# -*- coding: utf-8 -*-
"""
Created on Sat Apr 28 20:14:38 2018
@author: yanghe
"""
import numpy as np
import tensorflow as tf
import pygame
from voice_tool import *
pygame.mixer.init()
track4=pygame.mixer.Sound("./raw_data/dev/2.wav")
track4.play()
X = np.load("./XY_train/X.npy")
Y = np.load("./XY_train/Y.npy")
Y = np.reshape(Y, (-1, 1375))
X_dev = np.load("./XY_dev/X_dev.npy")
Y_dev = np.load("./XY_dev/Y_dev.npy")
Y_dev = np.reshape(Y_dev, (-1, 1375))
def get_weight_l2(shape ,name, regulariztion_rate=None):
weight = tf.get_variable('weight_%s'%str(name), shape=shape, initializer=tf.truncated_normal_initializer(stddev=0.1) )
bias = tf.get_variable('bias_%s'%str(name), shape=[shape[-1]], initializer=tf.constant_initializer(0.1) )
if regulariztion_rate != None :
tf.add_to_collection('loss' ,tf.contrib.layers.l2_regularizer(regulariztion_rate)(weight))
return bias , weight
def model(inputs):
inputs = tf.reshape(inputs, (-1, 5511, 1, 101))
with tf.variable_scope('conv1'):
conv1_biases,conv1_weight = get_weight_l2(shape=[15, 1, 101, 128],name='layer1')
conv1 = tf.nn.conv2d(inputs, conv1_weight, strides=[1,1,1,1], padding='SAME')
relu1 = tf.nn.relu(tf.nn.bias_add(conv1 , conv1_biases))
pool1 = tf.nn.max_pool(relu1, ksize=[1,15,1,1], strides=[1,4,1,1], padding='VALID')
dorp1 = tf.nn.dropout(pool1,keep_prob )
dorp1 = tf.reshape(dorp1, (-1, 1375, 128))
dorp1 = tf.transpose(dorp1, [1, 0, 2])
with tf.variable_scope('rnn'):
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(128, forget_bias=1.0)
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=keep_prob)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * 2)
outputs, _ = tf.nn.dynamic_rnn(cell, dorp1, dtype=tf.float32)
outputs = tf.reshape(outputs, (-1, 1375, 128, 1))
with tf.variable_scope('conv2'):
conv2_biases,conv2_weight = get_weight_l2(shape=[1, 128, 1, 1],name='layer2')
conv2 = tf.nn.conv2d(outputs, conv2_weight, strides=[1,1,1,1], padding='SAME')
sig1 = tf.nn.sigmoid(tf.nn.bias_add(conv2 , conv2_biases))
sig1 = tf.reshape(sig1, (-1, 1375))
return sig1
def train():
global_step = tf.Variable(0, trainable=False)
with tf.variable_scope("moving_average"):
variable_averages = tf.train.ExponentialMovingAverage(moving_average_decay, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.variable_scope('voice') as scope:
pred = model(input_data)
with tf.variable_scope("train_step"):
learning_rate = tf.train.exponential_decay(
learning_rate_base,
global_step,
1,
learing_rate_decay,
staircase=True)
cross_entropy = tf.reduce_mean(-tf.reduce_sum( targets* tf.log(pred) ,reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy, global_step=global_step)
with tf.control_dependencies([train_step , variables_averages_op]):
train_op = tf.no_op(name='train')
saver=tf.train.Saver()
with tf.Session() as sess :
tf.global_variables_initializer().run()
for i in range(training_steps):
_= sess.run(train_op , feed_dict={input_data:X , targets:Y,keep_prob:0.8})
if i % 2 == 0 :
loss = sess.run(cross_entropy , feed_dict={input_data:X_dev , targets:Y_dev,keep_prob:1.0})
print("After %d training step(s), the model loss is %g " % (i, loss))
saver.save(sess , 'saver/moedl_voce_3.ckpt')
def predict():
filename = "./raw_data/dev/2.wav"
x = graph_spectrogram(filename)
x = x.swapaxes(0,1)
x = np.expand_dims(x, axis=0)
with tf.variable_scope('voice') as scope:
scope.reuse_variables()
prediction = model(input_data)
saver=tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver.restore(sess,'saver/moedl_voce_1.ckpt')
predict_= sess.run(prediction,feed_dict={input_data:x,keep_prob:1.0})
print(predict_)
chime_on_activate(filename, predict_, 0.5)
track4=pygame.mixer.Sound("./chime_output.wav")
track4.play()
keep_prob = tf.placeholder(tf.float32)
input_data = tf.placeholder(tf.float32, [None, 5511, 101])
targets = tf.placeholder(tf.float32,[None, 1375])
training_steps = 50
learning_rate_base = 0.01
learing_rate_decay = 0.99
moving_average_decay = 0.99
train()
#predict()
filename = "./raw_data/dev/2.wav"
x = graph_spectrogram(filename)
x = x.swapaxes(0,1)
x = np.expand_dims(x, axis=0)
with tf.variable_scope('voice') as scope:
scope.reuse_variables()
prediction = model(input_data)
saver=tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver.restore(sess , 'saver/moedl_voce_3.ckpt')
predict_= sess.run(prediction,feed_dict={input_data:x,keep_prob:1.0})
chime_on_activate(filename, predict_, 0.9995656)
track4=pygame.mixer.Sound("./chime_output.wav")
track4.play()
