def test_mfcc_correctness(data_format, n_mfccs):
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
melgram = librosa.power_to_db(librosa.feature.melspectrogram(src_mono)) # mel, time
mfcc_ref = librosa.feature.mfcc(
S=melgram, n_mfcc=n_mfccs, norm='ortho'
) # 'ortho' -> 5% mismatch but..
expand_dim = (0, 3) if data_format in (_CH_LAST_STR, _CH_DEFAULT_STR) else (0, 1)
melgram_batch = np.expand_dims(melgram.T, expand_dim)
model = tf.keras.Sequential()
model.add(
LogmelToMFCC(n_mfccs=n_mfccs, data_format=data_format, input_shape=melgram_batch.shape[1:])
)
mfcc_kapre = model.predict(melgram_batch)
ch_axis = 1 if data_format == _CH_FIRST_STR else 3
mfcc_kapre = np.squeeze(mfcc_kapre, axis=ch_axis)
mfcc_kapre = mfcc_kapre[0].T
if n_mfccs > 1:
np.testing.assert_allclose(mfcc_ref[1:], mfcc_kapre[1:], atol=1e-4)
np.testing.assert_allclose(mfcc_ref[0], mfcc_kapre[0] / np.sqrt(2.0), atol=1e-4)
def test_frame_correctness(frame_length, data_format):
hop_length = frame_length // 2
n_ch = 1
src_mono, batch_src, input_shape = get_audio(data_format=data_format, n_ch=n_ch, length=1000)
model = tf.keras.Sequential()
model.add(
Frame(
frame_length=frame_length,
hop_length=hop_length,
pad_end=False,
data_format=data_format,
input_shape=input_shape,
)
)
frames_ref = librosa.util.frame(src_mono, frame_length, hop_length).T # (time, frame_length)
if data_format in (_CH_DEFAULT_STR, _CH_LAST_STR):
frames_ref = np.expand_dims(frames_ref, axis=2)
else:
frames_ref = np.expand_dims(frames_ref, axis=0)
frames_kapre = model.predict(batch_src)[0]
np.testing.assert_equal(frames_kapre, frames_ref)
def test_energy_correctness(data_format):
frame_length = 4
hop_length = frame_length // 2
n_ch = 1
src_mono, batch_src, input_shape = get_audio(
data_format=data_format, n_ch=n_ch, length=frame_length * 2
)
sr = 22050
ref_duration = 0.1
model = tf.keras.Sequential()
model.add(
Energy(
sample_rate=sr,
ref_duration=ref_duration,
frame_length=frame_length,
hop_length=hop_length,
pad_end=False,
data_format=data_format,
input_shape=input_shape,
)
)
energies_kapre = model.predict(batch_src)[0]
frames_ref = librosa.util.frame(src_mono, frame_length, hop_length).T # (time, frame_length)
nor_coeff = ref_duration / (frame_length / sr)
energies_ref = nor_coeff * np.sum(frames_ref ** 2, axis=1) # (time, )
if data_format in (_CH_DEFAULT_STR, _CH_LAST_STR):
energies_ref = np.expand_dims(energies_ref, axis=1)
else:
energies_ref = np.expand_dims(energies_ref, axis=0)
np.testing.assert_allclose(energies_kapre, energies_ref, atol=1e-5)
def test_save_load():
"""test saving/loading of models that has stft, melspectorgrma, and log frequency."""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last',
n_ch=1)
# test STFT save/load
save_load_compare(STFT(input_shape=input_shape, pad_begin=True), batch_src,
allclose_complex_numbers)
# test melspectrogram save/load
save_load_compare(
get_melspectrogram_layer(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test log frequency spectrogram save/load
save_load_compare(
get_log_frequency_spectrogram_layer(input_shape=input_shape,
return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test stft_mag_phase
save_load_compare(
get_stft_mag_phase(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
)
# test stft mag
save_load_compare(get_stft_magnitude_layer(input_shape=input_shape),
batch_src, np.testing.assert_allclose)
def test_save_load(data_format):
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
# test Frame save/load
save_load_compare(
Frame(frame_length=128, hop_length=64, input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
)
# test Energy save/load
save_load_compare(
Energy(frame_length=128, hop_length=64, input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
)
# test mu law layers
save_load_compare(
MuLawEncoding(quantization_channels=128),
batch_src,
np.testing.assert_allclose,
)
save_load_compare(
MuLawDecoding(quantization_channels=128),
np.arange(0, 256, 1).reshape((1, 256, 1)),
np.testing.assert_allclose,
)
