Develop a Voice Deepfake AI Model

Objective: Design and implement a deep learning model capable of creating voice deepfakes. The model should learn from a provided training set and report accuracies on a test set.
Specifications:
Dataset:

Input: A set of paired audio samples (real voice and corresponding textual transcript).
Size: At least 50 hours of audio for training.
Format: WAV/MP3 with a sample rate of 16kHz.
Model Architecture:

Use a combination of sequence-to-sequence model for text-to-spectrogram conversion (e.g., Tacotron 2) and a neural vocoder for spectrogram-to-audio conversion (e.g., WaveNet or WaveGlow).
Training:

Preprocess the audio to extract Mel-spectrograms.
Use the Mel-spectrograms as input for the Tacotron 2 model and the textual transcript as the target.
Train the neural vocoder using the real Mel-spectrograms and corresponding audio.
Evaluation:

Split the dataset into training (80%), validation (10%), and test sets (10%).
Train the model on the training set, validate its performance on the validation set.
After training, test the model's accuracy on the test set using objective metrics like Mel Cepstral Distortion (MCD) or Perceptual Evaluation of Speech Quality (PESQ). Also, consider subjective evaluation techniques like Mean Opinion Score (MOS).
Post-Processing:

Implement Griffin-Lim or use a pretrained vocoder like WaveNet to convert generated Mel-spectrograms to audible waveforms.
Output:
The model should accept text as input and produce an audio file in WAV format as output.


if you have other ideas how to implement, you are welcome to suggest.
initial demo required. our side representative will use the code to train. tagged data.