def test_audio_stim():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'barber.wav'))
assert round(stim.duration) == 57
assert stim.sampling_rate == 11025
stim = AudioStim(join(audio_dir, 'homer.wav'))
assert round(stim.duration) == 3
assert stim.sampling_rate == 11025
def test_get_filename():
url = 'https://archive.org/download/999WavFiles/TANKEN.WAV'
audio = AudioStim(url=url)
with audio.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
url = 'https://archive.org/download/NIX-C-1987-11903/1987_11903L.jpg'
image = ImageStim(url=url)
with image.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
def test_get_filename():
url = 'http://www.bobainsworth.com/wav/simpsons/themodyn.wav'
audio = AudioStim(url=url)
with audio.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
url = 'https://via.placeholder.com/350x150'
image = ImageStim(url=url)
with image.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
def test_get_filename():
url = 'http://www.bobainsworth.com/wav/simpsons/themodyn.wav'
audio = AudioStim(url=url)
with audio.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
url = 'https://via.placeholder.com/350x150'
image = ImageStim(url=url)
with image.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
def test_get_filename():
url = 'http://www.bobainsworth.com/wav/simpsons/themodyn.wav'
audio = AudioStim(url=url)
with audio.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
url = 'https://tuition.utexas.edu/sites/all/themes/tuition/logo.png'
image = ImageStim(url=url)
with image.get_filename() as filename:
assert exists(filename)
assert not exists(filename)
def test_googleAPI_converter():
stim = AudioStim(join(AUDIO_DIR, 'homer.wav'))
conv = GoogleSpeechAPIConverter()
out_stim = conv.transform(stim)
assert type(out_stim) == ComplexTextStim
text = [elem.text for elem in out_stim]
assert 'thermodynamics' in text or 'obey' in text
def test_IBMSpeechAPIConverter():
stim = AudioStim(join(AUDIO_DIR, 'homer.wav'), onset=4.2)
conv = IBMSpeechAPIConverter()
out_stim = conv.transform(stim)
assert conv.validate_keys()
assert isinstance(out_stim, ComplexTextStim)
first_word = next(w for w in out_stim)
assert isinstance(first_word, TextStim)
assert first_word.duration > 0
assert first_word.onset is not None
second_word = [w for w in out_stim][1]
assert second_word.onset > 4.2
num_words = len(out_stim.elements)
full_text = [elem.text for elem in out_stim]
assert 'thermodynamics' in full_text or 'obey' in full_text
conv2 = IBMSpeechAPIConverter(resolution='phrases')
out_stim = conv2.transform(stim)
assert isinstance(out_stim, ComplexTextStim)
first_phrase = next(w for w in out_stim)
assert isinstance(first_phrase, TextStim)
full_text = first_phrase.text
assert len(full_text.split()) > 1
assert 'thermodynamics' in full_text or 'obey' in full_text
assert len(out_stim.elements) < num_words
conv = IBMSpeechAPIConverter(username='******', password='******')
assert not conv.validate_keys()
def test_ibmAPI_converter():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'homer.wav'), onset=4.2)
conv = IBMSpeechAPIConverter()
out_stim = conv.transform(stim)
assert isinstance(out_stim, ComplexTextStim)
first_word = next(w for w in out_stim)
assert isinstance(first_word, TextStim)
assert first_word.duration > 0
assert first_word.onset is not None
second_word = [w for w in out_stim][1]
assert second_word.onset > 4.2
num_words = len(out_stim.elements)
full_text = [elem.text for elem in out_stim]
assert 'thermodynamics' in full_text or 'obey' in full_text
conv2 = IBMSpeechAPIConverter(resolution='phrases')
out_stim = conv2.transform(stim)
assert isinstance(out_stim, ComplexTextStim)
first_phrase = next(w for w in out_stim)
assert isinstance(first_phrase, TextStim)
full_text = first_phrase.text
assert len(full_text.split()) > 1
assert 'thermodynamics' in full_text or 'obey' in full_text
assert len(out_stim.elements) < num_words
def test_mfcc_energy_extractor():
audio = AudioStim(join(AUDIO_DIR, 'barber.wav'))
n_mels = 48
ext = MFCCEnergyExtractor(register='low', n_mels=n_mels)
data = ext.transform(audio).data.iloc[:, 4:]
assert data.shape == (1221, n_mels)
assert np.isclose(data.iloc[0].sum(), 695.7308991156441)
