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])))