基于keras的简易语音识别

简介

最近忽然看到不是基于kaldi的ASR代码，尝试了一下发现效果还不错，搬上来记录一下。

源码地址：

https://pan.baidu.com/s/1tFlZkMJmrMTD05cd_zxmAg

提取码：ndrr

数据集需要自行下载。

数据集

数据集使用的是清华大学的thchs30中文数据，data文件夹中包含（.wav文件和.trn文件；trn文件里存放的是.wav文件的文字描述:第一行为词，第二行为拼音，第三行为音素）.

模型预测

先直接解释有了训好的模型后如何使用，代码如下：

# -*- coding: utf-8 -*-
from keras.models import load_model
from keras import backend as K
import numpy as np
import librosa
from python_speech_features import mfcc
import pickle
import glob

wavs = glob.glob('A2_8.wav')
print(wavs)
with open('dictionary.pkl', 'rb') as fr:
    [char2id, id2char, mfcc_mean, mfcc_std] = pickle.load(fr)

mfcc_dim = 13
model = load_model('asr.h5')

index = np.random.randint(len(wavs))
print(wavs[index])

## 读取数据，并去除掉没说话的起始结束时间
audio, sr = librosa.load(wavs[index])
energy = librosa.feature.rmse(audio)
frames = np.nonzero(energy >= np.max(energy) / 5)
indices = librosa.core.frames_to_samples(frames)[1]
audio = audio[indices[0]:indices[-1]] if indices.size else audio[0:0]
X_data = mfcc(audio, sr, numcep=mfcc_dim, nfft=551)
X_data = (X_data - mfcc_mean) / (mfcc_std + 1e-14)
print(X_data.shape)

pred = model.predict(np.expand_dims(X_data, axis=0))
pred_ids = K.eval(K.ctc_decode(pred, [X_data.shape[0]], greedy=False, beam_width=10, top_paths=1)[0][0])
pred_ids = pred_ids.flatten().tolist()
print(''.join([id2char[i] for i in pred_ids]))

模型训练

模型采用了 TDNN 网络结构，并直接通过字符级别来预测，直接根据常见度将字符对应成数字标签。整个流程而言，

• 先将一个个语音样本变成MFCC特征，即一个样本的维度为time*num_MFCC，time维度将被补齐到batch里最长的time。
• 将批量样本送入网络，采用1d卷积，仅在时间轴上卷积，一个样本的输出维度为time*(num_words+1)，加的1代表预测了空状态。
• 通过CTC Loss计算损失

# -*- coding: utf-8 -*-
#导入相关的库
from keras.models import Model
from keras.layers import Input, Activation, Conv1D, Lambda, Add, Multiply, BatchNormalization
from keras.optimizers import Adam, SGD
from keras import backend as K
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

import random
import pickle
import glob
from tqdm import tqdm
import os

from python_speech_features import mfcc
import scipy.io.wavfile as wav
import librosa
from IPython.display import Audio



#读取数据集文件
text_paths = glob.glob('data/*.trn')
total = len(text_paths)
print(total)

with open(text_paths[0], 'r', encoding='utf8') as fr:
    lines = fr.readlines()
    print(lines)



#数据集文件trn内容读取保存到数组中
texts = []
paths = []
for path in text_paths:
    with open(path, 'r', encoding='utf8') as fr:
        lines = fr.readlines()
        line = lines[0].strip('\n').replace(' ', '')
        texts.append(line)
        paths.append(path.rstrip('.trn'))

print(paths[0], texts[0])



#定义mfcc数
mfcc_dim = 13

#根据数据集标定的音素读入
def load_and_trim(path):
    audio, sr = librosa.load(path)
    energy = librosa.feature.rmse(audio)
    frames = np.nonzero(energy >= np.max(energy) / 5)
    indices = librosa.core.frames_to_samples(frames)[1]
    audio = audio[indices[0]:indices[-1]] if indices.size else audio[0:0]

    return audio, sr

#可视化，显示语音文件的MFCC图
def visualize(index):
    path = paths[index]
    text = texts[index]
    print('Audio Text:', text)

    audio, sr = load_and_trim(path)
    plt.figure(figsize=(12, 3))
    plt.plot(np.arange(len(audio)), audio)
    plt.title('Raw Audio Signal')
    plt.xlabel('Time')
    plt.ylabel('Audio Amplitude')
    plt.show()

