├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── ar-cnn ├── AutoRegressiveCNN.ipynb ├── LICENSE ├── README.md ├── augmentation.py ├── constants.py ├── data_generator.py ├── images │ ├── addnotes.gif │ ├── download-composition.png │ ├── merged_pianoroll.png │ ├── pianoroll.png │ ├── removenotes.gif │ └── unet.png ├── inference.py ├── inference_parameters.json ├── losses.py ├── model.py ├── requirements.txt ├── sample_inputs │ ├── deck_the_halls.midi │ ├── me_and_my_jar_by_Jonathan_Coulton.midi │ ├── merry_christmas.midi │ ├── new_world.midi │ ├── ode_to_joy.midi │ └── twinkle_twinkle.midi ├── sample_outputs │ ├── ode_to_joy │ │ ├── ode_to_joy_input.mid │ │ └── ode_to_joy_output.mid │ └── ode_to_joy_offtune │ │ ├── ode_to_joy_offtune_input.mid │ │ └── ode_to_joy_offtune_output.mid └── utils │ ├── __init__.py │ ├── generate_training_plots.py │ └── midi_utils.py ├── gan ├── GAN.ipynb ├── Lifecycle_configurations.sh ├── README.md ├── dataset │ ├── JSPChorales.h5 │ ├── eval.npy │ └── train.npy ├── images │ ├── Piano.png │ ├── create_notebook.png │ ├── ddis.pdf │ ├── ddis.png │ ├── dgan.pdf │ ├── dgan.png │ ├── dgen.pdf │ ├── dgen.png │ ├── gan_notebook.png │ ├── kernel-busy.png │ ├── kernel-empty.png │ ├── kernel-error.PNG │ ├── lab-2 │ ├── notebook-instance.PNG │ ├── notebook-stop.png │ ├── notebook_git_settings.png │ ├── notebook_instance_settings.png │ ├── open-instance.PNG │ ├── open-notebook.PNG │ ├── open_jupyter.png │ ├── permissions_and_encryption.png │ ├── pianoroll2.png │ ├── pianoroll_timesteps.png │ ├── run-notebook.PNG │ └── set-kernel.PNG ├── input_twinkle_twinkle.mid ├── original_midi │ ├── MIDI-0.mid │ ├── MIDI-1.mid │ ├── MIDI-10.mid │ ├── MIDI-100.mid │ ├── MIDI-101.mid │ ├── MIDI-102.mid │ ├── MIDI-103.mid │ ├── MIDI-104.mid │ ├── MIDI-105.mid │ ├── MIDI-106.mid │ ├── MIDI-107.mid │ ├── MIDI-108.mid │ ├── MIDI-109.mid │ ├── MIDI-11.mid │ ├── MIDI-110.mid │ ├── MIDI-111.mid │ ├── MIDI-112.mid │ ├── MIDI-113.mid │ ├── MIDI-114.mid │ ├── MIDI-115.mid │ ├── MIDI-116.mid │ ├── MIDI-117.mid │ ├── MIDI-118.mid │ ├── MIDI-119.mid │ ├── MIDI-12.mid │ ├── MIDI-120.mid │ ├── MIDI-121.mid │ ├── MIDI-122.mid │ ├── MIDI-123.mid │ ├── MIDI-124.mid │ ├── MIDI-125.mid │ ├── MIDI-126.mid │ ├── MIDI-127.mid │ ├── MIDI-128.mid │ ├── MIDI-129.mid │ ├── MIDI-13.mid │ ├── MIDI-130.mid │ ├── MIDI-131.mid │ ├── MIDI-132.mid │ ├── MIDI-133.mid │ ├── MIDI-134.mid │ ├── MIDI-135.mid │ ├── MIDI-136.mid │ ├── MIDI-137.mid │ ├── MIDI-138.mid │ ├── MIDI-139.mid │ ├── MIDI-14.mid │ ├── MIDI-140.mid │ ├── MIDI-141.mid │ ├── MIDI-142.mid │ ├── MIDI-143.mid │ ├── MIDI-144.mid │ ├── MIDI-145.mid │ ├── MIDI-146.mid │ ├── MIDI-147.mid │ ├── MIDI-148.mid │ ├── MIDI-149.mid │ ├── MIDI-15.mid │ ├── MIDI-150.mid │ ├── MIDI-151.mid │ ├── MIDI-152.mid │ ├── MIDI-153.mid │ ├── MIDI-154.mid │ ├── MIDI-155.mid │ ├── MIDI-156.mid │ ├── MIDI-157.mid │ ├── MIDI-158.mid │ ├── MIDI-159.mid │ ├── MIDI-16.mid │ ├── MIDI-160.mid │ ├── MIDI-161.mid │ ├── MIDI-162.mid │ ├── MIDI-163.mid │ ├── MIDI-164.mid │ ├── MIDI-165.mid │ ├── MIDI-166.mid │ ├── MIDI-167.mid │ ├── MIDI-168.mid │ ├── MIDI-169.mid │ ├── MIDI-17.mid │ ├── MIDI-170.mid │ ├── MIDI-171.mid │ ├── MIDI-172.mid │ ├── MIDI-173.mid │ ├── MIDI-174.mid │ ├── MIDI-175.mid │ ├── MIDI-176.mid │ ├── MIDI-177.mid │ ├── MIDI-178.mid │ ├── MIDI-179.mid │ ├── MIDI-18.mid │ ├── MIDI-180.mid │ ├── MIDI-181.mid │ ├── MIDI-182.mid │ ├── MIDI-183.mid │ ├── MIDI-184.mid │ ├── MIDI-185.mid │ ├── MIDI-186.mid │ ├── MIDI-187.mid │ ├── MIDI-188.mid │ ├── MIDI-189.mid │ ├── MIDI-19.mid │ ├── MIDI-190.mid │ ├── MIDI-191.mid │ ├── MIDI-192.mid │ ├── MIDI-193.mid │ ├── MIDI-194.mid │ ├── MIDI-195.mid │ ├── MIDI-196.mid │ ├── MIDI-197.mid │ ├── MIDI-198.mid │ ├── MIDI-199.mid │ ├── MIDI-2.mid │ ├── MIDI-20.mid │ ├── MIDI-200.mid │ ├── MIDI-201.mid │ ├── MIDI-202.mid │ ├── MIDI-203.mid │ ├── MIDI-204.mid │ ├── MIDI-205.mid │ ├── MIDI-206.mid │ ├── MIDI-207.mid │ ├── MIDI-208.mid │ ├── MIDI-209.mid │ ├── MIDI-21.mid │ ├── MIDI-210.mid │ ├── MIDI-211.mid │ ├── MIDI-212.mid │ ├── MIDI-213.mid │ ├── MIDI-214.mid │ ├── MIDI-215.mid │ ├── MIDI-216.mid │ ├── MIDI-217.mid │ ├── MIDI-218.mid │ ├── MIDI-219.mid │ ├── MIDI-22.mid │ ├── MIDI-220.mid │ ├── MIDI-221.mid │ ├── MIDI-222.mid │ ├── MIDI-223.mid │ ├── MIDI-224.mid │ ├── MIDI-225.mid │ ├── MIDI-226.mid │ ├── MIDI-227.mid │ ├── MIDI-228.mid │ ├── MIDI-23.mid │ ├── MIDI-24.mid │ ├── MIDI-25.mid │ ├── MIDI-26.mid │ ├── MIDI-27.mid │ ├── MIDI-28.mid │ ├── MIDI-29.mid │ ├── MIDI-3.mid │ ├── MIDI-30.mid │ ├── MIDI-31.mid │ ├── MIDI-32.mid │ ├── MIDI-33.mid │ ├── MIDI-34.mid │ ├── MIDI-35.mid │ ├── MIDI-36.mid │ ├── MIDI-37.mid │ ├── MIDI-38.mid │ ├── MIDI-39.mid │ ├── MIDI-4.mid │ ├── MIDI-40.mid │ ├── MIDI-41.mid │ ├── MIDI-42.mid │ ├── MIDI-43.mid │ ├── MIDI-44.mid │ ├── MIDI-45.mid │ ├── MIDI-46.mid │ ├── MIDI-47.mid │ ├── MIDI-48.mid │ ├── MIDI-49.mid │ ├── MIDI-5.mid │ ├── MIDI-50.mid │ ├── MIDI-51.mid │ ├── MIDI-52.mid │ ├── MIDI-53.mid │ ├── MIDI-54.mid │ ├── MIDI-55.mid │ ├── MIDI-56.mid │ ├── MIDI-57.mid │ ├── MIDI-58.mid │ ├── MIDI-59.mid │ ├── MIDI-6.mid │ ├── MIDI-60.mid │ ├── MIDI-61.mid │ ├── MIDI-62.mid │ ├── MIDI-63.mid │ ├── MIDI-64.mid │ ├── MIDI-65.mid │ ├── MIDI-66.mid │ ├── MIDI-67.mid │ ├── MIDI-68.mid │ ├── MIDI-69.mid │ ├── MIDI-7.mid │ ├── MIDI-70.mid │ ├── MIDI-71.mid │ ├── MIDI-72.mid │ ├── MIDI-73.mid │ ├── MIDI-74.mid │ ├── MIDI-75.mid │ ├── MIDI-76.mid │ ├── MIDI-77.mid │ ├── MIDI-78.mid │ ├── MIDI-79.mid │ ├── MIDI-8.mid │ ├── MIDI-80.mid │ ├── MIDI-81.mid │ ├── MIDI-82.mid │ ├── MIDI-83.mid │ ├── MIDI-84.mid │ ├── MIDI-85.mid │ ├── MIDI-86.mid │ ├── MIDI-87.mid │ ├── MIDI-88.mid │ ├── MIDI-89.mid │ ├── MIDI-9.mid │ ├── MIDI-90.mid │ ├── MIDI-91.mid │ ├── MIDI-92.mid │ ├── MIDI-93.mid │ ├── MIDI-94.mid │ ├── MIDI-95.mid │ ├── MIDI-96.mid │ ├── MIDI-97.mid │ ├── MIDI-98.mid │ └── MIDI-99.mid ├── requirements.sh └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── display_utils.cpython-37.pyc │ ├── inference_utils.cpython-37.pyc │ ├── metrics_utils.cpython-37.pyc │ ├── midi_utils.cpython-37.pyc │ └── path_utils.cpython-37.pyc │ ├── display_utils.py │ ├── inference_utils.py │ ├── metrics_utils.py │ ├── midi_utils.py │ └── path_utils.py ├── reinvent-labs ├── LICENSE ├── Loss-functions.md ├── Music-terminologies.md ├── README.md ├── lab-1 │ ├── README.md │ └── images │ │ ├── aws-region.jpg │ │ ├── clone-models.jpg │ │ ├── create-model-button.jpg │ │ ├── create-models.jpg │ │ ├── files │ │ ├── lab1-access-console.png │ │ ├── lab1-compose music-get started.png │ │ ├── lab1-custom-music.png │ │ ├── lab1-generate-composition.PNG │ │ ├── lab1-get-started.png │ │ ├── lab1-model-default.PNG │ │ ├── lab1-play-default.PNG │ │ ├── lab1-record.PNG │ │ ├── model-name.jpg │ │ ├── models.jpg │ │ └── music-studio.jpg ├── lab-2 │ ├── GAN.ipynb │ ├── Lifecycle_configurations.sh │ ├── README.md │ ├── dataset │ │ ├── JSPChorales.h5 │ │ ├── eval.npy │ │ └── train.npy │ ├── images │ │ ├── Piano.png │ │ ├── create_notebook.png │ │ ├── ddis.pdf │ │ ├── ddis.png │ │ ├── dgan.pdf │ │ ├── dgan.png │ │ ├── dgen.pdf │ │ ├── dgen.png │ │ ├── gan_notebook.png │ │ ├── kernel-busy.png │ │ ├── kernel-empty.png │ │ ├── kernel-error.PNG │ │ ├── lab-2 │ │ ├── notebook-instance.PNG │ │ ├── notebook-stop.png │ │ ├── notebook_git_settings.png │ │ ├── notebook_instance_settings.png │ │ ├── open-instance.PNG │ │ ├── open-notebook.PNG │ │ ├── open_jupyter.png │ │ ├── permissions_and_encryption.png │ │ ├── pianoroll2.png │ │ ├── pianoroll_timesteps.png │ │ ├── run-notebook.PNG │ │ └── set-kernel.PNG │ ├── input_twinkle_twinkle.mid │ ├── original_midi │ │ ├── MIDI-0.mid │ │ ├── MIDI-1.mid │ │ ├── MIDI-10.mid │ │ ├── MIDI-100.mid │ │ ├── MIDI-101.mid │ │ ├── MIDI-102.mid │ │ ├── MIDI-103.mid │ │ ├── MIDI-104.mid │ │ ├── MIDI-105.mid │ │ ├── MIDI-106.mid │ │ ├── MIDI-107.mid │ │ ├── MIDI-108.mid │ │ ├── MIDI-109.mid │ │ ├── MIDI-11.mid │ │ ├── MIDI-110.mid │ │ ├── MIDI-111.mid │ │ ├── MIDI-112.mid │ │ ├── MIDI-113.mid │ │ ├── MIDI-114.mid │ │ ├── MIDI-115.mid │ │ ├── MIDI-116.mid │ │ ├── MIDI-117.mid │ │ ├── MIDI-118.mid │ │ ├── MIDI-119.mid │ │ ├── MIDI-12.mid │ │ ├── MIDI-120.mid │ │ ├── MIDI-121.mid │ │ ├── MIDI-122.mid │ │ ├── MIDI-123.mid │ │ ├── MIDI-124.mid │ │ ├── MIDI-125.mid │ │ ├── MIDI-126.mid │ │ ├── MIDI-127.mid │ │ ├── MIDI-128.mid │ │ ├── MIDI-129.mid │ │ ├── MIDI-13.mid │ │ ├── MIDI-130.mid │ │ ├── MIDI-131.mid │ │ ├── MIDI-132.mid │ │ ├── MIDI-133.mid │ │ ├── MIDI-134.mid │ │ ├── MIDI-135.mid │ │ ├── MIDI-136.mid │ │ ├── MIDI-137.mid │ │ ├── MIDI-138.mid │ │ ├── MIDI-139.mid │ │ ├── MIDI-14.mid │ │ ├── MIDI-140.mid │ │ ├── MIDI-141.mid │ │ ├── MIDI-142.mid │ │ ├── MIDI-143.mid │ │ ├── MIDI-144.mid │ │ ├── MIDI-145.mid │ │ ├── MIDI-146.mid │ │ ├── MIDI-147.mid │ │ ├── MIDI-148.mid │ │ ├── MIDI-149.mid │ │ ├── MIDI-15.mid │ │ ├── MIDI-150.mid │ │ ├── MIDI-151.mid │ │ ├── MIDI-152.mid │ │ ├── MIDI-153.mid │ │ ├── MIDI-154.mid │ │ ├── MIDI-155.mid │ │ ├── MIDI-156.mid │ │ ├── MIDI-157.mid │ │ ├── MIDI-158.mid │ │ ├── MIDI-159.mid │ │ ├── MIDI-16.mid │ │ ├── MIDI-160.mid │ │ ├── MIDI-161.mid │ │ ├── MIDI-162.mid │ │ ├── MIDI-163.mid │ │ ├── MIDI-164.mid │ │ ├── MIDI-165.mid │ │ ├── MIDI-166.mid │ │ ├── MIDI-167.mid │ │ ├── MIDI-168.mid │ │ ├── MIDI-169.mid │ │ ├── MIDI-17.mid │ │ ├── MIDI-170.mid │ │ ├── MIDI-171.mid │ │ ├── MIDI-172.mid │ │ ├── MIDI-173.mid │ │ ├── MIDI-174.mid │ │ ├── MIDI-175.mid │ │ ├── MIDI-176.mid │ │ ├── MIDI-177.mid │ │ ├── MIDI-178.mid │ │ ├── MIDI-179.mid │ │ ├── MIDI-18.mid │ │ ├── MIDI-180.mid │ │ ├── MIDI-181.mid │ │ ├── MIDI-182.mid │ │ ├── MIDI-183.mid │ │ ├── MIDI-184.mid │ │ ├── MIDI-185.mid │ │ ├── MIDI-186.mid │ │ ├── MIDI-187.mid │ │ ├── MIDI-188.mid │ │ ├── MIDI-189.mid │ │ ├── MIDI-19.mid │ │ ├── MIDI-190.mid │ │ ├── MIDI-191.mid │ │ ├── MIDI-192.mid │ │ ├── MIDI-193.mid │ │ ├── MIDI-194.mid │ │ ├── MIDI-195.mid │ │ ├── MIDI-196.mid │ │ ├── MIDI-197.mid │ │ ├── MIDI-198.mid │ │ ├── MIDI-199.mid │ │ ├── MIDI-2.mid │ │ ├── MIDI-20.mid │ │ ├── MIDI-200.mid │ │ ├── MIDI-201.mid │ │ ├── MIDI-202.mid │ │ ├── MIDI-203.mid │ │ ├── MIDI-204.mid │ │ ├── MIDI-205.mid │ │ ├── MIDI-206.mid │ │ ├── MIDI-207.mid │ │ ├── MIDI-208.mid │ │ ├── MIDI-209.mid │ │ ├── MIDI-21.mid │ │ ├── MIDI-210.mid │ │ ├── MIDI-211.mid │ │ ├── MIDI-212.mid │ │ ├── MIDI-213.mid │ │ ├── MIDI-214.mid │ │ ├── MIDI-215.mid │ │ ├── MIDI-216.mid │ │ ├── MIDI-217.mid │ │ ├── MIDI-218.mid │ │ ├── MIDI-219.mid │ │ ├── MIDI-22.mid │ │ ├── MIDI-220.mid │ │ ├── MIDI-221.mid │ │ ├── MIDI-222.mid │ │ ├── MIDI-223.mid │ │ ├── MIDI-224.mid │ │ ├── MIDI-225.mid │ │ ├── MIDI-226.mid │ │ ├── MIDI-227.mid │ │ ├── MIDI-228.mid │ │ ├── MIDI-23.mid │ │ ├── MIDI-24.mid │ │ ├── MIDI-25.mid │ │ ├── MIDI-26.mid │ │ ├── MIDI-27.mid │ │ ├── MIDI-28.mid │ │ ├── MIDI-29.mid │ │ ├── MIDI-3.mid │ │ ├── MIDI-30.mid │ │ ├── MIDI-31.mid │ │ ├── MIDI-32.mid │ │ ├── MIDI-33.mid │ │ ├── MIDI-34.mid │ │ ├── MIDI-35.mid │ │ ├── MIDI-36.mid │ │ ├── MIDI-37.mid │ │ ├── MIDI-38.mid │ │ ├── MIDI-39.mid │ │ ├── MIDI-4.mid │ │ ├── MIDI-40.mid │ │ ├── MIDI-41.mid │ │ ├── MIDI-42.mid │ │ ├── MIDI-43.mid │ │ ├── MIDI-44.mid │ │ ├── MIDI-45.mid │ │ ├── MIDI-46.mid │ │ ├── MIDI-47.mid │ │ ├── MIDI-48.mid │ │ ├── MIDI-49.mid │ │ ├── MIDI-5.mid │ │ ├── MIDI-50.mid │ │ ├── MIDI-51.mid │ │ ├── MIDI-52.mid │ │ ├── MIDI-53.mid │ │ ├── MIDI-54.mid │ │ ├── MIDI-55.mid │ │ ├── MIDI-56.mid │ │ ├── MIDI-57.mid │ │ ├── MIDI-58.mid │ │ ├── MIDI-59.mid │ │ ├── MIDI-6.mid │ │ ├── MIDI-60.mid │ │ ├── MIDI-61.mid │ │ ├── MIDI-62.mid │ │ ├── MIDI-63.mid │ │ ├── MIDI-64.mid │ │ ├── MIDI-65.mid │ │ ├── MIDI-66.mid │ │ ├── MIDI-67.mid │ │ ├── MIDI-68.mid │ │ ├── MIDI-69.mid │ │ ├── MIDI-7.mid │ │ ├── MIDI-70.mid │ │ ├── MIDI-71.mid │ │ ├── MIDI-72.mid │ │ ├── MIDI-73.mid │ │ ├── MIDI-74.mid │ │ ├── MIDI-75.mid │ │ ├── MIDI-76.mid │ │ ├── MIDI-77.mid │ │ ├── MIDI-78.mid │ │ ├── MIDI-79.mid │ │ ├── MIDI-8.mid │ │ ├── MIDI-80.mid │ │ ├── MIDI-81.mid │ │ ├── MIDI-82.mid │ │ ├── MIDI-83.mid │ │ ├── MIDI-84.mid │ │ ├── MIDI-85.mid │ │ ├── MIDI-86.mid │ │ ├── MIDI-87.mid │ │ ├── MIDI-88.mid │ │ ├── MIDI-89.mid │ │ ├── MIDI-9.mid │ │ ├── MIDI-90.mid │ │ ├── MIDI-91.mid │ │ ├── MIDI-92.mid │ │ ├── MIDI-93.mid │ │ ├── MIDI-94.mid │ │ ├── MIDI-95.mid │ │ ├── MIDI-96.mid │ │ ├── MIDI-97.mid │ │ ├── MIDI-98.mid │ │ └── MIDI-99.mid │ ├── requirements.sh │ └── utils │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── display_utils.cpython-37.pyc │ │ ├── inference_utils.cpython-37.pyc │ │ ├── metrics_utils.cpython-37.pyc │ │ ├── midi_utils.cpython-37.pyc │ │ └── path_utils.cpython-37.pyc │ │ ├── display_utils.py │ │ ├── inference_utils.py │ │ ├── metrics_utils.py │ │ ├── midi_utils.py │ │ └── path_utils.py └── reinvent-workshop-deck │ ├── DeepComposer-reInvent19-Workshop.pdf │ └── readme.md └── transformer-xl ├── .ipynb_checkpoints └── transformerxl-checkpoint.ipynb ├── LICENSE ├── README.md ├── images ├── attention_fig.png ├── dataloader.jpg ├── decoder.png ├── event_rep.jpg ├── nucleus.jpg ├── pianoroll.png ├── top-k.jpg ├── transformer.png ├── vanilla.gif └── xl.gif ├── pretrained_checkpoints ├── exp.yaml └── losses.pickle ├── requirements.txt ├── transformerxl.ipynb └── utils ├── midi_utils.py ├── music_encoder.py ├── performance_event_repo.py ├── performance_vocab.txt └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | ## Code of Conduct 2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 4 | opensource-codeofconduct@amazon.com with any additional questions or comments. 