Don't Send Every Audio File Straight to the AI Model

An audio file may appear flawless but still yield subpar AI outputs. Despite a successful technical check, the AI model might return an unsatisfactory response once played. It's easy to blame the model, but often the issue lies in treating every decodable file as a valid model input. Inadequate audio, such as being too quiet, clipped, empty, multichannel, or outside the model's trained conditions, can lead to predictable problems in the pipeline.

A small policy layer should precede inference to catch such issues, choose safer paths, and prevent false success claims. To begin, verify if the file contains an audio stream, its codec, duration, sample rate, channel count, and layout. Tools like ffprobe can retrieve this metadata as JSON without decoding the entire file. By doing so, you can identify malformed uploads and prevent quiet assumptions from propagating through the pipeline.

For instance, a speech model expecting mono audio should not encounter a six-channel layout during inference. Blindly downmixing is risky as it may mix different microphones or cancel partial signals. Metadata alone cannot confirm the file's usefulness; hence, inspect a bounded portion of the decoded signal. Basic measurements include peak and RMS levels, long silent regions, samples near full scale, active audio or speech ratio, and channel imbalance.

FFmpeg's astats and silencedetect filters can perform a basic server-side pass. Silence level and duration are examples, not standards, as whispered interviews, screen recordings, and field recordings may require different thresholds. A voice activity detector can distinguish speech from non-silent sounds. Extremely low-level recordings necessitate a different approach.

Rather than normalizing every upload to the same target, decide if the input is unusually low for the pipeline. If so, apply bounded gain with headroom before running the model. If the recording is already clipped or lacks usable speech, gain is not the solution. The decision thresholds should stem from tested failures, not arbitrary mastering guide numbers.

Instead of a single "valid" boolean, produce a processing plan. A file can be decodable but still warrant conservative processing or manual review. An example TypeScript interface for audio inspection includes decodability, audio stream count, duration, peak and RMS levels, clip ratio, and active audio ratio. Similarly, an audio policy defines minimum and maximum duration, active audio ratio, clipped sample ratio limits, calibration settings, target RMS values, and maximum gain.

Based on these inputs, generate a processing plan specifying the disposition (process, review, or reject), processing mode (standard or conservative), preGain in dB, and reasons for the decision.