def cross_entropy(logits,
labels,
input_length=None,
label_length=None,
smoothing=0.0,
reduction=tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS):
'''
cross entropy function for classfication and seq classfication
:param, label_length, for seq task, this for target seq length, e.g. a b c </s>, 4
'''
del input_length
onehot_labels = tf.cond(pred=tf.equal(
tf.rank(logits) - tf.rank(labels), 1),
true_fn=lambda: tf.one_hot(
labels, tf.shape(logits)[-1], dtype=tf.int32),
false_fn=lambda: labels)
if label_length is not None:
weights = utils.len_to_mask(label_length)
else:
weights = 1.0
loss = tf.losses.softmax_cross_entropy(onehot_labels=onehot_labels,
logits=logits,
weights=weights,
label_smoothing=smoothing,
reduction=reduction)
return loss
def test_generate_data(self):
for batch_mode in [True, False]:
task_name = self.config['data']['task']['name']
self.config['data']['task']['batch_mode'] = batch_mode
self.config['data']['task']['dummy'] = False
task = registers.task[task_name](self.config, self.mode)
with self.cached_session(use_gpu=False, force_gpu=False):
for uttid, feats, src_lens, targets, tgt_lens in task.generate_data(
):
logging.debug('uttid : {}'.format(uttid))
logging.debug("feats : {}, shape : {}".format(
feats, feats.shape))
logging.debug("targets : {}, shape : {}".format(
targets, targets.shape))
logging.debug('src_len : {}'.format(src_lens))
logging.debug('tgt_len : {}'.format(tgt_lens))
self.assertDTypeEqual(feats, np.float32)
self.assertDTypeEqual(src_lens, np.int64)
self.assertDTypeEqual(targets, np.int64)
self.assertDTypeEqual(tgt_lens, np.int64)
if batch_mode:
self.assertEqual(len(uttid.shape), 1)
self.assertEqual(len(feats.shape), 3)
self.assertEqual(len(targets.shape), 2)
self.assertEqual(len(src_lens.shape), 1)
self.assertEqual(len(tgt_lens.shape), 1)
else:
self.assertEqual(tf.rank(uttid).numpy(), 0)
self.assertEqual(len(feats.shape), 2)
self.assertEqual(len(targets.shape), 1)
self.assertEqual(tf.rank(src_lens).numpy(), 0)
self.assertEqual(tf.rank(tgt_lens).numpy(), 0)
def test_detla_delta(self):
''' test delta delta'''
with self.cached_session(use_gpu=False, force_gpu=False):
feat = tf.constant(self.data[None, :], dtype=tf.float32)
output = py_x_ops.delta_delta(feat,
order=self.order,
window=self.window)
self.assertEqual(tf.rank(output).eval(), tf.rank(feat).eval())
self.assertEqual(output.shape,
(1, self.feat_dim * (self.order + 1)))
self.assertAllClose(output.eval(), self.output_true[None, :])
def test_framepow(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
input_data = input_data / 32768
framepow = Framepow.params({
'window_length': 0.025,
'frame_length': 0.010
}).instantiate()
framepow_test = framepow(input_data, sample_rate)
output_true = np.array([
0.000018, 0.000011, 0.000010, 0.000010, 0.000010, 0.000010,
0.000008, 0.000009, 0.000009, 0.000009, 0.000009, 0.000011,
0.090164, 0.133028, 0.156547, 0.053551, 0.056670, 0.097706,
0.405659, 2.119505, 4.296845, 6.139090, 6.623638, 6.136467,
7.595072, 7.904415, 7.655983, 6.771016, 5.706427, 4.220942,
3.259599, 2.218259, 1.911394, 2.234246, 3.056905, 2.534153,
0.464354, 0.013493, 0.021231, 0.148362, 0.364829, 0.627266,
