Ahmad Zahir AI Voice Project

In the rich musical history of Afghanistan, few voices have stirred the soul quite like Ahmad Zahir. Though his voice was silenced too soon, his legacy continues to inspire generations of music lovers across the world.

In this project, we explore an extraordinary intersection of tradition and technology — how artificial intelligence and machine learning have been used to faithfully recreate Ahmad Zahir’s voice, note by note, breath by breath.

From gathering rare recordings to training deep neural networks, this project isn't just about innovation — it's about memory, culture, and preserving a voice that once moved a nation.

Ahmad Zahir AI Voice project leverages a combination of advanced machine learning and signal processing techniques to create a robust and realistic voice synthesis system. Using TensorFlow, the core deep learning framework, the project trains a neural network model to generate human-like speech. The process starts with G2P (grapheme-to-phoneme) conversion, ensuring that the text is properly transcribed into phonemes for accurate pronunciation. Audio features are then extracted using Librosa, which provides a comprehensive suite of tools for analyzing and processing audio signals, while FFMpeg handles the conversion of audio formats to ensure compatibility with the model.

To refine the model and gain insights into the training process, TensorBoard is utilized for visualizing key metrics such as loss functions and training accuracy, allowing for effective debugging and optimization. Praat-Parselmouth helps with phonetic analysis, enabling fine-grained control over pitch, intensity, and speech rate to improve the naturalness of the generated voice.

The project also incorporates standard libraries like NumPy and Pandas for data manipulation and analysis, while Scikit-learn and SciPy are used for a variety of machine learning tasks, such as feature selection, optimization, and statistical analysis. Finally, Matplotlib is used for visualizing data, generating plots that provide clear insights into the performance of the model and its audio outputs.

By combining these cutting-edge tools, the project achieves a high-quality, customizable AI voice that can be adapted to various applications, from virtual assistants to voiceovers in media production.

Project Highlights:

  • Realistic Speech Synthesis: Through deep learning and data preprocessing, the AI voice generates clear, natural-sounding speech.

  • Speech Analysis & Optimization: Key audio features are fine-tuned with libraries like Librosa and Praat-Parselmouth to achieve better pitch control and speech patterns.

  • Visualization Tools: TensorBoard and Matplotlib provide detailed insights into model performance and training statistics.

Potential Applications:

  • Virtual Assistants for interactive, human-like responses.

  • Audio Books & Podcasts with dynamic and engaging voiceovers.

  • Accessibility Tools for visually impaired users, offering better auditory interaction with digital platforms.