Merged with fingerprinter and recognizer cleanup

dejavu/__init__.py

@@ -0,0 +1,181 @@
+from dejavu.database import SQLDatabase
+from dejavu.convert import Converter
+import fingerprint
+from scipy.io import wavfile
+from multiprocessing import Process
+import wave, os
+import random
+
+DEBUG = False
+
+class Dejavu():
+
+    def __init__(self, config):
+    
+        self.config = config
+        
+        # initialize db
+        database = SQLDatabase(
+            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_HOSTNAME),
+            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_USERNAME),
+            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_PASSWORD),
+            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_DATABASE))
+        self.db = database
+        
+        # create components
+        self.converter = Converter()
+        #self.fingerprinter = Fingerprinter(self.config)
+        self.db.setup()
+
+        # get songs previously indexed
+        self.songs = self.db.get_songs()
+        self.songnames_set = set() # to know which ones we've computed before
+        if self.songs:
+            for song in self.songs:
+                song_id = song[SQLDatabase.FIELD_SONG_ID]
+                song_name = song[SQLDatabase.FIELD_SONGNAME]
+                self.songnames_set.add(song_name)
+                print "Added: %s to the set of fingerprinted songs..." % song_name
+
+    def chunkify(self, lst, n):
+        """
+            Splits a list into roughly n equal parts. 
+            http://stackoverflow.com/questions/2130016/splitting-a-list-of-arbitrary-size-into-only-roughly-n-equal-parts
+        """
+        return [lst[i::n] for i in xrange(n)]
+
+    def do_fingerprint(self, path, output, extensions, nprocesses):
+
+        # convert files, shuffle order
+        files = self.converter.find_files(path, extensions)
+        random.shuffle(files)
+        files_split = self.chunkify(files, nprocesses)
+
+        # split into processes here
+        processes = []
+        for i in range(nprocesses):
+
+            # need database instance since mysql connections shouldn't be shared across processes
+            sql_connection = SQLDatabase( 
+                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_HOSTNAME),
+                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_USERNAME),
+                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_PASSWORD),
+                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_DATABASE))
+
+            # create process and start it
+            p = Process(target=self.fingerprint_worker, args=(files_split[i], sql_connection, output))
+            p.start()
+            processes.append(p)
+
+        # wait for all processes to complete
+        for p in processes:
+            p.join()
+            
+        # delete orphans
+        # print "Done fingerprinting. Deleting orphaned fingerprints..."
+        # TODO: need a more performant query in database.py for the 
+        #self.fingerprinter.db.delete_orphans()
+
+    def fingerprint_worker(self, files, sql_connection, output):
+
+        for filename, extension in files:
+
+            # if there are already fingerprints in database, don't re-fingerprint or convert
+            song_name = os.path.basename(filename).split(".")[0]
+            if DEBUG and song_name in self.songnames_set: 
+                print("-> Already fingerprinted, continuing...")
+                continue