0.494912, 0.366029, 0.315408, 0.312441, 0.323796, 0.267505,
0.152856, 0.045305
])
self.assertEqual(tf.rank(framepow_test).eval(), 1)
self.assertAllClose(framepow_test.eval().flatten()[:50],
output_true)
def test_sfb(self):
''' test sfb op'''
with self.cached_session(use_gpu=False, force_gpu=False):
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
power_spc, phase_spc = py_x_ops.analyfiltbank(
input_data, sample_rate)
logging.info('Shape of power_spc: {}'.format(
power_spc.eval().shape))
logging.info('Shape of phase_spc: {}'.format(
phase_spc.eval().shape))
output = py_x_ops.synthfiltbank(power_spc.eval(), phase_spc.eval(),
sample_rate)
self.assertEqual(tf.rank(output).eval(), 1)
logging.info('Shape of recovered signal: {}'.format(
output.eval().shape))
# beginning 400 samples are different, due to the overlap and add
self.assertAllClose(output.eval().flatten()[500:550],
input_data[500:550],
rtol=1e-4,
atol=1e-4)
def test_fbank(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
config = {
'window_length': 0.025,
'output_type': 1,
'frame_length': 0.010,
'snip_edges': True
}
fbank = Fbank.params(config).instantiate()
fbank_test = fbank(input_data, sample_rate)
self.assertEqual(tf.rank(fbank_test).eval(), 3)
real_fank_feats = np.array(
[[3.768338, 4.946218, 6.289874, 6.330853, 6.761764, 6.884573],
[3.803553, 5.450971, 6.547878, 5.796172, 6.397846, 7.242926]])
self.assertAllClose(
np.squeeze(fbank_test.eval()[0:2, 0:6, 0]),
real_fank_feats,
rtol=1e-05,
atol=1e-05)
def accuracy(logits, labels):
''' accuracy candies
params:
logits: [B, ..., D]
labels: [B, ...]
return:
accuracy tensor
'''
with tf.name_scope('accuracy'):
assert_rank = tf.assert_equal(tf.rank(logits), tf.rank(labels) + 1)
assert_shape = tf.assert_equal(tf.shape(logits)[:-1], tf.shape(labels))
with tf.control_dependencies([assert_rank, assert_shape]):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int64)
labels = tf.cast(labels, tf.int64)
return tf.reduce_mean(
tf.cast(tf.equal(predictions, labels), dtype=tf.float32))
def test_spectrum(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
pitch = Pitch.params({
'window_length': 0.025,
'soft_min_f0': 10.0
}).instantiate()
pitch_test = pitch(input_data, sample_rate)
self.assertEqual(tf.rank(pitch_test).eval(), 2)
output_true = np.array(
[
[0.03881124, 0.3000031, - 0.02324523],
[0.006756478, 0.3000097, 0.01047742],
[0.02455365, 0.3000154, 0.00695902],
[0.02453586, 0.3000221, 0.008448198],
[0.03455311, 0.3000307, - 0.07547269],
[0.04293294, 0.3000422, - 0.04193667]
]
)
def test_plp(self):
wav_path = str(
Path(PACKAGE_ROOT_DIR).joinpath('layers/ops/data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
input_data = input_data / 32768
plp = Plp.params({
'window_length': 0.025,
'frame_length': 0.010,
'plp_order': 12
}).instantiate()
plp_test = plp(input_data, sample_rate)
output_true = np.array(
[[-0.209490, -0.326126, 0.010536, -0.027167, -0.117118],
[-0.020293, -0.454695, -0.104243, 0.001560, -0.234854],
[-0.015118, -0.444044, -0.156695, -0.086221, -0.319310],
[-0.031856, -0.130708, 0.047435, -0.089916, -0.160247],
[0.052763, -0.271487, 0.011329, 0.025320, 0.012851]])
self.assertEqual(tf.rank(plp_test).eval(), 2)
