
earshot
Ears for coding agents: let AI hear, review and make sound. CPU-only, open source.
The Lens
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
Updated Oct 2026
earshot gives coding agents ears. An agent can build a product video in code but can't hear that the whoosh lands late or the key click sounds cheap. earshot turns audio into text an agent can reason about: what a file sounds like, how well it matches a description, where the best moment sits in a long recording, and a timestamped critique of a whole film. MIT licensed and free.
It runs on CPU as a CLI, a Python API and an MCP server, plus two agent skills. The first run downloads about 1.1 GB of open models, then it works offline. Each CLI call reloads the models in 20 to 40 seconds, so use the MCP server for real work. Version 0.3 adds 201 sound effects generated from code.
No paid tier; the only paid piece is an optional Gemini critique on your own key behind an explicit upload flag. Solo, small teams and larger teams: free, installed per machine.
The catch: it's brand new, one author, alpha, and the README admits its sound search can game its own models. And don't pip install earshot: that PyPI name belongs to an unrelated voice-detection library. Install from the repo.
Free vs Self-Hosted vs Paid
fully freeFree: Everything, MIT for the code: describe, match, rank, find, compare, realness, timeline, sheet, critique, measure, forge, fit, the 201-sound sfx bank, kits, gallery, the MCP server and two agent skills (earshot, real-sound). Models are downloaded from their authors: CLAP (Apache-2.0) and the AST AudioSet tagger published by MIT (BSD-3-Clause).
Self-hosted: Local only. Python 3.10+, ffmpeg/ffprobe on PATH, CPU PyTorch; first model use downloads about 1.1 GB into .cache/hf, then runs offline. measure, sfx and kit need no models at all. Runs as an MCP server via python -m earshot mcp.
Paid: No paid tier. The optional earshot llm FILE --yes-upload critique sends audio to Google Gemini on your own GEMINI_API_KEY, billed by Google; it is off unless you pass that flag.
Free and local; the only spend is optional Gemini API usage if you opt into the audio-LLM critique.
What to do by team size
- Solo
- free; clone, install CPU PyTorch, add the MCP server
- Small team
- free; each person installs it locally
- Medium team
- free; nothing to share or host
- Large team
- free; vet it first, it is a single-author alpha
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License: MIT License
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About
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- Leon Lin (User)
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