+
+            # convert to WAV
+            wavout_path = self.converter.convert(filename, extension, Converter.WAV, output, song_name)
+
+            # insert song name into database
+            song_id = sql_connection.insert_song(song_name)
+
+            # for each channel perform FFT analysis and fingerprinting
+            channels, Fs = self.extract_channels(wavout_path)
+            for c in range(len(channels)):
+                channel = channels[c]
+                print "-> Fingerprinting channel %d of song %s..." % (c+1, song_name)
+                hashes = fingerprint.fingerprint(channel, Fs=Fs)
+                sql_connection.insert_hashes(song_id, hashes)
+
+            # only after done fingerprinting do confirm
+            sql_connection.set_song_fingerprinted(song_id)
+
+    def extract_channels(self, path):
+        """
+            Reads channels from disk.
+            Returns a tuple with (channels, sample_rate)
+        """
+        channels = []
+        Fs, frames = wavfile.read(path)
+        wave_object = wave.open(path)
+        nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
+        #assert Fs == self.fingerprinter.Fs
+
+        for channel in range(nchannels):
+            channels.append(frames[:, channel])
+        return (channels, Fs)
+    
+    def fingerprint_file(self, filepath, song_name=None):
+        # TODO: replace with something that handles all audio formats
+        channels, Fs = self.extract_channels(path)
+        if not song_name:
+            song_name = os.path.basename(filename).split(".")[0]
+        song_id = self.db.insert_song(song_name)
+        
+        for data in channels:
+            hashes = fingerprint.fingerprint(data, Fs=Fs)
+            self.db.insert_hashes(song_id, hashes)
+    
+    def find_matches(self, samples, Fs=fingerprint.DEFAULT_FS):
+        hashes = fingerprint.fingerprint(samples, Fs=Fs)
+        return self.db.return_matches(hashes)
+    
+    def align_matches(self, matches):
+        """
+            Finds hash matches that align in time with other matches and finds
+            consensus about which hashes are "true" signal from the audio.
+            
+            Returns a dictionary with match information.
+        """
+        # align by diffs
+        diff_counter = {}
+        largest = 0
+        largest_count = 0
+        song_id = -1
+        for tup in matches:
+            sid, diff = tup
+            if not diff in diff_counter:
+                diff_counter[diff] = {}
+            if not sid in diff_counter[diff]:
+                diff_counter[diff][sid] = 0
+            diff_counter[diff][sid] += 1
+
+            if diff_counter[diff][sid] > largest_count:
+                largest = diff
+                largest_count = diff_counter[diff][sid]
+                song_id = sid
+
+        if DEBUG: 
+            print("Diff is %d with %d offset-aligned matches" % (largest, largest_count))
+        
+        # extract idenfication      
+        song = self.db.get_song_by_id(song_id)
+        if song:
+            songname = song.get(SQLDatabase.FIELD_SONGNAME, None)
+        else:
+            return None
+        
+        if DEBUG: 
+            print("Song is %s (song ID = %d) identification took %f seconds" % (songname, song_id, elapsed))
+        
+        # return match info
+        song = {
+            "song_id" : song_id,
+            "song_name" : songname,
+            "confidence" : largest_count
+        }
+            
+        return song

