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train_transfer.py
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train_transfer.py
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# -*- coding: utf-8 -*-
# /usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/dc_tts
Modified by sean leary. learysean1@hotmail.com
https://github.com/SeanPLeary/dc_tts-transfer-learning
'''
from __future__ import print_function
from tqdm import tqdm
from data_load import get_batch, load_vocab
from hyperparams import Hyperparams as hp
from modules import *
from networks import TextEnc, AudioEnc, AudioDec, Attention, SSRN
import tensorflow as tf
from utils import *
import sys
class Graph:
def __init__(self, num=1, mode="train"):
'''
Args:
num: Either 1 or 2. 1 for Text2Mel 2 for SSRN.
mode: Either "train" or "synthesize".
'''
# Load vocabulary
self.char2idx, self.idx2char = load_vocab()
# Set flag
training = True if mode=="train" else False
# Graph
# Data Feeding
## L: Text. (B, N), int32
## mels: Reduced melspectrogram. (B, T/r, n_mels) float32
## mags: Magnitude. (B, T, n_fft//2+1) float32
if mode=="train":
self.L, self.mels, self.mags, self.fnames, self.num_batch = get_batch()
self.prev_max_attentions = tf.ones(shape=(hp.B,), dtype=tf.int32)
self.gts = tf.convert_to_tensor(guided_attention())
else: # Synthesize
self.L = tf.placeholder(tf.int32, shape=(None, None))
self.mels = tf.placeholder(tf.float32, shape=(None, None, hp.n_mels))
self.prev_max_attentions = tf.placeholder(tf.int32, shape=(None,))
if num==1 or (not training):
with tf.variable_scope("Text2Mel"):
# Get S or decoder inputs. (B, T//r, n_mels)
self.S = tf.concat((tf.zeros_like(self.mels[:, :1, :]), self.mels[:, :-1, :]), 1)
# Networks
with tf.variable_scope("TextEnc"):
self.K, self.V = TextEnc(self.L, training=training) # (N, Tx, e)
with tf.variable_scope("AudioEnc"):
self.Q = AudioEnc(self.S, training=training)
with tf.variable_scope("Attention"):
# R: (B, T/r, 2d)
# alignments: (B, N, T/r)
# max_attentions: (B,)
self.R, self.alignments, self.max_attentions = Attention(self.Q, self.K, self.V,
mononotic_attention=(not training),
prev_max_attentions=self.prev_max_attentions)
with tf.variable_scope("AudioDec"):
self.Y_logits, self.Y = AudioDec(self.R, training=training) # (B, T/r, n_mels)
else: # num==2 & training. Note that during training,
# the ground truth melspectrogram values are fed.
with tf.variable_scope("SSRN"):
self.Z_logits, self.Z = SSRN(self.mels, training=training)
if not training:
# During inference, the predicted melspectrogram values are fed.
with tf.variable_scope("SSRN"):
self.Z_logits, self.Z = SSRN(self.Y, training=training)
with tf.variable_scope("gs"):
self.global_step = tf.Variable(0, name='global_step', trainable=False)
if training:
if num==1: # Text2Mel
# mel L1 loss
self.loss_mels = tf.reduce_mean(tf.abs(self.Y - self.mels))
# mel binary divergence loss
self.loss_bd1 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.Y_logits, labels=self.mels))
# guided_attention loss
self.A = tf.pad(self.alignments, [(0, 0), (0, hp.max_N), (0, hp.max_T)], mode="CONSTANT", constant_values=-1.)[:, :hp.max_N, :hp.max_T]
self.attention_masks = tf.to_float(tf.not_equal(self.A, -1))
self.loss_att = tf.reduce_sum(tf.abs(self.A * self.gts) * self.attention_masks)
self.mask_sum = tf.reduce_sum(self.attention_masks)
self.loss_att /= self.mask_sum
# total loss
self.loss = self.loss_mels + self.loss_bd1 + self.loss_att
tf.summary.scalar('train/loss_mels', self.loss_mels)
tf.summary.scalar('train/loss_bd1', self.loss_bd1)
tf.summary.scalar('train/loss_att', self.loss_att)