ext = MFCCEnergyExtractor(register='high', n_mels=n_mels)
data = ext.transform(audio).data.iloc[:, 4:]
assert data.shape == (1221, n_mels)
assert np.isclose(data.iloc[100].sum(), 107.06728965998656)
ext2 = MFCCEnergyExtractor(n_mfcc=64,
n_coefs=8,
hop_length=512,
n_mels=n_mels,
register='low')
data = ext2.transform(audio).data.iloc[:, 4:]
assert data.shape == (1221, n_mels)
assert np.isclose(data.iloc[650].sum(), 884.4713338141073)
ext2 = MFCCEnergyExtractor(n_mfcc=64,
n_coefs=8,
hop_length=512,
n_mels=n_mels,
register='high')
data = ext2.transform(audio).data.iloc[:, 4:]
assert data.shape == (1221, n_mels)
assert np.isclose(data.iloc[601].sum(), 210.857157)
def test_chroma_extractors():
audio = AudioStim(join(AUDIO_DIR, "barber.wav"))
ext = ChromaSTFTExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 14)
assert np.isclose(df['onset'][1], 0.01161)
assert np.isclose(df['duration'][0], 0.01161)
assert np.isclose(df['chroma_2'][0], 0.417595)
ext2 = ChromaSTFTExtractor(n_chroma=6, n_fft=1024, hop_length=256)
df = ext2.transform(audio).to_df()
assert df.shape == (9763, 8)
assert np.isclose(df['onset'][1], 0.005805)
assert np.isclose(df['duration'][0], 0.005805)
assert np.isclose(df['chroma_5'][0], 0.732480)
ext = ChromaCQTExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 14)
assert np.isclose(df['chroma_cqt_2'][0], 0.286443)
ext = ChromaCENSExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 14)
assert np.isclose(df['chroma_cens_2'][0], 0.217814)
def test_spectral_extractors():
audio = AudioStim(join(AUDIO_DIR, 'barber.wav'))
ext = SpectralCentroidExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 5)
assert np.isclose(df['onset'][1], 0.04644)
assert np.isclose(df['duration'][0], 0.04644)
assert np.isclose(df['spectral_centroid'][0], 1144.98145)
ext2 = SpectralCentroidExtractor(n_fft=1024, hop_length=256)
df = ext2.transform(audio).to_df()
assert df.shape == (2441, 5)
assert np.isclose(df['onset'][1], 0.02322)
assert np.isclose(df['duration'][0], 0.02322)
assert np.isclose(df['spectral_centroid'][0], 866.20176)
ext = SpectralBandwidthExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 5)
assert np.isclose(df['spectral_bandwidth'][0], 1172.96090)
ext = SpectralContrastExtractor(fmin=100.0)
df = ext.transform(audio).to_df()
assert df.shape == (1221, 11)
assert np.isclose(df['spectral_contrast_band_4'][0], 25.637166)
ext = SpectralRolloffExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 5)
assert np.isclose(df['spectral_rolloff'][0], 2492.46826)
def test_compound_stim():
audio_dir = join(get_test_data_path(), 'audio')
audio = AudioStim(join(audio_dir, 'crowd.mp3'))
image1 = ImageStim(join(get_test_data_path(), 'image', 'apple.jpg'))
image2 = ImageStim(join(get_test_data_path(), 'image', 'obama.jpg'))
filename = join(get_test_data_path(), 'video', 'small.mp4')
video = VideoStim(filename)
text = ComplexTextStim(text="The quick brown fox jumped...")
stim = CompoundStim([audio, image1, image2, video, text])
assert len(stim.elements) == 5
assert isinstance(stim.video, VideoStim)
assert isinstance(stim.complex_text, ComplexTextStim)
assert isinstance(stim.image, ImageStim)
with pytest.raises(AttributeError):
stim.nonexistent_type
assert stim.video_frame is None
# Test iteration
len([e for e in stim]) == 5
imgs = stim.get_stim(ImageStim, return_all=True)
assert len(imgs) == 2
assert all([isinstance(im, ImageStim) for im in imgs])
also_imgs = stim.get_stim('image', return_all=True)
assert imgs == also_imgs
def test_spectral_extractors():
audio = AudioStim(join(AUDIO_DIR, "barber.wav"))
ext = SpectralCentroidExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 3)
assert np.isclose(df['onset'][1], 0.01161)
assert np.isclose(df['duration'][0], 0.01161)
assert np.isclose(df['spectral_centroid'][0], 817.53095)
ext2 = SpectralCentroidExtractor(n_fft=1024, hop_length=256)
df = ext2.transform(audio).to_df()
assert df.shape == (9763, 3)
assert np.isclose(df['onset'][1], 0.005805)
assert np.isclose(df['duration'][0], 0.005805)
assert np.isclose(df['spectral_centroid'][0], 1492.00515)
ext = SpectralBandwidthExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 3)
assert np.isclose(df['spectral_bandwidth'][0], 1056.66227)
