mirror of
https://github.com/correl/dejavu.git
synced 2024-11-23 11:09:52 +00:00
Fixed various small things that weren't caught before.
- Fixes SQL queries for table creations - Table creation is now down in reverse order to accompany the foreign key - Fixed a typo in the BaseRecognizer that caused it to not work - Changed configuration passed to Dejavu into a (nested) dictionary
This commit is contained in:
Wessie
parent
1c9eddc3a2
commit
3b72768f94
3 changed files with 59 additions and 74 deletions
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@ -8,20 +8,14 @@ import random
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DEBUG = False
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class Dejavu():
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class Dejavu(object):
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def __init__(self, config):
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self.config = config
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# initialize db
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database = SQLDatabase(
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_HOSTNAME),
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_USERNAME),
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_PASSWORD),
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_DATABASE))
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self.db = database
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self.db = SQLDatabase(**config.get("database", {}))
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# create components
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self.converter = Converter()
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#self.fingerprinter = Fingerprinter(self.config)
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@ -30,16 +24,16 @@ class Dejavu():
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# get songs previously indexed
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self.songs = self.db.get_songs()
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self.songnames_set = set() # to know which ones we've computed before
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if self.songs:
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for song in self.songs:
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song_id = song[SQLDatabase.FIELD_SONG_ID]
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song_name = song[SQLDatabase.FIELD_SONGNAME]
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self.songnames_set.add(song_name)
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print "Added: %s to the set of fingerprinted songs..." % song_name
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for song in self.songs:
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song_name = song[self.db.FIELD_SONGNAME]
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self.songnames_set.add(song_name)
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print "Added: %s to the set of fingerprinted songs..." % song_name
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def chunkify(self, lst, n):
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"""
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Splits a list into roughly n equal parts.
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Splits a list into roughly n equal parts.
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http://stackoverflow.com/questions/2130016/splitting-a-list-of-arbitrary-size-into-only-roughly-n-equal-parts
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"""
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return [lst[i::n] for i in xrange(n)]
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@ -55,25 +49,19 @@ class Dejavu():
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processes = []
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for i in range(nprocesses):
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# need database instance since mysql connections shouldn't be shared across processes
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sql_connection = SQLDatabase(
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_HOSTNAME),
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_USERNAME),
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_PASSWORD),
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self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_DATABASE))
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# create process and start it
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p = Process(target=self.fingerprint_worker, args=(files_split[i], sql_connection, output))
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p = Process(target=self.fingerprint_worker,
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args=(files_split[i], self.db, output))
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p.start()
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processes.append(p)
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# wait for all processes to complete
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for p in processes:
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p.join()
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# delete orphans
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# print "Done fingerprinting. Deleting orphaned fingerprints..."
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# TODO: need a more performant query in database.py for the
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# TODO: need a more performant query in database.py for the
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#self.fingerprinter.db.delete_orphans()
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def fingerprint_worker(self, files, sql_connection, output):
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@ -82,7 +70,7 @@ class Dejavu():
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# if there are already fingerprints in database, don't re-fingerprint or convert
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song_name = os.path.basename(filename).split(".")[0]
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if DEBUG and song_name in self.songnames_set:
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if DEBUG and song_name in self.songnames_set:
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print("-> Already fingerprinted, continuing...")
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continue
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@ -117,27 +105,27 @@ class Dejavu():
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for channel in range(nchannels):
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channels.append(frames[:, channel])
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return (channels, Fs)
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def fingerprint_file(self, filepath, song_name=None):
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# TODO: replace with something that handles all audio formats
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channels, Fs = self.extract_channels(path)
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if not song_name:
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song_name = os.path.basename(filename).split(".")[0]
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song_id = self.db.insert_song(song_name)
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for data in channels:
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hashes = fingerprint.fingerprint(data, Fs=Fs)
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self.db.insert_hashes(song_id, hashes)
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def find_matches(self, samples, Fs=fingerprint.DEFAULT_FS):
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hashes = fingerprint.fingerprint(samples, Fs=Fs)
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return self.db.return_matches(hashes)
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def align_matches(self, matches):
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"""
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Finds hash matches that align in time with other matches and finds
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consensus about which hashes are "true" signal from the audio.
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Returns a dictionary with match information.
