目的：識別三個單詞（bed，cat，happy）

import librosa
import os
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
import numpy as np
from tqdm import tqdm

二、定義所需函式

def get_labels(path=DATA_PATH):
    labels = os.listdir(path)
    label_indices = np.arange(0, len(labels))
    return
 
 labels, label_indices, to_categorical(label_indices)


def wav2mfcc(file_path):
    wave, sr = librosa.load(file_path, mono=True, sr=None)

    mfcc = librosa.feature.mfcc(wave, sr=16000)

    return mfcc


def save_data_to_array(path=DATA_PATH):
    labels, _, _ = get_labels(path)

    for label in labels:

        mfcc_vectors = []

        wavfiles = [path + label + '/'
 
 + wavfile for wavfile in os.listdir(path + '/' + label)]
        for wavfile in tqdm(wavfiles, "Saving vectors of label - '{}'".format(label)):
            mfcc = wav2mfcc(wavfile)
            mfcc_vectors.append(mfcc)
        np.save(label + '.npy', mfcc_vectors)


def get_train_test(split_ratio=0.6
 
, random_state=42):

    labels, indices, _ = get_labels(DATA_PATH)


    X = np.load(labels[0] + '.npy')
    y = np.zeros(X.shape[0])


    for i, label in enumerate(labels[1:]):
        x = np.load(label + '.npy')
        X = np.vstack((X, x))
        y = np.append(y, np.full(x.shape[0], fill_value= (i + 1)))

    assert X.shape[0] == len(y)

    return train_test_split(X, y, test_size= (1 - split_ratio), random_state=random_state, shuffle=True)



def prepare_dataset(path=DATA_PATH):
    labels, _, _ = get_labels(path)
    data = {}
    for label in labels:
        data[label] = {}
        data[label]['path'] = [path  + label + '/' + wavfile for wavfile in os.listdir(path + '/' + label)]

        vectors = []

        for wavfile in data[label]['path']:
            wave, sr = librosa.load(wavfile, mono=True, sr=None)


            mfcc = librosa.feature.mfcc(wave, sr=16000)
            vectors.append(mfcc)

        data[label]['mfcc'] = vectors

    return data


def load_dataset(path=DATA_PATH):
    data = prepare_dataset(path)

    dataset = []

    for key in data:
        for mfcc in data[key]['mfcc']:
            dataset.append((key, mfcc))

    return dataset[:100]

三、定義模型

def get_model():
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', input_shape=(feature_dim_1, feature_dim_2, channel)))
    model.add(Conv2D(48, kernel_size=(2, 2), activation='relu'))
    model.add(Conv2D(120, kernel_size=(2, 2), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.25))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.4))
    model.add(Dense(num_classes, activation='softmax'))
    model.compile(loss=keras.losses.categorical_crossentropy,
                  optimizer=keras.optimizers.Adadelta(),
                  metrics=['accuracy'])
    return model

四、填入資料

%load_ext autoreload
#自動載入模組
%autoreload 2
#%aimport每次執行鍵入的Python程式碼之前，每次重新載入所有模組（排除的除外）。

from preprocess import *
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.utils import to_categorical


feature_dim_2 = 32


save_data_to_array(max_len=feature_dim_2)


X_train, X_test, y_train, y_test = get_train_test()


feature_dim_1 = 20
channel = 1
epochs = 50
batch_size = 100
verbose = 1
num_classes = 3


X_train = X_train.reshape(X_train.shape[0], feature_dim_1, feature_dim_2, channel)
X_test = X_test.reshape(X_test.shape[0], feature_dim_1, feature_dim_2, channel)

y_train_hot = to_categorical(y_train)
y_test_hot = to_categorical(y_test)

五、評價模型

model = get_model()
model.fit(X_train, y_train_hot, batch_size=batch_size, epochs=epochs, verbose=verbose, validation_data=(X_test, y_test_hot))

模型準確率在0.95
大家可以親自嘗試一下。