def main():
print('start...')
a = open(meta_path, 'r').readlines()
# NN->PPG
# Set up network
mfcc_pl = tf.placeholder(dtype=tf.float32,
shape=[None, None, MFCC_DIM],
name='mfcc_pl')
classifier = CNNBLSTMCalssifier(out_dims=PPG_DIM,
n_cnn=3,
cnn_hidden=256,
cnn_kernel=3,
n_blstm=2,
lstm_hidden=128)
predicted_ppgs = tf.nn.softmax(classifier(inputs=mfcc_pl)['logits'])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
print('Restoring model from {}'.format(ckpt_path))
saver.restore(sess, ckpt_path)
cnt = 0
bad_list = []
for wav_f in tqdm(a):
try:
wav_f = wav_f.strip()
print('process:', wav_f)
# 提取声学参数
wav_arr = load_wav(wav_f)
mfcc_feats = wav2unnormalized_mfcc(wav_arr)
ppgs = sess.run(
predicted_ppgs,
feed_dict={mfcc_pl: np.expand_dims(mfcc_feats, axis=0)})
ppgs = np.squeeze(ppgs)
mel_feats = wav2normalized_db_mel(wav_arr)
spec_feats = wav2normalized_db_spec(wav_arr)
# /datapool/home/hujk17/ppg_decode_spec_10ms_sch_Multi/inference_findA_Multi_log_dir/2020-11-09T11-09-00/0_sample_spec.wav
fname = wav_f.split('/')[-1].split('.')[0]
save_mel_rec_name = fname + '_mel_rec.wav'
save_spec_rec_name = fname + '_spec_rec.wav'
assert ppgs.shape[0] == mfcc_feats.shape[0]
assert mfcc_feats.shape[0] == mel_feats.shape[
0] and mel_feats.shape[0] == spec_feats.shape[0]
write_wav(os.path.join(rec_wav_dir, save_mel_rec_name),
normalized_db_mel2wav(mel_feats))
write_wav(os.path.join(rec_wav_dir, save_spec_rec_name),
normalized_db_spec2wav(spec_feats))
check_ppg(ppgs)
# 存储ppg参数
ppg_save_name = os.path.join(ppg_dir, fname + '.npy')
np.save(ppg_save_name, ppgs)
cnt += 1
except Exception as e:
bad_list.append(wav_f)
print(str(e))
# break
print('good:', cnt)
print('bad:', len(bad_list))
print(bad_list)
return
def main():
#这一部分用于处理LJSpeech格式的数据集
a = open(meta_path, 'r').readlines()
a = [i.strip().split('|')[0] for i in a]
a = PPG_get_restore(a, ppg_dir, ppg_dir, mel_dir, spec_dir)
# NN->PPG
# Set up network
mfcc_pl = tf.placeholder(dtype=tf.float32, shape=[None, None, MFCC_DIM], name='mfcc_pl')
classifier = CNNBLSTMCalssifier(out_dims=PPG_DIM, n_cnn=3, cnn_hidden=256, cnn_kernel=3, n_blstm=2, lstm_hidden=128)
predicted_ppgs = tf.nn.softmax(classifier(inputs=mfcc_pl)['logits'])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
print('Restoring model from {}'.format(ckpt_path))
saver.restore(sess, ckpt_path)
cnt = 0
bad_list = []
for fname in tqdm(a):
try:
# 提取声学参数
# print('aaaaaaaaaaa111111111111111111111111111')
wav_f = os.path.join(wav_dir, fname + '.wav')
wav_arr = load_wav(wav_f)
# print('0000000000000000')
mfcc_feats = wav2unnormalized_mfcc(wav_arr)
# print('111111111111111111111111111')
ppgs = sess.run(predicted_ppgs, feed_dict={mfcc_pl: np.expand_dims(mfcc_feats, axis=0)})
# print('5555555111111111111111111111111111')
ppgs = np.squeeze(ppgs)
# print('66666666666S111111111111111111111111111')
mel_feats = wav2normalized_db_mel(wav_arr)
spec_feats = wav2normalized_db_spec(wav_arr)
# print('222222222111111111111111111111111111')
# 验证声学参数提取的对
save_name = fname + '.npy'
save_mel_rec_name = fname + '_mel_rec.wav'
save_spec_rec_name = fname + '_spec_rec.wav'
assert ppgs.shape[0] == mfcc_feats.shape[0]
assert mfcc_feats.shape[0] == mel_feats.shape[0] and mel_feats.shape[0] == spec_feats.shape[0]
write_wav(os.path.join(rec_wav_dir, save_mel_rec_name), normalized_db_mel2wav(mel_feats))
write_wav(os.path.join(rec_wav_dir, save_spec_rec_name), normalized_db_spec2wav(spec_feats))
# print('11111111111111333333333331111111111111')
check_ppg(ppgs)
# 存储声学参数
mfcc_save_name = os.path.join(mfcc_dir, save_name)
ppg_save_name = os.path.join(ppg_dir, save_name)
mel_save_name = os.path.join(mel_dir, save_name)
spec_save_name = os.path.join(spec_dir, save_name)
np.save(mfcc_save_name, mfcc_feats)
np.save(ppg_save_name, ppgs)
np.save(mel_save_name, mel_feats)
np.save(spec_save_name, spec_feats)
f_good_meta.write(fname + '\n')
cnt += 1
except Exception as e:
bad_list.append(fname)
print(str(e))
# break
print('good:', cnt)
print('bad:', len(bad_list))
print(bad_list)
return