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reformatting and styling

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This commit is contained in:
Eren Gölge
2021-04-12 11:47:39 +02:00
parent 9011dddf77
commit f519012dea
159 changed files with 6589 additions and 6429 deletions
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tests/test_vocoder_gan_datasets.py
+34 -34
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@@ -1,9 +1,9 @@
import os
import numpy as np
from tests import get_tests_path, get_tests_input_path, get_tests_output_path
from torch.utils.data import DataLoader
from tests import get_tests_input_path, get_tests_output_path, get_tests_path
from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config
from TTS.vocoder.datasets.gan_dataset import GANDataset
@@ -13,32 +13,33 @@ file_path = os.path.dirname(os.path.realpath(__file__))
OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
os.makedirs(OUTPATH, exist_ok=True)
C = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
C = load_config(os.path.join(get_tests_input_path(), "test_config.json"))
test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
ok_ljspeech = os.path.exists(test_data_path)
def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_pairs, return_segments, use_noise_augment, use_cache, num_workers):
'''Run dataloader with given parameters and check conditions '''
def gan_dataset_case(
batch_size, seq_len, hop_len, conv_pad, return_pairs, return_segments, use_noise_augment, use_cache, num_workers
):
"""Run dataloader with given parameters and check conditions """
ap = AudioProcessor(**C.audio)
_, train_items = load_wav_data(test_data_path, 10)
dataset = GANDataset(ap,
train_items,
seq_len=seq_len,
hop_len=hop_len,
pad_short=2000,
conv_pad=conv_pad,
return_pairs=return_pairs,
return_segments=return_segments,
use_noise_augment=use_noise_augment,
use_cache=use_cache)
loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
drop_last=True)
dataset = GANDataset(
ap,
train_items,
seq_len=seq_len,
hop_len=hop_len,
pad_short=2000,
conv_pad=conv_pad,
return_pairs=return_pairs,
return_segments=return_segments,
use_noise_augment=use_noise_augment,
use_cache=use_cache,
)
loader = DataLoader(
dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=True, drop_last=True
)
max_iter = 10
count_iter = 0
@@ -59,9 +60,8 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_pairs, retur
mel = ap.melspectrogram(audio)
# the first 2 and the last 2 frames are skipped due to the padding
# differences in stft
max_diff = abs((feat - mel[:, :feat.shape[-1]])[:, 2:-2]).max()
assert max_diff <= 0, f' [!] {max_diff}'
max_diff = abs((feat - mel[:, : feat.shape[-1]])[:, 2:-2]).max()
assert max_diff <= 0, f" [!] {max_diff}"
# return random segments or return the whole audio
if return_segments:
@@ -90,18 +90,18 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_pairs, retur
def test_parametrized_gan_dataset():
''' test dataloader with different parameters '''
""" test dataloader with different parameters """
params = [
[32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, False, True, 0],
[32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, False, True, 4],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, True, True, 0],
[1, C.audio['hop_length'], C.audio['hop_length'], 0, True, True, True, True, 0],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, True, True, True, True, 0],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, True, 0],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, False, True, 0],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, True, True, False, 0],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, False, False, 0],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, False, False, 0]
[32, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 0],
[32, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 4],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, True, True, 0],
[1, C.audio["hop_length"], C.audio["hop_length"], 0, True, True, True, True, 0],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 2, True, True, True, True, 0],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, False, True, True, 0],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, True, False, True, 0],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, False, True, True, False, 0],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, False, False, False, 0],
[1, C.audio["hop_length"] * 10, C.audio["hop_length"], 0, True, False, False, False, 0],
]
for param in params:
print(param)