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udpate gan_datasets tests

This commit is contained in:
Eren Gölge 2021-04-08 11:52:35 +02:00
parent 0ee0458309
commit ba80e82520
1 changed files with 45 additions and 33 deletions
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58
tests/test_vocoder_gan_datasets.py
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@ -19,8 +19,8 @@ test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
ok_ljspeech = os.path.exists(test_data_path) ok_ljspeech = os.path.exists(test_data_path)
def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers): 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 ''' '''Run dataloader with given parameters and check conditions '''
ap = AudioProcessor(**C.audio) ap = AudioProcessor(**C.audio)
_, train_items = load_wav_data(test_data_path, 10) _, train_items = load_wav_data(test_data_path, 10)
dataset = GANDataset(ap, dataset = GANDataset(ap,
@ -29,6 +29,7 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
hop_len=hop_len, hop_len=hop_len,
pad_short=2000, pad_short=2000,
conv_pad=conv_pad, conv_pad=conv_pad,
return_pairs=return_pairs,
return_segments=return_segments, return_segments=return_segments,
use_noise_augment=use_noise_augment, use_noise_augment=use_noise_augment,
use_cache=use_cache) use_cache=use_cache)
@ -42,31 +43,41 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
max_iter = 10 max_iter = 10
count_iter = 0 count_iter = 0
# return random segments or return the whole audio def check_item(feat, wav):
if return_segments: """Pass a single pair of features and waveform"""
for item1, _ in loader:
feat1, wav1 = item1
# feat2, wav2 = item2
expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2) expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
# check shapes # check shapes
assert np.all(feat1.shape == expected_feat_shape), f" [!] {feat1.shape} vs {expected_feat_shape}" assert np.all(feat.shape == expected_feat_shape), f" [!] {feat.shape} vs {expected_feat_shape}"
assert (feat1.shape[2] - conv_pad * 2) * hop_len == wav1.shape[2] assert (feat.shape[2] - conv_pad * 2) * hop_len == wav.shape[2]
# check feature vs audio match # check feature vs audio match
if not use_noise_augment: if not use_noise_augment:
for idx in range(batch_size): for idx in range(batch_size):
audio = wav1[idx].squeeze() audio = wav[idx].squeeze()
feat = feat1[idx] feat = feat[idx]
mel = ap.melspectrogram(audio) mel = ap.melspectrogram(audio)
# the first 2 and the last 2 frames are skipped due to the padding # the first 2 and the last 2 frames are skipped due to the padding
# differences in stft # differences in stft
max_diff = abs((feat - mel[:, :feat1.shape[-1]])[:, 2:-2]).max() max_diff = abs((feat - mel[:, :feat.shape[-1]])[:, 2:-2]).max()
assert max_diff <= 0, f' [!] {max_diff}' assert max_diff <= 0, f' [!] {max_diff}'
# return random segments or return the whole audio
if return_segments:
if return_pairs:
for item1, item2 in loader:
feat1, wav1 = item1
feat2, wav2 = item2
check_item(feat1, wav1)
check_item(feat2, wav2)
count_iter += 1
else:
for item1 in loader:
feat1, wav1 = item1
check_item(feat1, wav1)
count_iter += 1 count_iter += 1
# if count_iter == max_iter:
# break
else: else:
for item in loader: for item in loader:
feat, wav = item feat, wav = item
@ -81,15 +92,16 @@ def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, us
def test_parametrized_gan_dataset(): def test_parametrized_gan_dataset():
''' test dataloader with different parameters ''' ''' test dataloader with different parameters '''
params = [ params = [
[32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 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, False, True, 4], [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, 0], [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, 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, 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, False, 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, False, 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, True, True, False, 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, False, False, 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: for param in params:
print(param) print(param)