# test mfcc layer
expand_dim = (0, 3) if data_format in (_CH_LAST_STR, _CH_DEFAULT_STR) else (0, 1)
save_load_compare(
LogmelToMFCC(n_mfccs=10),
np.expand_dims(librosa.power_to_db(librosa.feature.melspectrogram(src_mono).T), expand_dim),
np.testing.assert_allclose,
)
def test_log_spectrogram_fail():
"""test if log spectrogram layer works well"""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last',
n_ch=1)
_ = get_log_frequency_spectrogram_layer(input_shape,
return_decibel=True,
log_n_bins=200)
def test_mag_phase(data_format):
n_ch = 1
n_fft, hop_length, win_length = 512, 256, 512
src_mono, batch_src, input_shape = get_audio(data_format=data_format, n_ch=n_ch)
mag_phase_layer = get_stft_mag_phase(
input_shape=input_shape,
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
input_data_format=data_format,
output_data_format=data_format,
)
model = tensorflow.keras.models.Sequential()
model.add(mag_phase_layer)
mag_phase_kapre = model(batch_src)[0] # a 2d image shape
ch_axis = 0 if data_format == 'channels_first' else 2 # non-batch
mag_phase_ref = np.stack(
librosa.magphase(
librosa.stft(
src_mono, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False,
).T
),
axis=ch_axis,
)
np.testing.assert_equal(mag_phase_kapre.shape, mag_phase_ref.shape)
# magnitude test
np.testing.assert_allclose(
np.take(mag_phase_kapre, [0,], axis=ch_axis,),
np.take(mag_phase_ref, [0,], axis=ch_axis,),
atol=2e-4,
)
def placeholder(*args, **kwargs):
global root, voices_menu
words = []
while True:
try:
word = input('type text to search:\n')
if not word:
break
words.append(word)
except KeyboardInterrupt:
break
print('fetching...')
voices = SelectionMenu([])
i = 0
for text, id in get_audio(words):
i += 1
sel = SelectionMenu([], 'Choose phrase')
si = SubmenuItem(text[:60], sel, voices)
sel.append_item(FunctionItem(f"Play \"{text[:80]}\"",
play_wrapper(id)))
sel.append_item(FunctionItem(f"Save \"{text[:80]}\"",
save_wrapper(id)))
voices.append_item(si)
voices.title = f'Tip for long outputs: to exit press `1` and arrow up'
string = '+'.join(words)
submenu_item = SubmenuItem(f'Found {string} voices: {i}', voices, root)
root.append_item(submenu_item)
def test_perfectly_reconstructing_stft_istft(waveform_data_format,
stft_data_format, hop_ratio):
n_ch = 1
src_mono, batch_src, input_shape = get_audio(
data_format=waveform_data_format, n_ch=n_ch)
time_axis = 1 if waveform_data_format == 'channels_first' else 0 # non-batch!
len_src = input_shape[time_axis]
n_fft = 2048
hop_length = int(2048 * hop_ratio)
n_added_frames = int(1 / hop_ratio) - 1
stft, istft = get_perfectly_reconstructing_stft_istft(
stft_input_shape=input_shape,
n_fft=n_fft,
hop_length=hop_length,
waveform_data_format=waveform_data_format,
stft_data_format=stft_data_format,
)
# Test - [STFT -> ISTFT]
model = tf.keras.models.Sequential([stft, istft])
recon_waveform = model(batch_src)
# trim off the pad_begin part
len_pad_begin = n_fft - hop_length
if waveform_data_format == 'channels_first':
recon_waveform = recon_waveform[:, :,
len_pad_begin:len_pad_begin + len_src]
else:
recon_waveform = recon_waveform[:, len_pad_begin:len_pad_begin +
len_src, :]
np.testing.assert_allclose(batch_src, recon_waveform, atol=1e-5)
# Test - [ISTFT -> STFT]
S = librosa.stft(src_mono, n_fft=n_fft, hop_length=hop_length).T.astype(
np.complex64) # (time, freq)
ch_axis = 1 if stft_data_format == 'channels_first' else 3 # batch shape
S = np.expand_dims(S, (0, ch_axis))
model = tf.keras.models.Sequential([istft, stft])
recon_S = model(S)
# trim off the frames coming from zero-pad result
n = n_added_frames
n_added_frames += n
if stft_data_format == 'channels_first':
if n != 0:
S = S[:, :, n:-n, :]
recon_S = recon_S[:, :, n_added_frames:-n_added_frames, :]
else:
if n != 0:
S = S[:, n:-n, :, :]
recon_S = recon_S[:, n_added_frames:-n_added_frames, :, :]
np.testing.assert_equal(S.shape, recon_S.shape)
allclose_complex_numbers(S, recon_S)
def test_spectrogram_correctness_more(data_format, window_name):
def _get_stft_model(following_layer=None):
