Speaker Identification using GMM on MFCC

main.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import glob
import librosa
import numpy as np
import os
import sklearn.mixture
import sys

def load(audio_path):
    y, sr = librosa.load(audio_path)
    y_trim = librosa.effects.remix(y, intervals=librosa.effects.split(y))
    mfcc = librosa.feature.mfcc(y=y_trim, sr=sr)
    return mfcc.T

def fit(frames, test_ratio=0.05, n_components=16):
    index = np.arange(len(frames))
    np.random.shuffle(index)

    train_idx = index[int(len(index) * test_ratio):]
    test_idx = index[:int(len(index) * test_ratio)]

    gmm = sklearn.mixture.GaussianMixture(n_components=n_components)
    gmm.fit(frames[train_idx])

    return gmm, frames[test_idx]

def predict(gmms, test_frame):
    scores = []
    for gmm_name, gmm in gmms.items():
        scores.append((gmm_name, gmm.score(test_frame)))
    return sorted(scores, key=lambda x: x[1], reverse=True)

def evaluate(gmms, test_frames):
    correct = 0

    for name in test_frames:
        best_name, best_score = predict(gmms, test_frames[name])[0]
        print 'Ground Truth: %s, Predicted: %s, Score: %f' % (name, best_name, best_score)
        if name == best_name:
            correct += 1

    print 'Overall Accuracy: %f%%' % (float(correct) / len(test_frames))

if __name__ == '__main__':
    gmms, test_frames = {}, {}

    for filename in glob.glob(os.path.join(sys.argv[1], '*.wav')):
        name = os.path.splitext(os.path.basename(filename))[0]
        print 'Processing %s ...' % name
        gmms[name], test_frames[name] = fit(load(filename))

    evaluate(gmms, test_frames)

    for filename in glob.glob(os.path.join(sys.argv[2], '*.wav')):
        result = predict(gmms, load(filename))
        print '%s: %s' % (os.path.basename(filename), ' / '.join(map(lambda x: '%s = %f' % x, result[:5])))

You might try to train something simpler to identify issues in your code. For example, training with only 2 examples, and debugging the data as it navigates through the training module. Most errors are usually in between. After you manage to overfit that toy experiment, you should start increasing the data with such datasets.