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models.py
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models.py
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# A Wavenet For Source Separation - Francesc Lluis - 25.10.2018
# Models.py
import keras
import util
import os
import numpy as np
import layers
import logging
#Singing Voice Separation Wavenet Model
class SingingVoiceSeparationWavenet():
def __init__(self, config, load_checkpoint=None, input_length=None, target_field_length=None, print_model_summary=False):
self.config = config
self.verbosity = config['training']['verbosity']
self.num_stacks = self.config['model']['num_stacks']
if type(self.config['model']['dilations']) is int:
self.dilations = [2 ** i for i in range(0, self.config['model']['dilations'] + 1)]
elif type(self.config['model']['dilations']) is list:
self.dilations = self.config['model']['dilations']
self.receptive_field_length = util.compute_receptive_field_length(config['model']['num_stacks'], self.dilations,
config['model']['filters']['lengths']['res'],
1)
if input_length is not None:
self.input_length = input_length
self.target_field_length = self.input_length - (self.receptive_field_length - 1)
if target_field_length is not None:
self.target_field_length = target_field_length
self.input_length = self.receptive_field_length + (self.target_field_length - 1)
else:
self.target_field_length = config['model']['target_field_length']
self.input_length = self.receptive_field_length + (self.target_field_length - 1)
self.target_padding = config['model']['target_padding']
self.padded_target_field_length = self.target_field_length + 2 * self.target_padding
self.half_target_field_length = self.target_field_length / 2
self.half_receptive_field_length = self.receptive_field_length / 2
self.num_residual_blocks = len(self.dilations) * self.num_stacks
self.activation = keras.layers.Activation('relu')
self.samples_of_interest_indices = self.get_padded_target_field_indices()
self.target_sample_indices = self.get_target_field_indices()
self.optimizer = self.get_optimizer()
self.out_1_loss = self.get_out_1_loss()
self.out_2_loss = self.get_out_2_loss()
self.metrics = self.get_metrics()
self.epoch_num = 0
self.checkpoints_path = ''
self.samples_path = ''
self.history_filename = ''
self.config['model']['num_residual_blocks'] = self.num_residual_blocks
self.config['model']['receptive_field_length'] = self.receptive_field_length
self.config['model']['input_length'] = self.input_length
self.config['model']['target_field_length'] = self.target_field_length
self.config['model']['type'] = 'singing-voice'
self.model = self.setup_model(load_checkpoint, print_model_summary)
def setup_model(self, load_checkpoint=None, print_model_summary=False):
self.checkpoints_path = os.path.join(self.config['training']['path'], 'checkpoints')
self.samples_path = os.path.join(self.config['training']['path'], 'samples')
self.history_filename = 'history_' + self.config['training']['path'][
self.config['training']['path'].rindex('/') + 1:] + '.csv'
model = self.build_model()
if os.path.exists(self.checkpoints_path) and util.dir_contains_files(self.checkpoints_path):
if load_checkpoint is not None:
last_checkpoint_path = load_checkpoint
self.epoch_num = 0
else:
checkpoints = os.listdir(self.checkpoints_path)
checkpoints.sort(key=lambda x: os.stat(os.path.join(self.checkpoints_path, x)).st_mtime)
last_checkpoint = checkpoints[-1]
last_checkpoint_path = os.path.join(self.checkpoints_path, last_checkpoint)
self.epoch_num = int(last_checkpoint[11:16])
print 'Loading model from epoch: %d' % self.epoch_num
model.load_weights(last_checkpoint_path)
else:
print 'Building new model...'
