1duo

Download CMake from: https://cmake.org/download/

wget https://cmake.org/files/v3.12/cmake-3.12.3.tar.gz

Compile from source and install

tar zxvf cmake-3.*

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

yevrah

################################################################################
# Method 1: Install using rpm packages (credit to DarkMukke)
#

rpm -Uvh http://mirror.ghettoforge.org/distributions/gf/gf-release-latest.gf.el7.noarch.rpm
rpm --import http://mirror.ghettoforge.org/distributions/gf/RPM-GPG-KEY-gf.el7

# WARNING: removing  vim-minimal uninstalls `sudo` if you skip the second step
#          make sure to at least run `yum install sudo`
yum -y remove vim-minimal vim-common vim-enhanced

cheind

__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017'
__license__ = 'BSD'

"""Trains a HMM based on gradient descent optimization. 

The parameters (theta) of the model are transition and 
emission probabilities, as well as the initial state probabilities. 

Given a start solution, the negative log likelihood of data given the 

higepon

import numpy as np
import tensorflow as tf
from tensorflow.python.layers import core as layers_core

hparams = tf.contrib.training.HParams(
    batch_size=3,
    encoder_length=4,
    decoder_length=5,
    num_units=6,
    src_vocab_size=7,

candlewill

深度学习于语音合成研究综述

本文综述近年来深度学习用于语音合成的一些方法。

WaveNet

在自回归生成模型在图像和文本领域广泛应用的时候，WaveNet [4] 尝试将这些思想应用于语音领域。仿照PixelRNN (van den Oord et al., 2016)图像生成的做法， WaveNet依据之前采样点来生成下一个采样点。生成下一个采样点的模型为CNN结构。为了生成指定说话人的声音，以及生成指定文本的声音，引入了全局条件和局部条件，来控制合成内容。为了扩大感受野，带洞卷积，使filter的按照指数扩张。

WaveNet存在的问题是，1） 每次预测一个采样点，速度太慢；2）如果用于TTS，那初始采样点选择将会很重要；3）以及需要文本前端的支持，前端分析出错，将直接影响合成效果。

peterroelants

"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim
from tensorflow.contrib.learn import ModeKeys
from tensorflow.contrib.learn import learn_runner


# Show debugging output

awni

"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.

jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

cloverstd

yum install -y boost boost-devel
yum install -y make gcc gcc-c++ kernel-devel python-devel
wget https://github.com/arvidn/libtorrent/releases/download/libtorrent-1_0_10/libtorrent-rasterbar-1.0.10.tar.gz
tar zxvf libtorrent-rasterbar-1.0.10.tar.gz
cd libtorrent-rasterbar-1.0.10.tar.gz
./configure --disable-debug --with-boost-libdir=/usr/lib64 --disable-encryption --enable-python-binding
make && make install
export LD_LIBRARY_PATH=/usr/local/lib/
cd bindings/python
python setup.py build