def sdr_improvement(self, s_target, s_approx, with_perm=False): # B S L or B P S L mix = tf.tile(tf.expand_dims(self.x_mix, 1) ,[1, self.S, 1]) s_target_norm = tf.reduce_sum(tf.square(s_target), axis=-1) s_approx_norm = tf.reduce_sum(tf.square(s_approx), axis=-1) mix_norm = tf.reduce_sum(tf.square(mix), axis=-1) s_target_s_2 = tf.square(tf.reduce_sum(s_target*s_approx, axis=-1)) s_target_mix_2 = tf.square(tf.reduce_sum(s_target*mix, axis=-1)) sep = 1.0/((s_target_norm*s_approx_norm)/s_target_s_2 - 1.0) separated = 10. * log10(sep) non_separated = 10. * log10(1.0/((s_target_norm*mix_norm)/s_target_mix_2 - 1.0)) loss = (s_target_norm*s_approx_norm)/s_target_s_2 val = separated - non_separated val = tf.reduce_mean(val , -1) # Mean over speakers if not with_perm: val = tf.reduce_mean(val , -1) # Mean over batches else: val = tf.reduce_mean(val , 0) # Mean over batches val = tf.reduce_min(val, -1) return val, loss
def init_separator(self):
if self.plugged:
if self.abs_input:
self.X = tf.abs(self.X)
if self.normalize_input == '01':
self.normalization01
elif self.normalize_input == 'meanstd':
self.normalization_mean_std
self.prediction
else:
# STFT
self.preprocessing
# Apply a certain function
if self.pre_func == 'sqrt':
self.X = tf.sqrt(self.X)
elif self.pre_func == 'log':
self.X = log10(self.X + 1e-12)
# Apply normalization
if self.normalize_input == '01':
self.normalization01
elif self.normalize_input == 'meanstd':
self.normalization_mean_std
# Apply silent mask
if self.silent_threshold > 0:
max_ = tf.reduce_max(self.X, [1, 2], keep_dims=True)
mask = tf.cast(
tf.less(max_ - self.X, self.silent_threshold / 20.),
tf.float32)
self.X = mask * self.X
if self.add_dilated:
self.dilated
self.prediction
#TODO TO IMPROVE !
if self.args['model_folder'] is None:
# if 'inference' not in self.folder and 'enhance' not in self.folder and 'finetuning' not in self.folder:
self.cost_model = self.cost
self.finish_construction()
self.optimize
def __init__(self, plugged=False, *args, **kwargs):
super(Separator, self).__init__(plugged, *args, **kwargs)
self.num_speakers = kwargs['tot_speakers']
self.layer_size = kwargs['layer_size']
self.embedding_size = kwargs['embedding_size']
self.normalize = kwargs['no_normalize']
self.nb_layers = kwargs['nb_layers']
self.a = kwargs['mask_a']
self.b = kwargs['mask_b']
self.normalize_input = kwargs['normalize_separator']
self.abs_input = kwargs['abs_input']
# Loss Parameters
self.loss_with_silence = kwargs['silence_loss']
self.threshold_silence_loss = kwargs['threshold_silence_loss']
self.function_mask = kwargs['function_mask']
# Kmeans Parameters
self.beta = kwargs['beta_kmeans']
self.threshold = kwargs['threshold']
self.with_silence = kwargs['with_silence']
self.nb_tries = kwargs['nb_tries']
self.nb_steps = kwargs['nb_steps']
self.graph = tf.get_default_graph()
self.plugged = plugged
# If the Separator is not independant but using a front layer
if self.plugged:
self.F = kwargs['filters']
with self.graph.as_default():
self.training = self.graph.get_tensor_by_name('inputs/is_training:0')
front = self.graph.get_tensor_by_name('front/output:0')
self.B = tf.shape(self.graph.get_tensor_by_name('inputs/non_mix_input:0'))[0]
with tf.name_scope('split_front'):
self.X = tf.reshape(front[:self.B, :, :, :], [self.B, -1, self.F]) # Mix input [B, T, N]
