Qafar-af Speech Command Recognition: Recognizing keywords

This repository is demonstrates audio preprocessing and building a machine learning model for Qafaraf and Amharic speech command recognition. The audio files in the WAV format is organized in seven different folders for each command and each WAV audio file are 1 second long with 16khz sampling rate the following five voice command.

• "Qembis", (start)
• "Derreh", (back)
• "Gurra", (turn left)
• "Migda", (turn right)
• "Solis", (stop)
• "Noise"
• "unknown"

Qafar-af and Amharic Speech Command Recognition (QSCR)

In this notebook the speech command recognition model is build and trained using the collected Qafar-af audio dataset. The Qafar-af Audio Dataset is contain seven folders that have five keyword, and noise an unknown class organized in seven class the using Qafar-af audio dataset a

Multi-class audio classification involves categorizing audio samples into multiple predefined classes or categories. This can be accomplished using various machine learning and signal processing techniques. Here's a simplified step-by-step process:

1. Data Collection and Preprocessing Data Collection: Gather a diverse dataset containing audio samples for each class you want to classify. Preprocessing: Convert audio files into a standardized format (e.g., WAV, MP3) and extract features that represent the audio content effectively. Common features include Mel-Frequency Cepstral Coefficients (MFCCs), spectrograms, or other time-frequency representations.

2. Feature Extraction Extract Features: Utilize signal processing methods (like Fast Fourier Transform, Short-Time Fourier Transform, or other spectral analysis techniques) to extract numerical representations (features) from the audio files. These features should capture relevant information about the sound, such as its frequency content, amplitude, and temporal characteristics.

3. Model Selection and Training Model Selection: Choose an appropriate machine learning model or technique for classification. Common models include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or hybrid architectures. Training: Train the chosen model using the extracted features and corresponding class labels. This involves feeding the model with labeled data to learn the patterns and relationships between features and classes.

4. Model Evaluation Validation: Evaluate the trained model's performance on a separate dataset (validation set) to assess its accuracy, precision, recall, and other relevant metrics. This helps in understanding how well the model generalizes to new, unseen data. Fine-tuning: Adjust model hyperparameters or architecture based on validation results to improve performance.

5. Prediction and Deployment Prediction: Use the trained model to classify new audio samples into their respective classes. This involves feeding new audio data through the model and obtaining predictions for their classes. Deployment: Deploy the model in your desired application or system for real-world usage.

6. Tools and Libraries Librosa: Python library for audio analysis and feature extraction. TensorFlow or PyTorch: Deep learning frameworks commonly used for building neural network models for audio classification. Scikit-learn: Useful for traditional machine learning models and preprocessing techniques.

7. Challenges

   • Data Quality: Noisy or inconsistent audio data can affect model performance.
   • Class Imbalance: Unequal representation of classes might lead to biased models.

• Tips
  • Data Augmentation: Generate more training data by applying transformations to existing audio samples (e.g., adding noise, pitch shifting).
  • Transfer Learning: Utilize pre-trained models or features learned from similar audio datasets to boost performance.
  • Ensemble Methods: Combine predictions from multiple models for improved accuracy. Would you like further details on any specific step or technique involved in multi-class audio classification?

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