    feature = mfcc(audio, sr, numcep=mfcc_dim, nfft=551)
    print('Shape of MFCC:', feature.shape)

    fig = plt.figure(figsize=(12, 5))
    ax = fig.add_subplot(111)
    im = ax.imshow(feature, cmap=plt.cm.jet, aspect='auto')
    plt.title('Normalized MFCC')
    plt.ylabel('Time')
    plt.xlabel('MFCC Coefficient')
    plt.colorbar(im, cax=make_axes_locatable(ax).append_axes('right', size='5%', pad=0.05))
    ax.set_xticks(np.arange(0, 13, 2), minor=False);
    plt.show()

    return path


Audio(visualize(0))



#提取音频特征并存储
features = []
for i in tqdm(range(total)):
    path = paths[i]
    audio, sr = load_and_trim(path)
    features.append(mfcc(audio, sr, numcep=mfcc_dim, nfft=551))

print(len(features), features[0].shape)



#随机选择100个数据集
samples = random.sample(features, 100)
samples = np.vstack(samples)
#平均MFCC的值为了归一化处理
mfcc_mean = np.mean(samples, axis=0)
#计算标准差为了归一化
mfcc_std = np.std(samples, axis=0)
print(mfcc_mean)
print(mfcc_std)
#归一化特征
features = [(feature - mfcc_mean) / (mfcc_std + 1e-14) for feature in features]



#将数据集读入的标签和对应id存储列表
chars = {}
for text in texts:
    for c in text:
        chars[c] = chars.get(c, 0) + 1

chars = sorted(chars.items(), key=lambda x: x[1], reverse=True)
chars = [char[0] for char in chars]
print(len(chars), chars[:100])

char2id = {c: i for i, c in enumerate(chars)}
id2char = {i: c for i, c in enumerate(chars)}




data_index = np.arange(total)
np.random.shuffle(data_index)
train_size = int(0.9 * total)
test_size = total - train_size
train_index = data_index[:train_size]
test_index = data_index[train_size:]
#神经网络输入和输出X,Y的读入数据集特征
X_train = [features[i] for i in train_index]
Y_train = [texts[i] for i in train_index]
X_test = [features[i] for i in test_index]
Y_test = [texts[i] for i in test_index]

batch_size = 16

#定义训练批次的产生，一次训练16个
def batch_generator(x, y, batch_size=batch_size):
    offset = 0
    while True:
        offset += batch_size

        if offset == batch_size or offset >= len(x):
            data_index = np.arange(len(x))
            np.random.shuffle(data_index)
            x = [x[i] for i in data_index]
            y = [y[i] for i in data_index]
            offset = batch_size

        X_data = x[offset - batch_size: offset]
        Y_data = y[offset - batch_size: offset]

        X_maxlen = max([X_data[i].shape[0] for i in range(batch_size)])
        Y_maxlen = max([len(Y_data[i]) for i in range(batch_size)])

        X_batch = np.zeros([batch_size, X_maxlen, mfcc_dim])
        Y_batch = np.ones([batch_size, Y_maxlen]) * len(char2id)
        X_length = np.zeros([batch_size, 1], dtype='int32')
        Y_length = np.zeros([batch_size, 1], dtype='int32')

        for i in range(batch_size):
            X_length[i, 0] = X_data[i].shape[0]
            X_batch[i, :X_length[i, 0], :] = X_data[i]

            Y_length[i, 0] = len(Y_data[i])
            Y_batch[i, :Y_length[i, 0]] = [char2id[c] for c in Y_data[i]]

        inputs = {'X': X_batch, 'Y': Y_batch, 'X_length': X_length, 'Y_length': Y_length}
        outputs = {'ctc': np.zeros([batch_size])}

        yield (inputs, outputs)





epochs = 50
num_blocks = 3
filters = 128

X = Input(shape=(None, mfcc_dim,), dtype='float32', name='X')
Y = Input(shape=(None,), dtype='float32', name='Y')
X_length = Input(shape=(1,), dtype='int32', name='X_length')
Y_length = Input(shape=(1,), dtype='int32', name='Y_length')

#卷积1层      # 一维卷积，默认channels_last，即通道维(MFCC特征维)放最后，对时间维进行卷积
def conv1d(inputs, filters, kernel_size, dilation_rate):
    return Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='causal', activation=None,
                  dilation_rate=dilation_rate)(inputs)

