MNIST-Audio-Digit-Classifier

This project utilizes deep learning techniques for the classification of the free-spoken-digit-dataset, offering an audio-oriented counterpart to the traditional MNIST dataset.

Getting Started

To run this project, ensure that you have Python 3 installed on your system. Follow these steps:

1. Create a Python environment using the following commands:

   conda create -n audio python=3
   activate audio

2. Install the necessary libraries listed in the requirements.txt file:

   pip install -r requirements.txt

3. Clone this repository and navigate to the root folder.

4. Run the main.py file:

   python src/main.py

Enhanced Workflow

When you execute the main.py file, it will perform the following workflow:

1. Folder Preparation:

   • Create the necessary folders for organizing data, models, and reports:

     • data/processed: Processed data will be stored here.

     • data/production_data: Random audio samples for production testing will be placed here.

     • models: Trained models will be saved in this folder.

     • reports: Graphical representations and evaluation metrics will be stored here.

       

2. Data Arrangement:

   • Arrange raw data into folders by digits and save them into the data/processed_data folder.

     

3. Production Testing Data Selection:

   • Select random audio samples from each digit to create a production testing dataset. These samples will be stored in the data/production_data folder.

     

4. Graphical Representations:

   • Take random audio files, plot graphical representations, and save the figures into the reports folder.

     

5. Feature Extraction:

   • Extract log mel spectrograms from the processed data to construct the dataset for training and evaluation.

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6. Data Splitting:

   • Split the audio features into train, validation, and test sets to prepare for model training.

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7. Model Training:

   • Train the models using the prepared dataset.

8. Performance Assessment:

   • Assess the models' performances using metrics such as Accuracy, Precision, Recall, etc.

   • Save figures such as Confusion Matrix, AUC ROC, loss and accuracy curves into the reports folder.

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9. Model Saving:

   • Save the trained models in the models folder for future use during production.

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In addition to images, refer to console logs for a clear understanding of the program's evolution.

Testing the App in Production

Let's try the app in production by following these steps:

1. Run the Application:

   • Execute the app.py file to launch the application.

2. Access the Application:

   • Open your web browser and go to http://127.0.0.1:5000.

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3. Choose Model for Prediction:

   • Select the desired model for prediction from the available options:
     • CNN Model: http://127.0.0.1:5000/predict_using_cnn/
     • Conv1D Model: http://127.0.0.1:5000/predict_using_conv1d/
     • LSTM Model: http://127.0.0.1:5000/predict_using_lstm/
     • Hybrid Model: http://127.0.0.1:5000/predict_using_hybrid/

4. Audio File Prediction:

   • Choose an audio file from the data/production_data folder.

5. Insert Parameter:

   • Insert the selected file's name as a parameter in the prediction route URL.

6. Run Prediction:

   • Execute the link to observe the prediction output.

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References

1. Audio-Classification with Seth Adams: Seth Adams' repository on Audio-Classification, offering valuable insights and code that contributed to the development of this project.

2. Deep Learning (Audio) Application: From Design to Deployment: A video tutorial providing a comprehensive overview of designing and deploying deep learning applications for audio, influencing the development of this project.

3. Deep Learning for Audio Classification: A series of videos covering deep learning techniques specifically tailored for audio classification, serving as a valuable resource during the project.

Feel free to explore these references for deeper insights and guidance on audio classification, deep learning, and related topics.

Happy coding!