5 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Licensing varies between subdirectories. Check each subdirectory for its corresponding license. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## AWS DeepComposer 2 | 3 | Welcome to AWS DeepComposer. This repository includes: 4 | 5 | 6 | ## [re:Invent 2019 workshop labs](reinvent-labs) 7 | 8 | ## [Training a custom GAN model](gan) 9 | 10 | ## [Training an autoregressive convolutional neural network(AR-CNN) model](ar-cnn) 11 | 12 | ## [Training a Transformer-XL model](transformer-xl) 13 | 14 | 15 | ## License 16 | 17 | Licensing varies between subdirectories. Check each subdirectory for its corresponding license. 18 | -------------------------------------------------------------------------------- /ar-cnn/LICENSE: -------------------------------------------------------------------------------- 1 | The MIT-Zero License 2 | 3 | Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so. 11 | 12 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 13 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 14 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 15 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 16 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 17 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 18 | THE SOFTWARE. -------------------------------------------------------------------------------- /ar-cnn/constants.py: -------------------------------------------------------------------------------- 1 | # The MIT-Zero License 2 | 3 | # Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. 4 | 5 | # Permission is hereby granted, free of charge, to any person obtaining a copy 6 | # of this software and associated documentation files (the "Software"), to deal 7 | # in the Software without restriction, including without limitation the rights 8 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | # copies of the Software, and to permit persons to whom the Software is 10 | # furnished to do so. 11 | 12 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 13 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 14 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 15 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 16 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 17 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 18 | # THE SOFTWARE. 19 | 20 | import os 21 | 22 | class Constants(): 23 | # Make it a multiple of the batch size for best (balanced) performance 24 | samples_per_ground_truth_data_item = 8 25 | training_validation_split = 0.9 26 | # Number of Bars 27 | bars = 8 28 | # Number of Beats Per Bar 29 | beats_per_bar = 4 30 | beat_resolution = 4 31 | # number of bars to be shifted 32 | bars_shifted_per_sample = 4 33 | # Total number of pitches in a Pianoroll 34 | number_of_pitches = 128 35 | # Total number of Tracks 36 | number_of_channels = 1 37 | output_file_path = "outputs/output_{}.mid" 38 | tempo = 100 # 100 bpm 39 | -------------------------------------------------------------------------------- /ar-cnn/images/addnotes.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/ar-cnn/images/addnotes.gif -------------------------------------------------------------------------------- /ar-cnn/images/download-composition.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/ar-cnn/images/download-composition.png -------------------------------------------------------------------------------- /ar-cnn/images/merged_pianoroll.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/ar-cnn/images/merged_pianoroll.png -------------------------------------------------------------------------------- /ar-cnn/images/pianoroll.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/ar-cnn/images/pianoroll.png -------------------------------------------------------------------------------- /ar-cnn/images/removenotes.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/ar-cnn/images/removenotes.gif -------------------------------------------------------------------------------- /ar-cnn/images/unet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/ar-cnn/images/unet.png -------------------------------------------------------------------------------- /ar-cnn/inference_parameters.json: -------------------------------------------------------------------------------- 1 | { 2 | "temperature": 1, 3 | "maxNotesAdded": 500, 4 | "maxPercentageOfInitialNotesRemoved": 100, 5 | "samplingIterations": 100 6 | } 7 | -------------------------------------------------------------------------------- /ar-cnn/losses.py: -------------------------------------------------------------------------------- 1 | # The MIT-Zero License 2 | 3 | # Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. 4 | 5 | # Permission is hereby granted, free of charge, to any person obtaining a copy 6 | # of this software and associated documentation files (the "Software"), to deal 7 | # in the Software without restriction, including without limitation the rights 8 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | # copies of the Software, and to permit persons to whom the Software is 10 | # furnished to do so. 11 | 12 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 13 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 14 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 15 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 16 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 17 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 18 | # THE SOFTWARE. 