def tensors_to_rgb_images(inputs, colors, size_multiplier=1):
"""
Map all values of 'inputs' between [0, 1] and then into RGB color indices.
Gather colors from 'colors' using the indices.
Based on https://gist.github.com/jimfleming/c1adfdb0f526465c99409cc143dea97b
"""
tf.debugging.assert_rank(inputs, 3, message="tensors_to_rgb_images expects batches of 2 dimensional tensors with shape [batch, cols, rows].")
tf.debugging.assert_rank(colors, 2, message="tensors_to_rgb_images expects a colormap of shape [color, component].")
tf.debugging.assert_equal(tf.shape(colors)[1], 3, message="tensors_to_rgb_images expects an RGB colormap.")
# Scale features between 0 and 1 to produce a grayscale image
inputs = features.feature_scaling(inputs, tf.constant(0.0), tf.constant(1.0))
# Map linear colormap over all grayscale values [0, 1] to produce an RGB image
indices = tf.cast(tf.math.round(inputs * tf.cast(tf.shape(colors)[0] - 1, tf.float32)), tf.int32)
tf.debugging.assert_non_negative(indices, message="Negative color indices")
images = tf.gather(colors, indices, axis=0, batch_dims=0)
tf.debugging.assert_rank(images, 4, message="Gathering colors failed, output images do not have a channel dimension. Make sure the inputs have a known shape.")
# Here it is assumed the output images are going to Tensorboard
images = tf.image.transpose(images)
images = tf.image.flip_up_down(images)
# Rows and columns from shape
old_size = tf.cast(tf.shape(images)[1:3], tf.float32)
new_size = tf.cast(size_multiplier * old_size, tf.int32)
images = tf.image.resize(images, new_size)
tf.debugging.assert_all_finite(images, message="Tensor conversion to RGB images failed, non-finite values in output")
return images
def extract_features(signals, sample_rates, feattype, spec_kwargs, melspec_kwargs, mfcc_kwargs, db_spec_kwargs, feat_scale_kwargs, window_norm_kwargs):
tf.debugging.assert_rank(signals, 2, message="Input signals for feature extraction must be batches of mono signals without channels, i.e. of shape [B, N] where B is batch size and N number of samples.")
tf.debugging.assert_equal(sample_rates, [sample_rates[0]], message="Different sample rates in a single batch not supported, all signals in the same batch should have the same sample rate.")
#TODO batches with different sample rates (probably not worth the effort)
sample_rate = sample_rates[0]
X = audio_features.spectrograms(signals, sample_rate, **spec_kwargs)
tf.debugging.assert_all_finite(X, "spectrogram failed")
if feattype in ("melspectrogram", "logmelspectrogram", "mfcc"):
X = audio_features.melspectrograms(X, sample_rate=sample_rate, **melspec_kwargs)
tf.debugging.assert_all_finite(X, "melspectrogram failed")
if feattype in ("logmelspectrogram", "mfcc"):
X = tf.math.log(X + 1e-6)
tf.debugging.assert_all_finite(X, "logmelspectrogram failed")
if feattype == "mfcc":
coef_begin = mfcc_kwargs.get("coef_begin", 1)
coef_end = mfcc_kwargs.get("coef_end", 13)
mfccs = tf.signal.mfccs_from_log_mel_spectrograms(X)
X = mfccs[..., coef_begin:coef_end]
tf.debugging.assert_all_finite(X, "mfcc failed")
elif feattype in ("db_spectrogram",):
X = audio_features.power_to_db(X, **db_spec_kwargs)
tf.debugging.assert_all_finite(X, "db_spectrogram failed")
if feat_scale_kwargs:
X = features.feature_scaling(X, **feat_scale_kwargs)
tf.debugging.assert_all_finite(X, "feature scaling failed")
if window_norm_kwargs:
X = features.window_normalization(X, **window_norm_kwargs)
tf.debugging.assert_all_finite(X, "window normalization failed")
return X
def test_feature_scaling(self):
for rank in range(1, 5):
for _ in range(300):
delta = np.random.uniform(1, 1e3)
min = np.random.uniform(-delta, delta)
max = min + np.random.uniform(0, delta / 2)
x = np.random.normal(0,
delta**2,
size=np.random.randint(2, 20, size=rank))
for axis in [None] + list(range(rank)):
y = features.feature_scaling(x, min, max,
axis=axis).numpy()
assert not np.isnan(y).any()
assert y.shape == x.shape
assert np.abs(y.min(axis=axis) - min).max() < 1e-9
assert np.abs(y.max(axis=axis) - max).max() < 1e-9