# compute with kapre
stft_model = tensorflow.keras.models.Sequential()
stft_model.add(
STFT(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
window_name=window_name,
pad_end=False,
input_data_format=data_format,
output_data_format=data_format,
input_shape=input_shape,
name='stft',
)
)
if following_layer is not None:
stft_model.add(following_layer)
return stft_model
n_fft = 512
hop_length = 256
n_ch = 2
src_mono, batch_src, input_shape = get_audio(data_format=data_format, n_ch=n_ch)
win_length = n_fft # test with x2
# compute with librosa
S_ref = librosa.core.stft(
src_mono,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
center=False,
window=window_name.replace('_window', '') if window_name else 'hann',
).T # (time, freq)
S_ref = np.expand_dims(S_ref, axis=2) # time, freq, ch=1
S_ref = np.tile(S_ref, [1, 1, n_ch]) # time, freq, ch=n_ch
if data_format == 'channels_first':
S_ref = np.transpose(S_ref, (2, 0, 1)) # ch, time, freq
stft_model = _get_stft_model()
S_complex = stft_model.predict(batch_src)[0] # 3d representation
allclose_complex_numbers(S_ref, S_complex)
# test Magnitude()
stft_mag_model = _get_stft_model(Magnitude())
S = stft_mag_model.predict(batch_src)[0] # 3d representation
np.testing.assert_allclose(np.abs(S_ref), S, atol=2e-4)
# # test Phase()
stft_phase_model = _get_stft_model(Phase())
S = stft_phase_model.predict(batch_src)[0] # 3d representation
allclose_phase(np.angle(S_complex), S)
def test_save_load(save_format):
"""test saving/loading of models that has stft, melspectorgrma, and log frequency."""
src_mono, batch_src, input_shape = get_audio(data_format='channels_last',
n_ch=1)
# test STFT save/load
save_load_compare(
STFT(input_shape=input_shape, pad_begin=True),
batch_src,
allclose_complex_numbers,
save_format,
STFT,
)
# test ConcatenateFrequencyMap
specs_batch = np.random.randn(2, 3, 5, 4).astype(np.float32)
save_load_compare(
ConcatenateFrequencyMap(input_shape=specs_batch.shape[1:]),
specs_batch,
np.testing.assert_allclose,
save_format,
ConcatenateFrequencyMap,
)
if save_format == 'tf':
# test melspectrogram save/load
save_load_compare(
get_melspectrogram_layer(input_shape=input_shape,
return_decibel=True),
batch_src,
np.testing.assert_allclose,
save_format,
)
# test log frequency spectrogram save/load
save_load_compare(
get_log_frequency_spectrogram_layer(input_shape=input_shape,
return_decibel=True),
batch_src,
np.testing.assert_allclose,
save_format,
)
# test stft_mag_phase
save_load_compare(
get_stft_mag_phase(input_shape=input_shape, return_decibel=True),
batch_src,
np.testing.assert_allclose,
save_format,
)
# test stft mag
save_load_compare(
get_stft_magnitude_layer(input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
save_format,
)
def test_save_load_channel_swap(data_format, save_format):
src_mono, batch_src, input_shape = get_audio(data_format='channels_last', n_ch=1)
save_load_compare(
ChannelSwap(input_shape=input_shape),
batch_src,
np.testing.assert_allclose,
save_format=save_format,
layer_class=ChannelSwap,
training=None,
)
def test_channel_swap_correctness(n_ch, data_format, data_type):
len_src = 256
src_mono, batch_src, input_shape = get_audio(data_format=data_format,
n_ch=n_ch,
length=len_src)
model = tf.keras.Sequential()
model.add(ChannelSwap(input_shape=input_shape, ))
# consistent during inference
kapre_ref = model.predict(batch_src)
for _ in range(100):
kapre_again = model.predict(batch_src)
np.testing.assert_equal(kapre_ref, kapre_again)
ch_axis = 1 if data_format == _CH_FIRST_STR else 2 # to be changed for 2d data type
def test_log_spectrogram_runnable(data_format):
"""test if log spectrogram layer works well"""
src_mono, batch_src, input_shape = get_audio(data_format=data_format,
n_ch=1)
_ = get_log_frequency_spectrogram_layer(input_shape, return_decibel=True)
_ = get_log_frequency_spectrogram_layer(input_shape, return_decibel=False)
def test_melspectrogram_correctness(n_fft, sr, hop_length, n_ch, data_format,
amin, dynamic_range, n_mels, mel_f_min,
mel_f_max):
"""Test the correctness of melspectrogram.
