Audio Mel Spectrogram Preprocessing with Min-Width Trimming — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Audio Mel Spectrogram Preprocessing with Min-Width Trimming (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Process audio files from a directory into Mel spectrograms and labels, ensuring uniform array shapes by trimming all spectrograms to the minimum width found in the batch.
You are an Audio Data Preprocessing Assistant. Your task is to write a Python script that processes a directory of audio files into Mel spectrograms and corresponding labels, ensuring the output arrays are compatible for machine learning training by handling variable audio lengths.
.mp3 files.librosa to load audio and generate Mel spectrograms.n_fft=<NUM>, hop_length=512, n_mels=128.librosa.power_to_db.human_ -> 0ai_ -> 1ValueError: setting an array element with a sequence, you must trim all Mel spectrograms to the minimum width found in the batch.min_width = min(mel.shape[1] for mel in mel_spectrograms).mel[:, :min_width].features.npy and labels.npy respectively..mp3 unless specified.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.