tensorflow练习11:语音识别程序
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找工作的事情暂时告一段落,感觉还需要不断提升自己,不说多少了,直接步入正题。
语音识别是人机交互、人工智能等领域必不可少的一个研究领域,下面就以该例为标准。
使用的数据集:THCHS30(Dong Wang, Xuewei Zhang, Zhiyong Zhang发布的开放语音数据集);
地址: 语音文件:http://data.cslt.org/thchs30/zip/wav.tgz
对应文本:http://data.cslt.org/thchs30/zip/doc.tgz
下载后,解压放到data文件夹下。
tensorflow环境:0.12.1
1)加载文件、分词等预处理操作:
#coding=utf-8import tensorflow as tfimport numpy as npimport osfrom collections import Counterimport librosaimport time#训练样本路径wav_path = 'data/wav/train'label_file = 'data/doc/trans/train.word.txt'# 获得训练用的wav文件路径列表def get_wave_files(wav_path=wav_path): wav_files = [] for (dirpath,dirnames,filenames) in os.walk(wav_path):#访问文件夹下的所有文件 #os.walk() 方法用于通过在目录树种游走输出在目录中的文件名,向上或者向下 for filename in filenames: if filename.endswith('.wav') or filename.endswith('.WAV'): #endswith() 方法用于判断字符串是否以指定后缀结尾,如果以指定后缀结尾返回True,否则返回False filename_path = os.sep.join([dirpath,filename])#定义文件路径(连) if os.stat(filename_path).st_size < 240000:#st_size文件的大小,以位为单位 continue wav_files.append(filename_path)#加载文件 return wav_fileswav_files = get_wave_files()#获取文件名列表#读取wav文件对应的labeldef get_wav_label(wav_files=wav_files,label_file=label_file): labels_dict = {} with open(label_file,encoding='utf-8') as f: for label in f : label =label.strip('\n') label_id = label.split(' ',1)[0] label_text = label.split(' ',1)[1] labels_dict[label_id]=label_text#以字典格式保存相应内容 labels=[] new_wav_files = [] for wav_file in wav_files: wav_id = os.path.basename(wav_file).split('.')[0] #得到相应的文件名后进行'.'分割 if wav_id in labels_dict: labels.append(labels_dict[wav_id])#存在该标签则放入 new_wav_files.append(wav_file) return new_wav_files,labels#返回标签和对应的文件wav_files,labels = get_wav_label()#得到标签和对应的语音文件print("加载训练样本:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))print("样本数:",len(wav_files))#词汇表(参考对话、诗词生成)all_words = []for label in labels: all_words += [word for word in label]counter = Counter(all_words)count_pairs =sorted(counter.items(),key=lambda x: -x[1])words,_=zip(*count_pairs)words_size =len(words)#词汇表尺寸print('词汇表大小:',words_size)#词汇映射成id表示word_num_map = dict(zip(words,range(len(words))))to_num = lambda word: word_num_map.get(word,len(words))#词汇映射函数labels_vector =[list(map(to_num,label)) for label in labels]label_max_len= np.max([len(label) for label in labels_vector])#获取最长字数print('最长句子的字数:',label_max_len)wav_max_len=0for wav in wav_files: wav,sr = librosa.load(wav,mono=True)#处理语音信号的库librosa #加载音频文件作为a floating point time series.(可以是wav,mp3等格式)mono=True:signal->mono mfcc=np.transpose(librosa.feature.mfcc(wav,sr),[1,0])#转置特征参数 #librosa.feature.mfcc特征提取函数 if len(mfcc)>wav_max_len: wav_max_len = len(mfcc)print("最长的语音:",wav_max_len)
以上程序加载训练文件并进行分词等操作。
2)定义初始训练细节步骤:
batch_size=16#每次取16个文件n_batch = len(wav_files)//batch_size#大约560个batchpointer =0#全局变量初值为0,定义该变量用以逐步确定batchdef get_next_batches(batch_size): global pointer batches_wavs = [] batches_labels = [] for i in range(batch_size): wav,sr=librosa.load(wav_files[pointer],mono=True) mfcc =np.transpose(librosa.feature.mfcc(wav,sr),[1,0]) batches_wavs.append(mfcc.tolist())#转换成列表表存入 batches_labels.append(labels_vector[pointer]) pointer+=1 #补0对齐 for mfcc in batches_wavs: while len(mfcc)<wav_max_len: mfcc.append([0]*20)#补一个全0列表 for label in batches_labels: while len(label)<label_max_len: label.append(0) return batches_wavs,batches_labelsX=tf.placeholder(dtype=tf.float32,shape=[batch_size,None,20])#定义输入格式sequence_len = tf.reduce_sum(tf.cast(tf.not_equal(tf.reduce_sum(X,reduction_indices=2), 0.), tf.int32), reduction_indices=1)Y= tf.placeholder(dtype=tf.int32,shape=[batch_size,None])#输出格式
以上代码确定一些训练的细节。
3)定义网络结构:
