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