ext = SpectralContrastExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 9)
assert np.isclose(df['spectral_contrast_band_4'][0], 25.09001)
ext = SpectralRolloffExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 3)
assert np.isclose(df['spectral_rolloff'][0], 1550.39063)
def test_transcribed_audio_stim():
audio = AudioStim(join(get_test_data_path(), 'audio', "barber_edited.wav"))
text_file = join(get_test_data_path(), 'text', "wonderful_edited.srt")
text = ComplexTextStim(text_file)
stim = TranscribedAudioCompoundStim(audio=audio, text=text)
assert isinstance(stim.audio, AudioStim)
assert isinstance(stim.complex_text, ComplexTextStim)
def test_chroma_extractors():
audio = AudioStim(join(AUDIO_DIR, 'barber.wav'))
ext = ChromaSTFTExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 16)
assert np.isclose(df['onset'][1], 0.04644)
assert np.isclose(df['duration'][0], 0.04644)
assert np.isclose(df['chroma_2'][0], 0.53129)
ext2 = ChromaSTFTExtractor(n_chroma=6, n_fft=1024, hop_length=256)
df = ext2.transform(audio).to_df()
assert df.shape == (2441, 10)
assert np.isclose(df['onset'][1], 0.02322)
assert np.isclose(df['duration'][0], 0.02322)
assert np.isclose(df['chroma_5'][0], 0.86870)
ext = ChromaCQTExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 16)
assert np.isclose(df['chroma_cqt_2'][0], 0.336481)
ext = ChromaCENSExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 16)
assert np.isclose(df['chroma_cens_2'][0], 0.136409)
def test_tempo_extractor():
audio = AudioStim(join(AUDIO_DIR, 'barber.wav'))
ext = TempoExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1, 5)
assert np.isclose(df['onset'][0], 0.0)
assert np.isclose(df['duration'][0], 0.04644)
assert np.isclose(df['tempo'][0], 117.453835)
def test_googleAPI_converter():
stim = AudioStim(join(AUDIO_DIR, 'obama_speech.wav'))
conv = GoogleSpeechAPIConverter()
out_stim = conv.transform(stim)
assert type(out_stim) == ComplexTextStim
text = [elem.text for elem in out_stim]
assert 'today' in text
assert 'United' in text
def test_audio_resampling_filter(target_sr, resample_type):
stim = AudioStim(join(AUDIO_DIR, 'homer.wav'))
filt = AudioResamplingFilter(target_sr, resample_type)
resampled = filt.transform(stim)
assert resampled.sampling_rate == target_sr
assert np.abs(target_sr * stim.duration - resampled.data.shape[0]) <= 1
def test_implicit_stim_conversion2():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'homer.wav'))
ext = LengthExtractor()
result = ext.transform(stim)
first_word = result[0].to_df()
assert 'text_length' in first_word.columns
assert first_word['text_length'][0] > 0
def test_stft_extractor():
stim = AudioStim(join(AUDIO_DIR, 'barber.wav'), onset=4.2)
ext = STFTAudioExtractor(frame_size=1., spectrogram=False,
freq_bins=[(100, 300), (300, 3000), (3000, 20000)])
result = ext.transform(stim)
df = result.to_df()
assert df.shape == (557, 5)
assert df['onset'][0] == 4.2
def test_tonnetz_extractor():
audio = AudioStim(join(AUDIO_DIR, "barber.wav"))
ext = TonnetzExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (4882, 8)
assert np.isclose(df['onset'][1], 0.01161)
assert np.isclose(df['duration'][0], 0.01161)
assert np.isclose(df['tonal_centroid_0'][0], -0.0264436)
def test_googleAPI_converter():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'homer.wav'))
conv = GoogleSpeechAPIConverter()
out_stim = conv.transform(stim)
assert type(out_stim) == ComplexTextStim
text = [elem.text for elem in out_stim]
assert 'thermodynamics' in text or 'obey' in text
def test_stft_extractor():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'barber.wav'))
ext = STFTAudioExtractor(frame_size=1., spectrogram=False,
freq_bins=[(100, 300), (300, 3000), (3000, 20000)])
result = ext.transform(stim)
df = result.to_df()
assert df.shape == (557, 5)
def test_librosa_extractor():
audio = AudioStim(join(AUDIO_DIR, 'barber.wav'))
ext = LibrosaFeatureExtractor(feature='rmse')
df = ext.transform(audio).to_df()
assert df.shape == (1221, 5)
assert np.isclose(df['onset'][1], 0.04644)
assert np.isclose(df['duration'][0], 0.04644)
assert np.isclose(df['rmse'][0], 0.25663)
def test_tonnetz_extractor():
audio = AudioStim(join(AUDIO_DIR, 'barber.wav'))
