FYP Ideas
AI-Based Image Generation for Social Media Content
• Overview: This web app would let users create custom images from text prompts.
Imagine you type "sunset at the beach with palm trees" into a prompt box, and the app
generates a beautiful image based on your description. Users could adjust styles like
making it look like a painting, a comic, or even a realistic photo. This tool would be
especially helpful for social media managers, marketers, and content creators who
want to generate unique visuals quickly.
• Key Features:
o Text-to-Image Generation: Users enter a description, and the app creates an
image based on it.
o Style Options: Users can select styles like "cartoon," "oil painting," or
"minimalistic" to apply to the image.
o Customization Controls: Adjust color themes or add filters to the generated
images.
o Download & Share: Users can download or directly share their creations on
social media.
• Technologies Involved:
o Generative Adversarial Networks (GANs): GANs are popular for creating
high-quality images. They work by having two neural networks—one
generates images, while the other tries to judge if they're real or fake. The
generator keeps learning and improving to make the images more realistic.
o Text-to-Image Models (e.g., Stable Diffusion, DALL-E): These models are
trained on large amounts of text and image pairs, allowing them to create
images from text descriptions. You could use an open-source model like
Stable Diffusion, which allows easy customization for different styles and
themes.
o Prompt Engineering: Fine-tuning the language input to get desired image
outputs. This involves designing the prompts in a way that generates more
relevant and refined images.
o Backend & Frontend: You could use Python frameworks like Flask or
Django for the backend, where the model runs, and React or Vue.js for the
frontend, where users interact with the web app.
o Cloud Services (e.g., AWS, Google Cloud): For hosting the app and
handling image processing tasks, as these models can be resource-intensive.
• How It Works:
o Users enter a description of what they want in the image.
o The text-to-image model processes the prompt to understand the elements
(like objects, colors, and mood).
o GANs and image processing layers generate the image based on the prompt.
o Users can then adjust styles and download the final image.
. Realistic Voice Cloning for Language Learning
• Overview: This app would help people who are learning new languages to hear
realistic pronunciations. Imagine typing a phrase like "Good morning" in the app,
choosing a specific accent or voice style, and hearing it spoken in a real-sounding
voice. This would be especially helpful for language learners who want to practice
with native accents or tones.
• Key Features:
o Text-to-Speech (TTS) with Cloned Voices: The app converts typed phrases
into speech using a cloned voice. Users can select the gender, tone, or accent
of the voice.
o Accent and Tone Customization: Choose accents (British, American, etc.)
and tones (formal, friendly, etc.) to match real-life speaking styles.
o Language Selection: Support multiple languages so users can practice
different ones.
o Audio Playback and Download: Listen to the generated speech or download
it for offline practice.
• Technologies Involved:
o Transformer-Based Models (Tacotron, WaveNet): Tacotron is a text-to-
speech model that turns text into sounds, while WaveNet is a deep neural
network for producing more natural-sounding audio. Together, they create
speech that sounds highly realistic.
o Voice Cloning Models: Voice cloning uses machine learning to create a
digital voice that sounds like a specific person. For example, if a model is
trained on someone’s voice, it can reproduce that voice saying any text you
provide.
o Speech Processing Libraries (like Librosa): Libraries like Librosa help
process audio signals, such as adjusting pitch or speed, to enhance the user’s
control over how the speech sounds.
o Frontend and Backend: Similar to the image generation app, you could use
Flask/Django for the backend to process TTS tasks and a frontend framework
like React for user interactions.
o Cloud or Edge Services: To handle large audio processing needs, services
like Azure Speech or AWS Polly can provide scalable, pre-trained TTS
models as well.
• How It Works:
o Users type a sentence in the app and choose the voice style they prefer.
o The app converts the text to speech by passing it through a TTS model.
o The chosen voice parameters are applied, like accent or pitch adjustments.
o The generated audio is played back to the user, helping them practice
pronunciation.
Both of these projects would provide practical, valuable skills in generative AI, enhancing
your portfolio and potentially drawing employer attention for their real-world applications.
Let me know if you need more details on any specific part!