dejavu/control.py

@@ -1,107 +0,0 @@
-from dejavu.database import SQLDatabase
-from dejavu.convert import Converter
-from dejavu.fingerprint import Fingerprinter
-from scipy.io import wavfile
-from multiprocessing import Process
-import wave, os
-import random
-
-class Dejavu():
-
-    def __init__(self, config):
-    
-        self.config = config
-
-        # create components
-        self.converter = Converter()
-        self.fingerprinter = Fingerprinter(self.config)
-        self.fingerprinter.db.setup()
-
-        # get songs previously indexed
-        self.songs = self.fingerprinter.db.get_songs()
-        self.songnames_set = set() # to know which ones we've computed before
-        if self.songs:
-            for song in self.songs:
-                song_id = song[SQLDatabase.FIELD_SONG_ID]
-                song_name = song[SQLDatabase.FIELD_SONGNAME]
-                self.songnames_set.add(song_name)
-                print "Added: %s to the set of fingerprinted songs..." % song_name
-
-    def chunkify(self, lst, n):
-        """
-            Splits a list into roughly n equal parts. 
-            http://stackoverflow.com/questions/2130016/splitting-a-list-of-arbitrary-size-into-only-roughly-n-equal-parts
-        """
-        return [lst[i::n] for i in xrange(n)]
-
-    def fingerprint(self, path, output, extensions, nprocesses):
-
-        # convert files, shuffle order
-        files = self.converter.find_files(path, extensions)
-        random.shuffle(files)
-        files_split = self.chunkify(files, nprocesses)
-
-        # split into processes here
-        processes = []
-        for i in range(nprocesses):
-
-            # need database instance since mysql connections shouldn't be shared across processes
-            sql_connection = SQLDatabase( 
-                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_HOSTNAME),
-                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_USERNAME),
-                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_PASSWORD),
-                self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_DATABASE))
-
-            # create process and start it
-            p = Process(target=self.fingerprint_worker, args=(files_split[i], sql_connection, output))
-            p.start()
-            processes.append(p)
-
-        # wait for all processes to complete
-        for p in processes:
-            p.join()
-            
-        # delete orphans
-        # print "Done fingerprinting. Deleting orphaned fingerprints..."
-        # TODO: need a more performant query in database.py for the 
-        #self.fingerprinter.db.delete_orphans()
-
-    def fingerprint_worker(self, files, sql_connection, output):
-
-        for filename, extension in files:
-
-            # if there are already fingerprints in database, don't re-fingerprint or convert
-            song_name = os.path.basename(filename).split(".")[0]
-            if song_name in self.songnames_set: 
-                print "-> Already fingerprinted, continuing..."
-                continue
-
-            # convert to WAV
-            wavout_path = self.converter.convert(filename, extension, Converter.WAV, output, song_name)
-
-            # insert song name into database
-            song_id = sql_connection.insert_song(song_name)
-
-            # for each channel perform FFT analysis and fingerprinting
-            channels = self.extract_channels(wavout_path)
-            for c in range(len(channels)):
-                channel = channels[c]
-                print "-> Fingerprinting channel %d of song %s..." % (c+1, song_name)
-                self.fingerprinter.fingerprint(channel, wavout_path, song_id, c+1)
-
-            # only after done fingerprinting do confirm
-            sql_connection.set_song_fingerprinted(song_id)
-
-    def extract_channels(self, path):
-        """
-            Reads channels from disk.
-        """
-        channels = []
-        Fs, frames = wavfile.read(path)
-        wave_object = wave.open(path)
-        nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
-        assert Fs == self.fingerprinter.Fs
-
-        for channel in range(nchannels):
-            channels.append(frames[:, channel])
-        return channels

dejavu/database.py

@@ -280,15 +280,11 @@ class SQLDatabase(Database):
     def return_matches(self, hashes):
         """
         Return the (song_id, offset_diff) tuples associated with
-        a list of
+        a list of (sha1, sample_offset) values.
-
-            sha1 => (None, sample_offset)
-
-        values.
         """
         # Create a dictionary of hash => offset pairs for later lookups
         mapper = {}
-        for hash, (_, offset) in hashes:
+        for hash, offset in hashes:
             mapper[hash.upper()] = offset
 