# Because the povey window is used instead of the hamming window in spectrum.
self.assertAllClose(
plp_test.eval()[50:55, 5:10], output_true, rtol=1e-02, atol=1e-02)
def ctc_lambda_loss(logits, labels, input_length, label_length, blank_index=0):
'''
ctc loss function
psram: logits, (B, T, D)
psram: input_length, (B, 1), input length of encoder
psram: labels, (B, T)
psram: label_length, (B, 1), label length for convert dense label to sparse
returns: loss, scalar
'''
ilen = tf.cond(
pred=tf.equal(tf.rank(input_length), 1),
true_fn=lambda: input_length,
false_fn=lambda: tf.squeeze(input_length),
)
ilen = tf.cast(ilen, tf.int32)
olen = tf.cond(
pred=tf.equal(tf.rank(label_length), 1),
true_fn=lambda: label_length,
false_fn=lambda: tf.squeeze(label_length))
olen = tf.cast(olen, tf.int32)
deps = [
tf.assert_rank(labels, 2, name='label_rank_check'),
tf.assert_rank(logits, 3, name='logits_rank_check'),
tf.assert_rank(ilen, 1, name='src_len_rank_check'), # input_length
tf.assert_rank(olen, 1, name='tgt_len_rank_check'), # output_length
]
labels, logits = ctc_data_transform(labels, logits, blank_index)
with tf.control_dependencies(deps):
# (B, 1)
# blank index is consistent with Espnet, zero
batch_loss = tf.nn.ctc_loss(
labels=labels,
inputs=logits,
sequence_length=ilen,
time_major=False,
preprocess_collapse_repeated=False,
ctc_merge_repeated=True,
ignore_longer_outputs_than_inputs=False)
return batch_loss
def test_FbankPitch(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
config = {'window_length': 0.025, 'output_type': 1, 'frame_length': 0.010}
fbank_pitch = FbankPitch.params(config).instantiate()
fbank_pitch_test = fbank_pitch(input_data, sample_rate)
self.assertEqual(tf.rank(fbank_pitch_test).eval(), 2)
print(fbank_pitch_test.eval()[0:2])
def test_fbank(self):
wav_path = str(
Path(PACKAGE_ROOT_DIR).joinpath(
'layers/ops/data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
config = {'window_length': 0.025, 'output_type': 1, 'frame_length': 0.010}
fbank = Fbank.params(config).instantiate()
fbank_test = fbank(input_data, sample_rate)
self.assertEqual(tf.rank(fbank_test).eval(), 3)
def compute_mel_filterbank_features(waveforms,
sample_rate=16000,
preemphasis=0.97,
frame_length=0.025,
frame_step=0.010,
fft_length=None,
lower_edge_hertz=80.0,
upper_edge_hertz=7600.0,
num_mel_bins=80,
log_noise_floor=1e-3,
apply_mask=True):
"""Implement mel-filterbank extraction using tf ops.
Args:
waveforms: float32 tensor with shape [max_len, nchannels]
sample_rate: sampling rate of the waveform
preemphasis: waveform high-pass filtering constant
frame_length: frame length in ms
frame_step: frame_Step in ms
fft_length: number of fft bins
lower_edge_hertz: lowest frequency of the filterbank
upper_edge_hertz: highest frequency of the filterbank
num_mel_bins: filterbank size
log_noise_floor: clip small values to prevent numeric overflow in log
apply_mask: When working on a batch of samples, set padding frames to zero
Returns:
filterbanks: a float32 tensor with shape [nchannles, max_len, num_bins]
"""
del log_noise_floor, apply_mask
spectrogram = powspec_feat(waveforms,
sr=sample_rate,
nfft=512 if not fft_length else fft_length,
winlen=frame_length,
winstep=frame_step,
lowfreq=lower_edge_hertz,
highfreq=upper_edge_hertz,
preemph=preemphasis)
# [channels, time, feat_dim]
fbank = fbank_feat(spectrogram,
sr=sample_rate,
feature_size=num_mel_bins,
nfft=512 if not fft_length else fft_length,
lowfreq=lower_edge_hertz,
highfreq=upper_edge_hertz)
# [time, feat_dim]
fbank = tf.cond(tf.equal(tf.rank(fbank), 3),
true_fn=lambda: fbank[0, :, :],
false_fn=lambda: fbank)
return fbank
