Introduction
With the rise of AI-powered music recommendations, developing a Similar Bollywood Song Finder using a Large Language Model (LLM) can enhance user experiences in music streaming applications. This system leverages Natural Language Processing (NLP) and Machine Learning (ML) to find Bollywood songs with similar lyrics, themes, and emotions based on user input.
With the rise of AI-powered music recommendations, developing a Similar Bollywood Song Finder using a Large Language Model (LLM) can enhance user experiences in music streaming applications. This system leverages Natural Language Processing (NLP) and Machine Learning (ML) to find Bollywood songs with similar lyrics, themes, and emotions based on user input.
This blog outlines the steps to create an LLM-powered song recommendation system by integrating text-based and audio-based features.
1. Define the Problem Statement
Before diving into model development, clearly define your use case. The Similar Bollywood Song Finder should:
- Accept a song title or lyrics as input.
- Identify similar Bollywood songs based on lyrics, genre, and mood.
- Utilize an LLM for NLP-based similarity and an audio embedding model for feature extraction.
A hybrid approach combining textual (lyrics) and acoustic (audio features) analysis will improve accuracy.
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2️. Data Collection & Preprocessing
A. Gather a Large Dataset
To train your model, collect a diverse dataset of Bollywood songs with metadata, lyrics, and audio features.
Sources:
- Lyrics: BollywoodLyrics.com, Genius API, Kaggle datasets, Musixmatch API.
- Audio Features: Spotify API provides tempo, energy, key, and danceability.
- Genre & Mood Labels: Manual tagging or datasets like Bollywood song databases.
Example Dataset Schema:
| Song Title | Artist | Lyrics | Genre | Mood | Audio Features |
| Tum Hi Ho | Arijit Singh | Hum tere bin ab… | Romantic | Sad | {tempo: 80, key: A minor, energy: 0.5} |
B. Data Cleaning & Preprocessing
- Lyrics Cleaning: Remove special characters, convert to lowercase, and tokenize.
- Stopword Removal: Eliminate common words (e.g., ‘hai’, ‘ke’, ‘aur’).
- Lemmatization: Convert words to their base forms (e.g., ‘chalte’ → ‘chalna’).
- Audio Feature Normalization: Scale numerical audio features for consistency.
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Code Snippet (Text Preprocessing in Python)
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words(‘hindi’)) # Use Hindi stopwords
def clean_lyrics(lyrics):
lyrics = re.sub(r'[^a-zA-Z\s]’, ”, lyrics.lower()) # Remove punctuation
tokens = word_tokenize(lyrics)
tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words] return ‘ ‘.join(tokens)
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3. Choose the Model Architecture
A hybrid model combining LLM-based textual analysis and audio embeddings works best.
A. LLM for Lyrics Similarity
Use a pretrained LLM such as:
- GPT-4 / LLaMA / Falcon for semantic understanding.
- IndicBERT / MuRIL (for Hindi & Bollywood songs) for better linguistic relevance.
- SBERT (Sentence-BERT) for sentence embeddings.
B. Audio Feature Analysis Model
Use CNN (Convolutional Neural Networks) or Autoencoders for extracting audio embeddings.
- VGGish (Google’s model) or OpenL3 for deep learning audio embeddings.
- Spotify’s audio feature API for additional insights.
4️. Model Training & Fine-Tuning
A. Train LLM on Lyrics Similarity
Fine-tune an LLM using triplet loss-based contrastive learning to improve similarity search.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(‘all-MiniLM-L6-v2’) # Lightweight & efficient
lyrics_embedding = model.encode(“Hum tere bin ab jee nahi sakte”)
B. Extract Audio Features Using Deep Learning
Use VGGish for audio feature extraction.
import tensorflow as tf
import tensorflow_hub as hub
vggish = hub.load(“https://tfhub.dev/google/vggish/1”)
audio_embedding = vggish(tf.random.uniform([1, 16000])) # Example input
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C. Combine Features and Train the Model
- Concatenate lyrics embeddings + audio embeddings.
- Train a neural network for similarity scoring.
Neural Network Model (Fusion of Text & Audio Embeddings)
import torch
import torch.nn as nn
class SimilarBollywoodSongFinder(nn.Module):
def __init__(self, input_dim):
super(SimilarBollywoodSongFinder, self).__init__()
self.fc = nn.Sequential(
nn.Linear(input_dim, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 1)
)
def forward(self, x):
return self.fc(x)
Train the model using Triplet Loss or Cosine Similarity Loss.
import torch.nn.functional as F
def similarity_loss(embedding1, embedding2):
return 1 – F.cosine_similarity(embedding1, embedding2)
5️. Deploy the Model
Once trained, deploy the model as an API or Web App.
A. Create a FastAPI Backend
from fastapi import FastAPI
app = FastAPI()
@app.get(“/similar_bollywood_songs”)
def find_similar(song_name: str):
# Call LLM + Audio model to find similar Bollywood songs
return {“similar_songs”: [“Tum Mile”, “Raabta”]}
B. Deploy on Cloud
- Serverless: AWS Lambda, Google Cloud Run.
- Containerization: Docker + Kubernetes.
Conclusion
Building an LLM-powered Similar Bollywood Song Finder requires combining NLP-based lyrics embeddings with deep learning-based audio embeddings. By leveraging pretrained models like GPT, IndicBERT, and VGGish, developers can create a highly accurate Bollywood music recommendation system.
Next Steps: Implement and test the model on real-world Bollywood song data to improve recommendations!
Complete Code
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import tensorflow as tf
import tensorflow_hub as hub
import nltk # This line was already in your code
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI
# Download NLTK stopwords data
nltk.download(‘stopwords’) # Download the stopwords dataset
# Data Preprocessing
lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words(‘hindi’)) # Use Hindi stopwords
def clean_lyrics(lyrics):
lyrics = re.sub(r'[^a-zA-Z\s]’, ”, lyrics.lower()) # Remove punctuation
tokens = word_tokenize(lyrics)
tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
return ‘ ‘.join(tokens)
# Load Sentence Transformer Model for Lyrics Embeddings
model = SentenceTransformer(‘all-MiniLM-L6-v2’)
def get_lyrics_embedding(lyrics):
return model.encode(lyrics)
# Load VGGish Model for Audio Feature Extraction
vggish = hub.load(“https://tfhub.dev/google/vggish/1”)
def get_audio_embedding(audio_signal):
return vggish(audio_signal)
# Neural Network Model (Fusion of Text & Audio Embeddings)
class SimilarBollywoodSongFinder(nn.Module):
def __init__(self, input_dim):
super(SimilarBollywoodSongFinder, self).__init__()
self.fc = nn.Sequential(
nn.Linear(input_dim, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, 1)
)
def forward(self, x):
return self.fc(x)
# Loss Function
def similarity_loss(embedding1, embedding2):
return 1 – F.cosine_similarity(embedding1, embedding2)
# API Deployment with FastAPI
app = FastAPI()
@app.get(“/similar_bollywood_songs”)
def find_similar(song_name: str):
# Placeholder function for similarity search
return {“similar_songs”: [“Tum Mile”, “Raabta”]}
# Run the API (for local testing)
if __name__ == “__main__”:
import uvicorn
uvicorn.run(app, host=”0.0.0.0″, port=8000)
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