         # Get an iteratable of all the hashes we need

dejavu/fingerprint.py

@@ -13,19 +13,117 @@ import time
 import hashlib
 import pickle
 
+IDX_FREQ_I = 0
+IDX_TIME_J = 1
+
+DEFAULT_FS = 44100
+DEFAULT_WINDOW_SIZE = 4096
+DEFAULT_OVERLAP_RATIO = 0.5
+DEFAULT_FAN_VALUE = 15
+
+DEFAULT_AMP_MIN = 10
+PEAK_NEIGHBORHOOD_SIZE = 20
+MIN_HASH_TIME_DELTA = 0
+
+def fingerprint(channel_samples,
+            Fs=DEFAULT_FS, 
+            wsize=DEFAULT_WINDOW_SIZE, 
+            wratio=DEFAULT_OVERLAP_RATIO, 
+            fan_value=DEFAULT_FAN_VALUE, 
+            amp_min=DEFAULT_AMP_MIN):
+    """
+        FFT the channel, log transform output, find local maxima, then return
+        locally sensitive hashes. 
+    """
+    # FFT the signal and extract frequency components
+    arr2D = mlab.specgram(
+        channel_samples, 
+        NFFT=wsize, 
+        Fs=Fs,
+        window=mlab.window_hanning,
+        noverlap=int(wsize * wratio))[0]
+
+    # apply log transform since specgram() returns linear array
+    arr2D = 10 * np.log10(arr2D)
+    arr2D[arr2D == -np.inf] = 0 # replace infs with zeros
+    
+    # find local maxima
+    local_maxima = get_2D_peaks(arr2D, plot=False, amp_min=amp_min)
+
+    # return hashes
+    return generate_hashes(local_maxima, fan_value=fan_value)
+
+def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
+
+    # http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.morphology.iterate_structure.html#scipy.ndimage.morphology.iterate_structure
+    struct = generate_binary_structure(2, 1)
+    neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
+
+    # find local maxima using our fliter shape
+    local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D 
+    background = (arr2D == 0)
+    eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
+    detected_peaks = local_max - eroded_background # this is a boolean mask of arr2D with True at peaks
+
+    # extract peaks
+    amps = arr2D[detected_peaks]
+    j, i = np.where(detected_peaks) 
+
+    # filter peaks
+    amps = amps.flatten()
+    peaks = zip(i, j, amps)
+    peaks_filtered = [x for x in peaks if x[2] > amp_min] # freq, time, amp
+    
+    # get indices for frequency and time
+    frequency_idx = [x[1] for x in peaks_filtered]
+    time_idx = [x[0] for x in peaks_filtered]
+
+    if plot:
+        # scatter of the peaks
+        fig, ax = plt.subplots()
+        ax.imshow(arr2D)
+        ax.scatter(time_idx, frequency_idx)
+        ax.set_xlabel('Time')
+        ax.set_ylabel('Frequency')
+        ax.set_title("Spectrogram of \"Blurred Lines\" by Robin Thicke");
+        plt.gca().invert_yaxis()
+        plt.show()
+
+    return zip(frequency_idx, time_idx)
+
+def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
+    """
+    Hash list structure:
+       sha1-hash[0:20]    time_offset
+    [(e05b341a9b77a51fd26, 32), ... ]
+    """
+    fingerprinted = set() # to avoid rehashing same pairs
+    hashes = []
+
+    for i in range(len(peaks)):
+        for j in range(fan_value):
+            if i+j < len(peaks) and not (i, i+j) in fingerprinted:
+                
+                freq1 = peaks[i][IDX_FREQ_I]
+                freq2 = peaks[i+j][IDX_FREQ_I]
+                t1 = peaks[i][IDX_TIME_J]
+                t2 = peaks[i+j][IDX_TIME_J]
+                t_delta = t2 - t1
+                
+                if t_delta >= MIN_HASH_TIME_DELTA:
+                    h = hashlib.sha1("%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
+                    hashes.append((h.hexdigest()[0:20], t1))
+                
+                # ensure we don't repeat hashing
+                fingerprinted.add((i, i+j))
+    return hashes
+
+# TODO: move all of the below to a class with DB access
+
+
 class Fingerprinter():
 
-    IDX_FREQ_I = 0
+
-    IDX_TIME_J = 1
-    
-    DEFAULT_FS = 44100
-    DEFAULT_WINDOW_SIZE = 4096
-    DEFAULT_OVERLAP_RATIO = 0.5
-    DEFAULT_FAN_VALUE = 15
-    
-    DEFAULT_AMP_MIN = 10
-    PEAK_NEIGHBORHOOD_SIZE = 20
-    MIN_HASH_TIME_DELTA = 0
 
     def __init__(self, config, 
             Fs=DEFAULT_FS, 
@@ -35,12 +133,7 @@ class Fingerprinter():
             amp_min=DEFAULT_AMP_MIN):
 
         self.config = config
-        database = SQLDatabase(
+        
-            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_HOSTNAME),
-            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_USERNAME),
-            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_PASSWORD),
-            self.config.get(SQLDatabase.CONNECTION, SQLDatabase.KEY_DATABASE))
-        self.db = database
 
         self.Fs = Fs
         self.dt = 1.0 / self.Fs
@@ -55,104 +148,15 @@ class Fingerprinter():
         hashes = self.process_channel(samples, song_id=sid)
         print "Generated %d hashes" % len(hashes)
         self.db.insert_hashes(hashes)
-
+    
+    # TODO: put this in another module
     def match(self, samples):
         """Used for matching unknown songs"""
         hashes = self.process_channel(samples)
         matches = self.db.return_matches(hashes)
         return matches
 
