acidcat

Audio metadata explorer and analysis tool -- like exiftool, but for audio.

Reads BPM, key, duration, tags, and format info from WAV, AIFF, MP3, FLAC, OGG, Opus, M4A, MIDI, and Serum presets. Zero dependencies for core metadata. Optional librosa analysis for BPM/key detection and ML feature extraction.

Also ships per-library SQLite indexes (acidcat index) tracked in a small global registry, plus an MCP server (acidcat-mcp) so an LLM can query your whole collection across libraries by bpm, key, tags, or full-text.

Install

git clone https://github.com/hed0rah/acidcat.git
cd acidcat
pip install -e .                # core + mutagen (WAV/AIFF/MIDI/Serum/MP3/FLAC/OGG/Opus/M4A)
pip install -e .[analysis]      # + librosa BPM/key detection
pip install -e .[ml]            # + sklearn similarity/clustering
pip install -e .[mcp]           # + MCP server (acidcat-mcp)
pip install -e .[all]           # everything

Quick Start

# single file -- instant metadata
acidcat kick_808.wav
acidcat loop.mp3
acidcat pad.flac

# pipe from stdin
cat file.wav | acidcat
curl https://example.com/loop.mp3 | acidcat -

# JSON output for piping
acidcat kick_808.wav -f json | jq .BPM

# deep analysis with librosa
acidcat kick_808.wav --deep

# scan a mixed-format directory
acidcat scan ~/Samples/Breaks -n 200

Supported Formats

Format	Extension	What acidcat reads
WAV	.wav	BPM, key, loop points, beats, ACID/SMPL chunks, LIST/INFO
AIFF	.aif	Duration, format, name, author, copyright, markers
MP3	.mp3	BPM, key, title, artist, album, genre, comment (ID3v2)
FLAC	.flac	BPM, key, title, artist, album, genre (Vorbis Comment)
OGG	.ogg	BPM, key, title, artist, album, genre (Vorbis Comment)
Opus	.opus	BPM, key, title, artist (Vorbis Comment)
M4A	.m4a	BPM, key, title, artist, album, genre (iTunes atoms)
MIDI	.mid	BPM, key sig, time sig, tracks, note count/range
Serum	.SerumPreset	Preset name, author, tags, description

Commands

Command	Description
acidcat FILE	Show metadata for a single file (auto-detected)
acidcat DIR	Batch-scan a directory (auto-detected)
acidcat -	Read from stdin
acidcat info FILE	Explicit single-file metadata dump
acidcat scan DIR	Batch-scan with CSV output
acidcat chunks FILE	Walk RIFF chunks -- offsets, sizes, parsed fields
acidcat survey DIR	Count chunk types across a directory tree
acidcat detect FILE|DIR	Estimate BPM/key using librosa
acidcat features DIR	Extract 50+ audio features for ML
acidcat similar CSV find TARGET	Find similar samples by features
acidcat similar CSV cluster	Cluster samples by audio characteristics
acidcat search CSV query TEXT	Text-based sample search (legacy CSV)
acidcat dump FILE CHUNK [...]	Hex-dump specific RIFF chunks
acidcat index DIR	Upsert DIR into the global SQLite index
acidcat query [flags]	Filter the global index by bpm/key/tag/text

Global Flags

-f, --format {table,json,csv}   Output format (default: table)
-o, --output FILE               Write output to file
-q, --quiet                     Suppress progress output
-v, --verbose                   Extra detail
-n, --num N                     Max files to scan (default: 500)
--has CHUNKS                    Filter by chunk IDs (comma-separated)
--deep                          Include librosa analysis

Dependency Groups

Group	What it adds	Commands enabled
(none)	mutagen (base)	info, scan, chunks, survey, dump for WAV/AIFF/MIDI/Serum/MP3/FLAC/OGG/Opus/M4A
[analysis]	librosa, numpy, scipy	detect, info --deep
[ml]	+ pandas, scikit-learn	features, similar, search
[mcp]	mcp SDK	acidcat-mcp stdio server
[all]	everything	all commands, all formats

