技术标签: 深度学习之TensorFlow入门、原理与进阶实战
9-23 yuyinchall
准备一批带有文字标注的语音样本,构建BiRNN网络,通过该语料样本进行训练,实现一个能够识别语音的神经网络模型
程序:
import numpy as np
import time
import tensorflow as tf
from tensorflow.python.ops import ctc_ops
from collections import Counter
#2 样本读取
## 自定义
yuyinutils = __import__("9-24 yuyinutils")
sparse_tuple_to_texts_ch = yuyinutils.sparse_tuple_to_texts_ch
ndarray_to_text_ch = yuyinutils.ndarray_to_text_ch
get_audio_and_transcriptch = yuyinutils.get_audio_and_transcriptch
pad_sequences = yuyinutils.pad_sequences
sparse_tuple_from = yuyinutils.sparse_tuple_from
get_wavs_lables = yuyinutils.get_wavs_lables
tf.reset_default_graph()
b_stddev = 0.046875
h_stddev = 0.046875
n_hidden = 1024
n_hidden_1 = 1024
n_hidden_2 = 1024
n_hidden_5 = 1024
n_cell_dim = 1024
n_hidden_3 = 2 * 1024
keep_dropout_rate = 0.95
relu_clip = 20
#使用3个1024节点的全连接层,然后是一个双向RNN,最后接上2个全连接层,并且都带有dropout层。这里使用的激活函数是带截断的Relu,截断值设为20。学习参数的初始化使用标准差为0.046875的random_normal。keep_dropout_rate为0.95.
def BiRNN_model(batch_x, seq_length, n_input, n_context, n_character, keep_dropout):
# batch_x_shape: [batch_size, n_steps, n_input + 2*n_input*n_context]
batch_x_shape = tf.shape(batch_x)
# 将输入转成时间序列优先
batch_x = tf.transpose(batch_x, [1, 0, 2])
# 再转成2维传入第一层
batch_x = tf.reshape(batch_x,
[-1, n_input + 2 * n_input * n_context]) # (n_steps*batch_size, n_input + 2*n_input*n_context)
# 使用clipped RELU activation and dropout.
# 1st layer
with tf.name_scope('fc1'):
b1 = variable_on_cpu('b1', [n_hidden_1], tf.random_normal_initializer(stddev=b_stddev))
h1 = variable_on_cpu('h1', [n_input + 2 * n_input * n_context, n_hidden_1],
tf.random_normal_initializer(stddev=h_stddev))
layer_1 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(batch_x, h1), b1)), relu_clip)
layer_1 = tf.nn.dropout(layer_1, keep_dropout)
# 2nd layer
with tf.name_scope('fc2'):
b2 = variable_on_cpu('b2', [n_hidden_2], tf.random_normal_initializer(stddev=b_stddev))
h2 = variable_on_cpu('h2', [n_hidden_1, n_hidden_2], tf.random_normal_initializer(stddev=h_stddev))
layer_2 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(layer_1, h2), b2)), relu_clip)
layer_2 = tf.nn.dropout(layer_2, keep_dropout)
# 3rd layer
with tf.name_scope('fc3'):
b3 = variable_on_cpu('b3', [n_hidden_3], tf.random_normal_initializer(stddev=b_stddev))
h3 = variable_on_cpu('h3', [n_hidden_2, n_hidden_3], tf.random_normal_initializer(stddev=h_stddev))
layer_3 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(layer_2, h3), b3)), relu_clip)
layer_3 = tf.nn.dropout(layer_3, keep_dropout)
# 双向rnn
with tf.name_scope('lstm'):
# Forward direction cell:
lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_cell_dim, forget_bias=1.0, state_is_tuple=True)
lstm_fw_cell = tf.contrib.rnn.DropoutWrapper(lstm_fw_cell,
input_keep_prob=keep_dropout)
# Backward direction cell:
lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_cell_dim, forget_bias=1.0, state_is_tuple=True)
lstm_bw_cell = tf.contrib.rnn.DropoutWrapper(lstm_bw_cell,
input_keep_prob=keep_dropout)
# `layer_3` `[n_steps, batch_size, 2*n_cell_dim]`
layer_3 = tf.reshape(layer_3, [-1, batch_x_shape[0], n_hidden_3])
outputs, output_states = tf.nn.bidirectional_dynamic_rnn(cell_fw=lstm_fw_cell,
cell_bw=lstm_bw_cell,
inputs=layer_3,
dtype=tf.float32,
time_major=True,
sequence_length=seq_length)
# 连接正反向结果[n_steps, batch_size, 2*n_cell_dim]
outputs = tf.concat(outputs, 2)
# to a single tensor of shape [n_steps*batch_size, 2*n_cell_dim]
outputs = tf.reshape(outputs, [-1, 2 * n_cell_dim])
with tf.name_scope('fc5'):
b5 = variable_on_cpu('b5', [n_hidden_5], tf.random_normal_initializer(stddev=b_stddev))
h5 = variable_on_cpu('h5', [(2 * n_cell_dim), n_hidden_5], tf.random_normal_initializer(stddev=h_stddev))
layer_5 = tf.minimum(tf.nn.relu(tf.add(tf.matmul(outputs, h5), b5)), relu_clip)
layer_5 = tf.nn.dropout(layer_5, keep_dropout)
with tf.name_scope('fc6'):
# 全连接层用于softmax分类
b6 = variable_on_cpu('b6', [n_character], tf.random_normal_initializer(stddev=b_stddev))
h6 = variable_on_cpu('h6', [n_hidden_5, n_character], tf.random_normal_initializer(stddev=h_stddev))
layer_6 = tf.add(tf.matmul(layer_5, h6), b6)