# Non mix input [B, T, N, S]
self.X_input = tf.identity(self.X)
self.X_non_mix = tf.transpose(tf.reshape(front[self.B:, :, :, :], [self.B, self.S, -1, self.F]), [0,2,3,1])
with tf.name_scope('create_masks'):
# # Batch of Masks (bins label)
# # shape = [ batch size, T, F, S]
argmax = tf.argmax(tf.abs(self.X_non_mix), axis=3)
self.y = tf.one_hot(argmax, self.S, self.a, self.b)
self.y_test_export = tf.reshape(self.y[:, :, :, 0], [self.B, -1])
if self.function_mask == 'linear':
max_ = tf.reduce_max(tf.abs(self.X), [1, 2], keep_dims=True)
self.y = self.y * tf.expand_dims(tf.abs(self.X)/max_, 3)
elif self.function_mask == 'sqrt':
max_ = tf.reduce_max(tf.abs(self.X), [1, 2], keep_dims=True)
self.y = self.y * tf.expand_dims(tf.sqrt(tf.abs(self.X)/max_), 3)
elif self.function_mask == 'square':
max_ = tf.reduce_max(tf.abs(self.X), [1, 2], keep_dims=True)
self.y = self.y * tf.expand_dims(tf.square(tf.abs(self.X)/max_), 3)
if self.loss_with_silence:
max_ = tf.reduce_max(tf.abs(self.X), [1, 2], keep_dims=True)
log_compare = log10(tf.divide(max_, tf.abs(self.X)))
mask = tf.cast(log_compare < self.threshold_silence_loss, tf.float32)
tf.summary.image('separator/silence_mask', tf.expand_dims(mask,3), max_outputs=1)
self.y = self.y * tf.expand_dims(mask, 3)
# Speakers indices used in the mixtures
# shape = [ batch size, #speakers]
self.I = tf.get_default_graph().get_tensor_by_name('inputs/indicies:0')
else:
# STFT hyperparams
self.window_size = kwargs['window_size']
self.hop_size = kwargs['hop_size']
# Network hyperparams
self.F = kwargs['window_size']//2 +1
def cost(self):
"""
Construct the cost function op for the negative sampling cost
"""
if self.loss_with_silence:
max_ = tf.reduce_max(tf.abs(self.X), [1, 2], keep_dims=True)
log_compare = log10(tf.divide(max_, tf.abs(self.X)))
mask = tf.cast(log_compare < self.threshold_silence_loss,
tf.float32)
tf.summary.image('separator/silence_mask',
tf.expand_dims(mask, 3),
max_outputs=1)
y_a_b = self.y * tf.expand_dims(mask, 3)
y_0_1 = (self.y + 1.0) / 2.0 * tf.expand_dims(mask, 3)
else:
y_a_b = self.y
y_0_1 = (self.y + 1.0) / 2.0
tf.summary.image('mask/true/1',
tf.abs(tf.expand_dims(y_0_1[:, :, :, 0], 3)))
tf.summary.image('mask/true/2',
tf.abs(tf.expand_dims(y_0_1[:, :, :, 1], 3)))
# Get the embedded T-F vectors from the network
embedding = self.prediction # [B, T, F, E]
embedding_broad = tf.expand_dims(embedding, 4) # [B, T, F, E, 1]
y_broad = tf.expand_dims(y_0_1, 3) # [B, T, F, 1, S]
v_mean = tf.reduce_sum(embedding_broad * y_broad, [1, 2]) / (
1e-12 + tf.expand_dims(tf.reduce_sum(y_0_1, [1, 2]), 1)
) # [B, E, S]
#
# Reconstruction loss
#
with tf.name_scope('reconstruction_loss'):
v_mean_broad = tf.expand_dims(v_mean, 1) # [B, 1, E, S]
v_mean_broad = tf.expand_dims(v_mean_broad, 1) # [B, 1, 1, E, S]
assignments = tf.reduce_sum(v_mean_broad * embedding_broad,
3) # [B, T, F, S]
assignments = tf.nn.sigmoid(assignments) # [B, T, F, S]
masked_input = tf.expand_dims(self.X_input, 3) * assignments
# X_non_mix [B, T, F, S]
cost_recons = tf.reduce_mean(tf.square(self.X_non_mix -
masked_input),
axis=[1, 2])
cost_recons = tf.reduce_mean(
cost_recons, axis=-1) # Mean among all speakers [B, S]
cost_recons = tf.reduce_mean(cost_recons)
tf.summary.scalar('value', cost_recons)
#
# Constrast loss
#
with tf.name_scope('source_contrastive_loss'):
speaker_vectors = tf.nn.l2_normalize(self.speaker_vectors, 1)