# 定义神经网络def speech_to_text_network(n_dim=128, n_blocks=3): #卷积层输出 out = conv1d_layer(input_tensor=X, size=1, dim=n_dim, activation='tanh', scale=0.14, bias=False) # skip connections def residual_block(input_sensor, size, rate): conv_filter = aconv1d_layer(input_sensor, size=size, rate=rate, activation='tanh', scale=0.03, bias=False) conv_gate = aconv1d_layer(input_sensor, size=size, rate=rate, activation='sigmoid', scale=0.03, bias=False) out = conv_filter * conv_gate out = conv1d_layer(out, size=1, dim=n_dim, activation='tanh', scale=0.08, bias=False) return out + input_sensor, out skip = 0 for _ in range(n_blocks): for r in [1, 2, 4, 8, 16]: out, s = residual_block(out, size=7, rate=r)#根据采样频率发生变化 skip += s #两层卷积 logit = conv1d_layer(skip, size=1, dim=skip.get_shape().as_list()[-1], activation='tanh', scale=0.08, bias=False) logit = conv1d_layer(logit, size=1, dim=words_size, activation=None, scale=0.04, bias=True) return logit
其中,上述代码中skip connection是CNN中的一种训练技巧,具体可以参照博客:极深网络(ResNet/DenseNet): Skip Connection为何有效及其它
上述代码中:conv1d_layer与aconv1d_layer代码如下:
#第一层卷积conv1d_index = 0def conv1d_layer(input_tensor,size,dim,activation,scale,bias): global conv1d_index with tf.variable_scope('conv1d_'+str(conv1d_index)): W= tf.get_variable('W', (size, input_tensor.get_shape().as_list()[-1], dim), dtype=tf.float32, initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale)) if bias: b= tf.get_variable('b',[dim],dtype=tf.float32,initializer=tf.constant_initializer(0)) out = tf.nn.conv1d(input_tensor, W, stride=1, padding='SAME')#输出与输入同纬度 if not bias: beta = tf.get_variable('beta', dim, dtype=tf.float32, initializer=tf.constant_initializer(0)) gamma = tf.get_variable('gamma', dim, dtype=tf.float32, initializer=tf.constant_initializer(1)) #均值 mean_running = tf.get_variable('mean', dim, dtype=tf.float32, initializer=tf.constant_initializer(0)) #方差 variance_running = tf.get_variable('variance', dim, dtype=tf.float32, initializer=tf.constant_initializer(1)) mean, variance = tf.nn.moments(out, axes=range(len(out.get_shape()) - 1)) #可以根据矩(均值和方差)来做normalize,见tf.nn.moments def update_running_stat(): decay =0.99 #mean_running、variance_running更新操作 update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)), variance_running.assign(variance_running * decay + variance * (1 - decay))] with tf.control_dependencies(update_op): return tf.identity(mean), tf.identity(variance) #返回mean,variance m, v = tf.cond(tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]), update_running_stat, lambda: (mean_running, variance_running)) out = tf.nn.batch_normalization(out, m, v, beta, gamma, 1e-8)#batch_normalization if activation == 'tanh': out = tf.nn.tanh(out) if activation == 'sigmoid': out = tf.nn.sigmoid(out) conv1d_index += 1 return out# aconv1d_layeraconv1d_index = 0def aconv1d_layer(input_tensor, size, rate, activation, scale, bias): global aconv1d_index with tf.variable_scope('aconv1d_' + str(aconv1d_index)): shape = input_tensor.get_shape().as_list()#以list的形式返回tensor的shape W = tf.get_variable('W', (1, size, shape[-1], shape[-1]), dtype=tf.float32, initializer=tf.random_uniform_initializer(minval=-scale, maxval=scale)) if bias: b = tf.get_variable('b', [shape[-1]], dtype=tf.float32, initializer=tf.constant_initializer(0)) out = tf.nn.atrous_conv2d(tf.expand_dims(input_tensor, dim=1), W, rate=rate, padding='SAME') #tf.expand_dims(input_tensor,dim=1)==>在第二维添加了一维,rate:采样率 out = tf.squeeze(out, [1])#去掉第二维 #同上 if not bias: beta = tf.get_variable('beta', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(0)) gamma = tf.get_variable('gamma', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(1)) mean_running = tf.get_variable('mean', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(0)) variance_running = tf.get_variable('variance', shape[-1], dtype=tf.float32, initializer=tf.constant_initializer(1)) mean, variance = tf.nn.moments(out, axes=range(len(out.get_shape()) - 