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"""
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# align by diffs
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@ -158,24 +146,24 @@ class Dejavu():
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largest_count = diff_counter[diff][sid]
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song_id = sid
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if DEBUG:
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if DEBUG:
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print("Diff is %d with %d offset-aligned matches" % (largest, largest_count))
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# extract idenfication
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# extract idenfication
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song = self.db.get_song_by_id(song_id)
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if song:
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songname = song.get(SQLDatabase.FIELD_SONGNAME, None)
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else:
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return None
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if DEBUG:
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if DEBUG:
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print("Song is %s (song ID = %d) identification took %f seconds" % (songname, song_id, elapsed))
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# return match info
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song = {
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"song_id" : song_id,
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"song_name" : songname,
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"confidence" : largest_count
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}
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return song
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return song
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@ -70,7 +70,7 @@ class SQLDatabase(Database):
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`%s` binary(10) not null,
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`%s` mediumint unsigned not null,
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`%s` int unsigned not null,
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INDEX(%s),
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PRIMARY KEY(%s),
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UNIQUE(%s, %s, %s),
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FOREIGN KEY (%s) REFERENCES %s(%s) ON DELETE CASCADE
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) ENGINE=INNODB;""" % (
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@ -157,8 +157,8 @@ class SQLDatabase(Database):
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fingerprints associated with them.
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"""
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with self.cursor() as cur:
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cur.execute(self.CREATE_FINGERPRINTS_TABLE)
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cur.execute(self.CREATE_SONGS_TABLE)
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cur.execute(self.CREATE_FINGERPRINTS_TABLE)
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cur.execute(self.DELETE_UNFINGERPRINTED)
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def empty(self):
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@ -1,6 +1,6 @@
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from multiprocessing import Queue, Process
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from dejavu.database import SQLDatabase
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import dejavu.fingerprint
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import dejavu.fingerprint as fingerprint
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from dejavu import Dejavu
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from scipy.io import wavfile
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import wave
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@ -12,60 +12,57 @@ import array
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class BaseRecognizer(object):
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def __init__(self, dejavu):
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self.dejavu = dejavu
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self.Fs = dejavu.fingerprint.DEFAULT_FS
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self.Fs = fingerprint.DEFAULT_FS
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def _recognize(self, *data):
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matches = []
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for d in data:
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matches.extend(self.dejavu.find_matches(data, Fs=self.Fs))
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matches.extend(self.dejavu.find_matches(d, Fs=self.Fs))
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return self.dejavu.align_matches(matches)
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def recognize(self):
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pass # base class does nothing
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def recognize(self):
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pass # base class does nothing
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class WaveFileRecognizer(BaseRecognizer):
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def __init__(self, dejavu, filename=None):
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super(WaveFileRecognizer, self).__init__(dejavu)
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self.filename = filename
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def recognize_file(self, filename):
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Fs, frames = wavfile.read(filename)
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self.Fs = Fs
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wave_object = wave.open(filename)
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nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
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channels = []
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for channel in range(nchannels):
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channels.append(frames[:, channel])
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t = time.time()
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match = self._recognize(*channels)
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t = time.time() - t
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if match:
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match['match_time'] = t
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return match
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def recognize(self):
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return self.recognize_file(self.filename)
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class MicrophoneRecognizer(BaseRecognizer):
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CHUNK = 8192 # 44100 is a multiple of 1225
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FORMAT = pyaudio.paInt16
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CHANNELS = 2
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RATE = 44100
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def __init__(self, dejavu, seconds=None):
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super(MicrophoneRecognizer, self).__init__(dejavu)
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self.audio = pyaudio.PyAudio()
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@ -75,52 +72,52 @@ class MicrophoneRecognizer(BaseRecognizer):
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self.chunk_size = CHUNK
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self.rate = RATE
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self.recorded = False
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def start_recording(self, channels=CHANNELS, rate=RATE, chunk=CHUNK):
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self.chunk_size = chunk
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self.channels = channels
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self.recorded = False
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self.rate = rate
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if self.stream:
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self.stream.stop_stream()
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self.stream.close()
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self.stream = self.audio.open(format=FORMAT,
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channels=channels,
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rate=rate,
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input=True,
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frames_per_buffer=chunk)
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self.data = [[] for i in range(channels)]
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def process_recording(self):
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data = self.stream.read(self.chunk_size)
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nums = np.fromstring(data, np.int16)
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for c in range(self.channels):
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self.data[c].extend(nums[c::c+1])
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def stop_recording(self):
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self.stream.stop_stream()
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self.stream.close()
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self.stream = None
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self.recorded = True
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def recognize_recording(self):
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if not self.recorded:
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raise NoRecordingError("Recording was not complete/begun")
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return self._recognize(*self.data)
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def get_recorded_time(self):
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return len(self.data[0]) / self.rate
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def recognize(self):
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self.start_recording()
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for i in range(0, int(self.rate / self.chunk * self.seconds)):
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self.process_recording()
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self.stop_recording()
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return self.recognize_recording()
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class NoRecordingError(Exception):
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pass
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