TensorFlow 中文语音识别

本文转载自

http://blog.topspeedsnail.com/archives/10696

数据集下载参见该文。

其中下面的代码进行了一些小小的调整。

其中包含缩进、版本方面(作者是python 3.5)、求wav_max_len方面（并行）、tf.nn.ctc_loss的参数顺序。

（具体改变可与原文链接中的代码进行对比）

并在程序中给出了一些小注解。

感谢作者将这么好的资料开源，吾辈只能望洋兴叹。

#coding: utf-8import tensorflow as tfimport numpy as npimport osfrom collections import Counterimport librosafrom joblib import Parallel, delayedwav_path = 'train'label_file = "train.word.txt"def get_wav_files(wav_path = wav_path):    wav_files = []    for (dirpath, dirnames, filenames) in os.walk(wav_path):        for filename in filenames:            if filename.endswith(".wav") or filename.endswith(".WAV"):                filename_path = os.sep.join([dirpath, filename])                if os.stat(filename_path).st_size < 240000:                    continue                wav_files.append(filename_path)    return wav_fileswav_files = get_wav_files()def get_wav_label(wav_files = wav_files, label_file = label_file):    labels_dict = {}    with open(label_file, "r", encoding='utf-8') as f:        for label in f:            label = label.strip("\n")            label_id, label_text = label.split(' ', 1)            labels_dict[label_id] = label_text    labels = []    new_wav_files = []    for wav_file in wav_files:        wav_id = os.path.basename(wav_file).split(".")[0]        if wav_id in labels_dict:            labels.append(labels_dict[wav_id])            new_wav_files.append(wav_file)    return new_wav_files, labelsdef get_wav_length(wav):    import numpy as np    import librosa    print(wav)    wav, sr = librosa.load(wav)    mfcc = np.transpose(librosa.feature.mfcc(wav, sr), [1, 0])    return len(mfcc)pointer = 0def get_next_batches(batch_size, wav_max_len):    global pointer    batches_wavs = []    batches_labels = []    for i in range(batch_size):        wav, sr = librosa.load(wav_files[pointer])        mfcc = np.transpose(librosa.feature.mfcc(wav, sr), [1,0])        batches_wavs.append(mfcc.tolist())        batches_labels.append(labels_vector[pointer])        pointer += 1    # 取零补齐    # label append 0 , 0 对应的字符    # mfcc 默认的计算长度为20(n_mfcc of mfcc) 作为channel length    for mfcc in batches_wavs:        while len(mfcc) < wav_max_len:            mfcc.append([0]*20)    for label in batches_labels:        while len(label) < label_max_len:            label.append(0)    return batches_wavs, batches_labelsconv1d_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') + (b if bias else 0)        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))            mean_running = tf.get_variable('mean', dim, dtype=tf.float32, initializer=tf.constant_initializer(0))            variance_running = tf.get_variable('variance', dim, dtype=tf.float32, initializer=tf.constant_initializer(1))            mean, variance = tf.nn.moments(out, axes=list(range(len(out.get_shape()) - 1)))            def update_running_stat():                decay = 0.99                # 定义了均值方差指数衰减 见 http://blog.csdn.net/liyuan123zhouhui/article/details/70698264                update_op = [mean_running.assign(mean_running * decay + mean * (1 - decay)), variance_running.assign(variance_running * decay + variance * (1 - decay))]                # 指定先执行均值方差的更新运算 见 http://blog.csdn.net/u012436149/article/details/72084744                with tf.control_dependencies(update_op):                    return tf.identity(mean), tf.identity(variance)            # 条件运算(https://applenob.github.io/tf_9.html) 按照作者这里的指定 是不进行指数衰减的            m, v = tf.cond(tf.Variable(False, trainable=False), 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)        elif activation == 'sigmoid':            out = tf.nn.sigmoid(out)        conv1d_index += 1        return out# 极黑卷积层 https://www.zhihu.com/question/57414498# 其输入参数中要包含一个大于 1 的rate 输出 channels与输入相同aconv1d_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()        # 利用 2 维极黑卷积函数计算相应 1 维卷积，expand_dims squeeze做了相应维度处理        # 实际 上一个 tf.nn.conv1d 在之前的tensorflow版本中是没有的，其的一个实现也是经过维度调整后调用 tf.nn.conv2d        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')        out = tf.squeeze(out, [1])        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=list(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), 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)        elif activation == 'sigmoid':            out = tf.nn.sigmoid(out)        aconv1d_index += 1        return outdef speech_to_text_network(n_dim = 128, n_blocks = 3):    out = conv1d_layer(input_tensor=X, size=1, dim = n_dim, activation='tanh', scale=0.14, bias=False)    def residual_block(input_sensor, size, rate):        conv_filter = aconv1d_layer(input_tensor=input_sensor, size=size, rate=rate, activation='tanh', scale=0.03, bias=False)        conv_gate = aconv1d_layer(input_tensor=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)            skip += s    logit = conv1d_layer(skip, size = 1, dim = skip.get_shape().as_list()[-1], activation='tanh', scale = 0.08, bias=False)    # 最后卷积层输出是词汇表大小    logit = conv1d_layer(logit, size = 1, dim = words_size, activation = None, scale = 0.04, bias = True)    return logit# 作者自己定义了优化器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(wav_max_len):    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, dense_shape=tf.cast(tf.shape(Y), tf.int64))    loss = tf.nn.ctc_loss(target, logit, 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())        saver = tf.train.Saver(tf.global_variables())        for epoch in range(16):            sess.run(tf.assign(lr, 0.001 * (0.97 ** epoch)))            global pointer            pointer = 0            for batch in range(n_batch):                batches_wavs, batches_labels = get_next_batches(batch_size, wav_max_len)                train_loss, _ = sess.run([loss, optimizer_op], feed_dict={X: batches_wavs, Y: batches_labels})                print(epoch, batch, train_loss)            if epoch % 5 == 0:                saver.save(sess, r'/speech/module', global_step=epoch)# 训练#train_speech_to_text_network()# 语音识别# 把batch_size改为1def 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)if __name__ == "__main__":    wav_files = get_wav_files()    wav_files, labels = get_wav_label()    print(u"样本数 ：", len(wav_files))    all_words = []    for label in labels:        # 字符分解        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)    print(u"词汇表大小：", words_size)    word_num_map = dict(zip(words, range(len(words))))    # 当字符不在已经收集的words中时，赋予其应当的num，这是一个动态的结果    to_num = lambda word: word_num_map.get(word, len(words))    # 将单个file的标签映射为num 返回对应list,最终all file组成嵌套list    labels_vector = [list(map(to_num, label)) for label in labels]    label_max_len = np.max([len(label) for label in labels_vector])    print(u"最长句子的字数:" + str(label_max_len))    # 下面仅仅计算了语音特征相应的最长的长度。    # 如果仅仅是计算长度是否需要施加变换后计算长度？    parallel_read = False    if parallel_read:        wav_max_len = np.max(Parallel(n_jobs=7)(delayed(get_wav_length)(wav) for wav in wav_files))    else:        wav_max_len = 673    print("最长的语音", wav_max_len)    batch_size = 16    n_batch = len(wav_files) // batch_size    X = tf.placeholder(dtype=tf.float32, shape=[batch_size, None, 20])    # 实际mfcc中的元素并非同号，不严格的情况下如此得到序列长度也是可行的    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])    train_speech_to_text_network(wav_max_len)