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select_sentence_style3.py
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select_sentence_style3.py
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import tensorflow as tf, pickle as pkl, os
import numpy as np
import random
import tqdm
import sys
import time
import hjk_tools.audio as audio
import math
import codecs
from best_tacotron.hyperparameter_style import HyperParams
from best_tacotron.train_model_sentence_style3 import Tacotron
from matplotlib import pyplot
import shutil
import csv
# import pandas as pd
train_data_num = 6148
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
hp = HyperParams()
data_name = 'sr16_aB_sentence_style3'
save_path = os.path.join('model', data_name)
save_data_att_path = os.path.join(save_path, 'style_dict.pkl')
model_name = "TTS"
tfrecord_train_path = './data/sr16_aB_sorted_train.tfrecords'
tfrecord_dev_path = './data/sr16_aB_sorted_dev.tfrecords'
pkl_train_id_path = './data/sr16_aB_sorted_train_id.pkl'
pkl_train_path = './data/sr16_aB_sorted_train.pkl'
pkl_dev_path = './data/sr16_aB_sorted_dev.pkl'
tb_logs_path = os.path.join('logs', data_name) + '/'
dev_txt_path = os.path.join('logs', data_name) + '/dev_loss.txt'
def parse_single_example(example_proto):
features = {"key": tf.FixedLenFeature([], tf.string),
"frames": tf.FixedLenFeature([], tf.int64),
"char_txt": tf.FixedLenFeature([], tf.string),
"txt_raw": tf.FixedLenFeature([], tf.string),
"txt_len": tf.FixedLenFeature([], tf.int64),
"log_mel_raw": tf.FixedLenFeature([], tf.string),
"log_stftm_raw": tf.FixedLenFeature([], tf.string)}
parsed = tf.parse_single_example(example_proto, features=features)
key = parsed["key"]
frames = tf.cast(parsed["frames"], tf.int32)
char_txt = parsed["char_txt"]
txt = tf.decode_raw(parsed["txt_raw"], tf.int32)
txt_len = tf.cast(parsed["txt_len"], tf.int32)
log_mel = tf.reshape(tf.decode_raw(parsed["log_mel_raw"], tf.float32), (frames, hp.seq2seq_dim))
log_stftm = tf.reshape(tf.decode_raw(parsed["log_stftm_raw"], tf.float32), (frames, hp.post_dim))
return {"key":key, "frames": frames, "char_txt": char_txt, "txt":txt, "txt_len":txt_len, "log_mel": log_mel, "log_stftm": log_stftm}
# return {"frames": frames, "txt":txt, "txt_len":txt_len, "log_mel": log_mel, "log_stftm": log_stftm}
def get_dataset(tfrecord_path, shuffle_buf, repeat_times):
dataset = tf.data.TFRecordDataset(tfrecord_path)
dataset = dataset.map(parse_single_example)
dataset = dataset.padded_batch(hp.batch_size, padded_shapes={
"key": (),
"frames": (),
"char_txt": (),
"txt": [None],
"txt_len": (),
"log_mel": [None, hp.seq2seq_dim],
"log_stftm": [None, hp.post_dim]}, padding_values={
"key": "",
"frames": 0,
"char_txt": "",
"txt": np.int32(0),
"txt_len": 0,
"log_mel": np.float32(np.log(0.01)),
"log_stftm": np.float32(np.log(0.01))})
dataset = dataset.shuffle(shuffle_buf)
dataset = dataset.repeat(repeat_times)
# dataset = dataset.batch(hp.batch_size)
return dataset
def init_next_batch(tfrecord_path, shuffle_buf, repeat_times):
data_set = get_dataset(tfrecord_path, shuffle_buf, repeat_times)
iterator = data_set.make_one_shot_iterator()
next_item = iterator.get_next()
return next_item
def get_next_batch(sess, next_item):
t = sess.run(next_item)
# while t['txt'].shape[0] != 32:
# t = sess.run(next_item)
# print('not 32 batch happen')
# print('frames:', t['frames'])
return t['txt'], t['txt_len'], t['log_mel'], t['log_stftm'], t['key'], t['char_txt']
def post_next_batch(batch_inp, batch_inp_mask, batch_mel_gtruth, batch_spec_gtruth, stats_meta):
init_time_stamp = batch_mel_gtruth.shape[1]
fix_time_stamp = init_time_stamp // hp.reduction_rate * hp.reduction_rate
batch_mel_gtruth = batch_mel_gtruth[:, 0: fix_time_stamp]
batch_spec_gtruth = batch_spec_gtruth[:, 0: fix_time_stamp]
