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4d978f3541
This involves yet another new plugin method: album_distance. This leaves as the last major puzzle piece for lastid the ability to augment the initial search into MB (i.e., can start a search using fingerprinted metadata).
109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
# This file is part of beets.
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# Copyright 2010, Adrian Sampson.
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#
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# Permission is hereby granted, free of charge, to any person obtaining
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# a copy of this software and associated documentation files (the
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# "Software"), to deal in the Software without restriction, including
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# without limitation the rights to use, copy, modify, merge, publish,
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# distribute, sublicense, and/or sell copies of the Software, and to
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# permit persons to whom the Software is furnished to do so, subject to
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# the following conditions:
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#
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# The above copyright notice and this permission notice shall be
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# included in all copies or substantial portions of the Software.
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"""Adds Last.fm acoustic fingerprinting support to the autotagger.
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Requires the pylastfp library.
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"""
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from __future__ import with_statement
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from beets.plugins import BeetsPlugin
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from beets import autotag
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import lastfp
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import logging
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API_KEY = '2dc3914abf35f0d9c92d97d8f8e42b43'
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# The amplification factor for distances calculated from fingerprinted
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# data. With this set to 2.0, for instance, "fingerprinted" track titles
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# will be considered twice as important as track titles from ID3 tags.
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DISTANCE_SCALE = 2.0
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log = logging.getLogger('beets')
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_match_cache = {}
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def match(path, metadata=None):
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"""Gets the metadata from Last.fm for the indicated track. Returns
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a dictionary with these keys: rank, artist, artist_mbid, title,
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track_mbid. May return None if fingerprinting or lookup fails.
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Caches the result, so multiple calls may be made efficiently.
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"""
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if path in _match_cache:
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return _match_cache[path]
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# Actually perform fingerprinting and lookup.
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try:
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xml = lastfp.gst_match(API_KEY, path, metadata)
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matches = lastfp.parse_metadata(xml)
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except lastfp.FingerprintError:
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# Fail silently and cache the failure.
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matches = None
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match = matches[0] if matches else None
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_match_cache[path] = match
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return match
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class LastIdPlugin(BeetsPlugin):
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def track_distance(self, item, info):
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last_data = match(item.path)
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if not last_data:
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# Match failed.
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return 0.0, 0.0
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dist, dist_max = 0.0, 0.0
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# Track title distance.
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dist += autotag._ie_dist(last_data['title'],
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info['title']) \
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* autotag.TRACK_TITLE_WEIGHT
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dist_max += autotag.TRACK_TITLE_WEIGHT
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# MusicBrainz track ID.
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if last_data['track_mbid']:
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log.debug('Last track ID match: %s/%s' %
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(last_data['track_mbid'], track_data['id']))
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if last_data['track_mbid'] != track_data['id']:
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dist += autotag.TRACK_ID_WEIGHT
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dist_max += autotag.TRACK_ID_WEIGHT
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log.debug('Last data: %s; distance: %f' %
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(str(last_data), dist/dist_max))
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return dist * DISTANCE_SCALE, dist_max * DISTANCE_SCALE
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def album_distance(self, items, info):
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# Get "fingerprinted" artists for each track.
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artists = []
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artist_ids = []
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for item in items:
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last_data = match(item.path)
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if last_data:
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artists.append(last_data['artist'])
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if last_data['artist_mbid']:
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artist_ids.append(last_data['artist_mbid'])
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# Vote on the most popular artist.
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last_artist = autotag._plurality(artists)
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last_artist_id = autotag._plurality(artist_ids)
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# Compare artist to MusicBrainz metadata.
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dist, dist_max = 0.0, 0.0
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dist += autotag._ie_dist(last_artist, info['artist']) \
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* autotag.ARTIST_WEIGHT
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dist_max += autotag.ARTIST_WEIGHT
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log.debug('Last artist (%s/%s) distance: %f' %
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(last_artist, info['artist'], dist/dist_max))
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#fixme: artist MBID currently ignored (as in vanilla tagger)
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return dist, dist_max
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