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160 lines
5 KiB
Python
160 lines
5 KiB
Python
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from dejavu.control import Dejavu
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from dejavu.recognize import Recognizer
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from dejavu.convert import Converter
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from dejavu.database import SQLDatabase
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from ConfigParser import ConfigParser
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from scipy.io import wavfile
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import matplotlib.pyplot as plt
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import warnings
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import pyaudio
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import os, wave, sys
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import random
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import numpy as np
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warnings.filterwarnings("ignore")
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config = ConfigParser()
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config.read("dejavu.cnf")
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dejavu = Dejavu(config)
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recognizer = Recognizer(dejavu.fingerprinter, config)
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def test_recording_lengths(recognizer):
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# settings for run
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RATE = 44100
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FORMAT = pyaudio.paInt16
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padding_seconds = 10
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SONG_PADDING = RATE * padding_seconds
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OUTPUT_FILE = "output.wav"
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p = pyaudio.PyAudio()
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c = Converter()
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files = c.find_files("va_us_top_40/wav/", [".wav"])[-25:]
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total = len(files)
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recording_lengths = [4]
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correct = 0
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count = 0
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score = {}
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for r in recording_lengths:
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RECORD_LENGTH = RATE * r
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for tup in files:
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f, ext = tup
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# read the file
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#print "reading: %s" % f
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Fs, frames = wavfile.read(f)
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wave_object = wave.open(f)
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nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
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# chose at random a segment of audio to play
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possible_end = num_frames - SONG_PADDING - RECORD_LENGTH
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possible_start = SONG_PADDING
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if possible_end - possible_start < RECORD_LENGTH:
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print "ERROR! Song is too short to sample based on padding and recording seconds preferences."
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sys.exit()
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start = random.randint(possible_start, possible_end)
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end = start + RECORD_LENGTH + 1
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# get that segment of samples
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channels = []
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frames = frames[start:end, :]
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wav_string = frames.tostring()
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# write to disk
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wf = wave.open(OUTPUT_FILE, 'wb')
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wf.setnchannels(nchannels)
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wf.setsampwidth(p.get_sample_size(FORMAT))
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wf.setframerate(RATE)
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wf.writeframes(b''.join(wav_string))
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wf.close()
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# play and test
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correctname = os.path.basename(f).replace(".wav", "").replace("_", " ")
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inp = raw_input("Click ENTER when playing %s ..." % OUTPUT_FILE)
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song = recognizer.listen(seconds=r+1, verbose=False)
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print "PREDICTED: %s" % song['song_name']
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print "ACTUAL: %s" % correctname
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if song['song_name'] == correctname:
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correct += 1
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count += 1
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print "Currently %d correct out of %d in total of %d" % (correct, count, total)
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score[r] = (correct, total)
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print "UPDATE AFTER %d TRIAL: %s" % (r, score)
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return score
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def plot_match_time_trials():
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# I did this manually
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t = np.array([1, 2, 3, 4, 5, 6, 7, 8, 10, 15, 25, 30, 45, 60])
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m = np.array([.47, .79, 1.1, 1.5, 1.8, 2.18, 2.62, 2.8, 3.65, 5.29, 8.92, 10.63, 16.09, 22.29])
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mplust = t + m
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# linear regression
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A = np.matrix([t, np.ones(len(t))])
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print A
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w = np.linalg.lstsq(A.T, mplust)[0]
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line = w[0] * t + w[1]
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print "Equation for line is %f * record_time + %f = time_to_match" % (w[0], w[1])
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# and plot
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plt.title("Recording vs Matching time for \"Get Lucky\" by Daft Punk")
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plt.xlabel("Time recorded (s)")
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plt.ylabel("Time recorded + time to match (s)")
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#plt.scatter(t, mplust)
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plt.plot(t, line, 'r-', t, mplust, 'o')
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plt.show()
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def plot_accuracy():
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# also did this manually
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secs = np.array([1, 2, 3, 4, 5, 6])
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correct = np.array([27.0, 43.0, 44.0, 44.0, 45.0, 45.0])
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total = 45.0
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correct = correct / total
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plt.title("Dejavu Recognition Accuracy as a Function of Time")
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plt.xlabel("Time recorded (s)")
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plt.ylabel("Accuracy")
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plt.plot(secs, correct)
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plt.ylim([0.0, 1.05])
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plt.show()
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def plot_hashes_per_song():
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squery = """select song_name, count(song_id) as num
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from fingerprints
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natural join songs
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group by song_name
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order by count(song_id) asc;"""
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sql = SQLDatabase(username="root", password="root", database="dejavu", hostname="localhost")
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cursor = sql.connection.cursor()
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cursor.execute(squery)
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counts = cursor.fetchall()
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songs = []
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count = []
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for item in counts:
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songs.append(item['song_name'].replace("_", " ")[4:])
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count.append(item['num'])
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pos = np.arange(len(songs)) + 0.5
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.barh(pos, count, align='center')
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ax.set_yticks(pos, tuple(songs))
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ax.axvline(0, color='k', lw=3)
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ax.set_xlabel('Number of Fingerprints')
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ax.set_title('Number of Fingerprints by Song')
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ax.grid(True)
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plt.show()
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#plot_accuracy()
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#score = test_recording_lengths(recognizer)
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#plot_match_time_trials()
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#plot_hashes_per_song()
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