The repo contains code for deploying a solution on AWS. We will be using Fast API for building the app.
- fastapi==0.63.0
- librosa==0.8.0
- matplotlib==3.1.2
- numpy==1.19.5
- Pillow==8.1.0
- pypesq==1.2.4
- pystoi==0.3.3
- starlette==0.13.6
- torch==1.7.1
- torchaudio==0.7.2
- tqdm==4.47.0
- uvicorn==0.13.3
- Werkzeug==1.0.1
The files and folders in this project and their usage is as follows.
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models: Contains pytorch model architecture used in this project. These models are loaded and updated using the saved checkpoints to run inference.
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sound_samples: Containes samples to run inference.
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static: Contains static files, css files, images to display and js files.
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templates: Contains templates for web app.
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uploads: Folder to store uploaded files.
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utils: Utility files for various supporting functions. Contains files for creating spectrograms, parameters for inference, data file to pre and post process audio files.
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app.py app file
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main.py File containing all of the functionality in the app. It imports certain other files to run properly.
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Inference.py Script to run inference o the audio clips.
To run the app locally run the following
git clone git@github.com:speakupai/ml_deployment.git
cd ml_deployment
$ python app.py
The app should look like this