batch_mel_gtruth = (batch_mel_gtruth - stats_meta["log_mel_mean"]) / stats_meta["log_mel_std"]
batch_spec_gtruth = (batch_spec_gtruth - stats_meta["log_stftm_mean"]) / stats_meta["log_stftm_std"]
# print(batch_inp.shape, batch_inp_mask.shape, batch_mel_gtruth.shape, batch_spec_gtruth.shape,
# )
# print(np.max(batch_inp), np.max(batch_inp_mask), np.max(batch_mel_gtruth), np.max(batch_spec_gtruth),
# )
# print(np.min(batch_inp), np.min(batch_inp_mask), np.min(batch_mel_gtruth), np.min(batch_spec_gtruth),
# )
return batch_inp, batch_inp_mask, batch_mel_gtruth, batch_spec_gtruth
def main():
# with tf.variable_scope('data'):
# inp = tf.placeholder(name='inp', shape=(None, None), dtype=tf.int32)
# inp_mask = tf.placeholder(name='inp_mask', shape=(None,), dtype=tf.int32)
# inp_id = tf.placeholder(name='inp_id', shape=(None,), dtype=tf.int32)
# seq2seq_gtruth = tf.placeholder(name='seq2seq_gtruth', shape=(None, None, hp.seq2seq_dim), dtype=tf.float32)
# post_gtruth = tf.placeholder(name='post_gtruth', shape=(None, None, hp.post_dim), dtype=tf.float32)
#
# train_meta_path = pkl_train_path
# assert os.path.exists(train_meta_path),\
# '[!] Train meta not exists! PATH: {}'.format(train_meta_path)
#
# dev_meta_path = pkl_dev_path
# assert os.path.exists(dev_meta_path), \
# '[!] Dev meta not exists! PATH: {}'.format(dev_meta_path)
#
# with open(train_meta_path, 'rb') as f:
# train_meta = pkl.load(f)
# train_meta['reduction_rate'] = hp.reduction_rate
#
# with open(dev_meta_path, 'rb') as f:
# dev_meta = pkl.load(f)
# dev_meta['reduction_rate'] = hp.reduction_rate
#
# train_model = Tacotron(inp=inp, inp_mask=inp_mask, inp_id = inp_id, seq2seq_gtruth=seq2seq_gtruth, post_gtruth=post_gtruth,
# hyper_params=hp, training=True, reuse=False)
#
# with tf.variable_scope('optimizer'):
# opt = tf.train.AdamOptimizer(train_model.exp_learning_rate_decay(0.001))
# # grad, var = zip(*opt.compute_gradients(train_model.loss))
# # with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
# # train_upd = opt.apply_gradients(zip(grad, var), global_step=train_model.global_step)
#
# grads_and_vars = opt.compute_gradients(train_model.loss)
# # for i, (grad, var) in enumerate(grads_and_vars):
# # # print(var.name)
# # if var.name.find('style_token:0') != -1:
# # grads_and_vars[i] = (grad / 200.0, var)
# # print(var.name)
# # print('hhhh time')
# # break
# with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
# train_upd = opt.apply_gradients(grads_and_vars, global_step=train_model.global_step)
#
#
# if not os.path.exists(save_path):
# os.makedirs(save_path)
# if not os.path.exists(tb_logs_path):
# os.makedirs(tb_logs_path)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
# train_model.sess = sess
# writer = tf.summary.FileWriter(tb_logs_path, filename_suffix='train', graph=sess.graph)
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
# ckpt = tf.train.get_checkpoint_state(save_path)
# saver = tf.train.Saver(max_to_keep=20)
# train_model.saver = saver
# ass_style_token = tf.placeholder(name="ass_style_token", shape=(1, hp.styles_kind, hp.style_dim), dtype=tf.float32)
# ass_opt = train_model.single_style_token.assign(ass_style_token)
# # ass_inp_att = tf.placeholder(name="ass_inp_att", shape=(None, hp.styles_kind),
# # dtype=tf.float32)
# # att_ass_opt = train_model.inp_att.assign(ass_inp_att)
select_key = ['LittleRedRidingHood/37_sr16k.wav',
'AMidsummerNightsDream/204_sr16k.wav',
'TheReluctantDragon/79_sr16k.wav',
'SorcerersApprentice/165_sr16k.wav',
'TheMousesWedding/66_1_sr16k.wav',
'AMidsummerNightsDream/102_0_sr16k.wav',
'PhantomOfTheOpera/171_sr16k.wav',
'PhantomOfTheOpera/174_sr16k.wav',
'RomeoAndJuliet/186_2_sr16k.wav'
# 'AMidsummerNightsDream/77_sr16k.wav'
]
train_next_item = init_next_batch(tfrecord_train_path, 1, 1)
