GRU-CTC中文语音识别

目录

• 基于keras的中文语音识别
• 音频文件特征提取
• 文本数据处理
• 数据格式处理
• 构建模型
• 模型训练及解码
• aishell数据转化

该项目github地址

基于keras的中文语音识别

• 该项目实现了GRU-CTC中文语音识别，所有代码都在gru_ctc_am.py中，包括：
  • 音频文件特征提取
  • 文本数据处理
  • 数据格式处理
  • 构建模型
  • 模型训练及解码
• 之外还包括将aishell数据处理为thchs30数据格式，合并数据进行训练。代码及数据放在gen_aishell_data中。

默认数据集为thchs30，参考gen_aishell_data中的数据及代码，也可以使用aishell的数据进行训练。

音频文件特征提取

# -----------------------------------------------------------------------------------------------------
'''
&usage:		[audio]对音频文件进行处理，包括生成总的文件列表、特征提取等
'''
# -----------------------------------------------------------------------------------------------------
# 生成音频列表
def genwavlist(wavpath):
	wavfiles = {}
	fileids = []
	for (dirpath, dirnames, filenames) in os.walk(wavpath):
		for filename in filenames:
			if filename.endswith('.wav'):
				filepath = os.sep.join([dirpath, filename])
				fileid = filename.strip('.wav')
				wavfiles[fileid] = filepath
				fileids.append(fileid)
	return wavfiles,fileids

# 对音频文件提取mfcc特征
def compute_mfcc(file):
	fs, audio = wav.read(file)
	mfcc_feat = mfcc(audio, samplerate=fs, numcep=26)
	mfcc_feat = mfcc_feat[::3]
	mfcc_feat = np.transpose(mfcc_feat)
	mfcc_feat = pad_sequences(mfcc_feat, maxlen=500, dtype='float', padding='post', truncating='post').T
	return mfcc_feat

文本数据处理

# -----------------------------------------------------------------------------------------------------
'''
&usage:		[text]对文本标注文件进行处理，包括生成拼音到数字的映射，以及将拼音标注转化为数字的标注转化
'''
# -----------------------------------------------------------------------------------------------------
# 利用训练数据生成词典
def gendict(textfile_path):
	dicts = []
	textfile = open(textfile_path,'r+')
	for content in textfile.readlines():
		content = content.strip('\n')
		content = content.split(' ',1)[1]
		content = content.split(' ')
		dicts += (word for word in content)
	counter = Counter(dicts)
	words = sorted(counter)
	wordsize = len(words)
	word2num = dict(zip(words, range(wordsize)))
	num2word = dict(zip(range(wordsize), words))
	return word2num, num2word #1176个音素

# 文本转化为数字
def text2num(textfile_path):
	lexcion,num2word = gendict(textfile_path)
	word2num = lambda word:lexcion.get(word, 0)
	textfile = open(textfile_path, 'r+')
	content_dict = {}
	for content in textfile.readlines():
		content = content.strip('\n')
		cont_id = content.split(' ',1)[0]
		content = content.split(' ',1)[1]
		content = content.split(' ')
		content = list(map(word2num,content))
		add_num = list(np.zeros(50-len(content)))
		content = content + add_num
		content_dict[cont_id] = content
	return content_dict,lexcion

数据格式处理

# -----------------------------------------------------------------------------------------------------
'''
&usage:		[data]数据生成器构造，用于训练的数据生成，包括输入特征及标注的生成，以及将数据转化为特定格式
'''
# -----------------------------------------------------------------------------------------------------
# 将数据格式整理为能够被网络所接受的格式，被data_generator调用
def get_batch(x, y, train=False, max_pred_len=50, input_length=500):
    X = np.expand_dims(x, axis=3)
    X = x # for model2
#     labels = np.ones((y.shape[0], max_pred_len)) *  -1 # 3 # , dtype=np.uint8
    labels = y
    input_length = np.ones([x.shape[0], 1]) * ( input_length - 2 )
#     label_length = np.ones([y.shape[0], 1])
    label_length = np.sum(labels > 0, axis=1)
    label_length = np.expand_dims(label_length,1)
    inputs = {'the_input': X,
              'the_labels': labels,
              'input_length': input_length,
              'label_length': label_length,
              }
    outputs = {'ctc': np.zeros([x.shape[0]])}  # dummy data for dummy loss function
    return (inputs, outputs)

