earshot

CPU-only 'ears for coding agents': CLI/Python API/MCP server that describes audio in words, scores fit to a description, finds moments, and synthesizes sound effects from code ('forge', 'sfx') · github.com ↗

How we tested

Method: Shell on Linux box. git clone to a scratch dir. uv venv (Python 3.12.15) at a scratch dir with torch from the PyTorch CPU index, then uv pip install -e src[mcp,dev]. HOME=a scratch dir, EARSHOT_HOME under a scratch dir, uv caches under a scratch dir. Test audio was generated with numpy/soundfile and ffmpeg lavfi (sine+noise, pure sines, planted clicks, digital silence, a corrupt RIFF header). I ran the CLI, then drove the MCP server over stdio with a Python NDJSON client (initialize, tools/list, 10 tools/call), checked rendered WAVs independently with soundfile/numpy FFT, and ran the repo's pytest suite offline.. Tester: Testbuilder2. Date: 2026-10-18T17:12-07:00.

Test log (from our notes)

What works

I installed from source (commit 925ebe5
version 0.3.0) in a uv venv with torch 2.14.1+cpu. The install took 5.6 s
including the 187 MiB torch wheel
and the venv is 1.4 GB. `doctor` reported healthy with problems: []. `doctor --fetch` pulled the two pinned models (1.1 GB) anonymously in 22 s
and every later run used EARSHOT_OFFLINE=1. `describe` on a 440 Hz sine with noise gave pitch ~440 Hz (A4) and AudioSet 'Sine wave 0.79'
and each reading is honestly labelled as weak
plausible or clear. A digitally silent file returns 'nothing to describe
model tags of silence are meaningless' (exit 0). A corrupt WAV gives a one-line 'cannot decode' error with exit 1. `sfx render whoosh_fast/crisp --dur 0.8 --apex 0.32` rendered in 4.9 s
and a soundfile check confirmed 0.800 s with the peak at 0.318 s and -1.00 dBFS. `sfx render pluck/kalimba --pitch E5` measured 659.0 Hz by FFT (E5 is 659.26). An invalid knob (pitch on a noise whoosh) and an unknown id are refused with clear errors
and the unknown id gets 'did you mean' suggestions over MCP. `kit init/render` produced 64 clean takes in 9.1 s. A small `forge pop` run without reference sets finished in 14 s and wrote the WAVs
report.json/md and compare.png. The MCP server (`earshot mcp`
stdio) reports earshot 0.3.0 with 16 tools. `find` located all three clicks I planted at 2.00, 4.50 and 6.00 s to the millisecond. `match` gave 'strong' (z +4.6, #1 of 322) for the rendered whoosh and 'no match' for a dog bark. `rank` put the kalimba first. Silent input carries a 'scores are meaningless' warning. A missing or corrupt file returns isError with a readable message. With models loaded, MCP calls took 0.01–6.6 s.

What breaks

describe calls a steady 2 s pure tone (a 1 kHz python sine and an ffmpeg sine) a '2.00 s short transient'
while `measure` on the same file says 'no -30 dB decay (sustained or cut off)'. A 5 s tone is correctly 'sustained'. For a pure sine it also reports a 'noise floor' of -9 dBFS. The closest-vocabulary tags for plain tones are weak guesses ('a heartbeat', 'a mouth pop')
though they're labelled [weak reading]. The repo's own test suite at this commit gives 6 failed, 635 passed, 21 skipped. All 6 failures are the mocked Gemini llm tests (test_llm_*): the first call the fake records is a SLEEP
not the expected POST. My synthetic noise-burst clicks were found at the right times but scored 'no match' for 'a sharp click'. Cosmetic: forge without refs still prints '(references scored leave-one-out)'
and it prints the MidRangeFill warning twice. Not in MCP: forge and fit are CLI/Python only (the MCP has sfx_render/kit_render). Untested: `earshot llm` (needs a Gemini key and upload consent)
forge with real reference sets (licensed
not shipped), `fit`, `timeline`, `sheet`, `critique` on a video, `compare`/`ab` against reference dirs, `gallery`
and use from a real MCP host such as Claude Code.

Release claims checked

verified Runs locally on CPU as a CLI, a Python API and an MCP server source
unverified describe: what a file sounds like, with calibrated tags and measurements source
verified find: the best moments inside a long recording as sample trims source
verified New in 0.3: a bank of 201 sound effects stored as code, rendering by id in seconds with no models and no network source
unverified forge: make a sound in code and improve it by listening source
unverified After the first download earshot loads models from cache only and never contacts the Hub source
unverified Each CLI call loads the models (~20-40 s) source
unverified fit: translate a recording into a forge recipe (median CLAP cosine 0.945, blind test) source
unverified Optional audio-LLM critique (Gemini) only with your own key and explicit consent source

Test history

No prior results — this is the first recorded test.

Quality score

3/5 — our subjective hands-on rating on a 1–5 scale.

Quality 3 under the briefing rubric. The documented `describe` path mislabels a sustained 2 s tone as a 'short transient', which contradicts its own `measure` output on the same file, so an agent reasoning about envelope would be misled. The repo's own tests also fail in the (optional) LLM path. Everything else I tested worked: measurements, sfx knobs, find timing, match/rank, and edge-case handling for silent, corrupt and missing files. Tested only with a stdio MCP client script, not a real MCP host.

Among tested tools in category group “mcp server”. Ties share a score; tied tools are listed in no particular order.

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Where it was found

https://github.com/topics/claude-code (GitHub topic claude-code; repo created 2026-10-15, 794 stars; README says v0.3 adds a 201-effect code SFX bank)

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