19 | 20 | import keras 21 | from keras import backend as K 22 | 23 | 24 | class Loss(): 25 | @staticmethod 26 | def built_in_softmax_kl_loss(target, output): 27 | ''' 28 | Custom Loss Function 29 | :param target: ground truth values 30 | :param output: predicted values 31 | :return kullback_leibler_divergence loss 32 | ''' 33 | target = K.flatten(target) 34 | output = K.flatten(output) 35 | target = target / K.sum(target) 36 | output = K.softmax(output) 37 | return keras.losses.kullback_leibler_divergence(target, output) 38 | -------------------------------------------------------------------------------- /ar-cnn/requirements.txt: 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All Rights Reserved. 4 | 5 | # Permission is hereby granted, free of charge, to any person obtaining a copy 6 | # of this software and associated documentation files (the "Software"), to deal 7 | # in the Software without restriction, including without limitation the rights 8 | # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | # copies of the Software, and to permit persons to whom the Software is 10 | # furnished to do so. 11 | 12 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 13 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 14 | # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 15 | # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 16 | # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 17 | # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 18 | # THE SOFTWARE. 19 | 20 | import matplotlib.pyplot as plt 21 | from IPython.display import clear_output 22 | import keras 23 | import numpy as np 24 | 25 | 26 | class GenerateTrainingPlots(keras.callbacks.Callback): 27 | 28 | # Generates Training Vs Validation live plots 29 | def on_train_begin(self, logs={}): 30 | 31 | # Create training and validation loss lists 32 | self.training_loss = [] 33 | self.validation_loss = [] 34 | self.logs = [] 35 | 36 | def on_epoch_end(self, epoch, logs={}): 37 | 38 | self.logs.append(logs) 39 | self.training_loss.append(logs.get('loss')) 40 | self.validation_loss.append(logs.get('val_loss')) 41 | 42 | # Start generating plot after Epoch 2 43 | if len(self.training_loss) > 1: 44 | clear_output(wait=True) 45 | N = np.arange(0, len(self.training_loss)) 46 | plt.style.use("ggplot") 47 | plt.figure() 48 | plt.plot(N, self.training_loss, label="training_loss") 49 | plt.plot(N, self.validation_loss, label="validation_loss") 50 | plt.title( 51 | "Training Vs Validation Loss After Epoch {}".format(epoch + 1)) 52 | plt.xlabel("Epoch Number") 53 | plt.ylabel("Loss") 54 | plt.legend() 55 | plt.show() 56 | -------------------------------------------------------------------------------- /gan/Lifecycle_configurations.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -e 4 | 5 | # OVERVIEW 6 | # This script installs all necessary software for running the DeepComposer GAN notebook. 7 | 8 | sudo -u ec2-user -i <<'EOF' 9 | ENVIRONMENT=python3 10 | source /home/ec2-user/anaconda3/bin/activate "$ENVIRONMENT" 11 | 12 | conda update --all --y 13 | pip install tensorflow==1.14.0 14 | pip install numpy==1.16.4 15 | pip install pretty_midi 16 | pip install pypianoroll 17 | pip install music21 18 | pip install seaborn 19 | pip install --ignore-installed moviepy 20 | 21 | source /home/ec2-user/anaconda3/bin/deactivate 22 | EOF -------------------------------------------------------------------------------- /gan/README.md: -------------------------------------------------------------------------------- 1 | # Train a custom GAN model 2 | 3 | ## Goal 4 | 5 | As part of this repo, you will learn to build a custom GAN architecture and train the model using Amazon SageMaker. 6 | 7 | 8 | 9 | ## Prerequisites 10 | 11 | * Access to Amazon SageMaker 12 | 13 | ## Cost 14 | 15 | Using a ml.c5.4xlarge, the entire exercise take 3-4 hrs to run. Please see the [Amazon SageMaker pricing](https://aws.amazon.com/sagemaker/pricing/) for details. 16 | 17 | ## Setup 18 | 19 | First we create the Amazon SageMaker notebook instance. 20 | 21 | Navigate to Amazon SageMaker using the link: https://console.aws.amazon.com/sagemaker/home?region=us-east-1#/dashboard 22 | 23 | ![notebook-instance](images/notebook-instance.PNG) 24 | 25 | Click **Notebook instances** from the left navigation bar 26 | 27 | Select **Create notebook instance** 28 | 29 | ![create-notebook](images/create_notebook.png) 30 | 31 | Within the notebook instance creation form, select "c5.4xlarge" for **Notebook instance type** 32 | 33 | ![notebook-instance-settings](images/notebook_instance_settings.png) 34 | 35 | Set the following for **Permissions and encryption**: 36 | * IAM role: Use an existing role or create a new role 37 | * Root access: Enable 38 | * Encryption key: No Custom Encryption 39 | 40 | ![notebook-instance-settings](images/permissions_and_encryption.png) 41 | 42 | Set the following for **Git repositories**: 43 | * Repository: Clone a public Git repository to this notebook instance only 44 | * Git repository URL: https://github.com/aws-samples/aws-deepcomposer-samples 45 | 46 | ![notebook-instance-settings](images/notebook_git_settings.png) 47 | 48 | Click **Open Jupyter** 49 | 50 | ![open-notebook](images/open_jupyter.png) 51 | 52 | 53 | 54 | Click **gan** folder, then click **GAN.ipynb** 55 | 56 | ![GAN-notebook](images/gan_notebook.png) 57 | 58 | *You will likely be prompted to select kernel. Choose the drop down and select **conda_python3** as the kernel* 59 | 60 | ![set-kernel](images/set-kernel.PNG) 61 | 62 | This notebook contains instructions and code to create a custom GAN model from scratch. Follow the notebook content and run all cells to the end. 63 | 64 | ![run-notebook](images/run-notebook.PNG) 65 | 66 | To run the code cells, choose the code cell you want to run and click **Run** 67 | 68 | ![kernel-empty](images/kernel-empty.png) 69 | 70 | If the kernel has an empty circle, it means it is free and ready to execute the code 71 | 72 | ![kernel-busy](images/kernel-busy.png) 73 | 74 | If the kernel has a filled circle, it means it is busy. Wait for it to become free before you execute the next line of code. 75 | 76 | ## Next Steps 77 | 78 | **Congratulations on building a custom GAN model from scratch!** 79 | 80 | Now try using your model to create compositions based on your custom MIDI input. 