Examples

Metadata Exploration

# what chunks exist in your sample library?
acidcat survey ~/Samples/Loops -n 5000

# walk all chunks in a specific file
acidcat chunks ~/Samples/Loops/breakbeat.wav

# hex-dump the ACID and SMPL chunks
acidcat dump ~/Samples/Loops/breakbeat.wav acid smpl

# scan only files with ACID metadata
acidcat scan ~/Samples/Loops --has acid -n 200

# scan a directory with mixed formats (WAV, MP3, FLAC, etc.)
acidcat scan ~/Samples -n 500

BPM / Key Detection

# estimate BPM/key with librosa (for files without metadata)
acidcat detect ~/Samples/OneShots

# scan with librosa fallback for missing metadata
acidcat scan ~/Samples/Loops --fallback -n 100

ML Feature Extraction

# extract 50+ audio features to CSV
acidcat features ~/Samples/Loops -n 500

# generate normalized (StandardScaler) ML-ready dataset
acidcat features ~/Samples/Loops --ml-ready -n 500

Similarity & Clustering

# find 5 samples similar to index 0
acidcat similar features.csv find 0 -n 5

# k-means clustering
acidcat similar features.csv cluster -k 10 -o clustered.csv

Libraries (per-directory indexes)

acidcat scan writes a one-off CSV. acidcat index is the persistent path: each directory you index becomes a library with its own SQLite file, and a small global registry at ~/.acidcat/registry.db lets reads fan out across every library you have registered.

By default the per-library DB lives centrally at ~/.acidcat/libraries/<label>_<hash>.db. Pass --in-tree if you'd rather have the DB travel with the data at <library>/.acidcat/index.db.

# register and index a library (label defaults to basename of DIR)
acidcat index ~/Samples/Loops --label loops
acidcat index ~/Samples/OneShots --label oneshots

# show every registered library
acidcat index --list

# per-library stats
acidcat index --stats loops

# extract librosa features during indexing (slower, enables similarity)
acidcat index ~/Samples/Loops --label loops --features

# rebuild a library's DB from scratch
acidcat index ~/Samples/Loops --label loops --rebuild

# forget a library (registry only) vs remove it (deletes the DB file)
acidcat index --forget loops
acidcat index --remove loops

# list registered libraries whose DB file is missing on disk
acidcat index --orphans

# import a legacy <name>_tags.json into a library
acidcat index ~/Samples --label samples --import-tags old_tags.json

Nested libraries are rejected at registration time: if you've registered ~/Samples, you can't also register ~/Samples/Loops until you forget the parent.

Discovery

For users with many scattered packs, --discover walks a tree and registers every qualifying subdirectory as its own library in one pass.

# preview what would get registered (no writes)
acidcat index --discover ~/Samples --dry-run

# actually register them
acidcat index --discover ~/Samples

# tighter threshold and namespacing for a subset of your collection
acidcat index --discover /mnt/external/old_drives \
              --min-samples 50 --label-prefix "ext_"

A directory qualifies if its subtree (within --max-depth, default 3) contains at least --min-samples audio files (default 20). Non- qualifying parents are recursed into so packs nested inside catch-all folders still surface. Already-registered roots are skipped. The home directory is refused as a discover root to prevent runaway registration.

Querying

By default acidcat query fans out across every registered library and merges the results.

acidcat query --bpm 120:130 --key Am
acidcat query --tag drums --tag punchy --duration :1
acidcat query --text "dusty lofi" --limit 20
acidcat query --format mp3 --root loops
acidcat query --root loops,oneshots --bpm 128
acidcat query --bpm 128 --paths-only | xargs -I {} cp {} out/

--root accepts a label, an absolute path, or a comma-separated list. Override the registry on any command with --registry PATH or the ACIDCAT_REGISTRY environment variable.

MCP Server

acidcat-mcp is a stdio MCP server that exposes the registered libraries as structured tools. An LLM can ask "what libraries do I have?", search across them by metadata, find compatible keys via Camelot, or (with [analysis] installed) find similar samples by librosa feature cosine.

pip install -e .[tags,mcp]            # minimum for discovery + writes
pip install -e .[tags,analysis,mcp]   # unlock find_similar / analyze_*

Claude Desktop / Claude Code config:

{
  "mcpServers": {
    "acidcat": {
      "command": "acidcat-mcp"
    }
  }
}

Optional: pass --registry PATH on the server process or set ACIDCAT_REGISTRY if your registry lives outside the default location.

Tool tiers (each tool description starts with Fast., SLOW., or VERY SLOW. so the model self-selects):

• Fast (SQLite only): search_samples, get_sample, locate_sample, list_libraries, list_tags, list_keys, list_formats, index_stats, find_compatible
• Slow analysis (needs [analysis]): find_similar, analyze_sample, detect_bpm_key
• Index management: reindex, reindex_features, discover_libraries
• Write (marked destructive): register_library, forget_library, tag_sample, set_sample_description

License

MIT