if not os.path.exists(self.config['training']['path']):
os.mkdir(self.config['training']['path'])
if not os.path.exists(self.checkpoints_path):
os.mkdir(self.checkpoints_path)
self.epoch_num = 0
if not os.path.exists(self.samples_path):
os.mkdir(self.samples_path)
if print_model_summary:
model.summary()
model.compile(optimizer=self.optimizer,
loss={'data_output_1': self.out_1_loss, 'data_output_2': self.out_2_loss}, metrics=self.metrics)
self.config['model']['num_params'] = model.count_params()
config_path = os.path.join(self.config['training']['path'], 'config.json')
if not os.path.exists(config_path):
util.pretty_json_dump(self.config, config_path)
if print_model_summary:
util.pretty_json_dump(self.config)
return model
def get_optimizer(self):
return keras.optimizers.Adam(lr=self.config['optimizer']['lr'], decay=self.config['optimizer']['decay'],
epsilon=self.config['optimizer']['epsilon'])
def get_out_1_loss(self):
if self.config['training']['loss']['out_1']['weight'] == 0:
return lambda y_true, y_pred: y_true * 0
return lambda y_true, y_pred: self.config['training']['loss']['out_1']['weight'] * util.l1_l2_loss(
y_true, y_pred, self.config['training']['loss']['out_1']['l1'],
self.config['training']['loss']['out_1']['l2'])
def get_out_2_loss(self):
if self.config['training']['loss']['out_2']['weight'] == 0:
return lambda y_true, y_pred: y_true * 0
return lambda y_true, y_pred: self.config['training']['loss']['out_2']['weight'] * util.l1_l2_loss(
y_true, y_pred, self.config['training']['loss']['out_2']['l1'],
self.config['training']['loss']['out_2']['l2'])
def get_callbacks(self):
return [
keras.callbacks.EarlyStopping(patience=self.config['training']['early_stopping_patience'], verbose=1,
monitor='loss'),
keras.callbacks.ModelCheckpoint(os.path.join(self.checkpoints_path,
'checkpoint.{epoch:05d}-{val_loss:.3f}.hdf5')),
keras.callbacks.CSVLogger(os.path.join(self.config['training']['path'], self.history_filename), append=True)
]
def fit_model(self, train_set_generator, num_steps_train, test_set_generator, num_steps_test, num_epochs):
print('Fitting model with %d training num steps and %d test num steps...' % (num_steps_train, num_steps_test))
self.model.fit_generator(train_set_generator,
num_steps_train,
epochs=num_epochs,
validation_data=test_set_generator,
validation_steps=num_steps_test,
callbacks=self.get_callbacks(),
verbose=self.verbosity,
initial_epoch=self.epoch_num)
def separate_batch(self, inputs):
return self.model.predict_on_batch(inputs)
def get_target_field_indices(self):
target_sample_index = self.get_target_sample_index()
return range(target_sample_index - self.half_target_field_length,
target_sample_index + self.half_target_field_length + 1)
def get_padded_target_field_indices(self):
target_sample_index = self.get_target_sample_index()
return range(target_sample_index - self.half_target_field_length - self.target_padding,
target_sample_index + self.half_target_field_length + self.target_padding + 1)
def get_target_sample_index(self):
return int(np.floor(self.input_length / 2.0))
def get_metrics(self):
return [
keras.metrics.mean_absolute_error,
self.valid_mean_absolute_error
]
def valid_mean_absolute_error(self, y_true, y_pred):
return keras.backend.mean(
keras.backend.abs(y_true[:, 1:-2] - y_pred[:, 1:-2]))
def build_model(self):
data_input = keras.engine.Input(
shape=(self.input_length,),
name='data_input')
data_expanded = layers.AddSingletonDepth()(data_input)
data_out = keras.layers.Convolution1D(self.config['model']['filters']['depths']['res'],
self.config['model']['filters']['lengths']['res'], padding='same',