-    def process_channel(self, channel_samples, song_id=None):
+    # TODO: this function has nothing to do with fingerprinting. is it needed?
-        """
-            FFT the channel, log transform output, find local maxima, then return
-            locally sensitive hashes. 
-        """
-        # FFT the signal and extract frequency components
-        arr2D = mlab.specgram(
-            channel_samples, 
-            NFFT=self.window_size, 
-            Fs=self.Fs,
-            window=mlab.window_hanning,
-            noverlap=self.noverlap)[0]
-
-        # apply log transform since specgram() returns linear array
-        arr2D = 10 * np.log10(arr2D)
-        arr2D[arr2D == -np.inf] = 0 # replace infs with zeros
-        
-        # find local maxima
-        local_maxima = self.get_2D_peaks(arr2D, plot=False)
-
-        # return hashes
-        return self.generate_hashes(local_maxima, song_id=song_id)
-
-    def get_2D_peaks(self, arr2D, plot=False):
-
-        # http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.morphology.iterate_structure.html#scipy.ndimage.morphology.iterate_structure
-        struct = generate_binary_structure(2, 1)
-        neighborhood = iterate_structure(struct, Fingerprinter.PEAK_NEIGHBORHOOD_SIZE) 
-
-        # find local maxima using our fliter shape
-        local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D 
-        background = (arr2D == 0)
-        eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
-        detected_peaks = local_max - eroded_background # this is a boolean mask of arr2D with True at peaks
-
-        # extract peaks
-        amps = arr2D[detected_peaks]
-        j, i = np.where(detected_peaks) 
-
-        # filter peaks
-        amps = amps.flatten()
-        peaks = zip(i, j, amps)
-        peaks_filtered = [x for x in peaks if x[2] > self.amp_min] # freq, time, amp
-        
-        # get indices for frequency and time
-        frequency_idx = [x[1] for x in peaks_filtered]
-        time_idx = [x[0] for x in peaks_filtered]
-
-        if plot:
-            # scatter of the peaks
-            fig, ax = plt.subplots()
-            ax.imshow(arr2D)
-            ax.scatter(time_idx, frequency_idx)
-            ax.set_xlabel('Time')
-            ax.set_ylabel('Frequency')
-            ax.set_title("Spectrogram of \"Blurred Lines\" by Robin Thicke");
-            plt.gca().invert_yaxis()
-            plt.show()
-
-        return zip(frequency_idx, time_idx)
-
-    def generate_hashes(self, peaks, song_id=None):
-        """
-        Hash list structure:
-           sha1-hash[0:20]    song_id, time_offset
-        [(e05b341a9b77a51fd26,   (3, 32)), ... ]
-        """
-        fingerprinted = set() # to avoid rehashing same pairs
-        hashes = []
-
-        for i in range(len(peaks)):
-            for j in range(self.fan_value):
-                if i+j < len(peaks) and not (i, i+j) in fingerprinted:
-
-                    freq1 = peaks[i][Fingerprinter.IDX_FREQ_I]
-                    freq2 = peaks[i+j][Fingerprinter.IDX_FREQ_I]
-                    t1 = peaks[i][Fingerprinter.IDX_TIME_J]
-                    t2 = peaks[i+j][Fingerprinter.IDX_TIME_J]
-                    t_delta = t2 - t1
-                    
-                    if t_delta >= Fingerprinter.MIN_HASH_TIME_DELTA:
-                        h = hashlib.sha1("%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
-                        hashes.append((h.hexdigest()[0:20], (song_id, t1)))
-                    
-                    # ensure we don't repeat hashing
-                    fingerprinted.add((i, i+j))
-        return hashes
-
-    def insert_into_db(self, key, value):
-        self.db.insert(key, value)
-
     def print_stats(self):
 
