train.py

# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
By kyubyong park. kbpark.linguist@gmail.com. 
https://www.github.com/kyubyong/tacotron
'''

from __future__ import print_function
import tensorflow as tf
import numpy as np
import librosa

import os
from tqdm import tqdm

from hyperparams import Hyperparams as hp
from prepro import *
from networks import encode, decode1, decode2
from modules import *
from data_load import get_batch
from utils import shift_by_one
from prepro import load_vocab
                     
class Graph:
    def __init__(self, is_training=True):
        self.graph = tf.Graph()
        
        with self.graph.as_default():
            if is_training:
                self.x, self.y, self.z, self.num_batch = get_batch()
                self.decoder_inputs = shift_by_one(self.y)
            else: # Evaluation
                self.x = tf.placeholder(tf.int32, shape=(None, None))
                self.decoder_inputs = tf.placeholder(tf.float32, shape=(None, None, hp.n_mels*hp.r))

            # Encoder
            self.memory = encode(self.x, is_training=is_training) # (N, T, E)
             
            # Decoder
            self.outputs1 = decode1(self.decoder_inputs, self.memory) # (N, T', hp.n_mels*hp.r)
            self.outputs2 = decode2(self.outputs1, is_training=is_training) # (N, T', (1+hp.n_fft//2)*hp.r)
             
            if is_training:  
                # Loss
                if hp.loss_type=="l1": # L1 loss
                    self.loss1 = tf.abs(self.outputs1 - self.y)
                    self.loss2 = tf.abs(self.outputs2 - self.z)
                else: # L2 loss
                    self.loss1 = tf.squared_difference(self.outputs1, self.y)
                    self.loss2 = tf.squared_difference(self.outputs2, self.z)
                
                # Target masking
                if hp.target_zeros_masking:
                    self.loss1 *= tf.to_float(tf.not_equal(self.y, 0.))
                    self.loss2 *= tf.to_float(tf.not_equal(self.z, 0.))
                
                self.mean_loss1 = tf.reduce_mean(self.loss1)
                self.mean_loss2 = tf.reduce_mean(self.loss2)
                self.mean_loss = self.mean_loss1 + self.mean_loss2    
               
                # Training Scheme
                self.global_step = tf.Variable(0, name='global_step', trainable=False)
                self.optimizer = tf.train.AdamOptimizer(learning_rate=hp.lr)
                self.train_op = self.optimizer.minimize(self.mean_loss, global_step=self.global_step)
                   
                # Summmary 
                tf.summary.scalar('mean_loss1', self.mean_loss1)
                tf.summary.scalar('mean_loss2', self.mean_loss2)
                tf.summary.scalar('mean_loss', self.mean_loss)
                self.merged = tf.summary.merge_all()
         
def main():   
    g = Graph(); print("Training Graph loaded")
    
    with g.graph.as_default():
        # Load vocabulary 
        char2idx, idx2char = load_vocab()
        
        # Training 
        sv = tf.train.Supervisor(logdir=hp.logdir,
                                 save_model_secs=0)
        with sv.managed_session() as sess:
            for epoch in range(1, hp.num_epochs+1): 
                if sv.should_stop(): break
                for step in tqdm(range(g.num_batch), total=g.num_batch, ncols=70, leave=False, unit='b'):
                    sess.run(g.train_op)
                
                # Write checkpoint files at every epoch
                gs = sess.run(g.global_step) 
                sv.saver.save(sess, hp.logdir + '/model_epoch_%02d_gs_%d' % (epoch, gs))

if __name__ == '__main__':
    main()
    print("Done")