def delta_delta(feat, order=2):
'''
params:
feat: a tensor of shape [nframe, nfbank] or [nframe, nfbank, 1]
return: [nframe, nfbank, 3]
'''
feat = tf.cond(tf.equal(tf.rank(feat), 3),
true_fn=lambda: feat[:, :, 0],
false_fn=lambda: feat)
shape = tf.shape(feat)
# [nframe nfbank*3]
nframe = shape[0]
nfbank = shape[1]
delta = py_x_ops.delta_delta(feat, order=order)
feat_with_delta_delta = tf.reshape(delta, (nframe, nfbank, (order + 1)))
return feat_with_delta_delta
def test_FbankPitch(self):
wav_path = str(
Path(os.environ['MAIN_ROOT']).joinpath(
'delta/layers/ops/data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
config = {
'window_length': 0.025,
'output_type': 1,
'frame_length': 0.010,
'thres_autoc': 0.4
}
fbank_pitch = FbankPitch.params(config).instantiate()
fbank_pitch_test = fbank_pitch(input_data)
self.assertEqual(tf.rank(fbank_pitch_test).eval(), 2)
def test_framepow(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
framepow = Framepow.params({
'window_length': 0.025,
'frame_length': 0.010
}).instantiate()
framepow_test = framepow(input_data, sample_rate)
real_framepow_feats = np.array(
[9.819611, 9.328745, 9.247337, 9.26451, 9.266059])
self.assertEqual(tf.rank(framepow_test).eval(), 1)
self.assertAllClose(framepow_test.eval()[0:5], real_framepow_feats)
def test_cepstrum(self):
''' test cepstrum op'''
with self.cached_session(use_gpu=False, force_gpu=False):
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.cepstrum(input_data, sample_rate)
#pylint: disable=bad-whitespace
output_true = np.array(
[[0.525808, 0.579537, 0.159656, 0.014726, -0.1866810],
[0.225988, 1.557304, 3.381828, 0.132935, 0.7128600],
[-1.832759, -1.045178, 0.753158, 0.116107, -0.9307780],
[-0.696277, 1.333355, 1.590942, 2.041829, -0.0805630],
[-0.377375, 2.984320, 0.036302, 3.676640, 1.1709290]])
#pylint: enable=bad-whitespace
self.assertEqual(tf.rank(output).eval(), 2)
logging.info('Shape of cepstrum: {}'.format(output.shape))
self.assertAllClose(output.eval()[15:20, 7:12], output_true)
def test_fbank(self):
''' test fbank op'''
with self.cached_session(use_gpu=False, force_gpu=False):
data = np.arange(513)
spectrogram = tf.constant(data[None, None, :], dtype=tf.float32)
sample_rate = tf.constant(22050, tf.int32)
output = py_x_ops.fbank(spectrogram,
sample_rate,
filterbank_channel_count=20)
output_true = np.array([
1.887894, 2.2693727, 2.576507, 2.8156495, 3.036504, 3.2296343,
3.4274294, 3.5987632, 3.771217, 3.937401, 4.0988584, 4.2570987,
4.4110703, 4.563661, 4.7140336, 4.8626432, 5.009346, 5.1539173,
5.2992935, 5.442024
])
self.assertEqual(tf.rank(output).eval(), 3)
self.assertEqual(output.shape, (1, 1, 20))
self.assertAllClose(output.eval(), output_true[None, None, :])
def test_frmpow(self):
''' test frame_power op'''
with self.cached_session(use_gpu=False, force_gpu=False):
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.frame_pow(input_data, sample_rate)
output_true = np.array([
0.000018, 0.000011, 0.000010, 0.000010, 0.000010, 0.000010, 0.000008,
0.000009, 0.000009, 0.000009, 0.000009, 0.000011, 0.090164, 0.133028,
0.156547, 0.053551, 0.056670, 0.097706, 0.405659, 2.119505, 4.296845,
6.139090, 6.623638, 6.136467, 7.595072, 7.904415, 7.655983, 6.771016,
5.706427, 4.220942, 3.259599, 2.218259, 1.911394, 2.234246, 3.056905,
2.534153, 0.464354, 0.013493, 0.021231, 0.148362, 0.364829, 0.627266,
0.494912, 0.366029, 0.315408, 0.312441, 0.323796, 0.267505, 0.152856,
0.045305
])
self.assertEqual(tf.rank(output).eval(), 1)
logging.info('Shape of frame_power: {}'.format(output.eval().shape))
self.assertAllClose(output.eval().flatten()[:50], output_true)
