Test on ubuntu 16.04. Language: English
Tools: Festival && Merlin
1. download files and copy to merlin/tools
Test on ubuntu 16.04. Language: English
Tools: Festival && Merlin
1. download files and copy to merlin/tools
# redirect output and errors into file output.log: | |
nohup some_command > output.log 2>&1& | |
# abbreviated syntax for bash version >= ver.4: | |
nohup some_command &> output.log |
#!/bin/bash | |
# install CUDA Toolkit v8.0 | |
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network)) | |
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb" | |
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG} | |
sudo dpkg -i ${CUDA_REPO_PKG} | |
sudo apt-get update | |
sudo apt-get -y install cuda |
TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:
After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.
# -*- coding: utf-8 -*- | |
"""Example Google style docstrings. | |
This module demonstrates documentation as specified by the `Google Python | |
Style Guide`_. Docstrings may extend over multiple lines. Sections are created | |
with a section header and a colon followed by a block of indented text. | |
Example: | |
Examples can be given using either the ``Example`` or ``Examples`` | |
sections. Sections support any reStructuredText formatting, including |
import argparse | |
from collections import OrderedDict | |
import librosa | |
import numpy as np | |
import os | |
class Segment: | |
def __init__(self, start, end): | |
self.start = start |