         iterable = self.db.get_iterable_kv_pairs()
@@ -168,59 +172,10 @@ class Fingerprinter():
         for song_id, count in counter.iteritems():
             song_name = self.song_names[song_id]
             print "%s has %d spectrogram peaks" % (song_name, count)
-
+    
+    # this does... what? this seems to only be used for the above function
     def set_song_names(self, wpaths):
         self.song_names = wpaths
-        
+    
-    def align_matches(self, matches, starttime, record_seconds=0, verbose=False):
+    # TODO: put this in another module
-        """
-            Finds hash matches that align in time with other matches and finds
-            consensus about which hashes are "true" signal from the audio.
-            
-            Returns a dictionary with match information.
-        """
-        # align by diffs
-        diff_counter = {}
-        largest = 0
-        largest_count = 0
-        song_id = -1
-        for tup in matches:
-            sid, diff = tup
-            if not diff in diff_counter:
-                diff_counter[diff] = {}
-            if not sid in diff_counter[diff]:
-                diff_counter[diff][sid] = 0
-            diff_counter[diff][sid] += 1
 
-            if diff_counter[diff][sid] > largest_count:
-                largest = diff
-                largest_count = diff_counter[diff][sid]
-                song_id = sid
-
-        if verbose: 
-            print "Diff is %d with %d offset-aligned matches" % (largest, largest_count)
-        
-        # extract idenfication      
-        song = self.db.get_song_by_id(song_id)
-        if song:
-            songname = song.get(SQLDatabase.FIELD_SONGNAME, None)
-        else:
-            return None
-        songname = songname.replace("_", " ")
-        elapsed = time.time() - starttime
-        
-        if verbose: 
-            print "Song is %s (song ID = %d) identification took %f seconds" % (songname, song_id, elapsed)
-        
-        # return match info
-        song = {
-            "song_id" : song_id,
-            "song_name" : songname,
-            "match_time" : elapsed,
-            "confidence" : largest_count
-        }
-        
-        if record_seconds: 
-            song['record_time'] = record_seconds
-            
-        return song

dejavu/recognize.py

@@ -1,5 +1,7 @@
 from multiprocessing import Queue, Process
 from dejavu.database import SQLDatabase
+import dejavu.fingerprint
+from dejavu import Dejavu
 from scipy.io import wavfile
 import wave
 import numpy as np
@@ -8,65 +10,117 @@ import sys
 import time
 import array
 
-class Recognizer(object):
 
+class BaseRecognizer(object):
+    
+    def __init__(self, dejavu):
+        self.dejavu = dejavu
+        self.Fs = dejavu.fingerprint.DEFAULT_FS
+    
+    def _recognize(self, *data):
+        matches = []
+        for d in data:
+            matches.extend(self.dejavu.find_matches(data, Fs=self.Fs))
+        return self.dejavu.align_matches(matches)
+    
+    def recognize(self):
+        pass # base class does nothing
+    
+    
+
+
+class WaveFileRecognizer(BaseRecognizer):
+    
+    def __init__(self, dejavu, filename=None):
+        super(WaveFileRecognizer, self).__init__(dejavu)
+        self.filename = filename
+    
+    def recognize_file(self, filename):
+        Fs, frames = wavfile.read(filename)
+        self.Fs = Fs
+        
+        wave_object = wave.open(filename)
+        nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
+        
+        channels = []
+        for channel in range(nchannels):
+            channels.append(frames[:, channel])
+        
+        t = time.time()
+        match = self._recognize(*channels)
+        t = time.time() - t
+        
+        if match:
+            match['match_time'] = t
+        
+        return match
+    
+    def recognize(self):
+        return self.recognize_file(self.filename)
+
+
+class MicrophoneRecognizer(BaseRecognizer):
+    
     CHUNK = 8192 # 44100 is a multiple of 1225
     FORMAT = pyaudio.paInt16
     CHANNELS = 2
     RATE = 44100
-
-    def __init__(self, fingerprinter, config):
-
-        self.fingerprinter = fingerprinter
-        self.config = config
-        self.audio = pyaudio.PyAudio()
-        
-    def read(self, filename, verbose=False):
-        
-        # read file into channels            
-        channels = []
-        Fs, frames = wavfile.read(filename)
-        wave_object = wave.open(filename)
-        nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
-        for channel in range(nchannels):
-            channels.append(frames[:, channel])    
-        
-        # get matches
-        starttime = time.time()
-        matches = []
-        for channel in channels:
-            matches.extend(self.fingerprinter.match(channel))
-        
-        return self.fingerprinter.align_matches(matches, starttime, verbose=verbose)
     