def test_spectrum(self):
wav_path = str(
Path(PACKAGE_ROOT_DIR).joinpath('layers/ops/data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
spectrum = Spectrum.params({'window_length': 0.025}).instantiate()
spectrum_test = spectrum(input_data, sample_rate)
output_true = np.array(
[[-16.863441, -16.910473, -17.077059, -16.371634, -16.845686],
[-17.922068, -20.396345, -19.396944, -17.331493, -16.118851],
[-17.017776, -17.551350, -20.332376, -17.403994, -16.617926],
[-19.873854, -17.644503, -20.679525, -17.093716, -16.535091],
[-17.074402, -17.295971, -16.896650, -15.995432, -16.560730]])
self.assertEqual(tf.rank(spectrum_test).eval(), 2)
self.assertAllClose(spectrum_test.eval()[4:9, 4:9], output_true)
def test_plp(self):
''' test plp op'''
with self.cached_session(use_gpu=False, force_gpu=False):
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.plp(input_data, sample_rate)
#pylint: disable=bad-whitespace
output_true = np.array(
[[-0.209490, -0.326126, 0.010536, -0.027167, -0.117118],
[-0.020293, -0.454695, -0.104243, 0.001560, -0.234854],
[-0.015118, -0.444044, -0.156695, -0.086221, -0.319310],
[-0.031856, -0.130708, 0.047435, -0.089916, -0.160247],
[0.052763, -0.271487, 0.011329, 0.025320, 0.012851]])
#pylint: enable=bad-whitespace
self.assertEqual(tf.rank(output).eval(), 2)
logging.info('Shape of PLP: {}'.format(output.shape))
self.assertAllClose(
output.eval()[50:55, 5:10], output_true, rtol=1e-05, atol=1e-05)
def test_mfcc(self):
wav_path = str(
Path(PACKAGE_ROOT_DIR).joinpath('layers/ops/data/sm1_cln.wav'))
with self.session():
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
# config = {}
mfcc = Mfcc.params().instantiate()
mfcc_test = mfcc(input_data, sample_rate)
self.assertEqual(tf.rank(mfcc_test).eval(), 3)
real_mfcc_feats = np.array(
[[-30.58736, -7.088838, -10.67966, -1.646479, -4.36086],
[-30.73371, -6.128432, -7.930599, 3.208357, -1.086456]])
self.assertAllClose(np.squeeze(mfcc_test.eval()[0, 0:2, 1:6]),
real_mfcc_feats,
rtol=1e-05,
atol=1e-05)
def test_fbank(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
config = {
'window_length': 0.025,
'output_type': 1,
'frame_length': 0.010,
'snip_edges': True
}
fbank = Fbank.params(config).instantiate()
fbank_test = fbank(input_data, sample_rate)
self.assertEqual(tf.rank(fbank_test).eval(), 3)
if tf.executing_eagerly():
print(fbank_test.numpy()[0:2, 0:6, 0])
else:
print(fbank_test.eval()[0:2, 0:6, 0])
def test_spectrum(self):
wav_path = str(
Path(PACKAGE_ROOT_DIR).joinpath('layers/ops/data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
spectrum = Spectrum.params({
'window_length': 0.025,
'snip_edges': 1
}).instantiate()
spectrum_test = spectrum(input_data, sample_rate)
output_true = np.array(
[[9.819611, 2.84503, 3.660894, 2.7779, 1.212233],
[9.328745, 2.553949, 3.276319, 3.000918, 2.499342]])
self.assertEqual(tf.rank(spectrum_test).eval(), 2)
self.assertAllClose(
spectrum_test.eval()[0:2, 0:5], output_true, rtol=1e-05, atol=1e-05)
def test_spectrum(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
pitch = Pitch.params({
'window_length': 0.025,
'soft_min_f0': 10.0
}).instantiate()
pitch_test = pitch(input_data, sample_rate)
self.assertEqual(tf.rank(pitch_test).eval(), 2)
output_true = [[-0.1366025, 143.8855], [-0.0226383, 143.8855],
[-0.08464742, 143.8855], [-0.08458386, 143.8855],
[-0.1208689, 143.8855]]
self.assertAllClose(
pitch_test.eval()[0:5, :], output_true, rtol=1e-05, atol=1e-05)
def test_pitch(self):
''' test pitch op'''
with self.cached_session(use_gpu=False, force_gpu=False):
# read wave
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.pitch(input_data, sample_rate)
