Asthitwa offers advanced voice replication to mimic specific voices accurately, ensuring natural, comforting interactions. It excels in emotional support with pre-programmed messages and personalized NLP responses. Users can customize by uploading voice samples. Privacy and security are prioritized with secure storage and data control.
Requirements
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Voice Capture:
- Implement a function to record voice samples from the user. Ensure the recording is of high quality and sufficient length to capture the nuances of the user's voice.
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Voice Model Training:
- Use a machine learning framework (like TensorFlow or PyTorch) to train a voice synthesis model on the captured voice samples. Consider using existing voice synthesis architectures like Tacotron or WaveNet.
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User Confirmation:
- Once the initial model is trained, synthesize a sample voice clip using the model. Present this clip to the user for confirmation.
- If the user approves, proceed to save the model. If not, adjust the training parameters or request more voice samples and retrain.
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Model Saving:
- Save the trained model securely, ensuring that only the authenticated user can access it.
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Voice Output:
- Integrate the trained model into the voice assistant system. Ensure that the assistant uses the trained voice model to respond to user queries.
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User Interface:
- Develop a simple user interface that allows the user to record their voice, provide feedback on the synthesized voice, and interact with the voice assistant.
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Security and Privacy:
- Ensure that all voice data and user interactions are handled securely. Implement appropriate data protection and privacy measures.
Demo link https://drive.google.com/file/d/1SeQOip11CeN8FftVThnc8lbTvNjsP2mM/view?usp=sharing