Note that mel filterbank is tested separated
"""
def _get_melgram_model(return_decibel,
amin,
dynamic_range,
input_shape=None):
# compute with kapre
melgram_model = get_melspectrogram_layer(
n_fft=n_fft,
sample_rate=sr,
n_mels=n_mels,
mel_f_min=mel_f_min,
mel_f_max=mel_f_max,
win_length=win_length,
hop_length=hop_length,
input_data_format=data_format,
output_data_format=data_format,
return_decibel=return_decibel,
input_shape=input_shape,
db_amin=amin,
db_dynamic_range=dynamic_range,
)
return melgram_model
src_mono, batch_src, input_shape = get_audio(data_format=data_format,
n_ch=n_ch)
win_length = n_fft # test with x2
# compute with librosa
S_ref = librosa.feature.melspectrogram(
src_mono,
sr=sr,
n_fft=n_fft,
hop_length=hop_length,
win_length=win_length,
center=False,
power=1.0,
n_mels=n_mels,
fmin=mel_f_min,
fmax=mel_f_max,
).T
S_ref = np.expand_dims(S_ref, axis=2) # time, freq, ch=1
S_ref = np.tile(S_ref, [1, 1, n_ch]) # time, freq, ch=n_ch
if data_format == 'channels_first':
S_ref = np.transpose(S_ref, (2, 0, 1)) # ch, time, freq
# melgram
melgram_model = _get_melgram_model(return_decibel=False,
input_shape=input_shape,
amin=None,
dynamic_range=120.0)
S = melgram_model.predict(batch_src)[0] # 3d representation
np.testing.assert_allclose(S_ref, S, atol=1e-4)
# log melgram
melgram_model = _get_melgram_model(return_decibel=True,
input_shape=input_shape,
amin=amin,
dynamic_range=dynamic_range)
S = melgram_model.predict(batch_src)[0] # 3d representation
S_ref_db = librosa.power_to_db(S_ref,
ref=1.0,
amin=amin,
top_db=dynamic_range)
np.testing.assert_allclose(
S_ref_db, S, rtol=3e-3) # decibel is evaluated with relative tolerance
def test_spectrogram_tflite_correctness(
n_fft, hop_length, n_ch, data_format, batch_size, win_length, pad_end
):
def _get_stft_model(following_layer=None, tflite_compatible=False):
# compute with kapre
stft_model = tensorflow.keras.models.Sequential()
if tflite_compatible:
stft_model.add(
STFTTflite(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
window_name=None,
pad_end=pad_end,
input_data_format=data_format,
output_data_format=data_format,
input_shape=input_shape,
name='stft',
)
)
else:
stft_model.add(
STFT(
n_fft=n_fft,
win_length=win_length,
hop_length=hop_length,
window_name=None,
pad_end=pad_end,
input_data_format=data_format,
output_data_format=data_format,
input_shape=input_shape,
name='stft',
)
)
if following_layer is not None:
stft_model.add(following_layer)
return stft_model
src_mono, batch_src, input_shape = get_audio(
data_format=data_format, n_ch=n_ch, batch_size=batch_size
)
# tflite requires a known batch size
batch_size = batch_src.shape[0]
stft_model_tflite = _get_stft_model(tflite_compatible=True)
stft_model = _get_stft_model(tflite_compatible=False)
# test STFT()
S_complex_tflite = predict_using_tflite(stft_model_tflite, batch_src) # predict using tflite
# (batch, time, freq, chan, re/imag) - convert to complex number:
S_complex_tflite = tf.complex(
S_complex_tflite[..., 0], S_complex_tflite[..., 1]
) # (batch,time,freq,chan)
S_complex = stft_model.predict(batch_src) # predict using tf model
allclose_complex_numbers(S_complex, S_complex_tflite)
# test Magnitude()
stft_mag_model_tflite = _get_stft_model(MagnitudeTflite(), tflite_compatible=True)
stft_mag_model = _get_stft_model(Magnitude(), tflite_compatible=False)
S_lite = predict_using_tflite(stft_mag_model_tflite, batch_src) # predict using tflite
S = stft_mag_model.predict(batch_src) # predict using tf model
np.testing.assert_allclose(S, S_lite, atol=1e-4)
# # test approx Phase() same for tflite and non-tflite
stft_approx_phase_model_lite = _get_stft_model(
PhaseTflite(approx_atan_accuracy=500), tflite_compatible=True
)
stft_approx_phase_model = _get_stft_model(
Phase(approx_atan_accuracy=500), tflite_compatible=False
)
S_approx_phase_lite = predict_using_tflite(
stft_approx_phase_model_lite, batch_src
) # predict using tflite
S_approx_phase = stft_approx_phase_model.predict(
batch_src, batch_size=batch_size
) # predict using tf model
assert_approx_phase(S_approx_phase_lite, S_approx_phase, atol=1e-2, acceptable_fail_ratio=0.01)
# # test accuracy of approx Phase()
stft_phase_model = _get_stft_model(Phase(), tflite_compatible=False)
S_phase = stft_phase_model.predict(batch_src, batch_size=batch_size) # predict using tf model
assert_approx_phase(S_approx_phase_lite, S_phase, atol=1e-2, acceptable_fail_ratio=0.01)
