def separate(self):
# Input for KMeans algorithm [B, TF, E]
input_kmeans = tf.reshape(self.prediction, [self.B, -1, self.embedding_size])
self.embeddings = input_kmeans
# S speakers to separate, give self.X in input not to consider silent bins
kmeans = KMeans(nb_clusters=self.S, nb_tries=self.nb_tries, nb_iterations=self.nb_steps,
input_tensor=input_kmeans, beta=self.beta, latent_space_tensor=tf.abs(self.X) if self.with_silence else None,
threshold=self.threshold)
# Extract labels of each bins TF_i - labels [B, TF, 1]
_ , labels = kmeans.network
if self.beta is None:
# It produces hard assignements [0,1,0, ...]
self.masks = tf.one_hot(tf.cast(labels, tf.int32), self.S, 1.0, 0.0) # Create masks [B, TF, S]
else:
# It produces soft assignements
self.masks = labels
tf.summary.image('mask/predicted/1', tf.reshape(self.masks[:,:,0],[self.B, -1, self.F, 1]))
tf.summary.image('mask/predicted/2', tf.reshape(self.masks[:,:,1],[self.B, -1, self.F, 1]))
separated = tf.reshape(self.X, [self.B, -1, 1]) * self.masks # [B ,TF, S]
if not self.plugged:
separated = separated * tf.expand_dims((self.max_ - self.min_), 2) + tf.expand_dims(self.min_, 2)
separated = tf.square(separated)
separated = tf.reshape(separated, [self.B, -1, self.F, self.S])
separated = tf.transpose(separated, [0,3,1,2]) # [B, S, T, F]
separated = tf.reshape(separated, [self.B*self.S, -1, self.F, 1]) # [BS, T, F, 1]
return separated
def separate(self): input_kmeans = tf.reshape(self.prediction, [self.B, -1, self.E]) kmeans = KMeans(nb_clusters=2, nb_iterations=10, input_tensor=input_kmeans) _ , labels = kmeans.network masks = tf.one_hot(labels, 2, 1.0, 0.0, name='masks') self.ms = tf.reshape(masks, [self.B, -1, self.F, self.S]) separated = tf.reshape(self.X, [self.B, -1, 1]) * masks # [B ,TF, S] separated = tf.reshape(separated, [self.B, -1, self.F, self.S]) separated = tf.transpose(separated, [0,3,1,2]) separated = tf.reshape(separated, [self.B*self.S, -1, self.F, 1]) print separated return separated
def separate(self): # TODO for only when Ind is available, speaker info is given # Ind [B, S, 1] Ind = tf.expand_dims(self.Ind, 2) # U [S_tot, E] centroids = tf.gather_nd(self.speaker_centroids, Ind) input_kmeans = tf.reshape(self.prediction, [self.B, -1, self.E]) kmeans = KMeans(nb_clusters=2, nb_iterations=10, input_tensor=input_kmeans, centroids_init=centroids) _ , labels = kmeans.network masks = tf.one_hot(labels, 2, 1.0, 0.0) separated = tf.reshape(self.X, [self.B, -1, 1])* masks # [B ,TF, S] separated = tf.reshape(separated, [self.B, -1, self.F, self.S]) separated = tf.transpose(separated, [0,3,1,2]) separated = tf.reshape(separated, [self.B*self.S, -1, self.F, 1]) print separated return separated
def separate(self):
# Input for KMeans algorithm [B, TF, E]
input_kmeans = tf.reshape(self.prediction,
[self.B, -1, self.embedding_size])
# S speakers to separate, give self.X in input not to consider silent bins
kmeans = KMeans(nb_clusters=self.S,
nb_iterations=10,
input_tensor=input_kmeans,
latent_space_tensor=self.X)
# Extract labels of each bins TF_i - labels [B, TF, 1]
_, labels = kmeans.network
self.masks = tf.one_hot(labels, 2, 1.0, 0.0) # Create masks [B, TF, S]
separated = tf.reshape(self.X,
[self.B, -1, 1]) * self.masks # [B ,TF, S]
separated = tf.reshape(separated, [self.B, -1, self.F, self.S])
separated = tf.transpose(separated, [0, 3, 1, 2]) # [B, S, T, F]
separated = tf.reshape(
separated, [self.B * self.S, -1, self.F, 1]) # [BS, T, F, 1]
return separated
def separate(self): # TODO for only when Ind is available, speaker info is given # Ind [B, S, 1] # Ind = tf.expand_dims(self.I, 2) # U [S_tot, E] # centroids = tf.gather_nd(self.speaker_vectors, Ind) # Input for KMeans algorithm [B, TF, E] input_kmeans = tf.reshape(self.prediction, [self.B, -1, self.embedding_size]) # S speakers to separate, give self.X in input not to consider silent bins kmeans = KMeans(nb_clusters=self.S, nb_iterations=10, input_tensor=input_kmeans, latent_space_tensor=self.X) # Extract labels of each bins TF_i - labels [B, TF, 1] _ , labels = kmeans.network print labels self.masks = tf.one_hot(labels, 2, 1.0, 0.0) # Create masks [B, TF, S] separated = tf.reshape(self.X, [self.B, -1, 1])* self.masks # [B ,TF, S] separated = tf.reshape(separated, [self.B, -1, self.F, self.S]) separated = tf.transpose(separated, [0,3,1,2]) # [B, S, T, F] separated = tf.reshape(separated, [self.B*self.S, -1, self.F, 1]) # [BS, T, F, 1] return separated