Leonxlnx/earshot

选用指南

AI 根据 README 整理 · 2026/10/16 10:41:28

使用方式

通过命令行、Python API 或 MCP 服务器使用，支持多种音频分析命令。

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earshot turns sound into evidence an agent can reason about: what a file sounds like in words, how well it fits a description, the best moment inside a long recording, whether it is a real recording or code-synthesized, a second-by-second listening transcript of a whole film, an annotated picture of it, and a scorecard against professional launch films. It runs locally on CPU, as a CLI, a Python API and an MCP server. With ears, an agent can also **make its own sounds in code** and improve them by listening (`earshot forge`, below). ``` $ earshot describe enter.ogg

部署要求

需要 Python 3.10+ 和 ffmpeg/ffprobe，仅支持 CPU，首次使用需下载约 1.4GB 模型。

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getting better. After a search the made sounds pass every model check, held-out ones included, so the model checks stop telling made from real; the 1/3-octave measurement still can. The realness model is easy to fool (a modal resonator plus a room and a noise floor reads "real"). Judge a made sound by the model-free measurements, spectrograms next to the references, robust re-renders, and a human blind test. ## Install Python 3.10+ and ffmpeg / ffprobe on PATH. CPU only; no GPU is used. ```bash git clone https://github.com/Leonxlnx/earshot && cd earshot python -m venv .venv

.venv/bin/python -m pip install torch --index-url https://download.pytorch.org/whl/cpu # Windows: .venv/Scripts/python .venv/bin/python -m pip install -e ".[mcp,llm,dev]" .venv/bin/python -m earshot doctor --load ``` The first model use downloads two open models (~1.4 GB) into `.cache/hf` inside the repo (or `EARSHOT_HOME`): CLAP `laion/larger_clap_general` (text-audio embeddings) and the AudioSet tagger `MIT/ast-finetuned-audioset-10-10-0.4593`. After that earshot works offline.

已知限制

无法判断品味、情感、来源或许可证，无法处理语音内容，模型无法分析 14kHz 以上频率。

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"is this the kind of sound I asked for" on single exposed sounds, picking among candidates, obvious code-synthesized sources. Hints only: tags of events inside a busy mix, music genre and mood. It cannot judge taste or emotion, provenance or licences, speech content, or anything above 14 kHz from the models. The launch scorecard describes the style of the 7 films behind it, not every good film. Details in [docs/LIMITS.md](docs/LIMITS.md). ## Repo map ``` earshot/ the package: CLI, Python API, MCP server, CPU model wrappers