mirror of https://github.com/coqui-ai/TTS.git
tests updates
Eren Golge
parent
caae1af4f6
commit
dce1715e0f
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@ -61,7 +61,7 @@ class MyDataset(Dataset):
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self.use_phonemes = use_phonemes
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self.phoneme_cache_path = phoneme_cache_path
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self.phoneme_language = phoneme_language
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if not os.path.isdir(phoneme_cache_path):
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if use_phonemes and not os.path.isdir(phoneme_cache_path):
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os.makedirs(phoneme_cache_path)
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print(" > DataLoader initialization")
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print(" | > Data path: {}".format(root_path))
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@ -38,7 +38,7 @@ class CBHGTests(unittest.TestCase):
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class DecoderTests(unittest.TestCase):
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def test_in_out(self):
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layer = Decoder(in_features=256, memory_dim=80, r=2)
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layer = Decoder(in_features=256, memory_dim=80, r=2, memory_size=4, attn_windowing=False)
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dummy_input = T.rand(4, 8, 256)
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dummy_memory = T.rand(4, 2, 80)
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@ -6,7 +6,7 @@ import numpy as np
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from torch.utils.data import DataLoader
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from utils.generic_utils import load_config
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from utils.audio import AudioProcessor
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from datasets import TTSDataset, TTSDatasetCached, TTSDatasetMemory
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from datasets import TTSDataset
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from datasets.preprocess import ljspeech, tts_cache
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file_path = os.path.dirname(os.path.realpath(__file__))
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@ -41,7 +41,9 @@ class TestTTSDataset(unittest.TestCase):
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preprocessor=ljspeech,
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ap=self.ap,
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batch_group_size=bgs,
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min_seq_len=c.min_seq_len)
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min_seq_len=c.min_seq_len,
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max_seq_len=float("inf"),
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use_phonemes=False)
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dataloader = DataLoader(
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dataset,
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batch_size=batch_size,
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@ -190,366 +192,4 @@ class TestTTSDataset(unittest.TestCase):
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# check batch conditions
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assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
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assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
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class TestTTSDatasetCached(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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super(TestTTSDatasetCached, self).__init__(*args, **kwargs)
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self.max_loader_iter = 4
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self.c = load_config(os.path.join(c.data_path_cache, 'config.json'))
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self.ap = AudioProcessor(**self.c.audio)
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def _create_dataloader(self, batch_size, r, bgs):
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dataset = TTSDataset.MyDataset(
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c.data_path_cache,
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'tts_metadata.csv',
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r,
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c.text_cleaner,
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preprocessor=tts_cache,
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ap=self.ap,
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batch_group_size=bgs,
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min_seq_len=c.min_seq_len,
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max_seq_len=c.max_seq_len,
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cached=True)
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dataloader = DataLoader(
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dataset,
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batch_size=batch_size,
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shuffle=False,
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collate_fn=dataset.collate_fn,
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drop_last=True,
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num_workers=c.num_loader_workers)
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return dataloader, dataset
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def test_loader(self):
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if ok_ljspeech:
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dataloader, dataset = self._create_dataloader(2, c.r, 0)
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for i, data in enumerate(dataloader):
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if i == self.max_loader_iter:
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break
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text_input = data[0]
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text_lengths = data[1]
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linear_input = data[2]
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mel_input = data[3]
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mel_lengths = data[4]
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stop_target = data[5]
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item_idx = data[6]
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neg_values = text_input[text_input < 0]
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check_count = len(neg_values)
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assert check_count == 0, \
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" !! Negative values in text_input: {}".format(check_count)
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# TODO: more assertion here
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assert mel_input.shape[0] == c.batch_size
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assert mel_input.shape[2] == c.audio['num_mels']
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if self.ap.symmetric_norm:
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assert mel_input.max() <= self.ap.max_norm
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assert mel_input.min() >= -self.ap.max_norm
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assert mel_input.min() < 0
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else:
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assert mel_input.max() <= self.ap.max_norm
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assert mel_input.min() >= 0
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def test_batch_group_shuffle(self):
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if ok_ljspeech:
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dataloader, dataset = self._create_dataloader(2, c.r, 16)
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frames = dataset.items
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for i, data in enumerate(dataloader):
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if i == self.max_loader_iter:
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break
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text_input = data[0]
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text_lengths = data[1]
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linear_input = data[2]
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mel_input = data[3]
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mel_lengths = data[4]
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stop_target = data[5]
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item_idx = data[6]
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neg_values = text_input[text_input < 0]
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check_count = len(neg_values)
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assert check_count == 0, \
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" !! Negative values in text_input: {}".format(check_count)
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# TODO: more assertion here
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assert mel_input.shape[0] == c.batch_size
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assert mel_input.shape[2] == c.audio['num_mels']
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dataloader.dataset.sort_items()
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assert frames[0] != dataloader.dataset.items[0]
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def test_padding_and_spec(self):
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if ok_ljspeech:
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dataloader, dataset = self._create_dataloader(1, 1, 0)
