-
Notifications
You must be signed in to change notification settings - Fork 0
/
train_yesno.py.bak
224 lines (171 loc) · 7.96 KB
/
train_yesno.py.bak
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
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 2 21:11:57 2018
@author: sungkyun
FFTNet: A REAL-TIME SPEAKER-DEPENDENT NEURAL VOCODER (Zeyu Jin et al., 2018, ICASSP)
*CMU Arctic dataset:
- 1032 utterances for train
- 100 utterances for test
- cond. input = F0 and 25-dim MCC
*Preprocessing:
- 16Khz sampling mono audio
- 8-bit mu-law encoded wav
*Architecture:
- receptive field = 2048 (2^11)
- 11 FFT-layers (256 ch.)
- Total 1M parameters
- final 2 layers = FC() -> softmax(256) : see details in paper 2.3.2
*Training:
- minibatch of 5 x 5000 samples
- 100,000 steps = 500 iters
*Requirements:
pip install soundfile
pip install librosa
"""
import os
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.data import DataLoader
from util.loading_dataset import YesNoDataset # required for loading YesNo dataset
from util.utils import mu_law_decode # required for auditioning generated samples
# Parsing arguments
parser = argparse.ArgumentParser(description='FFTNet reimplementation')
parser.add_argument('-exp', '--exp_name', type=str, default='00', metavar='STR',
help='Generated samples will be located in the checkpoints/exp<exp_name> directory. Default="00"') #
parser.add_argument('-btr', '--batch_train', type=int, default=1, metavar='N',
help='Batch size for training. e.g. -btr 5')
parser.add_argument('-bts', '--batch_test', type=int, default=1, metavar='N',
help='Batch size for test. e.g. -bts 5')
parser.add_argument('-load', '--load', type=str, default=None, metavar='STR',
help='e.g. --load checkpoints/exp00/checkpoint_00')
args = parser.parse_args()
USE_GPU = torch.cuda.is_available()
RAND_SEED = 0
#%% Loading data
dset_train = YesNoDataset(csv_path='data/processed_yesno/train.csv', zpad_target_len=160000, transform=None) # TODO: feature-scaling
dset_test = YesNoDataset(csv_path='data/processed_yesno/test.csv', zpad_target_len=160000, transform=None)
train_loader = DataLoader(dset_train,
batch_size=args.batch_train,
shuffle=True,
num_workers=4,
pin_memory=True
) # number of CPU threads, practically, num_worker = 4 * num_GPU
test_loader = DataLoader(dset_test,
batch_size=args.batch_test,
shuffle=False,
num_workers=8,
pin_memory=True,
)
#%% Train & Test Functions
def train(epoch):
model.train()
train_loss = 0.
train_acc = []
total_data_sz = len(train_loader.dataset.file_ids)
for batch_idx, (_, X_mulaw, X_mfcc ) in enumerate(train_loader):
if USE_GPU:
X_mulaw, X_mfcc = X_mulaw.cuda(), X_mfcc.cuda()
X_mulaw, X_mfcc = Variable(X_mulaw), Variable(X_mfcc.float())
optimizer.zero_grad()
y = model(X_mulaw, X_mfcc)
loss = F.cross_entropy(input=y.view(-1, 256), target=X_mulaw.view(-1), size_average=True)
# input=y.view(-1, num_classes), where num_classes=256 for 8bit mu-law
# target=X_mulaw.view(-1)
loss.backward()
optimizer.step()
# Accuracy
pred = y.view(-1,256).data.max(1, keepdim=True)[1] # Get the index of the max log-probability from y
acc = pred.eq(X_mulaw.view(-1).data.view_as(pred)).cpu().sum().numpy() / len(pred) # Compute accuracy by comparing pred with X_mulaw
print('Train Epoch: {} [{}/{}], Loss = {:.6f}, Acc = {:.6f}'.format(
epoch, batch_idx * train_loader.batch_size, total_data_sz, loss.item(), acc))
train_loss += loss.item()
train_acc.append(acc)
return train_loss, np.mean(train_acc)
def generator(test_file_id, out_filename, recep_sz=2048, verbose=1000):
'''
Annotation
- X_mfcc : Original condition input
- total samples : The number of total samples to generate (=size(X_mfcc))
- x_mulaw_slice : A temporary input replacing X_mulaw. At the start, all zeros(encoded as 128s).