tf.summary.image('train/mel_gt', tf.expand_dims(tf.transpose(self.mels[:1], [0, 2, 1]), -1))
tf.summary.image('train/mel_hat', tf.expand_dims(tf.transpose(self.Y[:1], [0, 2, 1]), -1))
else: # SSRN
# mag L1 loss
self.loss_mags = tf.reduce_mean(tf.abs(self.Z - self.mags))
# mag binary divergence loss
self.loss_bd2 = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.Z_logits, labels=self.mags))
# total loss
self.loss = self.loss_mags + self.loss_bd2
tf.summary.scalar('train/loss_mags', self.loss_mags)
tf.summary.scalar('train/loss_bd2', self.loss_bd2)
tf.summary.image('train/mag_gt', tf.expand_dims(tf.transpose(self.mags[:1], [0, 2, 1]), -1))
tf.summary.image('train/mag_hat', tf.expand_dims(tf.transpose(self.Z[:1], [0, 2, 1]), -1))
# Training Scheme
self.lr = learning_rate_decay(hp.lr, self.global_step)
tvars = tf.trainable_variables()
tvars_new = []
for tvar in hp.selected_tvars:
tvars_new = tvars_new + [var for var in tvars if tvar in var.name]
# tvars_new = [var for var in tvars if ('SSRN/C_13') in var.name] + \
# [var for var in tvars if ('SSRN/C_14') in var.name] + \
# [var for var in tvars if ('SSRN/C_15') in var.name] + \
# [var for var in tvars if ('SSRN/C_16') in var.name] + \
# [var for var in tvars if ('Text2Mel/TextEnc/HC_11') in var.name] + \
# [var for var in tvars if ('Text2Mel/TextEnc/HC_12') in var.name] + \
# [var for var in tvars if ('Text2Mel/TextEnc/HC_13') in var.name] + \
# [var for var in tvars if ('Text2Mel/TextEnc/HC_14') in var.name] + \
# [var for var in tvars if ('Text2Mel/TextEnc/HC_15') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioEnc/HC_9') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioEnc/HC_10') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioEnc/HC_11') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioEnc/HC_12') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioEnc/HC_13') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioDec/HC_7') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioDec/C_8') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioDec/C_9') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioDec/C_10') in var.name] + \
# [var for var in tvars if ('Text2Mel/AudioDec/C_11') in var.name]
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)
#self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)
tf.summary.scalar("lr", self.lr)
## gradient clipping
self.gvs = self.optimizer.compute_gradients(self.loss, var_list=tvars_new)
self.clipped = []
for grad, var in self.gvs:
grad = tf.clip_by_value(grad, -1., 1.)
self.clipped.append((grad, var))
self.train_op = self.optimizer.apply_gradients(self.clipped, global_step=self.global_step)
# Summary
self.merged = tf.summary.merge_all()
if __name__ == '__main__':
# argument: 1 or 2. 1 for Text2mel, 2 for SSRN.
num = int(sys.argv[1])
g = Graph(num=num); print("Training Graph loaded")
logdir = hp.logdir + "-" + str(num)
sv = tf.train.Supervisor(logdir=logdir, save_model_secs=0, global_step=g.global_step)
#with sv.managed_session() as sess:
with sv.managed_session(config = tf.ConfigProto(allow_soft_placement=True)) as sess:
sv.saver.restore(sess, tf.train.latest_checkpoint(hp.restoredir + "-" + str(num)))
while 1:
for _ in tqdm(range(g.num_batch), total=g.num_batch, ncols=70, leave=False, unit='b'):
gs, _ = sess.run([g.global_step, g.train_op])
# Write checkpoint files at every 1k steps
if gs % 1000 == 0:
sv.saver.save(sess, logdir + '/model_gs_{}'.format(str(gs // 1000).zfill(3) + "k"))
if num==1:
# plot alignment
alignments = sess.run(g.alignments)
plot_alignment(alignments[0], str(gs // 1000).zfill(3) + "k", logdir)
# break
if gs > hp.num_iterations: break
print("Done")