sztuczna-empatia-kaczuszka/lab8/zad3/zad3.ipynb

!pip install librosa soundfile numpy sklearn pyaudio

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WARNING: Ignoring invalid distribution -andas (c:\users\user\anaconda3\lib\site-packages)
WARNING: Ignoring invalid distribution -andas (c:\users\user\anaconda3\lib\site-packages)

import librosa
import soundfile
import os, glob, pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score

#DataFlair - Extract features (mfcc, chroma, mel) from a sound file
def extract_feature(file_name, mfcc, chroma, mel):
    with soundfile.SoundFile(file_name) as sound_file:
        X = sound_file.read(dtype="float32")
        sample_rate=sound_file.samplerate
        if chroma:
            stft=np.abs(librosa.stft(X))
        result=np.array([])
        if mfcc:
            mfccs=np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
            result=np.hstack((result, mfccs))
        if chroma:
            chroma=np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
            result=np.hstack((result, chroma))
        if mel:
            mel=np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T,axis=0)
            result=np.hstack((result, mel))
    return result

#DataFlair - Emotions in the RAVDESS dataset
emotions={
  '01':'neutral',
  '02':'calm',
  '03':'happy',
  '04':'sad',
  '05':'angry',
  '06':'fearful',
  '07':'disgust',
  '08':'surprised'
}
#DataFlair - Emotions to observe
observed_emotions=['calm', 'happy', 'disgust']

#DataFlair - Load the data and extract features for each sound file

from pathlib import Path
path = str(Path.cwd())
def load_data(test_size=0.2):
    x,y=[],[]
    for file in glob.glob(f"{path}\data\Actor_*\*.wav"):
        file_name=os.path.basename(file)
        emotion=emotions[file_name.split("-")[2]]
        if emotion not in observed_emotions:
            continue
        feature=extract_feature(file, mfcc=True, chroma=True, mel=True)
        x.append(feature)
        y.append(emotion)
    return train_test_split(np.array(x), y, test_size=test_size, random_state=9)

#DataFlair - Split the dataset
x_train,x_test,y_train,y_test=load_data(test_size=0.25)

#DataFlair - Get the shape of the training and testing datasets
print((x_train.shape[0], x_test.shape[0]))

(432, 144)

#DataFlair - Get the number of features extracted
print(f'Features extracted: {x_train.shape[1]}')

Features extracted: 180

#DataFlair - Initialize the Multi Layer Perceptron Classifier
model=MLPClassifier(alpha=0.01, batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=500)

#DataFlair - Train the model
model.fit(x_train,y_train)

c:\Users\User\anaconda3\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
  warnings.warn(

MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),
              learning_rate='adaptive', max_iter=500)

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MLPClassifier

MLPClassifier(alpha=0.01, batch_size=256, hidden_layer_sizes=(300,),
              learning_rate='adaptive', max_iter=500)

#DataFlair - Predict for the test set
y_pred=model.predict(x_test)

#DataFlair - Calculate the accuracy of our model
accuracy=accuracy_score(y_true=y_test, y_pred=y_pred)
#DataFlair - Print the accuracy
print("Accuracy: {:.2f}%".format(accuracy*100))

Accuracy: 84.03%