use_bias=False,
name='initial_causal_conv')(data_expanded)
skip_connections = []
res_block_i = 0
for stack_i in range(self.num_stacks):
layer_in_stack = 0
for dilation in self.dilations:
res_block_i += 1
data_out, skip_out = self.dilated_residual_block(data_out, res_block_i, layer_in_stack, dilation, stack_i)
if skip_out is not None:
skip_connections.append(skip_out)
layer_in_stack += 1
data_out = keras.layers.Add()(skip_connections)
data_out = self.activation(data_out)
data_out = keras.layers.Convolution1D(self.config['model']['filters']['depths']['final'][0],
self.config['model']['filters']['lengths']['final'][0],
padding='same',
use_bias=False)(data_out)
data_out = self.activation(data_out)
data_out = keras.layers.Convolution1D(self.config['model']['filters']['depths']['final'][1],
self.config['model']['filters']['lengths']['final'][1], padding='same',
use_bias=False)(data_out)
data_out = keras.layers.Convolution1D(1, 1)(data_out)
data_out_vocals_1 = keras.layers.Lambda(lambda x: keras.backend.squeeze(x, 2),
output_shape=lambda shape: (shape[0], shape[1]), name='data_output_1')(
data_out)
data_out_vocals_2 = keras.layers.Lambda(lambda x: keras.backend.squeeze(x, 2),
output_shape=lambda shape: (shape[0], shape[1]), name='data_output_2')(
data_out)
return keras.engine.Model(inputs=[data_input], outputs=[data_out_vocals_1, data_out_vocals_2])
def dilated_residual_block(self, data_x, res_block_i, layer_i, dilation, stack_i):
original_x = data_x
data_out = keras.layers.Conv1D(2 * self.config['model']['filters']['depths']['res'],
self.config['model']['filters']['lengths']['res'],
dilation_rate=dilation, padding='same',
use_bias=False,
name='res_%d_dilated_conv_d%d_s%d' % (
res_block_i, dilation, stack_i),
activation=None)(data_x)
data_out_1 = layers.Slice(
(Ellipsis, slice(0, self.config['model']['filters']['depths']['res'])),
(self.input_length, self.config['model']['filters']['depths']['res']),
name='res_%d_data_slice_1_d%d_s%d' % (self.num_residual_blocks, dilation, stack_i))(data_out)
data_out_2 = layers.Slice(
(Ellipsis, slice(self.config['model']['filters']['depths']['res'],
2 * self.config['model']['filters']['depths']['res'])),
(self.input_length, self.config['model']['filters']['depths']['res']),
name='res_%d_data_slice_2_d%d_s%d' % (self.num_residual_blocks, dilation, stack_i))(data_out)
tanh_out = keras.layers.Activation('tanh')(data_out_1)
sigm_out = keras.layers.Activation('sigmoid')(data_out_2)
data_x = keras.layers.Multiply(name='res_%d_gated_activation_%d_s%d' % (res_block_i, layer_i, stack_i))(
[tanh_out, sigm_out])
data_x = keras.layers.Convolution1D(
self.config['model']['filters']['depths']['res'] + self.config['model']['filters']['depths']['skip'], 1,
padding='same', use_bias=False)(data_x)
res_x = layers.Slice((Ellipsis, slice(0, self.config['model']['filters']['depths']['res'])),
(self.input_length, self.config['model']['filters']['depths']['res']),
name='res_%d_data_slice_3_d%d_s%d' % (res_block_i, dilation, stack_i))(data_x)
skip_x = layers.Slice((Ellipsis, slice(self.config['model']['filters']['depths']['res'],
self.config['model']['filters']['depths']['res'] +
self.config['model']['filters']['depths']['skip'])),
(self.input_length, self.config['model']['filters']['depths']['skip']),
name='res_%d_data_slice_4_d%d_s%d' % (res_block_i, dilation, stack_i))(data_x)
skip_x = layers.Slice((slice(self.samples_of_interest_indices[0], self.samples_of_interest_indices[-1] + 1, 1),
Ellipsis), (self.padded_target_field_length, self.config['model']['filters']['depths']['skip']),
