classifier.py

# ----------------------------------------------------------------------------------- #
#                   Listening to audio files and working out on classifying
# ----------------------------------------------------------------------------------- #

import librosa
import numpy as np
import pandas as pd

# importing required modules for Classification
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

audio = "audio.wav"
y, sr = librosa.load(audio, mono=True, duration=1)

# features
chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
zcr = librosa.feature.zero_crossing_rate(y)
mfcc = librosa.feature.mfcc(y=y, sr=sr)

# loading up variables
chroma_stft = np.mean(chroma_stft)
spec_cent = np.mean(spec_cent)
spec_bw = np.mean(spec_bw)
rolloff = np.mean(rolloff)
zcr = np.mean(zcr)
mfcc = np.mean(mfcc)

# ----------------------------------------------------------------------------------- #
#                              Classification starts here
# ----------------------------------------------------------------------------------- #

dt = pd.read_csv("feature.csv")  # the dataset
# print(dt.head(0))
train, test = train_test_split(dt)

# print(train.shape)
# print(test.shape)

train_X = train[["chroma_stft", "spec_cent", "spec_bw", "rolloff", "zcr", "mfcc"]]
train_y = train.prognosis

test_X = test[["chroma_stft", "spec_cent", "spec_bw", "rolloff", "zcr", "mfcc"]]
test_y = test.prognosis

# knn
model = KNeighborsClassifier(n_neighbors=3)
model.fit(train_X, train_y)

val = np.array([chroma_stft, spec_cent, spec_bw, rolloff, zcr, mfcc])
val = val.reshape(1, -1)
prediction = model.predict(val)
print(prediction)