-    def listen(self, seconds=10, verbose=False):
+    def __init__(self, dejavu, seconds=None):
+        super(MicrophoneRecognizer, self).__init__(dejavu)
+        self.audio = pyaudio.PyAudio()
+        self.stream = None
+        self.data = []
+        self.channels = CHANNELS
+        self.chunk_size = CHUNK
+        self.rate = RATE
+        self.recorded = False
+    
+    def start_recording(self, channels=CHANNELS, rate=RATE, chunk=CHUNK):
+        self.chunk_size = chunk
+        self.channels = channels
+        self.recorded = False
+        self.rate = rate
+        
+        if self.stream:
+            self.stream.stop_stream()
+            self.stream.close()
+        
+        self.stream = self.audio.open(format=FORMAT,
+                                channels=channels,
+                                rate=rate,
+                                input=True,
+                                frames_per_buffer=chunk)
+        
+        self.data = [[] for i in range(channels)]
+    
+    def process_recording(self):
+        data = self.stream.read(self.chunk_size)
+        nums = np.fromstring(data, np.int16)
+        for c in range(self.channels):
+            self.data[c].extend(nums[c::c+1])
+    
+    def stop_recording(self):
+        self.stream.stop_stream()
+        self.stream.close()
+        self.stream = None
+        self.recorded = True
+        
+    def recognize_recording(self):
+        if not self.recorded:
+            raise NoRecordingError("Recording was not complete/begun")
+        return self._recognize(*self.data)
+    
+    def get_recorded_time(self):
+        return len(self.data[0]) / self.rate
+    
+    def recognize(self):
+        self.start_recording()
+        for i in range(0, int(self.rate / self.chunk * self.seconds)):
+            self.process_recording()
+        self.stop_recording()
+        return self.recognize_recording()
+    
+class NoRecordingError(Exception):
+    pass
 
-        # open stream
-        stream = self.audio.open(format=Recognizer.FORMAT,
-                        channels=Recognizer.CHANNELS,
-                        rate=Recognizer.RATE,
-                        input=True,
-                        frames_per_buffer=Recognizer.CHUNK)
-        
-        # record
-        if verbose: print("* recording")
-        left, right = [], []
-        for i in range(0, int(Recognizer.RATE / Recognizer.CHUNK * seconds)):
-            data = stream.read(Recognizer.CHUNK)
-            nums = np.fromstring(data, np.int16)
-            left.extend(nums[1::2])
-            right.extend(nums[0::2])
-        if verbose: print("* done recording")
-        
-        # close and stop the stream
-        stream.stop_stream()
-        stream.close()
-        
-        # match both channels
-        starttime = time.time()
-        matches = []
-        matches.extend(self.fingerprinter.match(left))
-        matches.extend(self.fingerprinter.match(right))
-        
-        # align and return
-        return self.fingerprinter.align_matches(matches, starttime, record_seconds=seconds, verbose=verbose)

go.py

@@ -1,4 +1,4 @@
-from dejavu.control import Dejavu
+from dejavu import Dejavu
 from ConfigParser import ConfigParser
 import warnings
 warnings.filterwarnings("ignore")