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‎.github/workflows/main.yaml‎

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# workflow name.
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name: Deploy KWS web app to Heroku.
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# Run workflow on every push to main branch.
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on:
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push:
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branches: [main]
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# Workflows jobs.
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jobs:
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build:
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runs-on: ubuntu-latest
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steps:
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# Check-out your repository.
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- name: Checkout
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uses: actions/checkout@v2
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- name: Build, Push and Release the KWS app as Docker container to Heroku.
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uses: gonuit/heroku-docker-deploy@v1.3.3 # GitHub action name (leave it as it is).
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with:
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email: ${{ secrets.HEROKU_EMAIL }}
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heroku_api_key: ${{ secrets.HEROKU_API_KEY }}
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heroku_app_name: ${{ secrets.HEROKU_APP_NAME }}
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# (Optional, default: "./")
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# Dockerfile directory
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dockerfile_directory: ./
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# (Optional, default: "Dockerfile")
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# Dockerfile name
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dockerfile_name: Dockerfile
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# (Optional, default: "")
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# Additional options of docker build command.
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docker_options: "--no-cache"
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# (Optional, default: "web")
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# Select the process type for which you want the docker container to be uploaded.
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# By default, this argument is set to "web".
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# For more information look at https://devcenter.heroku.com/articles/process-model
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process_type: web

‎.gitignore‎

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__pycache__/

‎Dockerfile‎

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FROM python:3.8
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COPY . /app
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WORKDIR /app
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RUN pip install -r requirements.txt
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EXPOSE $PORT
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CMD gunicorn --workers=5 --bind 0.0.0.0:$PORT app:app

‎README.md‎

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‎app.py‎

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#!/usr/bin/env python3
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"""
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@author: Jithin Sasikumar
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Script to create a web application that wraps the trained model to be used for inference using `FLASK API`.
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It facilitates the application to run from a server which defines every routes and functions to perform.
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The front-end is designed using `./templates/page.html` and its styles in `./static/page.css`
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Note:
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Make sure to define all the variables and valid paths in `.config_dir/config.yaml` to run this script
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without errors and issues.
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"""
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from kws_spotter import SpeechRecognition
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from omegaconf import OmegaConf
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from src.data import check_fileType
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from src.exception_handler import NotFoundError
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from flask import Flask, render_template, request, redirect, flash, abort
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app = Flask(__name__)
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app.config["SECRET_KEY"] = "MyKWSAppSecretKey"
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cfg = OmegaConf.load('./config_dir/config.yaml')
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@app.route('/')
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def home():
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"""
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Returns the result of calling render_template() with page.html
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"""
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return render_template('page.html')
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@app.route("/transcribe", methods = ["POST"])
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def transcribe():
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"""
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Returns the prediction from trained model artifact whenever transcribe route is called.
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It accepts file input (.wav) whenever user uploads the file, and make prediction using it.
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The `app.route()` decorator does the job of event handling by means of `jinja2` template
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engine.
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Raises
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------
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NotFoundError: Exception
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404 error, if any exception occurs.
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"""
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recognized_keyword = ""
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redirect(request.url)
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if request.method == "POST":
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audio_file = request.files["file"]
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if audio_file.filename == "":
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flash("File not found !!!", category="error")
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return render_template("page.html")
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elif not check_fileType(filename=audio_file.filename, extension=".wav"):
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flash(f"Unsupported file format. Please use only .wav files", category="error")
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return render_template("page.html")
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else:
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try:
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recognizer = SpeechRecognition(audio_file,
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cfg.paths.model_artifactory_dir,
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cfg.params.n_mfcc,
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cfg.params.mfcc_length,
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cfg.params.sampling_rate)
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recognized_keyword, label_probability = recognizer.predict()
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except NotFoundError:
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abort(404, description = "Sorry, something went wrong. Cannot predict from the model. Please try again !!!")
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return render_template(
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"page.html",
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recognized_keyword = f"Transcribed keyword: {recognized_keyword.title()}",
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label_probability = f"Predicted probability: {label_probability}"
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)
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if __name__ == "__main__":
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app.run(debug=False)

‎artifacts/2/asr_model_1.3/meta.yaml‎

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artifact_uri: file:/./artifacts/2/asr_model_1.3/model_artifacts
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end_time: 1659924195701
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entry_point_name: ''
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experiment_id: '2'
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lifecycle_stage: active
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name: ''
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run_name: asr_model_1.3
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run_id: 95d1e7e807f94e5aaacf87d19a193437
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run_uuid: 95d1e7e807f94e5aaacf87d19a193437
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source_name: ''
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source_type: 4
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source_version: ''
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start_time: 1659922818963
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status: 3
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tags: []
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user_id: Jithin Sasikumar

‎artifacts/2/asr_model_1.3/metrics/accuracy‎

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‎artifacts/2/asr_model_1.3/metrics/loss‎

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