pyacoustid 0.5

Installation

First, install the Chromaprint fingerprinting library by Lukas Lalinsky. (The library itself depends on an FFT library, but it’s smart enough to use an algorithm from software you probably already have installed; see the Chromaprint page for details.) This module can use either the Chromaprint dynamic library or the fpcalc command-line tool, which itself depends on libavcodec. If you use fpcalc, either ensure that it is on your $PATH or set the FPCALC environment variable to its location.

Then you can install this library from PyPI using pip:

$ pip install pyacoustid

This library uses audioread to do audio decoding (pip should automatically install this dependency), but it’s not really necessary if you already have decoded audio.

Running

You can run the included demonstration script, aidmatch.py, to test your installation:

$ python aidmatch.py mysterious_music.mp3

This will show the top metadata match from Acoustid’s database. The script uses audioread to decode music, so it should transparently use a media library available on your system (GStreamer, FFmpeg, MAD, or Core Audio).

Using in Your Code

The simplest way to use pyacoustid to identify audio files is to call the match function:

>>> import acoustid
>>> for score, recording_id, title, artist in acoustid.match(apikey, path):
>>>     ...

This convenience function uses audioread to decode audio and parses the response for you, pulling out the most important track metadata. It returns in iterable over tuples of relevant information. Everything happens in one fell swoop. There are also a number of “smaller” functions you can use to perform parts of the process:

• fingerprint(samplerate, channels, pcmiter): Generate a fingerprint for raw audio data. Specify the audio parameters and give an iterable containing blocks of PCM data.

• lookup(apikey, fingerprint, duration): Make a request to the Acoustid API to look up the fingerprint returned by the previous function. An API key is required, as is the length, in seconds, of the source audio. Returns a parsed JSON response.

• parse_lookup_result(data): Given a parsed JSON response, return an iterator over tuples containing the match score (a float between 0 and 1), the MusicBrainz recording ID, title, and artist name for each match.

The module internally performs thread-safe API rate limiting to 3 queries per second whenever the Web API is called, in accordance with the Web service documentation.

If you’re running your own Acoustid database server, you can set the base URL for all API calls with the set_base_url function.

Calls to the library can raise AcoustidError exceptions of two subtypes: FingerprintGenerationError and WebServiceError. Catch these exceptions if you want to proceed when audio can’t be decoded or no match is found on the server. NoBackendError, a subclass of FingerprintGenerationError, is used when the Chromaprint library or fpcalc command-line tool cannot be found.

Version History

0.5

Fix response parsing when recording has no artists or title. Fix compatibility with Python < 2.7. Add specific NoBackendError exception.

0.4

Fingerprinting can now fall back to using the fpcalc command-line tool instead of the Chromaprint dynamic library so the library can be used with the binary distributions (thanks to Lukas Lalinsky). Fingerprint submission (thanks to Alastair Porter). Data chunks can now be buffers as well as bytestrings (fixes compatibility with pymad).

0.3

Configurable API base URL. Result parser now generates all results instead of returning just one. Find the chromaprint library on Cygwin. New module names: chromaprint and acoustid (no package).

0.2

Compress HTTP requests and responses. Limit audio decoding to 120 seconds. Return score from convenience function.

0.1

Initial release.

Credits

This library is by Adrian Sampson. Chromaprint and Acoustid are by Lukas Lalinsky. This package includes the original ctypes-based bindings written by Lukas. The entire library is made available under the MIT license. pyacoustid was written to be used with beets, which you should probably check out.