tensorflow.python.framework.errors_impl.OutOfRangeError？ GitHub上andabi/ deep-voice-conversion/ 的解决方法

本例程序是用于人声转换的，将任意说话人的语音转换为特定说话人（English actress Kate Winslet）的声音。

GitHub地址： https://github.com/andabi/deep-voice-conversion

设置 train(logdir='./datasets/timit/TIMIT/TRAIN', queue=True)，程序报错如：

Traceback (most recent call last):

File "/home/human-machine/Speech/deep-voice-conversion-master/train1.py", line 90, in 
train(logdir='./logdir/default/train1', queue=True)
File "/home/human-machine/Speech/deep-voice-conversion-master/train1.py", line 57, in train
sess.run(train_op)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: PaddingFIFOQueue '_1_batch/padding_fifo_queue' is closed and has insufficient elements (requested 32, current size 0)

[[Node: batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batch/padding_fifo_queue, batch/n)]]

我的相关博客： https://blog.csdn.net/qq_34638161/article/details/80387829

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解决方法：

sess.run(tf.global_variables_initializer()) 前面加上一行：

sess.run(tf.local_variables_initializer())

sess.run(tf.global_variables_initializer())

注意：此处用 sess.run(tf.group(tf.local_variables_initializer(),tf.global_variables_initializer())) 不行

安装模块 ffmpeg：sudo apt install ffmpeg，安装后运行就没有报错了：tensorflow.python.framework.errors_impl.OutOfRangeError

FFmpeg：是一套可以用来记录、转换数字音频、视频，并能将其转化为流的开源计算机程序。

设置 train(logdir='./datasets/timit/TIMIT/TRAIN', queue=False) ，运行，出现报错，更改相应的文件：

data_load.py中的 phn_file = wav_file.replace("WAV.wav", "PHN").replace("wav", "PHN") 改为：

                              phn_file = wav_file.replace("WAV.wav", "phn").replace("wav", "phn") 

在 train(logdir='./datasets/timit/TIMIT/TRAIN', queue=False)下运行：

图一：

但是在 train(logdir='./datasets/timit/TIMIT/TRAIN', queue=True)下运行会卡顿在第一个epoch：

附上代码 tain1.py ：

# -*- coding: utf-8 -*-
# /usr/bin/python2

from __future__ import print_function
import hparams as hp
from hparams import logdir_path
from tqdm import tqdm

from modules import *
from models import Model
import eval1
from data_load import get_batch
import argparse


def train(logdir='./logdir/default/train1', queue=True):
    model = Model(mode="train1", batch_size=hp.Train1.batch_size, queue=queue)

    # Loss
    loss_op = model.loss_net1()

    # Accuracy
    acc_op = model.acc_net1()

    # Training Scheme
    global_step = tf.Variable(0, name='global_step', trainable=False)

    optimizer = tf.train.AdamOptimizer(learning_rate=hp.Train1.lr)
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'net/net1')
        train_op = optimizer.minimize(loss_op, global_step=global_step, var_list=var_list)

    # Summary
    # for v in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'net/net1'):
    #     tf.summary.histogram(v.name, v)
    tf.summary.scalar('net1/train/loss', loss_op)
    tf.summary.scalar('net1/train/acc', acc_op)

    summ_op = tf.summary.merge_all()

    session_conf = tf.ConfigProto(
        gpu_options=tf.GPUOptions(
            allow_growth=True,
        ),
    )

    # Training
    with tf.Session() as sess:
    # with tf.Session(config=session_conf) as sess:
        # Load trained model
        # sess.run(tf.local_variables_initializer())
        # sess.run(tf.global_variables_initializer())
        # sess.run(tf.global(tf.global_variables_initializer(), tf.local_variables_initializer())
        sess.run(tf.global_variables_initializer())
        model.load(sess, 'train1', logdir=logdir)

        writer = tf.summary.FileWriter(logdir, sess.graph)
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        # threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        for epoch in range(1, hp.Train1.num_epochs + 1):
            for step in tqdm(range(model.num_batch), total=model.num_batch, ncols=70, leave=False, unit='b'):
                if queue:
                    sess.run(train_op)
                else:
                    mfcc, ppg = get_batch(model.mode, model.batch_size)
                    sess.run(train_op, feed_dict={model.x_mfcc: mfcc, model.y_ppgs: ppg})

            # Write checkpoint files at every epoch
            if queue:
                summ, gs = sess.run([summ_op, global_step])
            else:
                summ, gs = sess.run([summ_op, global_step], feed_dict={model.x_mfcc: mfcc, model.y_ppgs: ppg})
            
            if epoch % hp.Train1.save_per_epoch == 0:
                tf.train.Saver().save(sess, '{}/epoch_{}_step_{}'.format(logdir, epoch, gs))

            # Write eval accuracy at every epoch
            with tf.Graph().as_default():
                eval1.eval(logdir=logdir, queue=False)

            writer.add_summary(summ, global_step=gs)

        writer.close()
        coord.request_stop()
        coord.join(threads)


def get_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument('case', type=str, help='experiment case name')
    # parser.add_argument('case', type=str, help='timit')
    arguments = parser.parse_args()
    return arguments

if __name__ == '__main__':
    # train(logdir='./logdir/default/train1', queue=True)
    train(logdir='./datasets/timit/TIMIT/TRAIN', queue=True)
    # train(logdir='./datasets/timit/TIMIT/TRAIN', queue=False)

    args = get_arguments()
    case = args.case
    logdir = '{}/{}/train1'.format(logdir_path, case)
    # train(logdir=logdir)
    train(logdir=logdir, queue=False)
    print("Done")