- x_mfcc_slice : A temporary condition input.
- y : One sample output of the model. This will be fed into x_mulaw_slice at the right-most column.
- pred : Prediction of one new generated sample
- out : Collection of mu_law_decode(pred)s.
'''
_, _, X_mfcc = test_loader.dataset.__getitem__(test_file_id) # X_mfcc: (CxL) np.ndarray
feat_dim = X_mfcc.shape[0]
total_samples = X_mfcc.shape[1]
X_mfcc = X_mfcc.reshape(1, feat_dim, total_samples) # BxCxL
# Initial input slices, filled with zeros.
x_mulaw_slice = Variable(torch.LongTensor(1, recep_sz) * 0 + 128, requires_grad=False).cuda() # all zeros(128s).
x_mfcc_slice = Variable(torch.FloatTensor(1, feat_dim, recep_sz) * 0., requires_grad=False).cuda()
out = []
model.eval() # .eval(): Not requires gradients.
for i in range(total_samples):
# New x_mfcc_slice: shift-left, then fill one 'cond' sample into the right-most column
x_mfcc_slice[:, :, 0:-1] = x_mfcc_slice[:, :, 1:]
x_mfcc_slice[:, :, -1] = torch.FloatTensor(X_mfcc[:, :, i])
y = model(x_mulaw_slice, x_mfcc_slice, gen_mod=True) # 1x1x256 (BxLxC)
pred = y.view(-1,256).data.max(1, keepdim=True)[1] # Predict: Get the index of the max log-probability from y
# New x_mulaw_slice: shift-left, then fill 'pred' into the right-most column
x_mulaw_slice[0, 0:-1] = x_mulaw_slice[0, 1:] # Shift-left
x_mulaw_slice[0, -1] = pred # Push 'pred'
# Collect generated sample
out.append(float(mu_law_decode(pred.cpu().numpy())))
# Print progress
if i % verbose == 0:
print('Generator: {}/{} samples ({:.2f}%)'.format(i, total_samples,
100 * i / total_samples ) )
# Save audio
import librosa
librosa.output.write_wav(out_filename, np.asarray(out), sr=16000)
def validate(epoch):
model.eval()
val_loss = 0.
# Not implemented yet
return val_loss
def load_checkpoint(filepath):
'''
Load pre-trained model.
'''
dt = torch.load(filepath)
model.load_state_dict(dt['state_dict'])
optimizer.load_state_dict(dt['optimizer'])
return 0
def save_checkpoint(state, accuracy, exp_name):
checkpoint_dir = 'checkpoints/' + exp_name
os.makedirs(checkpoint_dir, exist_ok=True)
filepath = checkpoint_dir + '/checkpoint.pth.tar'
torch.save(state, filepath)
def history_recorder():
'''
history_recorder():
- save training history as .csv files.
- save learning-curve as .png files
'''
return 0
def print_model_sz(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
print('Number of trainable parameters = {}'.format(sum([np.prod(p.size()) for p in model_parameters])) )
#%% Experiment: train
from FFTNet_dilconv import FFTNet # <-- implemented using 2x1 dilated conv.
#from FFTNet_split import FFTNet # <-- same with paper
model = FFTNet().cuda() if USE_GPU else FFTNet().cpu()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=1e-5)
print_model_sz(model)
load_checkpoint('checkpoints/00/checkpoint.pth.tar')
for epoch in range(100):
torch.manual_seed(RAND_SEED + epoch)
tr_loss, tr_acc = train(epoch)
save_checkpoint({'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),},
tr_acc, args.exp_name)
#%% Experiment: generation
test_file_id = 0 # Select 0~5 for different condition input
generator(test_file_id=test_file_id, out_filename='aaa.wav')