sounds.py

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

#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:
            mfcc=np.mean(librosa.feature.mfcc(y=X, sr=sample_rate,n_mfcc=40).T, axis=0)
        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(X, sr=sample_rate).T,axis=0)
            result=np.hstack((result, mel))
    
        return result


#Emotions in the RAVDESS dataset
emotions={
    '01':'neutral',
    '02':'calm',
    '03':'happy',
    '04':'sad',
    '05':'angry',
    '06':'fearful',
    '07':'disgust',
    '08':'surpried'
    }

#Emotions to observe
observed_emotions=['calm','happy','fearful','disgust']

#Load the data and extract features for each sound file
def load_data(test_size=0.2):
    x,y=[],[]
    for file in glob.glob("C:\\Users\\VC\\Downloads\\speech-emotion-recognition\\speech-emotion-recognition-ravdess-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)
    #prints all the features and emotions    
    print(x,y)    
    return train_test_split(np.array(x), y, test_size=test_size, random_state=9)
    

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

#Get the shape of the training and testing data sets
print((x_train.shape[0],x_test.shape[0]))

#Number of features extracted - count
print(f'Features extracted: {x_train.shape[1]}')

#Initializing multilayer Perceptron classifier - set the max iteration based on achieving the convergence point
model=MLPClassifier(alpha=0.01,batch_size=256, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive', max_iter=1000)

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

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

#Calculating accuracy of the model
accuracy=accuracy_score(y_true=y_test, y_pred=y_pred)

#Printing accuracy
print("Accuracy: {:.2f}%".format(accuracy*100))