81 | 82 | 83 | 84 | **Important: Remember to stop your Amazon SageMaker instances after you're done to avoid extra charges** 85 | 86 | 87 | 88 | ![notebook-stop](images/notebook-stop.png) -------------------------------------------------------------------------------- /gan/dataset/JSPChorales.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/gan/dataset/JSPChorales.h5 -------------------------------------------------------------------------------- /gan/dataset/eval.npy: -------------------------------------------------------------------------------- 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/gan/utils/inference_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | # this software and associated documentation files (the "Software"), to deal in 5 | # the Software without restriction, including without limitation the rights to 6 | # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | # the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | import tensorflow as tf 17 | import numpy as np 18 | 19 | from utils import path_utils, midi_utils, display_utils 20 | 21 | # --- local samples------------------------------------------------------------------ 22 | 23 | def load_melody_samples(n_sample=10): 24 | """Load the samples used for evaluation.""" 25 | 26 | sample_source_path = './dataset/eval.npy' 27 | 28 | data = np.load(sample_source_path) 29 | data = np.asarray(data, dtype=np.float32) # {-1, 1} 30 | 31 | random_idx = np.random.choice(len(data), n_sample, replace=False) 32 | 33 | sample_x = data[random_idx] 34 | 35 | sample_z = tf.random.truncated_normal((n_sample, 2, 8, 512)) 36 | 37 | print("Loaded {} melody samples".format(len(sample_x))) 38 | 39 | return sample_x, sample_z 40 | 41 | # --- Training ------------------------------------------------------------------ 42 | 43 | def generate_pianoroll(generator, conditioned_track, noise_vector=None): 44 | if noise_vector == None: 45 | noise_vector = tf.random.truncated_normal((1, 2, 8, 512)) 46 | return generator((conditioned_track, noise_vector), training=False) 47 | 48 | 49 | def generate_midi(generator, saveto_dir, input_midi_file='./Experiments/data/happy_birthday_easy.mid'): 50 | conditioned_track = midi_utils.get_conditioned_track(midi=input_midi_file) 51 | generated_pianoroll = generate_pianoroll(generator, conditioned_track) 52 | 53 | destination_path = path_utils.new_temp_midi_path(saveto_dir=saveto_dir) 54 | midi_utils.save_pianoroll_as_midi(generated_pianoroll.numpy(), destination_path=destination_path) 55 | return destination_path 56 | 57 | def show_generated_pianorolls(generator, eval_dir, input_midi_file='./Experiments/data/happy_birthday_easy.mid', n_pr = 4): 58 | conditioned_track = midi_utils.get_conditioned_track(midi=input_midi_file) 59 | for i in range(n_pr): 60 | generated_pianoroll = generate_pianoroll(generator, conditioned_track) 61 | display_utils.show_pianoroll(generated_pianoroll) -------------------------------------------------------------------------------- /gan/utils/midi_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | # this software and associated documentation files (the "Software"), to deal in 5 | # the Software without restriction, including without limitation the rights to 6 | # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | # the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | import tensorflow as tf 17 | import numpy as np 18 | import pypianoroll 19 | 20 | def save_pianoroll_as_midi(pianoroll, 21 | programs=[0, 0, 0, 0], 22 | is_drums=[False, False, False, False], 23 | tempo=100, # in bpm 24 | beat_resolution=4, # number of time steps 25 | destination_path=None 26 | ): 27 | 28 | pianoroll = pianoroll > 0 29 | 30 | # Reshape batched pianoroll array to a single pianoroll array 31 | pianoroll_ = pianoroll.reshape((-1, pianoroll.shape[2], pianoroll.shape[3])) 32 | 33 | # Create the tracks 34 | tracks = [] 35 | for idx in range(pianoroll_.shape[2]): 36 | tracks.append(pypianoroll.Track( 37 | pianoroll_[..., idx], programs[idx], is_drums[idx])) 38 | 39 | multitrack = pypianoroll.Multitrack( 40 | tracks=tracks, tempo=tempo, beat_resolution=beat_resolution) 41 | 42 | multitrack.write(destination_path) 43 | print('Midi saved to ', destination_path) 44 | return destination_path 45 | 46 | 47 | def get_conditioned_track(midi=None, phrase_length=32, beat_resolution=4): 48 | 49 | if not isinstance(midi, str): 50 | # ----------- Generation from preprocessed dataset ------------------ 51 | sample_x = midi 52 | sample_c = np.expand_dims(sample_x[..., 0], -1) 53 | else: 54 | # --------------- Generation from midi file ------------------------- 55 | midi_file = midi 56 | parsed = pypianoroll.Multitrack(beat_resolution=beat_resolution) 57 | parsed.parse_midi(midi_file) 58 | 59 | sample_c = parsed.tracks[0].pianoroll.astype(np.float32) 60 | 61 | # Remove initial steps that have no note-on events 62 | first_non_zero = np.nonzero(sample_c.sum(axis=1))[0][0] 63 | 64 | # Use the first 'phrase_length' steps as the primer 65 | sample_c = sample_c[first_non_zero: first_non_zero + phrase_length] 66 | 67 | # Binarize data (ignore velocity value) 68 | sample_c[sample_c > 0] = 1 69 | sample_c[sample_c <= 0] = -1 70 | 71 | sample_c = np.expand_dims(np.expand_dims(sample_c, 0), -1) # 1, 32, 128, 1 72 | 73 | sample_c = tf.convert_to_tensor(sample_c, dtype=tf.float32) 74 | return sample_c 75 | -------------------------------------------------------------------------------- /gan/utils/path_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | # this software and associated documentation files (the "Software"), to deal in 5 | # the Software without restriction, including without limitation the rights to 6 | # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | # the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | import os 17 | import glob 18 | 19 | def generated_midi_path_for_iteration(iteration, saveto_dir=None): 20 | return os.path.join(saveto_dir, 'iteration-{}.mid'.format(iteration)) 21 | 22 | def new_temp_midi_path(saveto_dir=None): 23 | mid_files = glob.glob(os.path.join(saveto_dir, 'temp-*.mid')) 24 | return os.path.join(saveto_dir, 'temp-%d.mid' % len(mid_files)) 25 | -------------------------------------------------------------------------------- /reinvent-labs/LICENSE: -------------------------------------------------------------------------------- 1 | Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | this software and associated documentation files (the "Software"), to deal in 5 | the Software without restriction, including without limitation the rights to 6 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | -------------------------------------------------------------------------------- /reinvent-labs/Music-terminologies.md: -------------------------------------------------------------------------------- 1 | ## Music Terminologies 2 | 3 | *__Pitch:__* how high or low a note is being perceived by our brain 4 | 5 | *__Duration:__* length of the sound 6 | 7 | *__Loudness:__* how ‘loud’ or ‘quiet’ the sound is 8 | 9 | *__Timbre:__* what makes sounds different from each other. It is what makes us differentiate from the same pitch played by a guitar versus a piano or your fellow programmer whistling Iron Maiden. 