ext = TonnetzExtractor()
df = ext.transform(audio).to_df()
assert df.shape == (1221, 10)
assert np.isclose(df['onset'][1], 0.04644)
assert np.isclose(df['duration'][0], 0.04644)
assert np.isclose(df['tonal_centroid_0'][0], -0.0391266)
def test_audioset_extractor(hop_size, top_n, target_sr):
verify_dependencies(['tensorflow'])
def compute_expected_length(stim, ext):
stft_par = ext.params.STFT_WINDOW_SECONDS - ext.params.STFT_HOP_SECONDS
tot_window = ext.params.PATCH_WINDOW_SECONDS + stft_par
ons = np.arange(start=0,
stop=stim.duration - tot_window,
step=hop_size)
return len(ons)
audio_stim = AudioStim(join(AUDIO_DIR, 'crowd.mp3'))
audio_filter = AudioResamplingFilter(target_sr=target_sr)
audio_resampled = audio_filter.transform(audio_stim)
# test with defaults and 44100 stimulus
ext = AudiosetLabelExtractor(hop_size=hop_size)
r_orig = ext.transform(audio_stim).to_df()
assert r_orig.shape[0] == compute_expected_length(audio_stim, ext)
assert r_orig.shape[1] == 525
assert np.argmax(r_orig.to_numpy()[:, 4:].mean(axis=0)) == 0
assert r_orig['duration'][0] == .975
assert all([
np.isclose(r_orig['onset'][i] - r_orig['onset'][i - 1], hop_size)
for i in range(1, r_orig.shape[0])
])
# test resampled audio length and errors
if target_sr >= 14500:
r_resampled = ext.transform(audio_resampled).to_df()
assert r_orig.shape[0] == r_resampled.shape[0]
else:
with pytest.raises(ValueError) as sr_error:
ext.transform(audio_resampled)
assert all([
substr in str(sr_error.value)
for substr in ['Upsample', str(target_sr)]
])
# test top_n option
ext_top_n = AudiosetLabelExtractor(top_n=top_n)
r_top_n = ext_top_n.transform(audio_stim).to_df()
assert r_top_n.shape[1] == ext_top_n.top_n + 4
assert np.argmax(r_top_n.to_numpy()[:, 4:].mean(axis=0)) == 0
# test label subset
labels = [
'Speech', 'Silence', 'Harmonic', 'Bark', 'Music', 'Bell', 'Steam',
'Rain'
]
ext_labels_only = AudiosetLabelExtractor(labels=labels)
r_labels_only = ext_labels_only.transform(audio_stim).to_df()
assert r_labels_only.shape[1] == len(labels) + 4
# test top_n/labels error
with pytest.raises(ValueError) as err:
AudiosetLabelExtractor(top_n=10, labels=labels)
assert 'Top_n and labels are mutually exclusive' in str(err.value)
def test_mean_amplitude_extractor():
audio = AudioStim(join(AUDIO_DIR, 'barber_edited.wav'))
text_file = join(get_test_data_path(), 'text', 'wonderful_edited.srt')
text = ComplexTextStim(text_file)
stim = TranscribedAudioCompoundStim(audio=audio, text=text)
ext = MeanAmplitudeExtractor()
result = ext.transform(stim).to_df()
targets = [-0.154661, 0.121521]
assert np.allclose(result['mean_amplitude'], targets)
def test_witaiAPI_converter():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'homer.wav'))
conv = WitTranscriptionConverter()
out_stim = conv.transform(stim)
assert type(out_stim) == ComplexTextStim
first_word = next(w for w in out_stim)
assert type(first_word) == TextStim
#assert '_' in first_word.name
text = [elem.text for elem in out_stim]
assert 'thermodynamics' in text or 'obey' in text
def test_ibm_speech_converter_large():
default = config.get_option('allow_large_jobs')
config.set_option('allow_large_jobs', False)
conv = IBMSpeechAPIConverter()
audio = AudioStim(join(AUDIO_DIR, 'silence.wav'))
with pytest.raises(ValueError):
conv.transform(audio)
config.set_option('allow_large_jobs', default)
def test_googleAPI_converter():
stim = AudioStim(join(AUDIO_DIR, 'obama_speech.wav'))
conv = GoogleSpeechAPIConverter()
assert conv.validate_keys()
out_stim = conv.transform(stim)
assert type(out_stim) == ComplexTextStim
text = [elem.text for elem in out_stim]
assert 'today' in text
assert 'United' in text
conv = GoogleSpeechAPIConverter(discovery_file='no/good.json')
assert not conv.validate_keys()
def test_WitTranscriptionConverter():
stim = AudioStim(join(AUDIO_DIR, 'homer.wav'), onset=4.2)
conv = WitTranscriptionConverter()
out_stim = conv.transform(stim)
assert type(out_stim) == ComplexTextStim
first_word = next(w for w in out_stim)
assert type(first_word) == TextStim
assert first_word.onset == 4.2
second_word = [w for w in out_stim][1]
assert second_word.onset == 4.2
text = [elem.text for elem in out_stim]
assert 'thermodynamics' in text or 'obey' in text