#标准化函数
def batchnorm(inputs):
    return BatchNormalization()(inputs)

#激活层函数
def activation(inputs, activation):
    return Activation(activation)(inputs)

#全连接层函数
def res_block(inputs, filters, kernel_size, dilation_rate):
    hf = activation(batchnorm(conv1d(inputs, filters, kernel_size, dilation_rate)), 'tanh')
    hg = activation(batchnorm(conv1d(inputs, filters, kernel_size, dilation_rate)), 'sigmoid')
    h0 = Multiply()([hf, hg])

    ha = activation(batchnorm(conv1d(h0, filters, 1, 1)), 'tanh')
    hs = activation(batchnorm(conv1d(h0, filters, 1, 1)), 'tanh')

    return Add()([ha, inputs]), hs


h0 = activation(batchnorm(conv1d(X, filters, 1, 1)), 'tanh')
shortcut = []
for i in range(num_blocks):
    for r in [1, 2, 4, 8, 16]:
        h0, s = res_block(h0, filters, 7, r)
        shortcut.append(s)

h1 = activation(Add()(shortcut), 'relu')
h1 = activation(batchnorm(conv1d(h1, filters, 1, 1)), 'relu')
#softmax损失函数输出结果
Y_pred = activation(batchnorm(conv1d(h1, len(char2id) + 1, 1, 1)), 'softmax')
sub_model = Model(inputs=X, outputs=Y_pred)

#计算损失函数
def calc_ctc_loss(args):
    y, yp, ypl, yl = args
    return K.ctc_batch_cost(y, yp, ypl, yl)


ctc_loss = Lambda(calc_ctc_loss, output_shape=(1,), name='ctc')([Y, Y_pred, X_length, Y_length])
#加载模型训练
model = Model(inputs=[X, Y, X_length, Y_length], outputs=ctc_loss)
#建立优化器
optimizer = SGD(lr=0.02, momentum=0.9, nesterov=True, clipnorm=5)
#激活模型开始计算
model.compile(loss={'ctc': lambda ctc_true, ctc_pred: ctc_pred}, optimizer=optimizer)


checkpointer = ModelCheckpoint(filepath='asr.h5', verbose=0)
lr_decay = ReduceLROnPlateau(monitor='loss', factor=0.2, patience=1, min_lr=0.000)
#开始训练
history = model.fit_generator(
    generator=batch_generator(X_train, Y_train),
    steps_per_epoch=len(X_train) // batch_size,
    epochs=epochs,
    validation_data=batch_generator(X_test, Y_test),
    validation_steps=len(X_test) // batch_size,
    callbacks=[checkpointer, lr_decay])



#保存模型
sub_model.save('asr.h5')
#将字保存在pl=pkl中
with open('dictionary.pkl', 'wb') as fw:
    pickle.dump([char2id, id2char, mfcc_mean, mfcc_std], fw)




train_loss = history.history['loss']
plt.plot(np.linspace(1, epochs, epochs), train_loss, label='train')
plt.legend(loc='upper right')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()



#下面是模型的预测效果，可见main.py
from keras.models import load_model
import pickle

with open('dictionary.pkl', 'rb') as fr:
    [char2id, id2char, mfcc_mean, mfcc_std] = pickle.load(fr)

sub_model = load_model('asr.h5')


def random_predict(x, y):
    index = np.random.randint(len(x))
    feature = x[index]
    text = y[index]

    pred = sub_model.predict(np.expand_dims(feature, axis=0))
    pred_ids = K.eval(K.ctc_decode(pred, [feature.shape[0]], greedy=False, beam_width=10, top_paths=1)[0][0])
    pred_ids = pred_ids.flatten().tolist()

    print('True transcription:\n-- ', text, '\n')
    print('Predicted transcription:\n-- ' + ''.join([id2char[i] for i in pred_ids]), '\n')


random_predict(X_train, Y_train)
random_predict(X_test, Y_test)

总结

对比其他的分类任务，语音识别多了个解码过程，这也导致了目前在常见的深度学习框架中还没有很好的ASR框架，目前而言，CTC的应用也导致出现了一些完全端到端的ASR系统，相信以后这也会是个大趋势。