output_true = np.array([
0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000,
122.823532, 117.647057, 116.788322, 116.788322, 119.402985,
119.402985, 119.402985, 119.402985, 119.402985, 123.076920,
124.031006, 125.000000, 132.065216, 139.130432, 139.130432,
137.931030, 126.108368, 114.285713, 115.107910, 122.070084,
129.032257, 130.081299, 130.081299, 129.032257, 130.081299,
131.147537, 129.032257, 125.000000, 120.300751, 115.107910
])
self.assertEqual(tf.rank(output).eval(), 1)
logging.info('Shape of pitch: {}'.format(output.eval().shape))
self.assertAllClose(output.eval().flatten()[:50], output_true)
def test_zcr(self):
''' test zcr op'''
with self.cached_session(use_gpu=False, force_gpu=False):
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
output = py_x_ops.zcr(input_data, sample_rate)
output_true = np.array([
0.406250, 0.418750, 0.425000, 0.407500, 0.393750, 0.392500,
0.388750, 0.417500, 0.427500, 0.456250, 0.447500, 0.386250,
0.357500, 0.282500, 0.232500, 0.262500, 0.282500, 0.295000,
0.220000, 0.157500, 0.125000, 0.107500, 0.100000, 0.092500,
0.092500, 0.095000, 0.097500, 0.105000, 0.100000, 0.112500,
0.120000, 0.132500, 0.130000, 0.135000, 0.112500, 0.120000,
0.090000, 0.080000, 0.070000, 0.080000, 0.087500, 0.092500,
0.097500, 0.097500, 0.112500, 0.090000, 0.065000, 0.087500,
0.175000, 0.240000
])
self.assertEqual(tf.rank(output).eval(), 1)
logging.info('Shape of zero-cross-rate: {}'.format(
output.eval().shape))
self.assertAllClose(output.eval().flatten()[:50], output_true)
def test_mfcc(self):
wav_path = str(Path(PACKAGE_OPS_DIR).joinpath('data/sm1_cln.wav'))
with self.cached_session(use_gpu=False, force_gpu=False):
read_wav = ReadWav.params().instantiate()
input_data, sample_rate = read_wav(wav_path)
config = {'use_energy': True}
mfcc = Mfcc.params(config).instantiate()
mfcc_test = mfcc(input_data, sample_rate)
self.assertEqual(tf.rank(mfcc_test).eval(), 3)
real_mfcc_feats = np.array([[
9.819611, -30.58736, -7.088838, -10.67966, -1.646479, -4.36086
], [
9.328745, -30.73371, -6.128432, -7.930599, 3.208357, -1.086456
]])
self.assertAllClose(np.squeeze(mfcc_test.eval()[0, 0:2, 0:6]),
real_mfcc_feats,
rtol=1e-05,
atol=1e-05)
def test_spectrum(self):
''' test spectrum op'''
with self.cached_session(use_gpu=False, force_gpu=False):
sample_rate, input_data = feat_lib.load_wav(self.wavpath, sr=16000)
logging.info(
f"input shape: {input_data.shape}, sample rate dtype: {sample_rate.dtype}"
)
self.assertEqual(sample_rate, 16000)
output = py_x_ops.spectrum(input_data, sample_rate)
#pylint: disable=bad-whitespace
output_true = np.array(
[[-16.863441, -16.910473, -17.077059, -16.371634, -16.845686],
[-17.922068, -20.396345, -19.396944, -17.331493, -16.118851],
[-17.017776, -17.551350, -20.332376, -17.403994, -16.617926],
[-19.873854, -17.644503, -20.679525, -17.093716, -16.535091],
[-17.074402, -17.295971, -16.896650, -15.995432, -16.560730]])
#pylint: enable=bad-whitespace
self.assertEqual(tf.rank(output).eval(), 2)
logging.info('Shape of spectrum: {}'.format(output.shape))
self.assertAllClose(output.eval()[4:9, 4:9], output_true)
def fbank_feat(powspec,
sr=8000,
feature_size=40,
nfft=512,
lowfreq=0,
highfreq=None):
''' powspec: [audio_channels, spectrogram_length, spectrogram_feat_dim]
return : [auido_chnnels, nframe, nfbank]
'''
del nfft
true_fn = lambda: tf.expand_dims(powspec, 0)
false_fn = lambda: powspec
powspec = tf.cond(tf.equal(tf.rank(powspec), 2), true_fn, false_fn)
feat = py_x_ops.fbank(
powspec,
sr,
filterbank_channel_count=feature_size,
lower_frequency_limit=lowfreq,
upper_frequency_limit=highfreq,
)
return feat