app.py

import streamlit as st
import whisper
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def cosine_sim(text1, text2):
    vectorizer = TfidfVectorizer().fit_transform([text1, text2])
    vectors = vectorizer.toarray()
    return cosine_similarity(vectors)[0, 1]
    
model = whisper.load_model("base")
st.audio("titanic.mp3")
st.write("Listen to music since you have to record 15seconds after that")

audio_value = st.experimental_audio_input("Sing Rest of music:🎙️")
lyrics = "Far across the distance And spaces between us You have come to show you go on"

if audio_value:
    with open("user_sing.mp3", "wb") as f:
        f.write(audio_value.getbuffer())

    user_lyrics = model.transcribe("user_sing.mp3")["text"]
    st.write(user_lyrics)
    similarity_score = cosine_sim(lyrics, user_lyrics)
    if similarity_score > 0.85:
        st.success('Awsome! You are doing great', icon="✅")
    else:
        st.error('Awful! Try harder next time', icon="🚨")