Classifier training long time due to the size data

Question

I have a problem to train my classifier.

I have 10 different kinds of music genres, each genre with 100 songs, after making an Mfccs I have a numpy array of (1293, 20)

If all together with np.vstack I have an array of (1293000, 20) and another for the labels.

When I run model.fit (features, labels), it takes a lot of time.

I have also tried with:

from sklearn.manifold import TSNE
X_embedded = TSNE (n_components = 2).fit_transform(features)
X_embedded.shape

I've tried to reduce the songs from 1000 to 100 but it's still taking a long time.

Any idea how I can classify songs with arrays with so much data?

I put some code:

scaler = sklearn.preprocessing.StandardScaler()
y, sr = librosa.load('EXAMPLE1')
mfcc = librosa.feature.mfcc(y, sr=sr, n_mfcc=20).T
mfcc_scaled = scaler.fit_transform(mfcc)
mfcc_scaled.shape # (1293, 20)

y, sr = librosa.load('/Users/josetorronteras/AnacondaProjects/Neural-Networks/genres/pop/pop.00044.au')
mfcc2 = librosa.feature.mfcc(y, sr=sr, n_mfcc=20).T
mfcc_scaled2 = scaler.fit_transform(mfcc2)
mfcc_scaled2.shape # (1293, 20)

tmp_arr = []
tmp_arr.append(mfcc_scaled)
tmp_arr.append(mfcc_scaled2)
mafcc_list = np.vstack(tmp_arr)

mafcc_list.shape # (2586, 20)
a0 = np.zeros(len(mfcc_scaled))
a1 = np.ones(len(mfcc_scaled2))

labels = np.concatenate((a0, a1))
labels.shape # (2586,)

Thanks