GSoC 2019 - Automatic Speech Recognition (German)

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Link to Repository of Code

Describe my work briefly

I happy to be the part of GSoC - 2019 working for the Distributed Little Red Hen Labs.

I utilized the community bonding period to discuss the deliverables with my mentors Jan Gorisch, Peter Uhrig, Elmar Noeth, and Sumit Vohra. Also getting myself acquainted with the Student Guide of Google Summer of Code, the GSoC timeline, the Redhen Student Guideline, and the Redhen Coding style guide.

Red Hen requires the code to be deployed in HPC servers. Below are some guidelines to access HPC server (courtesy of) Andrea Pagotto.

Login Steps

Login to Case Western Reserve University

  1. Submit User information to RedHen.
  2. RedHen will create an account at Case Western Reserve University (firstname.lastname@case.edu).
  3. On receiving the confirmation, you can Login at http://webmail.case.edu

Connecting to CWRU Network

  1. The Links provided in the previous Email can be used to set up the CWRU Network Login which will be a randomly assigned Username in the format abc123. This username can be used to connect to the VPN.

Connecting to VPN

To log into the VPN, download the Cisco AnyConnect.

Network: vpn.case.edu

Further, Login with the Username and Password to CWRU network. The APP will ask for the second Password, which can we set using DUO application from play store.

ssh to HPC

In the terminal type:

ssh caseID@rider.case.edu

where caseID is the abc123 CWRU login. More guidelines can be found here. Once in the HPC, the home directory will be /home/abc123, and the path where you will have room to store data will be /mnt/rds/redhen/gallina/home/abc123. Next, follow steps to set up ssh keys, as will be explained in an email from RedHen.

Using Singularity

In order to use Singularity, it must exist in the path. You can test Singularity by pulling from RedHen’s singularity hub, and running the container:

module pull singularity
singularity pull --name deepspeech-temp.img shub://RedHenLab/singularity_containers:deepspeech2

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