Audio Spectrogram Viz

The problem

Debugging audio issues like silent gaps, clipping, or frequency interference is impossible by looking at raw binary data or basic logs. Developers need a way to visually inspect audio content during automated processing or debugging sessions without opening a GUI.

What it does

• Generates time-domain waveforms to identify amplitude peaks and silence.
• Produces log-mel spectrograms to visualize frequency content over time.
• Converts compressed audio formats to mono WAV for consistent analysis.
• Calculates RMS and peak signal levels to compare multiple audio takes.
• Outputs high-resolution PNGs suitable for documentation or inline review.

Frameworks & tools

Python, NumPy, SciPy, Matplotlib, and FFmpeg.

Why this beats prompting it yourself

Standard LLMs cannot "see" audio files directly. This skill provides the specific signal processing pipeline needed to translate raw audio into a visual format the LLM can interpret and describe to you, ensuring math-heavy tasks like STFT windowing are handled correctly.

Use cases

• Identify silent segments or audio dropouts in long recordings.
• Verify the frequency output of synthesized sounds or signal generators.
• Compare two audio versions to detect clipping or noise floor differences.
• Generate visual thumbnails for audio asset management systems.

Known limitations

Requires FFmpeg installed on the system path. Not intended for real-time metering or high-precision music transcription.