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How to pick the best sound file from a set of downloads with Earshot

Rank candidate sound files against a text description and keep the closest match.

  • Top pick
  • Content
  • Intermediate
  • 30 minutes
  • From the README
  • Any agent

Who it is forA developer or agent that needs a specific sound, such as a key press or a click, and has several candidate files from a sound library.

The problemChoosing among many similar sound files by ear takes time, and a file name does not say how the sound really is.

The outcomeA sorted list of candidate files by fit to the description, plus a verdict on the top choice.

What you need first

  • Python and a working earshot install (the install steps are not in the README excerpt provided)
  • A CPU; earshot runs locally on CPU and needs no GPU
  • Sound files to test (WAV, OGG or MP4 with audio)
  • Optional: your own licensed reference recordings for forge (not included in the repo)

Steps

  1. Run earshot rank "a single mechanical key press" candidates/*.ogg to sort the files by fit. (inferred, check the README)
  2. Run earshot match candidates/top.ogg "a deep thocky mechanical keyboard press" --alts "a clicky blue switch keyboard" "a mouse click" to get the verdict for the best file. (inferred, check the README)
  3. Run earshot describe candidates/top.ogg to read what the file sounds like in words and its measurements. (inferred, check the README)

Prompt for your agent

Use the earshot repo at <repo path>. Run `earshot rank` over the files in <folder> for the description "<description>", then run `earshot match` on the top file. Report the ranked list and the match verdict (strong, plausible, weak or no match) with its z score. Verify that the reported file exists before you use it.

Caveat. The first call to each CLI command loads models for about 20 to 40 seconds, so use the MCP server or one Python script when you run many checks.

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