name='res_%d_keep_samples_of_interest_d%d_s%d' % (res_block_i, dilation, stack_i))(skip_x)
res_x = keras.layers.Add()([original_x, res_x])
return res_x, skip_x
# Multi-Instrument Separation Wavenet Model
class MultiInstrumentSeparationWavenet():
def __init__(self, config, load_checkpoint=None, input_length=None, target_field_length=None, print_model_summary=False):
self.config = config
self.verbosity = config['training']['verbosity']
self.num_stacks = self.config['model']['num_stacks']
if type(self.config['model']['dilations']) is int:
self.dilations = [2 ** i for i in range(0, self.config['model']['dilations'] + 1)]
elif type(self.config['model']['dilations']) is list:
self.dilations = self.config['model']['dilations']
self.receptive_field_length = util.compute_receptive_field_length(config['model']['num_stacks'], self.dilations,
config['model']['filters']['lengths']['res'],
1)
if input_length is not None:
self.input_length = input_length
self.target_field_length = self.input_length - (self.receptive_field_length - 1)
if target_field_length is not None:
self.target_field_length = target_field_length
self.input_length = self.receptive_field_length + (self.target_field_length - 1)
else:
self.target_field_length = config['model']['target_field_length']
self.input_length = self.receptive_field_length + (self.target_field_length - 1)
self.target_padding = config['model']['target_padding']
self.padded_target_field_length = self.target_field_length + 2 * self.target_padding
self.half_target_field_length = self.target_field_length / 2
self.half_receptive_field_length = self.receptive_field_length / 2
self.num_residual_blocks = len(self.dilations) * self.num_stacks
self.activation = keras.layers.Activation('relu')
self.samples_of_interest_indices = self.get_padded_target_field_indices()
self.target_sample_indices = self.get_target_field_indices()
self.optimizer = self.get_optimizer()
self.out_1_loss = self.get_out_1_loss()
self.out_2_loss = self.get_out_2_loss()
self.out_3_loss = self.get_out_3_loss()
self.metrics = self.get_metrics()
self.epoch_num = 0
self.checkpoints_path = ''
self.samples_path = ''
self.history_filename = ''
self.config['model']['num_residual_blocks'] = self.num_residual_blocks
self.config['model']['receptive_field_length'] = self.receptive_field_length
self.config['model']['input_length'] = self.input_length
self.config['model']['target_field_length'] = self.target_field_length
self.config['model']['type'] = 'multi-instrument'
self.model = self.setup_model(load_checkpoint, print_model_summary)
def setup_model(self, load_checkpoint=None, print_model_summary=False):
self.checkpoints_path = os.path.join(self.config['training']['path'], 'checkpoints')
self.samples_path = os.path.join(self.config['training']['path'], 'samples')
self.history_filename = 'history_' + self.config['training']['path'][
self.config['training']['path'].rindex('/') + 1:] + '.csv'
model = self.build_model()
if os.path.exists(self.checkpoints_path) and util.dir_contains_files(self.checkpoints_path):
if load_checkpoint is not None:
last_checkpoint_path = load_checkpoint
self.epoch_num = 0
else:
checkpoints = os.listdir(self.checkpoints_path)
checkpoints.sort(key=lambda x: os.stat(os.path.join(self.checkpoints_path, x)).st_mtime)
last_checkpoint = checkpoints[-1]
last_checkpoint_path = os.path.join(self.checkpoints_path, last_checkpoint)
self.epoch_num = int(last_checkpoint[11:16])
print 'Loading model from epoch: %d' % self.epoch_num
model.load_weights(last_checkpoint_path)
else:
print 'Building new model...'
