语音识别（五）：声音识别

MFCC,Mel Frequency Cepstrum Coefficient,梅尔频率倒谱系数
时间域-FFT->频率域->MFCC
带有时间离散分布特性的隐马尔科夫模型

# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
import warnings
import numpy as np
import scipy.io.wavfile as wf
import python_speech_features as sf
import hmmlearn.hmm as hl
warnings.filterwarnings('ignore',
                        category=DeprecationWarning)
np.seterr(all='ignore')


def search_speeches(directory, speeches):
    directory = os.path.normpath(directory)
    for entry in os.listdir(directory):
        label = directory[directory.rfind(os.path.sep) + 1:]
        path = os.path.join(directory, entry)
        if os.path.isdir(path):
            search_speeches(path, speeches)
        elif os.path.isfile(path) and path.endswith('.wav'):
            if label not in speeches:
                speeches[label] = []
            speeches[label].append(path)


train_speeches = {}
search_speeches('../../data/speeches/training',
                train_speeches)
train_x, train_y = [], []
for label, filenames in train_speeches.items():
    mfccs = np.array([])
    for filename in filenames:
        sample_rate, sigs = wf.read(filename)
        mfcc = sf.mfcc(sigs, sample_rate)
        if len(mfccs) == 0:
            mfccs = mfcc
        else:
            mfccs = np.append(mfccs, mfcc, axis=0)
    train_x.append(mfccs)
    train_y.append(label)
models = {}
for mfccs, label in zip(train_x, train_y):
    model = hl.GaussianHMM(n_components=4,
                           covariance_type='diag', n_iter=1000)
    models[label] = model.fit(mfccs)
test_speeches = {}
search_speeches('../../data/speeches/testing',
                test_speeches)
test_x, test_y = [], []
for label, filenames in test_speeches.items():
    mfccs = np.array([])
    for filename in filenames:
        sample_rate, sigs = wf.read(filename)
        mfcc = sf.mfcc(sigs, sample_rate)
        if len(mfccs) == 0:
            mfccs = mfcc
        else:
            mfccs = np.append(mfccs, mfcc, axis=0)
    test_x.append(mfccs)
    test_y.append(label)
pred_test_y = []
for mfccs in test_x:
    best_score, best_label = None, None
    for label, model in models.items():
        score = model.score(mfccs)
        if (best_score is None) or (best_score < score):
            best_score, best_label = score, label
    pred_test_y.append(best_label)
print(test_y)
print(pred_test_y)