-
Notifications
You must be signed in to change notification settings - Fork 0
/
eval_mcd.py
162 lines (122 loc) · 4.74 KB
/
eval_mcd.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2018-present, Papercup Technologies Limited
# All rights reserved.
import os
import argparse
import visdom
import numpy as np
from tqdm import tqdm
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
from data import NpzFolder, NpzLoader, TBPTTIter
from model import Loop
from utils import create_output_dir, wrap, check_grad
from dtw import dtw
def model_def(checkpoint, gpu=-1, valid_loader=None):
weights = torch.load(checkpoint,
map_location=lambda storage, loc: storage)
opt = torch.load(os.path.dirname(checkpoint) + '/args.pth')
train_args = opt[0]
train_args.noise = 0
#norm = opt[5]
#dict = {v: k for k, v in enumerate(code2phone)}
norm = np.load(valid_loader.dataset.npzs[0])['audio_norminfo']
model = Loop(train_args)
model.load_state_dict(weights)
if gpu >= 0:
model.cuda()
model.eval()
return model, norm
logSpecDbConst = 10.0 / np.log(10.0) * np.sqrt(2.0)
def logSpecDbDist(x, y):
diff = x - y
return logSpecDbConst * np.sqrt(np.inner(diff, diff))
def evaluate(model, norm, valid_loader, logging=None):
total = 0
total1 = 0
valid_enum = tqdm(valid_loader, desc='Valid')
dtw_cost = logSpecDbDist
cmp_mean = norm[0]
cmp_std = norm[1]
for txt, feat, spkr in valid_enum:
input = wrap(txt, volatile=True)
target = wrap(feat, volatile=True)
spkr = wrap(spkr, volatile=True)
#lan = wrap(lan)
feat = target[0]
# model.train()
#feat = torch.FloatTensor(*target[0].size())
#feat = Variable(target[0], volatile=True)
#feat = wrap(feat)
#with torch.no_grad():
output, attn = model([input, spkr], feat)
batch_size = attn.size(1)
tmp_loss = 0
ground_truths = target[0].cpu().data.numpy()
ground_truths = ground_truths * cmp_std + cmp_mean
synthesised = output.cpu().data.numpy()
synthesised = synthesised * cmp_std + cmp_mean
for i in range(batch_size):
length = target[1][i].cpu().data.numpy()[0]
ground_truth = ground_truths[:, i]
ground_truth = ground_truth[:length, :25]
synth = synthesised[:, i]
synth = synth[:length, :25]
unit_loss = dtw(ground_truth, synth, dtw_cost)
unit_loss /= length
tmp_loss += unit_loss
tmp_loss /= batch_size
total += tmp_loss
valid_enum.set_description('Valid (MCD %.2f)' %
(tmp_loss))
avg = total / len(valid_loader)
if logging:
logging.info('====> Test set loss: {:.4f}'.format(avg))
return avg
def main():
parser = argparse.ArgumentParser(description='PyTorch Loop')
# Env options:
parser.add_argument('--epochs', type=int, default=92, metavar='N',
help='number of epochs to train (default: 92)')
parser.add_argument('--seed', type=int, default=10, metavar='S',
help='random seed (default: 3)')
parser.add_argument('--expName', type=str, default='vctk', metavar='E',
help='Experiment name')
parser.add_argument('--data', default='data/vctk',
metavar='D', type=str, help='Data path')
parser.add_argument('--checkpoint', default='',
metavar='C', type=str, help='Checkpoint path')
parser.add_argument('--gpu', default=0,
metavar='G', type=int, help='GPU device ID')
# Data options
parser.add_argument('--max-seq-len', type=int, default=1000,
help='Max sequence length for tbptt')
parser.add_argument('--batch-size', type=int, default=64,
help='Batch size')
# Model options
parser.add_argument('--nspk', type=int, default=22,
help='Number of speakers')
# init
args = parser.parse_args()
args.expName = os.path.join('checkpoints', args.expName)
torch.cuda.set_device(args.gpu)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
logging = create_output_dir(args)
# data
valid_dataset = NpzFolder(args.data + '/numpy_features_valid', args.nspk == 1)
valid_loader = NpzLoader(valid_dataset,
max_seq_len=args.max_seq_len,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True)
# load model
model, norm = model_def(args.checkpoint, gpu=args.gpu, valid_loader=valid_loader)
# Begin!
eval_loss = evaluate(model, norm, valid_loader, logging)
if __name__ == '__main__':
main()