- Using https://ui.shadcn.com/docs we will make a UI.
- Using Spotify API connection will be made.
- Recommendation Model will be made before all of this: https://github.com/madhavthaker/spotify-recommendation-system
- Django + react will be the main framework.
Get the Data
Scrape or obtain a dataset of at least 20,000-50,000 music reviews, lyrics, genre descriptions etc.
Ensure this text data covers a wide range of music styles and opinions
Clean and preprocess the data to remove HTML, fix encoding issues etc.
Choose the Model
For starting out, use the 124M parameter GPT-2 model from OpenAI
Download from https://github.com/openai/gpt-2
Set Up Environment
Install Python 3.7+ and PyTorch
Install Hugging Face Transformers library
Set up on GPU instance if possible (Google Colab is free option)
Fine-Tune GPT-2
Use Hugging Face example scripts to fine-tune GPT-2 model
Load your music text dataset
Train on a subset first for 1-2 epochs to get a baseline
Define Prompts
Design prompts to provide model context on Spotify user's music data
Example: "Based on this user's playlists of [genre] music, here is my humorous review..."
Generate & Evaluate
Use model to generate funny reviews conditioned on prompts
Evaluate humor, coherence, music knowledge
Iterate by increasing epochs, model size, prompt tweaks
Integrate Spotify API
Study Spotify Web API docs to authenticate user
Fetch user's playlist/library data into your app
Build Front-End
Create website or app front-end where users login
Display their music data and feed it to model
Show generated funny review by your AI critic
Deploy & Expand
Deploy web app on service like Heroku
Optionally add text-to-speech output
Collect more data, try larger models like BLOOM
Keep Training
As you get more user music data, keep training the model
Techniques like reinforcement learning can improve humor
Start small with the 124M GPT-2, get the core working, then iterate on model size and capabilities. Consistency is key - keep pushing your training data quantity and diversity.