if not os.path.exists(self.config['training']['path']):
os.mkdir(self.config['training']['path'])
if not os.path.exists(self.checkpoints_path):
os.mkdir(self.checkpoints_path)
self.epoch_num = 0
if not os.path.exists(self.samples_path):
os.mkdir(self.samples_path)
if print_model_summary:
model.summary()
model.compile(optimizer=self.optimizer,
loss={'data_output_1': self.out_1_loss, 'data_output_2': self.out_2_loss,
'data_output_3': self.out_3_loss}, metrics=self.metrics)
self.config['model']['num_params'] = model.count_params()
config_path = os.path.join(self.config['training']['path'], 'config.json')
if not os.path.exists(config_path):
util.pretty_json_dump(self.config, config_path)
if print_model_summary:
util.pretty_json_dump(self.config)
return model
def get_optimizer(self):
return keras.optimizers.Adam(lr=self.config['optimizer']['lr'], decay=self.config['optimizer']['decay'],
epsilon=self.config['optimizer']['epsilon'])
def get_out_1_loss(self):
if self.config['training']['loss']['out_1']['weight'] == 0:
return lambda y_true, y_pred: y_true * 0
return lambda y_true, y_pred: self.config['training']['loss']['out_1']['weight'] * util.l1_l2_loss(
y_true, y_pred, self.config['training']['loss']['out_1']['l1'],
self.config['training']['loss']['out_1']['l2'])
def get_out_2_loss(self):
if self.config['training']['loss']['out_2']['weight'] == 0:
return lambda y_true, y_pred: y_true * 0
return lambda y_true, y_pred: self.config['training']['loss']['out_2']['weight'] * util.l1_l2_loss(
y_true, y_pred, self.config['training']['loss']['out_2']['l1'],
self.config['training']['loss']['out_2']['l2'])
def get_out_3_loss(self):
if self.config['training']['loss']['out_3']['weight'] == 0:
return lambda y_true, y_pred: y_true * 0
return lambda y_true, y_pred: self.config['training']['loss']['out_3']['weight'] * util.l1_l2_loss(
y_true, y_pred, self.config['training']['loss']['out_3']['l1'],
self.config['training']['loss']['out_3']['l2'])
def get_callbacks(self):
return [
keras.callbacks.EarlyStopping(patience=self.config['training']['early_stopping_patience'], verbose=1,
monitor='loss'),
keras.callbacks.ModelCheckpoint(os.path.join(self.checkpoints_path,
'checkpoint.{epoch:05d}-{val_loss:.3f}.hdf5')),
keras.callbacks.CSVLogger(os.path.join(self.config['training']['path'], self.history_filename), append=True)
]
def fit_model(self, train_set_generator, num_steps_train, test_set_generator, num_steps_test, num_epochs):
print('Fitting model with %d training num steps and %d test num steps...' % (num_steps_train, num_steps_test))
self.model.fit_generator(train_set_generator,
num_steps_train,
epochs=num_epochs,
validation_data=test_set_generator,
validation_steps=num_steps_test,
callbacks=self.get_callbacks(),
verbose=self.verbosity,
initial_epoch=self.epoch_num)
def separate_batch(self, inputs):
return self.model.predict_on_batch(inputs)
def get_target_field_indices(self):
target_sample_index = self.get_target_sample_index()
return range(target_sample_index - self.half_target_field_length,
target_sample_index + self.half_target_field_length + 1)
def get_padded_target_field_indices(self):
target_sample_index = self.get_target_sample_index()
return range(target_sample_index - self.half_target_field_length - self.target_padding,
target_sample_index + self.half_target_field_length + self.target_padding + 1)
def get_target_sample_index(self):
return int(np.floor(self.input_length / 2.0))
def get_metrics(self):
return [
keras.metrics.mean_absolute_error,
self.valid_mean_absolute_error
]
def valid_mean_absolute_error(self, y_true, y_pred):
return keras.backend.mean(
keras.backend.abs(y_true[:, 1:-2] - y_pred[:, 1:-2]))
def build_model(self):
data_input = keras.engine.Input(
shape=(self.input_length,),
name='data_input')
data_expanded = layers.AddSingletonDepth()(data_input)
data_out = keras.layers.Convolution1D(self.config['model']['filters']['depths']['res'],
self.config['model']['filters']['lengths']['res'], padding='same',
use_bias=False,
name='initial_causal_conv')(data_expanded)
skip_connections = []
res_block_i = 0
for stack_i in range(self.num_stacks):
layer_in_stack = 0