1)) def update_running_stat(): decay = 0.99 update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)), variance_running.assign(variance_running * decay + variance * (1 - decay))] with tf.control_dependencies(update_op): return tf.identity(mean), tf.identity(variance) m, v = tf.cond(tf.Variable(False, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES]), update_running_stat, lambda: (mean_running, variance_running)) out = tf.nn.batch_normalization(out, m, v, beta, gamma, 1e-8) if activation == 'tanh': out = tf.nn.tanh(out) if activation == 'sigmoid': out = tf.nn.sigmoid(out) aconv1d_index += 1 return out
4)训练代码:
#对优化类进行一些自定义操作。class MaxPropOptimizer(tf.train.Optimizer): def __init__(self, learning_rate=0.001, beta2=0.999, use_locking=False, name="MaxProp"): super(MaxPropOptimizer, self).__init__(use_locking, name) self._lr = learning_rate self._beta2 = beta2 self._lr_t = None self._beta2_t = None def _prepare(self): self._lr_t = tf.convert_to_tensor(self._lr, name="learning_rate") self._beta2_t = tf.convert_to_tensor(self._beta2, name="beta2") def _create_slots(self, var_list): for v in var_list: self._zeros_slot(v, "m", self._name) def _apply_dense(self, grad, var): lr_t = tf.cast(self._lr_t, var.dtype.base_dtype) beta2_t = tf.cast(self._beta2_t, var.dtype.base_dtype) if var.dtype.base_dtype == tf.float16: eps = 1e-7 else: eps = 1e-8 m = self.get_slot(var, "m") m_t = m.assign(tf.maximum(beta2_t * m + eps, tf.abs(grad))) g_t = grad / m_t var_update = tf.assign_sub(var, lr_t * g_t) return tf.group(*[var_update, m_t]) def _apply_sparse(self, grad, var): return self._apply_dense(grad, var)def train_speech_to_text_network(): print("开始训练:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) logit = speech_to_text_network() # CTC loss indices = tf.where(tf.not_equal(tf.cast(Y, tf.float32), 0.)) target = tf.SparseTensor(indices=indices, values=tf.gather_nd(Y, indices) - 1, shape=tf.cast(tf.shape(Y), tf.int64)) loss = tf.nn.ctc_loss(logit, target, sequence_len, time_major=False) # optimizer lr = tf.Variable(0.001, dtype=tf.float32, trainable=False) optimizer = MaxPropOptimizer(learning_rate=lr, beta2=0.99) var_list = [t for t in tf.trainable_variables()] gradient = optimizer.compute_gradients(loss, var_list=var_list) optimizer_op = optimizer.apply_gradients(gradient) with tf.Session() as sess: sess.run(tf.global_variables_initializer())#初始化变量 saver = tf.train.Saver(tf.global_variables()) for epoch in range(16): print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) print("第%d次循环迭代:"%(epoch)) sess.run(tf.assign(lr, 0.001 * (0.97 ** epoch))) global pointer pointer = 0#根据pointer来确定 for batch in range(n_batch): batches_wavs, batches_labels = get_next_batches(batch_size) train_loss, _ = sess.run([loss, optimizer_op], feed_dict={X: batches_wavs, Y: batches_labels}) print(epoch, batch, train_loss) if epoch % 5 == 0: print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) print("第%d次模型保存结果:"%(epoch//5)) saver.save(sess, './speech.module', global_step=epoch) print("结束训练时刻:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))# 训练train_speech_to_text_network()
以上设置16个epoch;非gpu训练时间大概2到3天。
5)测试使用代码:
def speech_to_text(wav_file): wav, sr = librosa.load(wav_file, mono=True) mfcc = np.transpose(np.expand_dims(librosa.feature.mfcc(wav, sr), axis=0), [0, 2, 1]) logit = speech_to_text_network() saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('.')) decoded = tf.transpose(logit, perm=[1, 0, 2]) decoded, _ = tf.nn.ctc_beam_search_decoder(decoded, sequence_len, merge_repeated=False) predict = tf.sparse_to_dense(decoded[0].indices, decoded[0].shape, decoded[0].values) + 1 output = sess.run(decoded, feed_dict={X: mfcc}) print(output)
以上仅为个人见解,欢迎各位批评指正。
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