T-717-SPST exercises

This repository contains excercises and supporting code for the course T-717-SPST at Reykjavik University. (Work in progress)

• Exercise 1: Digital Audio
• Exercise 2: Audio Manipulation
• Exercise 3: Domain Specific TTS
• Exercise 4: Script Design
• Exercise 5: Record Your Data
• Exercise 6: Festival in Docker (work in progress)
• Exercise 7: Ossian in AWS (work in progress)

Setting up environment

All the code in this repository is written in Python 3. The recommended approach is to create a python virtual environment:

• Create the virtual environment with one of the following:
  • macOS/Linux: python3 -m venv .env or virtualenv -p python3 ..env
  • Windows: python -m venv ./env or py -3 -m venv .env
• Activate it with source ./env/bin/activate if you are in the project directory. Otherwise you do source /path/to/your/environment/bin/activate.

You can however use Python in any way you see fit and perhaps you may have all the requirements already installed system wide.

Install Python requirements with pip install -r requirements.txt. You can of course install any additional python requirements using pip, just make sure you have your virtual environment activated when you do.

Using VS Code + Python (Optional)

To get the best experience make sure that your VS Code workspace is using the correct Python interpreter. If you are using a virtual environment then the workspace setting python.pythonPath has to be set to /path/to/venv/bin/python. Normally VS Code takes care of doing this for you by recognizing that there is a virtual environment in the workspace. If not:

• Make sure you have the VSC Python extension installed (search for ms-python.python in the extension search)
• Press the settings cog in the bottom left inside VSC and select settings.
• Select Workspace
• search for pythonpath and edit the value to point to your python interpreter as explained above.

You can read a more detailed document about python environments in VSC here.

Using this repository

I would recommend forking this repository before starting your work. By doing that you could easily version control your own work throughout the course. Notes:

• This repository will ignore *.wav files, except for the data/ directories in assignments 1-3. If you want to track your results as well, either save them in these ./data directories or modify the .gitignore file.
• This repository is a work in progress so when important changes are made, follow this to update your fork according to the upstream.

How to return the assignment

Each assignment has a README.md which includes the assignment description and what to turn in. You should return a PDF file where each question in README.md that is marked with (*) is answered. Try to adhere to the numbering in the README.md files. For example, label the answer to the first question in this assignment as 1.1. Furthermore:

• Each assignment has a template.py file. This file should be included in your submission with your own code filled in as well as any other helper functions you write to generate your results.
• Some assignments have an example.py file that shows how to use some of the functions that are given in tools.py.
• In some of the assignments you are asked to generate and save waveforms to disk. In those cases, it is good to include those as well. Turn in your assignment on the Canvas page for the course.