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app.py
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app.py
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from flask import Flask, request, render_template
import os
import numpy as np
from sklearn.preprocessing import StandardScaler
import pickle
import librosa
app = Flask(__name__)
# Load the saved model, encoder, and scaler using pickle
with open('model.pkl', 'rb') as f:
model = pickle.load(f)
with open('encoder.pkl', 'rb') as f:
encoder = pickle.load(f)
with open('scaler.pkl', 'rb') as f:
scaler = pickle.load(f)
def extract_features(file_path):
#here it loads a file and then return the audio time series=y and sampling rate=sr
y, sr = librosa.load(file_path, mono=True, duration=60)
#chroma short time fourier transform for knwoing the pitch classes
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
#root mean square it indicates the energy/amplitude of audio signal
rms = librosa.feature.rms(y=y)
#represents central of mass and brightness of sound
spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
#width of spectral band
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
#represents frequency below which specified percentage of total spectral energy lies
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
#Zero Crossing rate represents the rate at which the audio signal chnages its sign(measures how noisy data is)
zcr = librosa.feature.zero_crossing_rate(y)
#mel-frequency ceptral coefficients represents short term power spectrum of sound
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
#information about the spectral shape of the audio signal.
features = [np.mean(chroma_stft), np.mean(rms), np.mean(spec_cent), np.mean(spec_bw), np.mean(rolloff), np.mean(zcr)]
for e in mfcc:
features.append(np.mean(e))
return features
def predict_tal(file_path):
features = extract_features(file_path)
# predict the tabla tal
prediction = model.predict(scaler.transform([features])) # Standardize the feature array before prediction
# Assuming your model outputs probabilities for different tabla tals
predicted_tal_index = np.argmax(prediction)
tabla_tals = ['addhatrital', 'trital', 'ektal','rupak','jhaptal','bhajani','dadra','deepchandi']
predicted_tal = tabla_tals[predicted_tal_index]
return predicted_tal
# Define the endpoint for the home page
@app.route('/')
def home():
return render_template('index.html')
# Define the endpoint for the prediction API
@app.route('/predict', methods=['POST'])
def predict():
file = request.files['audio']
filename = file.filename
file_path = os.path.join('static', 'uploads', filename)
file.save(file_path)
predicted_tal = predict_tal(file_path)
return render_template('result.html', tal=predicted_tal)
if __name__ == '__main__':
app.run(debug=True)