source: python/demos/demo_pitch.py @ 4120fbc

feature/autosinkfeature/constantqfeature/pitchshiftfeature/pydocstringsfeature/timestretchpitchshiftsamplertimestretchyinfft+
Last change on this file since 4120fbc was 4120fbc, checked in by Paul Brossier <piem@piem.org>, 3 years ago

python/demos: python3 and double precision compatibility

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1#! /usr/bin/env python
2
3import sys
4from aubio import source, pitch
5
6if len(sys.argv) < 2:
7    print("Usage: %s <filename> [samplerate]" % sys.argv[0])
8    sys.exit(1)
9
10filename = sys.argv[1]
11
12downsample = 1
13samplerate = 44100 // downsample
14if len( sys.argv ) > 2: samplerate = int(sys.argv[2])
15
16win_s = 4096 // downsample # fft size
17hop_s = 512  // downsample # hop size
18
19s = source(filename, samplerate, hop_s)
20samplerate = s.samplerate
21
22tolerance = 0.8
23
24pitch_o = pitch("yin", win_s, hop_s, samplerate)
25pitch_o.set_unit("midi")
26pitch_o.set_tolerance(tolerance)
27
28pitches = []
29confidences = []
30
31# total number of frames read
32total_frames = 0
33while True:
34    samples, read = s()
35    pitch = pitch_o(samples)[0]
36    #pitch = int(round(pitch))
37    confidence = pitch_o.get_confidence()
38    #if confidence < 0.8: pitch = 0.
39    print("%f %f %f" % (total_frames / float(samplerate), pitch, confidence))
40    pitches += [pitch]
41    confidences += [confidence]
42    total_frames += read
43    if read < hop_s: break
44
45if 0: sys.exit(0)
46
47#print pitches
48import os.path
49from numpy import array, ma
50import matplotlib.pyplot as plt
51from demo_waveform_plot import get_waveform_plot, set_xlabels_sample2time
52
53skip = 1
54
55pitches = array(pitches[skip:])
56confidences = array(confidences[skip:])
57times = [t * hop_s for t in range(len(pitches))]
58
59fig = plt.figure()
60
61ax1 = fig.add_subplot(311)
62ax1 = get_waveform_plot(filename, samplerate = samplerate, block_size = hop_s, ax = ax1)
63plt.setp(ax1.get_xticklabels(), visible = False)
64ax1.set_xlabel('')
65
66def array_from_text_file(filename, dtype = 'float'):
67    filename = os.path.join(os.path.dirname(__file__), filename)
68    return array([line.split() for line in open(filename).readlines()],
69        dtype = dtype)
70
71ax2 = fig.add_subplot(312, sharex = ax1)
72ground_truth = os.path.splitext(filename)[0] + '.f0.Corrected'
73if os.path.isfile(ground_truth):
74    ground_truth = array_from_text_file(ground_truth)
75    true_freqs = ground_truth[:,2]
76    true_freqs = ma.masked_where(true_freqs < 2, true_freqs)
77    true_times = float(samplerate) * ground_truth[:,0]
78    ax2.plot(true_times, true_freqs, 'r')
79    ax2.axis( ymin = 0.9 * true_freqs.min(), ymax = 1.1 * true_freqs.max() )
80# plot raw pitches
81ax2.plot(times, pitches, '.g')
82# plot cleaned up pitches
83cleaned_pitches = pitches
84#cleaned_pitches = ma.masked_where(cleaned_pitches < 0, cleaned_pitches)
85#cleaned_pitches = ma.masked_where(cleaned_pitches > 120, cleaned_pitches)
86cleaned_pitches = ma.masked_where(confidences < tolerance, cleaned_pitches)
87ax2.plot(times, cleaned_pitches, '.-')
88#ax2.axis( ymin = 0.9 * cleaned_pitches.min(), ymax = 1.1 * cleaned_pitches.max() )
89#ax2.axis( ymin = 55, ymax = 70 )
90plt.setp(ax2.get_xticklabels(), visible = False)
91ax2.set_ylabel('f0 (midi)')
92
93# plot confidence
94ax3 = fig.add_subplot(313, sharex = ax1)
95# plot the confidence
96ax3.plot(times, confidences)
97# draw a line at tolerance
98ax3.plot(times, [tolerance]*len(confidences))
99ax3.axis( xmin = times[0], xmax = times[-1])
100ax3.set_ylabel('condidence')
101set_xlabels_sample2time(ax3, times[-1], samplerate)
102plt.show()
103#plt.savefig(os.path.basename(filename) + '.svg')
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