This document outlines the steps to create an AI voice bot that converts text input into audio output using a friend's voice. This project involves data collection, preprocessing, model training, and deployment. The goal is to achieve a natural and accurate replication of your friend's voice.

• Ensure Permission: Obtain explicit permission from your friend to use their voice data for this project.

• Equipment: Use a high-quality microphone in a quiet environment.
• Content: Record at least 1-2 hours of varied and clear speech to capture a wide range of phonetic contexts.

• Manual Transcription: Transcribe the audio manually for accuracy.
• Speech-to-Text Tools: Optionally, use speech-to-text services to assist with the transcription.

• Tools: Use tools like Praat to mark the start and end of each sentence or phrase.
• Segmentation: Save each segment as a separate audio file.

• Libraries: Use Python libraries such as pydub or librosa to normalize the audio files.

• Popular Choices:
  • Tacotron 2: Synthesizes natural-sounding speech.
  • WaveGlow or WaveNet: Converts the synthesized speech into waveform audio.

• Setup: Use a machine with a GPU for faster training. Install necessary libraries such as TensorFlow or PyTorch.

• Download Models: Obtain Tacotron 2 and WaveGlow from NVIDIA’s GitHub repositories.
• Organize Dataset: Ensure the audio and transcription files are in the required format.

• Adjustments: Modify hyperparameters and continue training based on initial results to improve accuracy.

• Testing: Provide text inputs to the trained model and generate audio outputs. Evaluate the quality and make necessary adjustments.

• Web Interface: Create a simple web application using Flask to input text and get audio output.

• Load and Use Model: Load the trained model and generate audio responses in the endpoint.

• Data Collection and Preprocessing:

  • Audacity: For recording and editing audio.
  • Praat: For audio segmentation.
  • Python Libraries: pydub, librosa.

• Model Training:

  • Frameworks: TensorFlow or PyTorch.
  • Models: NVIDIA’s Tacotron 2 and WaveGlow implementations.

• Deployment:

  • Web Applications: Flask or Django.
  • Desktop Applications: PyInstaller or Electron.

Following these steps will enable you to create a custom AI voice bot that accurately replicates your friend's voice. The process involves detailed technical steps, leveraging your engineering expertise to handle the complexities of data preprocessing, model training, and deployment.