# 将2维[n_steps*batch_size, n_character]转成3维 time-major [n_steps, batch_size, n_character].
layer_6 = tf.reshape(layer_6, [-1, batch_x_shape[0], n_character])
# Output shape: [n_steps, batch_size, n_character]
return layer_6
"""
used to create a variable in CPU memory.
"""
def variable_on_cpu(name, shape, initializer):
# Use the /cpu:0 device for scoped operations
with tf.device('/cpu:0'):
# Create or get apropos variable
var = tf.get_variable(name=name, shape=shape, initializer=initializer)
return var
wav_path = 'F:/shendu/yuyinchall/wav/wav/train'
label_file = 'F:/shendu/yuyinchall/doc/doc/trans/train.word.txt'
wav_files, labels = get_wavs_lables(wav_path, label_file)
print(wav_files[0], labels[0])
# wav/train/A11/A11_0.WAV -> 绿 是 阳春 烟 景 大块 文章 的 底色 四月 的 林 峦 更是 绿 得 鲜活 秀媚 诗意 盎然
print("wav:", len(wav_files), "label", len(labels))
'''----------------------------------------------------------------------'''
#3 建立批次获取样本函数
# 字表
all_words = []
for label in labels:
# print(label)
all_words += [word for word in label]
counter = Counter(all_words)
words = sorted(counter)
words_size = len(words)
word_num_map = dict(zip(words, range(words_size)))
print('字表大小:', words_size)
n_input = 26 # 计算美尔倒谱系数的个数
n_context = 9 # 对于每个时间点,要包含上下文样本的个数
batch_size = 8
def next_batch(labels, start_idx=0, batch_size=1, wav_files=wav_files):
filesize = len(labels)
end_idx = min(filesize, start_idx + batch_size)
idx_list = range(start_idx, end_idx)
txt_labels = [labels[i] for i in idx_list]
wav_files = [wav_files[i] for i in idx_list]
(source, audio_len, target, transcript_len) = get_audio_and_transcriptch(None,
wav_files,
n_input,
n_context, word_num_map, txt_labels)
start_idx += batch_size
# Verify that the start_idx is not larger than total available sample size
if start_idx >= filesize:
start_idx = -1
# Pad input to max_time_step of this batch
source, source_lengths = pad_sequences(source) # 如果多个文件将长度统一,支持按最大截断或补0
sparse_labels = sparse_tuple_from(target)
return start_idx, source, source_lengths, sparse_labels
next_idx, source, source_len, sparse_lab = next_batch(labels, 0, batch_size)
print(len(sparse_lab))
print(np.shape(source))
# print(sparse_lab)
t = sparse_tuple_to_texts_ch(sparse_lab, words)
print(t[0])
# source已经将变为前9(不够补空)+本身+后9,每个26,第一个顺序是第10个的数据。
'''---------------------------------------------------------------------'''
#1 定义占位符
# shape = [batch_size, max_stepsize, n_input + (2 * n_input * n_context)]
# the batch_size and max_stepsize每步都是变长的。
input_tensor = tf.placeholder(tf.float32, [None, None, n_input + (2 * n_input * n_context)],
name='input') # 语音log filter bank or MFCC features
# Use sparse_placeholder; will generate a SparseTensor, required by ctc_loss op.
targets = tf.sparse_placeholder(tf.int32, name='targets') # 文本
# 1d array of size [batch_size]
seq_length = tf.placeholder(tf.int32, [None], name='seq_length') # 序列长
keep_dropout = tf.placeholder(tf.float32)
'''----------------------------------------------------------------------'''