embedding = tf.nn.l2_normalize(embedding, -1)
I = tf.expand_dims(self.I, axis=2) # [B, S, 1]
# Gathering the speaker_vectors [|S|, E]
Vspeakers = tf.gather_nd(speaker_vectors, I) # [B, S, E]
# Expand the dimensions in preparation for broadcasting
Vspeakers_broad = tf.expand_dims(Vspeakers, 1)
Vspeakers_broad = tf.expand_dims(Vspeakers_broad,
1) # [B, 1, 1, S, E]
embedding_broad = tf.expand_dims(embedding, 3)
# Compute the dot product between the embedding vectors and speaker
# vectors
dot = tf.reduce_sum(Vspeakers_broad * embedding_broad, 4)
# Compute the cost for every element
sc_cost = -tf.log(tf.nn.sigmoid(y_a_b * dot))
sc_cost = tf.reduce_mean(
sc_cost, 3) # Average the cost over all speakers in the input
sc_cost = tf.reduce_mean(sc_cost,
0) # Average the cost over all batches
sc_cost = tf.reduce_mean(sc_cost)
tf.summary.scalar('value', sc_cost)
cost = sc_cost + cost_recons
tf.summary.scalar('total', cost)
return cost
def __init__(self,
nb_clusters,
centroids_init=None,
nb_tries=10,
nb_iterations=10,
input_tensor=None,
latent_space_tensor=None,
beta=None,
threshold=2.5,
assign_at_end=False):
self.nb_clusters = nb_clusters
self.nb_iterations = nb_iterations
self.nb_tries = nb_tries
self.latent_space_tensor = latent_space_tensor
self.beta = beta
self.assign_at_end = assign_at_end
if input_tensor is None:
self.graph = tf.Graph()
else:
self.graph = tf.get_default_graph()
with self.graph.as_default():
with tf.name_scope('kmeans'):
if input_tensor is None:
# Spectrogram, embeddings
# shape = [batch, L , E ]
self.X_in = tf.placeholder("float", [None, None, None],
name='Kmeans_input')
else:
self.X_in = input_tensor
# mean, _ = tf.nn.moments(self.X_in, axes=-1, keep_dims=True)
x_norm = tf.nn.l2_normalize(self.X_in, axis=-1)
self.b = tf.shape(x_norm)[0]
self.X_original = tf.identity(x_norm)
self.X = tf.expand_dims(x_norm, 1)
self.X = tf.tile(self.X, [1, self.nb_tries, 1, 1])
self.L = tf.shape(self.X)[-2]
self.E = tf.shape(self.X)[-1]
self.X = tf.reshape(self.X, [-1, self.L, self.E])
self.B = tf.shape(self.X)[0]
self.ones = tf.ones_like(self.X, tf.float32)
self.shifting = tf.tile(
tf.expand_dims(tf.range(self.B) * self.nb_clusters, 1),
[1, self.L])
if centroids_init is None:
def random_without_replace(b, l):
a = np.array([
np.random.choice(range(l),
size=self.nb_clusters,
replace=False) for _ in range(b)
])
return a.astype(np.int32)
y = tf.py_func(random_without_replace, [self.B, self.L],
tf.int32)
random = tf.reshape(y, [self.B, self.nb_clusters, 1])
# Take randomly 'nb_clusters' vectors from X
batch_range = tf.tile(
tf.reshape(tf.range(self.B, dtype=tf.int32),
shape=[self.B, 1, 1]),
[1, self.nb_clusters, 1])
indices = tf.concat([batch_range, random], axis=2)
self.centroid_init = tf.gather_nd(self.X, indices)
else:
self.centroids = tf.identity(centroids_init)
self.centroids = tf.tile(self.centroids,
[self.nb_tries, 1, 1])
if not self.latent_space_tensor is None:
latent_space_tensor = tf.reshape(latent_space_tensor,
[self.b, self.L])
log_lst = log10(
tf.divide(
tf.reduce_max(latent_space_tensor, [-1],
keep_dims=True),
latent_space_tensor))
self.notsilent_notry = tf.reshape(
tf.cast(log_lst < threshold, tf.float32),
[self.b, self.L, 1])
self.notsilent = tf.tile(self.notsilent_notry,
[self.nb_tries, 1, 1])
else:
self.notsilent = tf.ones([self.B, self.L, 1])
self.network