10 | 11 | 12 | ![piano](Lab%202/images/Piano.png) 13 | 14 | *__Note:__* a symbol denoting a musical sound. Represent the pitch and duration of a sound 15 | 16 | In western music, notes are represented with the first 7 letters (A, B, C, D, E, F and G) or in Europe by La, [t/s]i, Do, Re, Mi, Fa, Sol 17 | 18 | The eighth note, or octave, is given the same name as the first, but has double its frequency. 19 | 20 | To differentiate, we usually put a number after the letter 21 | E.g: C3 (middle C in the piano), C4 is one octave above (2x frequency of C3) 22 | 23 | 24 | 25 | -------------------------------------------------------------------------------- /reinvent-labs/README.md: -------------------------------------------------------------------------------- 1 | ## AWS DeepComposer 2 | 3 | Welcome to AWS DeepComposer workshop. Here are quick links to the lab. 4 | 5 | 6 | ## [Lab 1: Compose music with DeepComposer models](lab-1) 7 | 8 | ## [Lab 2: Train a custom GAN model](lab-2) 9 | 10 | ## Optional Reading 11 | 12 | ## [Loss Functions for GAN architecture](Loss-functions.md) 13 | 14 | ## [Music Terminologies Starter](Music-terminologies.md) 15 | 16 | ## [re:Invent DeepComposer workshop deck](reinvent-workshop-deck) 17 | 18 | 19 | ## License 20 | 21 | This library is licensed under the MIT-0 License. See the LICENSE file. -------------------------------------------------------------------------------- /reinvent-labs/lab-1/README.md: -------------------------------------------------------------------------------- 1 | ## Pre-requisites: (Read before you proceed) 2 | 1. Use Chrome Browser for this lab 3 | 4 | 5 | ## Access AWS DeepComposer console: 6 | 7 | Click on Deepcomposer link to get started: https://us-east-1.console.aws.amazon.com/deepcomposer 8 | 9 | ![access-console](images/lab1-access-console.png) 10 | 11 | Enter AWS account ID, IAM Username and Password provided 12 | 13 | Click Sign In 14 | 15 | **Note- You must access the console in N.Virginia (us-east-1) AWS region** You can use the dropdown to select the correct region. 16 | ![aws-region](images/aws-region.jpg) 17 | 18 | ## Get Started: 19 | ![music-studio](images/music-studio.jpg) 20 | 21 | Click **Music Studio** from the left navigation menu 22 | 23 | ![play-default](images/lab1-play-default.PNG) 24 | 25 | Click *play* to play the default input melody 26 | 27 | ![generate-composition](images/lab1-generate-composition.PNG) 28 | 29 | Click **Generate composition** to generate a composition. AI generated composition will be created 30 | 31 | Click *play* to play the new AI generated musical composition 32 | 33 | ## Input melody: 34 | 35 | ![record-composition](images/lab1-record.PNG) 36 | 37 | Click *record* to start recording 38 | 39 | ![custom-input](images/lab1-custom-music.png) 40 | 41 | Play the notes on the physical keyboard provided 42 | 43 | Stop recording by clicking the *record* button again 44 | 45 | Play the recorded music to verify the input. In case you don’t like recorded music, you may start recording again by clicking *record* 46 | 47 | ![model-jazz](images/lab1-model-default.PNG) 48 | 49 | Select **Jazz** model from *Model* 50 | 51 | Click **Generate Composition** to generate a composition based on the input melody you provided. *Note: This step will take few minutes to generate a composition inspired by the chosen genre* 52 | 53 | Click *play* to play the composition and enjoy the AI generated music 54 | 55 | Try experimenting with different genres or sample input melody 56 | 57 | **Congratulations! You have learnt how to use pre-trained models to generate new music** 58 | -------------------------------------------------------------------------------- /reinvent-labs/lab-1/images/aws-region.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/reinvent-labs/lab-1/images/aws-region.jpg -------------------------------------------------------------------------------- /reinvent-labs/lab-1/images/clone-models.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/reinvent-labs/lab-1/images/clone-models.jpg -------------------------------------------------------------------------------- /reinvent-labs/lab-1/images/create-model-button.jpg: 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/home/ec2-user/anaconda3/bin/deactivate 22 | EOF -------------------------------------------------------------------------------- /reinvent-labs/lab-2/README.md: -------------------------------------------------------------------------------- 1 | # Lab 2 - Train a custom GAN model 2 | 3 | ## Goal 4 | 5 | As part of this lab, you will learn to build a custom GAN architecture and train the model using Amazon SageMaker. 6 | 7 | 8 | 9 | ## Prerequisites 10 | 11 | * Access to Amazon SageMaker 12 | 13 | ## Cost 14 | 15 | Using a ml.c5.4xlarge, the entire exercise take 3-4 hrs to run. Please see the [Amazon SageMaker pricing](https://aws.amazon.com/sagemaker/pricing/) for details. 16 | 17 | ## Setup 18 | 19 | First we create the Amazon SageMaker notebook instance. 