate librosa loading, RMS rolling window, BPM |
metrics.py |
Pluggable Metric ABC + registry (RMS, Waveform, LUFS, Crest, PSR, True Peak, Spectrogram) |
audio_visualization_widget.py |
Embedded FigureCanvasQTAgg host |
font_manager.py |
Custom + system CJK font discovery, matplotlib/Qt config |
font_control_widget.py |
Font picker + size slider |
plot_control_widget.py |
Metric selector + refresh-plot button |
logger_setup.py |
CLI log-level parsing + custom TRACE level |
setup_fonts.py |
Diagnostic utility (run standalone) |
Adding a metric
Subclass Metric in metrics.py, implement compute(audio_file) -> data (the
heavy part, runs on the worker thread) and render(data, file_path) -> Figure
(cheap, runs on the GUI thread). Register the instance in the METRICS dict at
the bottom of the file — it shows up in the dropdown automatically.