def test_audio_data_without_normalization_raises_for_invalid_data(self):
nt.assert_raises(
ValueError,
lambda: display.Audio([1.001], rate=44100, normalize=False))
nt.assert_raises(
ValueError,
lambda: display.Audio([-1.001], rate=44100, normalize=False))
def test_audio_data_without_normalization(self):
max_int16 = numpy.iinfo(numpy.int16).max
for scale in [1, 0.5, 0.2]:
test_tone = get_test_tone(scale)
test_tone_max_abs = numpy.max(numpy.abs(test_tone))
expected_max_value = int(max_int16 * test_tone_max_abs)
audio = display.Audio(test_tone, rate=44100, normalize=False)
actual_max_value = numpy.max(numpy.abs(read_wav(audio.data)))
nt.assert_equal(actual_max_value, expected_max_value)
def test_audio_from_file():
path = pjoin(dirname(__file__), 'test.wav')
display.Audio(filename=path)
fname = 'f1.wav'
rate, data = scipy.io.wavfile.read(fname)
data = data.astype(np.float)
t = np.linspace(0, len(data)/rate, len(data))
plt.plot(t,data)
plt.ylabel('Magnitude')
plt.xlabel('Tempo (s)')
plt.show()
c = spec.Wav2Spectrogram() # Objeto que converte arquivos wav para espectrogramas
s = c.convert(open(fname, 'rb'), window_length=2048, window_step=1024, spectrum_type='log')
tr = trim.TrimSpectrogram()
s = tr.trim(s, min_freq=0, max_freq=5000)
d = s.data
d = d/np.max(d)
d = 1 - d
min_freq = s.metadata.min_freq
max_freq = s.metadata.max_freq
min_time = s.metadata.min_time
max_time = s.metadata.max_time
im = plt.imshow(d, aspect='auto', origin='lower', cmap=plt.cm.gray, extent=[min_time, max_time, min_freq/1000.0, max_freq/1000.0])
plt.xlabel('Time (s)')
plt.ylabel('Frequency (kHz)')
plt.show()
display.Audio(fname)
def test_audio_raises_for_nested_list(self):
stereo_signal = [list(get_test_tone())] * 2
nt.assert_raises(TypeError,
lambda: display.Audio(stereo_signal, rate=44100))
def test_audio_data_normalization(self):
expected_max_value = numpy.iinfo(numpy.int16).max
for scale in [1, 0.5, 2]:
audio = display.Audio(get_test_tone(scale), rate=44100)
actual_max_value = numpy.max(numpy.abs(read_wav(audio.data)))
nt.assert_equal(actual_max_value, expected_max_value)
def test_audio_from_list(self):
test_tone = get_test_tone()
audio = display.Audio(list(test_tone), rate=44100)
nt.assert_equal(len(read_wav(audio.data)), len(test_tone))
def test_audio_from_numpy_array(self):
test_tone = get_test_tone()
audio = display.Audio(test_tone, rate=44100)
nt.assert_equal(len(read_wav(audio.data)), len(test_tone))
def test_audio_from_list(self):
test_tone = get_test_tone()
audio = display.Audio(list(test_tone), rate=44100)
assert len(read_wav(audio.data)) == len(test_tone)
# Beat tracking example
from __future__ import print_function
import librosa
import IPython.lib.display as player
import pygame as pg
import tkinter
# 1. Get the file path to the included audio example
filename = "test3.mp3"
# 2. Load the audio as a waveform `y`
# Store the sampling rate as `sr`
y, sr = librosa.load(filename)
player.Audio(filename=filename)
# 3. Run the default beat tracker
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
# Run onset tracker
onset_frames = librosa.onset.onset_detect(y=y, sr=sr)
print('Estimated tempo: {:.2f} beats per minute'.format(tempo))
# 4. Convert the frame indices of beat events into timestamps
beat_times = librosa.frames_to_time(beat_frames, sr=sr)
onset_times = librosa.frames_to_time(onset_frames, sr=sr)
print('Saving output to beat_times.csv')
librosa.output.times_csv('beat_times.csv', beat_times)
librosa.output.times_csv('onset_times.csv', onset_times)
