Seasonal Generative Radio is a submission for Kontinuum 2022, which combines the changing seasons throughout the year with a well known composition by Vivaldi (Four Seasons) to generate a unique and ever evolving composition which will play for 365 days in a row if selected by the panel. The musical generation is supplemented with artificially generated narration, which is programmatically triggered to play every hour, similar to a radio presenter.

Our work combines a number of technologies, which can be run continually on a server if required.

The weather is reflective of the constantly changing conditions in which we live our day to day lives. While there is a certain level of predictable ranges in which we can expect the weather to vary over on any given day, the number of variables which combined describe the environment is a powerful stimuli to base a generative composition on.

Weather data captured as generative stimuli

wind speed: The speed of the wind, given in meters/sec
wind gust: The gust of the wind, given in meters/sec
wind deg: A meteorological measurement of the direction of the wind in degrees
temperature: Temperature in degrees celsius
temperature_max: Maximum temperature at the moment. This is maximal currently observed temperature (within large megalopolises and urban areas).
temperature_min: Minimum temperature at the moment. This is maximal currently observed temperature (within large megalopolises and urban areas).
feels_like: This temperature parameter accounts for the human perception of weather. Visibility: Human visibility in meters
Humidity: Humidity in the air described as a % value
Pressure: Atmospheric pressure Rain: Numerical representation of the amount of snowfall
Snow: Numerical representation of the amount of snowfall Clouds: Cloudiness described as a % value

wind speed: tempo
wind gust:
wind deg:
temperature:
temperature_max: Max distortion?
temperature_min: Min distortion?
feels_like:
Visibility:
Humidity: Pressure:
Rain:
Snow:
Clouds:

Luca to describe modulation of audio sources using weather data

• Describe programme used
• How the sound is modulated

To generate the narration every hour, we use a generative language model called GPT-Neo, an open source alternative to OpenAI's GPT-3. It is trained on the Pile dataset and achieves comparable results to OpenAI's GPT-3 model.

Interestingly, generative models take an input parameter called temperature to control how 'far' away from context the model can sway. As a result, the weather data also bears an impact on the generation of our narration, ensuring greater variability in the text.

Temperature is commonly a number around 1, as a result, we calculate temperature as: temperature_min / temperature_max

Note: Max Length could also be generated by using weather data, e.g. pressure * Humidity %

All generative language tasks require an initial prompt to trigger the model to start predicting the next word or set of words in a sequence. We keep ours generic enough to allow for a greater variety of outcomes over the course of each day:

Welcome to Seasonal Generation Radio, today we're going to be talking about

Start by setting up a new conda environment to run on the server

conda env create -f environment.yml
conda activate radiogen

Weather

The weather module can be run as a standalone script, or can be called from other modules.

parameters:
city: Name of the city you want to return data for
stream: (yes/no) - whether you want a stream returned or a single response
interval: (1s / 10s / 30s) - the interval at which the stream should be refreshed

To start a weather stream for London which refreshes every 10 seconds run:

python weather.py London yes 10s