#2 构建网络模型
# logits is the non-normalized output/activations from the last layer.
# logits will be input for the loss function.
# nn_model is from the import statement in the load_model function
logits = BiRNN_model(input_tensor, tf.to_int64(seq_length), n_input, n_context, words_size + 1, keep_dropout)
'''-----------'''
'''----------------------------------------------------------------------'''
#3 定义损失函数即优化器
# 调用ctc loss
avg_loss = tf.reduce_mean(ctc_ops.ctc_loss(targets, logits, seq_length))
# [optimizer]
learning_rate = 0.001
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(avg_loss)
'''----------------------------------------------------------------------'''
#4 定义解码,并评估模型节点
with tf.name_scope("decode"):
decoded, log_prob = ctc_ops.ctc_beam_search_decoder(logits, seq_length, merge_repeated=False)
with tf.name_scope("accuracy"):
distance = tf.edit_distance(tf.cast(decoded[0], tf.int32), targets)
# 计算label error rate (accuracy)
ler = tf.reduce_mean(distance, name='label_error_rate')
'''----------------------------------------------------------------------'''
#建立session并添加检查点处理
epochs = 100
savedir = "F:/shendu/yuyinchalltest/"
saver = tf.train.Saver(max_to_keep=1) # 生成saver
# create the session
sess = tf.Session()
# 没有模型的话,就重新初始化
sess.run(tf.global_variables_initializer())
kpt = tf.train.latest_checkpoint(savedir)
print("kpt:", kpt)
startepo = 0
if kpt != None:
saver.restore(sess, kpt)
ind = kpt.find("-")
startepo = int(kpt[ind + 1:])
print(startepo)
'''----------------------------------------------------------------------'''
#6 通过循环来迭代训练模型
# 准备运行训练步骤
section = '\n{0:=^40}\n'
print(section.format('Run training epoch'))
train_start = time.time()
for epoch in range(epochs): # 样本集迭代次数
epoch_start = time.time()
if epoch < startepo:
continue
print("epoch start:", epoch, "total epochs= ", epochs)
#######################run batch####
n_batches_per_epoch = int(np.ceil(len(labels) / batch_size))
print("total loop ", n_batches_per_epoch, "in one epoch,", batch_size, "items in one loop")
train_cost = 0
train_ler = 0
next_idx = 0
for batch in range(n_batches_per_epoch): # 一次batch_size,取多少次
# 取数据
next_idx, source, source_lengths, sparse_labels = \
next_batch(labels, next_idx, batch_size)
feed = {input_tensor: source, targets: sparse_labels, seq_length: source_lengths,
keep_dropout: keep_dropout_rate}
# 计算 avg_loss optimizer ;
batch_cost, _ = sess.run([avg_loss, optimizer], feed_dict=feed)
train_cost += batch_cost
'''----------------------------------------------------------------------'''
# 7 定期评估模型。输出模型解码结果
if (batch + 1) % 20 == 0:
print('loop:', batch, 'Train cost: ', train_cost / (batch + 1))
feed2 = {input_tensor: source, targets: sparse_labels, seq_length: source_lengths, keep_dropout: 1.0}
d, train_ler = sess.run([decoded[0], ler], feed_dict=feed2)
dense_decoded = tf.sparse_tensor_to_dense(d, default_value=-1).eval(session=sess)
dense_labels = sparse_tuple_to_texts_ch(sparse_labels, words)
counter = 0
print('Label err rate: ', train_ler)
for orig, decoded_arr in zip(dense_labels, dense_decoded):
# convert to strings
decoded_str = ndarray_to_text_ch(decoded_arr, words)
print(' file {}'.format(counter))
print('Original: {}'.format(orig))
print('Decoded: {}'.format(decoded_str))
counter = counter + 1
break
epoch_duration = time.time() - epoch_start
log = 'Epoch {}/{}, train_cost: {:.3f}, train_ler: {:.3f}, time: {:.2f} sec'
print(log.format(epoch, epochs, train_cost, train_ler, epoch_duration))
saver.save(sess, savedir + "yuyinch.cpkt", global_step=epoch)
train_duration = time.time() - train_start
print('Training complete, total duration: {:.2f} min'.format(train_duration / 60))
sess.close()
结果:
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