Whether your jam is reggae, hip-hop or electronic you can get creative and enter the latest AWS DeepComposer Chartbusters challenge! The Spin the Model challenge launches today and is open until August 23, 2020. AWS DeepComposer gives developers a creative way to get started with machine learning. Chartbusters is a monthly challenge where you can use AWS DeepComposer to create original compositions and compete to top the charts and win prizes.
To participate in the challenge you first need to train a model and create a composition using your dataset and the Amazon SageMaker notebook. You don’t need a physical keyboard to participate in the challenge. Next, you import the composition on the AWS DeepComposer console, and submit the composition to SoundCloud. When you submit a composition, AWS DeepComposer automatically adds it to the Spin the Model challenge playlist in SoundCloud.
You can use the A deep dive into training an AR-CNN model learning capsule available on the AWS DeepComposer console to learn the concepts to train a model. To access the learning capsule, sign in to the AWS DeepComposer console and choose learning capsules in the navigation pane. Choose A deep dive into training an AR-CNN model to begin learning.
Training a model
We have provided a sample notebook to create a custom model. To use the notebook, first create the Amazon SageMaker notebook instance.
- On the Amazon SageMaker console, under Notebook, choose Notebook instances.
- Choose Create notebook instance.
- For Notebook instance type, choose ml.c5.4xlarge.
- For IAM role, choose a new or existing role.
- For Root access, select Enable.
- For Encryption key, choose No Custom Encryption.
- For Repository, choose Clone a public Git repository to this notebook instance only.
- For Git repository URL, enter
- Choose Create notebook instance.
- Select your notebook instance and choose Open Jupyter.
- In the ar-cnn folder, choose
You’re likely prompted to choose a kernel.
- From the drop-down menu, choose conda_tensorflow_p36.
- Choose Set Kernel.
This notebook contains instructions and code to create and train a custom model from scratch.
- To run the code cells, choose the code cell you want to run and choose Run.
If the kernel has an empty circle, it means it’s available and ready to run the code.
If the kernel has a filled circle, it means the kernel is busy. Wait for it to become available before you run the next line of code.
- Provide the path for your dataset in the dataset summary section. Replace the current
data_dirpath with your dataset directory.
Your dataset should be in the .mid format.
- After you provide the dataset directory path, you can experiment by changing the hyperparameters to train the model.
Training a model typically takes 5 hours or longer, depending on the dataset size and hyperparameter choices.
- After you train the model, create a composition by using the code in the Inference section.
You can use the sample input MIDI files provided in the GitHub repo to generate a composition. Alternatively, you can play the input melody in AWS DeepComposer and download the melody to create a new composition.
- After you create your composition, download it by navigating to the /outputs folder and choosing the file to download.
Submitting your composition
You can now import your composition in AWS DeepComposer. This step is necessary to submit the composition to the Spin the Model Chartbusters challenge.
- On the AWS DeepComposer console, choose Input melody.
- For Source of input melody, choose Imported track.
- For Imported track, choose Choose file to upload the file.
- Use the AR-CNN algorithm to further enhance the input melody.
- To submit your composition for the challenge, choose Chartbusters in the navigation pane.
- Choose Submit a composition.
- Choose your composition from the drop-down menu.
- Provide a track name for your composition and choose Submit.
AWS DeepComposer submits your composition to the Spin the Model playlist on SoundCloud. You can choose Vote on SoundCloud on the console to review and listen to other submissions for the challenge.
Congratulations! You have submitted your entry for the AWS DeepComposer Chartbusters challenge. Invite your friends and family to listen to and like your composition!
Learn more about AWS DeepComposer Chartbusters at https://aws.amazon.com/deepcomposer/chartbusters.
About the Author
Jyothi Nookula is a Principal Product Manager for AWS AI devices. She loves to build products that delight her customers. In her spare time, she loves to paint and host charity fund raisers for her art exhibitions.
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