# 数据生成器，默认音频为thchs30\train,默认标注为thchs30\train.syllable,被模型训练方法fit_generator调用
def data_generate(wavpath = 'E:\\Data\\data_thchs30\\train', textfile = 'E:\\Data\\thchs30\\train.syllable.txt', bath_size=4):
	wavdict,fileids = genwavlist(wavpath)
	#print(wavdict)
	content_dict,lexcion = text2num(textfile)
	genloop = len(fileids)//bath_size
	print("all loop :", genloop)
	while True:
		feats = []
		labels = []
		# 随机选择某个音频文件作为训练数据
		i = random.randint(0,genloop-1)
		for x in range(bath_size):
			num = i * bath_size + x
			fileid = fileids[num]
			# 提取音频文件的特征
			mfcc_feat = compute_mfcc(wavdict[fileid])
			feats.append(mfcc_feat)
			# 提取标注对应的label值
			labels.append(content_dict[fileid])
		# 将数据格式修改为get_batch可以处理的格式
		feats = np.array(feats)
		labels = np.array(labels)
		# 调用get_batch将数据处理为训练所需的格式
		inputs, outputs = get_batch(feats, labels)
		yield inputs, outputs

构建模型

# -----------------------------------------------------------------------------------------------------
'''
&usage:		[net model]构件网络结构，用于最终的训练和识别
'''
# -----------------------------------------------------------------------------------------------------
# 被creatModel调用，用作ctc损失的计算
def ctc_lambda(args):
	labels, y_pred, input_length, label_length = args
	y_pred = y_pred[:, :, :]
	return K.ctc_batch_cost(labels, y_pred, input_length, label_length)

# 构建网络结构，用于模型的训练和识别
def creatModel():
	input_data = Input(name='the_input', shape=(500, 26))
	layer_h1 = Dense(512, activation="relu", use_bias=True, kernel_initializer='he_normal')(input_data)
	#layer_h1 = Dropout(0.3)(layer_h1)
	layer_h2 = Dense(512, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h1)
	layer_h3_1 = GRU(512, return_sequences=True, kernel_initializer='he_normal', dropout=0.3)(layer_h2)
	layer_h3_2 = GRU(512, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', dropout=0.3)(layer_h2)
	layer_h3 = add([layer_h3_1, layer_h3_2])
	layer_h4 = Dense(512, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h3)
	#layer_h4 = Dropout(0.3)(layer_h4)
	layer_h5 = Dense(1177, activation="relu", use_bias=True, kernel_initializer='he_normal')(layer_h4)
	output = Activation('softmax', name='Activation0')(layer_h5)
	model_data = Model(inputs=input_data, outputs=output)
	#ctc
	labels = Input(name='the_labels', shape=[50], dtype='float32')
	input_length = Input(name='input_length', shape=[1], dtype='int64')
	label_length = Input(name='label_length', shape=[1], dtype='int64')
	loss_out = Lambda(ctc_lambda, output_shape=(1,), name='ctc')([labels, output, input_length, label_length])
	model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)
	model.summary()
	ada_d = Adadelta(lr=0.01, rho=0.95, epsilon=1e-06)
	#model=multi_gpu_model(model,gpus=2)
	model.compile(loss={'ctc': lambda y_true, output: output}, optimizer=ada_d)
	#test_func = K.function([input_data], [output])
	print("model compiled successful!")
	return model, model_data

模型训练及解码

# -----------------------------------------------------------------------------------------------------
'''
&usage:		模型的解码，用于将数字信息映射为拼音
'''
# -----------------------------------------------------------------------------------------------------
# 对model预测出的softmax的矩阵，使用ctc的准则解码，然后通过字典num2word转为文字
def decode_ctc(num_result, num2word):
	result = num_result[:, :, :]
	in_len = np.zeros((1), dtype = np.int32)
	in_len[0] = 50;
	r = K.ctc_decode(result, in_len, greedy = True, beam_width=1, top_paths=1)
	r1 = K.get_value(r[0][0])
	r1 = r1[0]
	text = []
	for i in r1:
		text.append(num2word[i])
	return r1, text

# -----------------------------------------------------------------------------------------------------
'''
&usage:		模型的训练
'''
# -----------------------------------------------------------------------------------------------------
# 训练模型
def train():
	# 准备训练所需数据
	yielddatas = data_generate()
	# 导入模型结构，训练模型，保存模型参数
	model, model_data = creatModel()
	model.fit_generator(yielddatas, steps_per_epoch=2000, epochs=1)
	model.save_weights('model.mdl')
	model_data.save_weights('model_data.mdl')

# -----------------------------------------------------------------------------------------------------
'''
&usage:		模型的测试，看识别结果是否正确
'''
# -----------------------------------------------------------------------------------------------------
# 测试模型
def test():
	# 准备测试数据，以及生成字典
	word2num, num2word = gendict('E:\\Data\\thchs30\\train.syllable.txt')
	yielddatas = data_generate(bath_size=1)
	# 载入训练好的模型，并进行识别
	model, model_data = creatModel()
	model_data.load_weights('model_data.mdl')
	result = model_data.predict_generator(yielddatas, steps=1)
	# 将数字结果转化为文本结果
	result, text = decode_ctc(result, num2word)
	print('数字结果： ', result)
	print('文本结果：', text)

aishell数据转化

将aishell中的汉字标注转化为拼音标注，利用该数据与thchs30数据训练同样的网络结构。

该模型作为一个练手小项目。

没有使用语言模型，直接简单建模。

我的github: https://github.com/audier