with open(pkl_train_path, 'rb') as f:
train_meta = pkl.load(f)
train_meta['reduction_rate'] = hp.reduction_rate
with open(pkl_train_id_path, "rb") as f:
data_id_dict = pkl.load(f)
select_lst = dict()
select_lst['batch_key'] = []
select_lst['batch_inp'] = []
select_lst['batch_inp_mask'] = []
select_lst['batch_mel_gtruth'] = []
select_lst['batch_spec_gtruth'] = []
select_lst['batch_char_txt'] = []
select_lst['batch_id'] = []
while True:
try:
batch_inp, batch_inp_mask, batch_mel_gtruth, batch_spec_gtruth, batch_key, batch_char_txt = get_next_batch(sess,
train_next_item)
batch_inp, batch_inp_mask, batch_mel_gtruth, batch_spec_gtruth = post_next_batch(batch_inp, batch_inp_mask,
batch_mel_gtruth,
batch_spec_gtruth, train_meta)
for i, var in enumerate(batch_key):
var = var.decode()
for j in select_key:
# print(var)
if var.find(j) != -1:
print(var)
select_lst['batch_key'].append(var)
select_lst['batch_inp'].append(batch_inp[i])
select_lst['batch_inp_mask'].append(batch_inp_mask[i])
select_lst['batch_mel_gtruth'].append(batch_mel_gtruth[i])
select_lst['batch_spec_gtruth'].append(batch_spec_gtruth[i])
select_lst['batch_char_txt'].append(batch_char_txt[i].decode())
# print(batch_char_txt[i].decode())
# print(batch_char_txt[i])
# print('in:', select_lst['batch_char_txt'][0])
select_lst['batch_id'].append(data_id_dict[var])
except Exception as e:
print('stopped')
break
with open('selected_data_10.pkl', 'wb') as f:
pkl.dump(select_lst, f)
for i in range(len(select_key)):
print(select_lst['batch_key'][i], select_lst['batch_id'][i], select_lst['batch_char_txt'][i])
# print(select_lst['batch_key'], select_lst['batch_id'], select_lst['batch_char_txt'])
# if ckpt:
# ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
# saver.restore(sess, os.path.join(save_path, 'Tacotron-200000'))
# # print('restore path:', ckpt_name)
# # with open(save_data_att_path, "rb") as f:
# # data_style_att_dict = pkl.load(f)
# # print('load att dict')
# else:
# print('no restor, init all include style:')
# # np.random.seed(1)
# init_style_token = np.random.uniform(low=-1, high=1, size=(1, hp.styles_kind, hp.style_dim))
# print('look random:', np.max(init_style_token), np.min(init_style_token))
# sess.run(ass_opt, feed_dict={ass_style_token: init_style_token})
# data_style_att_dict = dict()
# print(data_id_dict)
# data_id_list = np.arange(0, train_data_num, dtype=np.int32)
# print(data_id_list)
# data_att = sess.run(train_model.inp_att, feed_dict={inp_id: data_id_list})
# print(data_att)
# key_list = list(data_id_dict.keys())
# ans = []
# cnt = [[]for _ in range(10)]
# for i in range(train_data_num):
# itm = info(i, key_list[i], data_att[i], np.argmax(data_att[i]), np.max(data_att[i]))
# ans.append(itm)
# cnt[itm.c].append(itm)
# for i in range(10):
# print(i, ':', len(cnt[i]))
# cnt[i] = sorted(cnt[i], key=lambda s:s.c_val, reverse=True)
# for j in range(10):
# print('class:', cnt[i][j].c, 'val:', cnt[i][j].c_val, cnt[i][j].key)
# select_by_class(cnt[i][j].key, cnt[i][j].c)
#
# # val_cnt = np.asarray(cnt[i][:].c_val)
# val_cnt = np.asarray(list(map(lambda s:s.c_val, cnt[i])))
# # print(val_cnt)
# # print(type(val_cnt))
# drawHist(val_cnt)
#
# #get data style vector
# data_style_vec = sess.run(train_model.single_style_token)
# data_style_vec = data_style_vec[0]
# ans = np.zeros((10, 10), dtype=np.float32)
# for i in range(10):
# for j in range(10):
# x = data_style_vec[i]
# y = data_style_vec[j]
# angle = x.dot(y) / (np.sqrt(x.dot(x)) * np.sqrt(y.dot(y)))
# ans[i][j] = angle
# print(i, '----', j, ':', angle)
#
# with open("test.csv", "w") as csvfile:
# writer = csv.writer(csvfile)
#
# # 先写入columns_name
# # writer.writerow({"index", "a_name", "b_name"})
# # 写入多行用writerows
# writer.writerows(ans)
if __name__ == '__main__':
main()