20 | 21 | Navigate to Amazon SageMaker using the link: https://console.aws.amazon.com/sagemaker/home?region=us-east-1#/dashboard 22 | 23 | ![notebook-instance](images/notebook-instance.PNG) 24 | 25 | Click **Notebook instances** from the left navigation bar 26 | 27 | Select **Create notebook instance** 28 | 29 | ![create-notebook](images/create_notebook.png) 30 | 31 | Within the notebook instance creation form, select "c5.4xlarge" for **Notebook instance type** 32 | 33 | ![notebook-instance-settings](images/notebook_instance_settings.png) 34 | 35 | Set the following for **Permissions and encryption**: 36 | * IAM role: Use an existing role or create a new role 37 | * Root access: Enable 38 | * Encryption key: No Custom Encryption 39 | 40 | ![notebook-instance-settings](images/permissions_and_encryption.png) 41 | 42 | Set the following for **Git repositories**: 43 | * Repository: Clone a public Git repository to this notebook instance only 44 | * Git repository URL: https://github.com/aws-samples/aws-deepcomposer-samples 45 | 46 | ![notebook-instance-settings](images/notebook_git_settings.png) 47 | 48 | Click **Open Jupyter** 49 | 50 | ![open-notebook](images/open_jupyter.png) 51 | 52 | 53 | 54 | Click **Lab 2** folder, then click **GAN.ipynb** 55 | 56 | ![GAN-notebook](images/gan_notebook.png) 57 | 58 | *You will likely be prompted to select kernel. Choose the drop down and select **conda_python3** as the kernel* 59 | 60 | ![set-kernel](images/set-kernel.PNG) 61 | 62 | This notebook contains instructions and code to create a custom GAN model from scratch. Follow the notebook content and run all cells to the end. 63 | 64 | ![run-notebook](images/run-notebook.PNG) 65 | 66 | To run the code cells, choose the code cell you want to run and click **Run** 67 | 68 | ![kernel-empty](images/kernel-empty.png) 69 | 70 | If the kernel has an empty circle, it means it is free and ready to execute the code 71 | 72 | ![kernel-busy](images/kernel-busy.png) 73 | 74 | If the kernel has a filled circle, it means it is busy. Wait for it to become free before you execute the next line of code. 75 | 76 | ## Next Steps 77 | 78 | **Congratulations on building a custom GAN model from scratch!** 79 | 80 | Now try using your model to create compositions based on your custom MIDI input. 81 | 82 | 83 | 84 | **Important: Remember to stop your Amazon SageMaker instances after you're done to avoid extra charges** 85 | 86 | 87 | 88 | ![notebook-stop](images/notebook-stop.png) -------------------------------------------------------------------------------- /reinvent-labs/lab-2/dataset/JSPChorales.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/reinvent-labs/lab-2/dataset/JSPChorales.h5 -------------------------------------------------------------------------------- /reinvent-labs/lab-2/dataset/eval.npy: 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All Rights Reserved. 2 | 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | # this software and associated documentation files (the "Software"), to deal in 5 | # the Software without restriction, including without limitation the rights to 6 | # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | # the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | import tensorflow as tf 17 | import numpy as np 18 | 19 | from utils import path_utils, midi_utils, display_utils 20 | 21 | # --- local samples------------------------------------------------------------------ 22 | 23 | def load_melody_samples(n_sample=10): 24 | """Load the samples used for evaluation.""" 25 | 26 | sample_source_path = './dataset/eval.npy' 27 | 28 | data = np.load(sample_source_path) 29 | data = np.asarray(data, dtype=np.float32) # {-1, 1} 30 | 31 | random_idx = np.random.choice(len(data), n_sample, replace=False) 32 | 33 | sample_x = data[random_idx] 34 | 35 | sample_z = tf.random.truncated_normal((n_sample, 2, 8, 512)) 36 | 37 | print("Loaded {} melody samples".format(len(sample_x))) 38 | 39 | return sample_x, sample_z 40 | 41 | # --- Training ------------------------------------------------------------------ 42 | 43 | def generate_pianoroll(generator, conditioned_track, noise_vector=None): 44 | if noise_vector == None: 45 | noise_vector = tf.random.truncated_normal((1, 2, 8, 512)) 46 | return generator((conditioned_track, noise_vector), training=False) 47 | 48 | 49 | def generate_midi(generator, saveto_dir, input_midi_file='./Experiments/data/happy_birthday_easy.mid'): 50 | conditioned_track = midi_utils.get_conditioned_track(midi=input_midi_file) 51 | generated_pianoroll = generate_pianoroll(generator, conditioned_track) 52 | 53 | destination_path = path_utils.new_temp_midi_path(saveto_dir=saveto_dir) 54 | midi_utils.save_pianoroll_as_midi(generated_pianoroll.numpy(), destination_path=destination_path) 55 | return destination_path 56 | 57 | def show_generated_pianorolls(generator, eval_dir, input_midi_file='./Experiments/data/happy_birthday_easy.mid', n_pr = 4): 58 | conditioned_track = midi_utils.get_conditioned_track(midi=input_midi_file) 59 | for i in range(n_pr): 60 | generated_pianoroll = generate_pianoroll(generator, conditioned_track) 61 | display_utils.show_pianoroll(generated_pianoroll) -------------------------------------------------------------------------------- /reinvent-labs/lab-2/utils/path_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | # Permission is hereby granted, free of charge, to any person obtaining a copy of 4 | # this software and associated documentation files (the "Software"), to deal in 5 | # the Software without restriction, including without limitation the rights to 6 | # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 7 | # the Software, and to permit persons to whom the Software is furnished to do so. 8 | 9 | # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 10 | # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 11 | # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 12 | # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 13 | # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 14 | # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | 16 | import os 17 | import glob 18 | 19 | def generated_midi_path_for_iteration(iteration, saveto_dir=None): 20 | return os.path.join(saveto_dir, 'iteration-{}.mid'.format(iteration)) 21 | 22 | def new_temp_midi_path(saveto_dir=None): 23 | mid_files = glob.glob(os.path.join(saveto_dir, 'temp-*.mid')) 24 | return os.path.join(saveto_dir, 'temp-%d.mid' % len(mid_files)) 25 | -------------------------------------------------------------------------------- /reinvent-labs/reinvent-workshop-deck/DeepComposer-reInvent19-Workshop.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/reinvent-labs/reinvent-workshop-deck/DeepComposer-reInvent19-Workshop.pdf -------------------------------------------------------------------------------- /reinvent-labs/reinvent-workshop-deck/readme.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /transformer-xl/images/attention_fig.