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Added automated release workflow for all commit tags
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.github/workflows/release.yaml

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# Name of the workflow
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name: Release
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# Run on every commit tag which begins with "v" (e.g., "v1.0.0")
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# Run on every commit tag which begins with `v - version number`
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# (e.g., "v1.0.0")
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on:
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push:
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tags:

README.md

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# Deploying an end-to-end keyword spotting model into cloud server using Flask and Docker with CI/CD pipeline
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This project promulgates a `pipeline` that `trains` end-to-end keyword spotting models using input audio files, `tracks` experiments by logging the model artifacts, parameters and metrics, `build` them as a web application followed by `dockerizing` them into a container and deploys the application containing trained model artifacts as a docker container into the cloud server with `CI/CD` integration and releases.
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This project promulgates a `pipeline` that `trains` end-to-end keyword spotting models using input audio files, `tracks` experiments by logging the model artifacts, parameters and metrics, `build` them as a web application followed by `dockerizing` them into a container and deploys the application containing trained model artifacts as a docker container into the cloud server with `CI/CD` integration and automated releases.
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## Author
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## Description
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The project is a concoction of `research` (audio signal processing, keyword spotting, ASR), `development` (audio data processing, deep neural network training, evaluation) and `deployment` (building model artifacts, web app development, docker, cloud PaaS) with integrating `CI/CD` pipelines.
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The project is a concoction of `research` (audio signal processing, keyword spotting, ASR), `development` (audio data processing, deep neural network training, evaluation) and `deployment` (building model artifacts, web app development, docker, cloud PaaS) with integrating `CI/CD` pipelines and automated releases.
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| ![flowchart](./images/KWS_flowchart_main.JPG) |
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