for dilation in self.dilations:
res_block_i += 1
data_out, skip_out = self.dilated_residual_block(data_out, res_block_i, layer_in_stack, dilation, stack_i)
if skip_out is not None:
skip_connections.append(skip_out)
layer_in_stack += 1
data_out = keras.layers.Add()(skip_connections)
data_out = self.activation(data_out)
data_out = keras.layers.Convolution1D(self.config['model']['filters']['depths']['final'][0],
self.config['model']['filters']['lengths']['final'][0],
padding='same',
use_bias=False)(data_out)
data_out = self.activation(data_out)
data_out = keras.layers.Convolution1D(self.config['model']['filters']['depths']['final'][1],
self.config['model']['filters']['lengths']['final'][1], padding='same',
use_bias=False)(data_out)
data_out = keras.layers.Convolution1D(3, 1)(data_out)
data_out_vocals = layers.Slice((Ellipsis, slice(0, 1)), (self.padded_target_field_length, 1),
name='slice_data_output_1')(data_out)
data_out_drums = layers.Slice((Ellipsis, slice(1, 2)), (self.padded_target_field_length, 1),
name='slice_data_output_2')(data_out)
data_out_bass = layers.Slice((Ellipsis, slice(2, 3)), (self.padded_target_field_length, 1),
name='slice_data_output_3')(data_out)
data_out_vocals = keras.layers.Lambda(lambda x: keras.backend.squeeze(x, 2),
output_shape=lambda shape: (shape[0], shape[1]), name='data_output_1')(
data_out_vocals)
data_out_drums = keras.layers.Lambda(lambda x: keras.backend.squeeze(x, 2),
output_shape=lambda shape: (shape[0], shape[1]), name='data_output_2')(
data_out_drums)
data_out_bass = keras.layers.Lambda(lambda x: keras.backend.squeeze(x, 2),
output_shape=lambda shape: (shape[0], shape[1]), name='data_output_3')(
data_out_bass)
return keras.engine.Model(inputs=[data_input], outputs=[data_out_vocals, data_out_drums, data_out_bass])
def dilated_residual_block(self, data_x, res_block_i, layer_i, dilation, stack_i):
original_x = data_x
data_out = keras.layers.Conv1D(2 * self.config['model']['filters']['depths']['res'],
self.config['model']['filters']['lengths']['res'],
dilation_rate=dilation, padding='same',
use_bias=False,
name='res_%d_dilated_conv_d%d_s%d' % (
res_block_i, dilation, stack_i),
activation=None)(data_x)
data_out_1 = layers.Slice(
(Ellipsis, slice(0, self.config['model']['filters']['depths']['res'])),
(self.input_length, self.config['model']['filters']['depths']['res']),
name='res_%d_data_slice_1_d%d_s%d' % (self.num_residual_blocks, dilation, stack_i))(data_out)
data_out_2 = layers.Slice(
(Ellipsis, slice(self.config['model']['filters']['depths']['res'],
2 * self.config['model']['filters']['depths']['res'])),
(self.input_length, self.config['model']['filters']['depths']['res']),
name='res_%d_data_slice_2_d%d_s%d' % (self.num_residual_blocks, dilation, stack_i))(data_out)
tanh_out = keras.layers.Activation('tanh')(data_out_1)
sigm_out = keras.layers.Activation('sigmoid')(data_out_2)
data_x = keras.layers.Multiply(name='res_%d_gated_activation_%d_s%d' % (res_block_i, layer_i, stack_i))(
[tanh_out, sigm_out])
data_x = keras.layers.Convolution1D(
self.config['model']['filters']['depths']['res'] + self.config['model']['filters']['depths']['skip'], 1,
padding='same', use_bias=False)(data_x)
res_x = layers.Slice((Ellipsis, slice(0, self.config['model']['filters']['depths']['res'])),
(self.input_length, self.config['model']['filters']['depths']['res']),
name='res_%d_data_slice_3_d%d_s%d' % (res_block_i, dilation, stack_i))(data_x)
skip_x = layers.Slice((Ellipsis, slice(self.config['model']['filters']['depths']['res'],
self.config['model']['filters']['depths']['res'] +
self.config['model']['filters']['depths']['skip'])),
(self.input_length, self.config['model']['filters']['depths']['skip']),
name='res_%d_data_slice_4_d%d_s%d' % (res_block_i, dilation, stack_i))(data_x)
skip_x = layers.Slice((slice(self.samples_of_interest_indices[0], self.samples_of_interest_indices[-1] + 1, 1),
Ellipsis), (self.padded_target_field_length, self.config['model']['filters']['depths']['skip']),
name='res_%d_keep_samples_of_interest_d%d_s%d' % (res_block_i, dilation, stack_i))(skip_x)
res_x = keras.layers.Add()([original_x, res_x])
return res_x, skip_x