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/attention_fig.png -------------------------------------------------------------------------------- /transformer-xl/images/dataloader.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/dataloader.jpg -------------------------------------------------------------------------------- /transformer-xl/images/decoder.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/decoder.png -------------------------------------------------------------------------------- /transformer-xl/images/event_rep.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/event_rep.jpg -------------------------------------------------------------------------------- /transformer-xl/images/nucleus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/nucleus.jpg -------------------------------------------------------------------------------- /transformer-xl/images/pianoroll.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/pianoroll.png -------------------------------------------------------------------------------- /transformer-xl/images/top-k.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/top-k.jpg -------------------------------------------------------------------------------- /transformer-xl/images/transformer.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/transformer.png -------------------------------------------------------------------------------- /transformer-xl/images/vanilla.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/vanilla.gif -------------------------------------------------------------------------------- /transformer-xl/images/xl.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/images/xl.gif -------------------------------------------------------------------------------- /transformer-xl/pretrained_checkpoints/exp.yaml: -------------------------------------------------------------------------------- 1 | # TrainConfig 2 | base_init: 3 | - normal 4 | - 0.01 5 | batch_size: 64 6 | clip: 1.0 7 | dim_inner: 1000 8 | dim_model: 500 9 | data_dir: data/jsb_chorales_numpy 10 | dropatt: 0.1 11 | dropout: 0.1 12 | embed_init: 13 | - normal 14 | - 0.01 15 | eval_batch_size: 2 16 | eval_interval: 500 17 | eval_mem_len: 512 18 | eval_tgt_len: 128 19 | log_interval: 100 20 | lr: 0.0005 21 | lr_min: 2.5e-05 22 | max_step: 100000 23 | mem_len: 512 24 | num_heads: 10 25 | num_layers: 4 26 | optim: adam 27 | plot_interval: 500 28 | plot_losses_while_training: false 29 | save_all_test_losses: true 30 | save_path: checkpoints/ 31 | scheduler: inv_sqrt 32 | seed: 101 33 | tgt_len: 128 34 | tie_embedding: true 35 | warmup_step: 4000 36 | weight_decay: 0.0 37 | -------------------------------------------------------------------------------- /transformer-xl/pretrained_checkpoints/losses.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-samples/aws-deepcomposer-samples/ef66010dc65d9a38b5c8fcb2e7fdf9cde01ee066/transformer-xl/pretrained_checkpoints/losses.pickle -------------------------------------------------------------------------------- /transformer-xl/requirements.txt: -------------------------------------------------------------------------------- 1 | pypianoroll==0.5.3 2 | music21==5.7.2 3 | texttable==1.6.2 4 | note_seq==0.0.2 5 | tqdm==4.42.1 6 | pretty_midi==0.2.9 7 | prettyprint==0.1.5 8 | torch==1.7.0 9 | autocfg==0.0.6 10 | -------------------------------------------------------------------------------- /transformer-xl/utils/midi_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import numpy as np 5 | from music21 import midi 6 | import pypianoroll 7 | from pypianoroll import Multitrack 8 | from texttable import Texttable 9 | import os 10 | from pprint import pprint 11 | 12 | def play_midi(input_midi): 13 | '''Takes path to an input and plays the midi file in the notebook cell 14 | :param input_midi: Path to midi file 15 | :return: 16 | ''' 17 | midi_object = midi.MidiFile() 18 | midi_object.open(input_midi) 19 | midi_object.read() 20 | midi_object.close() 21 | show_midi = midi.translate.midiFileToStream(midi_object) 22 | show_midi.show('midi') 23 | 24 | def find_files_by_extensions(root, exts=[]): 25 | def _has_ext(name): 26 | if not exts: 27 | return True 28 | name = name.lower() 29 | for ext in exts: 30 | if name.endswith(ext): 31 | return True 32 | return False 33 | 34 | for path, _, files in os.walk(root): 35 | for name in files: 36 | if _has_ext(name): 37 | yield os.path.join(path, name) 38 | 39 | def print_sample_array(split, parent_dir="data/jsb_chorales_numpy"): 40 | """ 41 | Prints a randomly sampled numpy array from the parent_dir 42 | """ 43 | 44 | midi_files = [ 45 | os.path.join(parent_dir, split, midi) 46 | for midi in os.listdir(os.path.join(parent_dir, split)) 47 | ] 48 | selection = np.random.choice(midi_files) 49 | pprint(np.load(selection)) 50 | -------------------------------------------------------------------------------- /transformer-xl/utils/utils.py: -------------------------------------------------------------------------------- 1 | # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | # SPDX-License-Identifier: Apache-2.0 3 | 4 | import numpy as np 5 | import os 6 | import torch 7 | from IPython import display 8 | import matplotlib.gridspec as gridspec 9 | from matplotlib import pyplot as plt 10 | import seaborn as sns 11 | 12 | def save_checkpoint( 13 | model, 14 | train_step, 15 | best_val_loss, 16 | save_path, 17 | name="checkpoint.pt", 18 | ): 19 | checkpoint = { 20 | "model": model.state_dict(), 21 | "train_step": train_step, 22 | "best_val_loss": best_val_loss, 23 | } 24 | 25 | print(f"Saving checkpoint to {save_path}") 26 | torch.save(checkpoint, os.path.join(save_path,name)) 27 | 28 | def plot_losses(train_losses, val_losses, test_losses): 29 | sns.set() 30 | display.clear_output(wait=True) 31 | fig = plt.figure(figsize=(15,5)) 32 | 33 | def plot_lines(loss_dic, color): 34 | iters = list(loss_dic.keys()) 35 | vals = [loss_dic[i] for i in iters] 36 | return plt.plot(iters, vals, color) 37 | 38 | line1, = plot_lines(train_losses, 'r') 39 | line2, = plot_lines(val_losses, 'k') 40 | line3, = plot_lines(test_losses, 'b') 41 | 42 | plt.xlabel('Iterations') 43 | plt.ylabel('Losses') 44 | plt.legend((line1, line2, line3), ('train-loss', 'val-loss', 'test-loss')) 45 | 46 | # return display.display(fig) 47 | 48 | 49 | --------------------------------------------------------------------------------