• 设为首页
  • 点击收藏
  • 手机版
    手机扫一扫访问
    迪恩网络手机版
  • 关注官方公众号
    微信扫一扫关注
    迪恩网络公众号

Azure-Samples/MachineLearning-MusicGeneration: Using Azure Machine Learning to b ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称(OpenSource Name):

Azure-Samples/MachineLearning-MusicGeneration

开源软件地址(OpenSource Url):

https://github.com/Azure-Samples/MachineLearning-MusicGeneration

开源编程语言(OpenSource Language):

Python 100.0%

开源软件介绍(OpenSource Introduction):

Music Generation with Azure Machine Learning

NOTE This content is no longer maintained. Visit the Azure Machine Learning Notebook project for sample Jupyter notebooks for ML and deep learning with Azure Machine Learning.

Sequence-to-Sequence model using multi-layered LSTM for music generation. For more detailed walkthrough see: blog

Prerequisites

The prerequisites to run this example are as follows:

  1. Make sure that you have properly installed Azure Machine Learning Workbench by following the Install and create Quickstart

  2. This example could be run on any compute context. However, it is recommended to run it on a GPU machine to accelerate the training process.

  3. Access to an Azure Blob Storage Account. See how to create and manage your storage account here

Create a new Workbench project

  1. Clone this repo to your local machine to /MachineLearning-MusicGeneration
  2. Open Azure Machine Learning Workbench
  3. On the Projects page, click the + sign and select Add Existing Folder as Project
  4. Delete the .git folder in the cloned repo as Azure Machine Learning Workbench currently cannot import projects that contain a git repo
  5. In the Add Existing Folder as Project pane, set the project directory to the location where this repo has been cloned and fill in the information for your new project
  6. Click Create

Setup compute environment

Setup remote VM as execution target

az ml computetarget attach --name "my_dsvm" --address "my_dsvm_ip_address" --username "my_name" --password "my_password" --type remotedocker

Configure my_dsvm.compute

baseDockerImage: microsoft/mmlspark:plus-gpu-0.7.91
nvidiaDocker: true

Configure my_dsvm.runconfig

To push models to Azure Blob Storage, add your storage account details to your .runconfig file:

EnvironmentVariables:
  "STORAGE_ACCOUNT_NAME": "<YOUR_AZURE_STORAGE_ACCOUNT_NAME>"
  "STORAGE_ACCOUNT_KEY": "<YOUR_AZURE_STORAGE_ACCOUNT_KEY>"
Framework: Python

For more info on Azure ML Workbench compute targets see documentation.

Train

To train your own model using a DSVM compte target

Prepare compute environment

az ml experiment -c prepare my_dsvm

Run the experiment

az ml experiment submit -c my_dsvm MusicGeneration/train.py

Generate Music (Predict)

az ml experiment submit -c my_dsvm MusicGeneration/score.py

Listen to your own music!

The song generated in the previous step will be saved in your Blob Storage conatiner. You can listen to the song by downloading the .mid file and playing it using any standard media player like Windows Media Player for example.

Data Credit

The dataset used for the experiments is available at (http://www.feelyoursound.com/scale-chords/)




鲜花

握手

雷人

路过

鸡蛋
该文章已有0人参与评论

请发表评论

全部评论

专题导读
热门推荐
阅读排行榜

扫描微信二维码

查看手机版网站

随时了解更新最新资讯

139-2527-9053

在线客服(服务时间 9:00~18:00)

在线QQ客服
地址:深圳市南山区西丽大学城创智工业园
电邮:jeky_zhao#qq.com
移动电话:139-2527-9053

Powered by 互联科技 X3.4© 2001-2213 极客世界.|Sitemap