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for i, data in enumerate(dataloader):
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if i == self.max_loader_iter:
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break
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text_input = data[0]
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text_lengths = data[1]
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linear_input = data[2]
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mel_input = data[3]
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mel_lengths = data[4]
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stop_target = data[5]
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item_idx = data[6]
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# check mel_spec consistency
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if item_idx[0].split('.')[-1] == 'npy':
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wav = np.load(item_idx[0])
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else:
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wav = self.ap.load_wav(item_idx[0])
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mel = self.ap.melspectrogram(wav)
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mel_dl = mel_input[0].cpu().numpy()
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assert (abs(mel.T).astype("float32") - abs(
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mel_dl[:-1])).sum() == 0, (
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abs(mel.T).astype("float32") - abs(mel_dl[:-1])).sum()
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# check mel-spec correctness
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mel_spec = mel_input[-1].cpu().numpy()
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wav = self.ap.inv_mel_spectrogram(mel_spec.T)
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self.ap.save_wav(wav,
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OUTPATH + '/mel_inv_dataloader_cache.wav')
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shutil.copy(item_idx[-1], OUTPATH + '/mel_target_dataloader_cache.wav')
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# check linear-spec
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linear_spec = linear_input[-1].cpu().numpy()
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wav = self.ap.inv_spectrogram(linear_spec.T)
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self.ap.save_wav(wav, OUTPATH + '/linear_inv_dataloader_cache.wav')
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shutil.copy(item_idx[-1], OUTPATH + '/linear_target_dataloader_cache.wav')
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# check the last time step to be zero padded
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assert mel_input[0, -1].sum() == 0
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assert mel_input[0, -2].sum() != 0
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assert stop_target[0, -1] == 1
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assert stop_target[0, -2] == 0
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assert stop_target.sum() == 1
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assert len(mel_lengths.shape) == 1
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assert mel_lengths[0] == mel_input[0].shape[0]
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# Test for batch size 2
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dataloader, dataset = self._create_dataloader(2, 1, 0)
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for i, data in enumerate(dataloader):
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if i == self.max_loader_iter:
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break
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text_input = data[0]
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text_lengths = data[1]
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linear_input = data[2]
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mel_input = data[3]
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mel_lengths = data[4]
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stop_target = data[5]
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item_idx = data[6]
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if mel_lengths[0] > mel_lengths[1]:
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idx = 0
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else:
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idx = 1
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# check the first item in the batch
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assert mel_input[idx, -1].sum() == 0
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assert mel_input[idx, -2].sum() != 0, mel_input
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assert stop_target[idx, -1] == 1
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assert stop_target[idx, -2] == 0
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assert stop_target[idx].sum() == 1
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assert len(mel_lengths.shape) == 1
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assert mel_lengths[idx] == mel_input[idx].shape[0]
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# check the second itme in the batch
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assert mel_input[1 - idx, -1].sum() == 0
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assert stop_target[1 - idx, -1] == 1
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assert len(mel_lengths.shape) == 1
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# check batch conditions
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assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
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# class TestTTSDatasetMemory(unittest.TestCase):
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# def __init__(self, *args, **kwargs):
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# super(TestTTSDatasetMemory, self).__init__(*args, **kwargs)
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# self.max_loader_iter = 4
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# self.c = load_config(os.path.join(c.data_path_cache, 'config.json'))
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# self.ap = AudioProcessor(**c.audio)
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# def test_loader(self):
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# if ok_ljspeech:
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# dataset = TTSDatasetMemory.MyDataset(
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# c.data_path_cache,
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# 'tts_metadata.csv',
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# c.r,
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# c.text_cleaner,
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# preprocessor=tts_cache,
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# ap=self.ap,
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# min_seq_len=c.min_seq_len)
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# dataloader = DataLoader(
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# dataset,
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# batch_size=2,
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# shuffle=True,
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# collate_fn=dataset.collate_fn,
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# drop_last=True,
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# num_workers=c.num_loader_workers)
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# for i, data in enumerate(dataloader):
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# if i == self.max_loader_iter:
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# break
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# text_input = data[0]
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# text_lengths = data[1]
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# linear_input = data[2]
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# mel_input = data[3]
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# mel_lengths = data[4]
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# stop_target = data[5]
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# item_idx = data[6]
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# neg_values = text_input[text_input < 0]
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# check_count = len(neg_values)
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# assert check_count == 0, \
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# " !! Negative values in text_input: {}".format(check_count)
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# # check mel-spec shape
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# assert mel_input.shape[0] == c.batch_size
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# assert mel_input.shape[2] == c.audio['num_mels']
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# assert mel_input.max() <= self.ap.max_norm
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# # check data range
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# if self.ap.symmetric_norm:
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# assert mel_input.max() <= self.ap.max_norm
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# assert mel_input.min() >= -self.ap.max_norm
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# assert mel_input.min() < 0
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# else:
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# assert mel_input.max() <= self.ap.max_norm
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# assert mel_input.min() >= 0
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# def test_batch_group_shuffle(self):
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# if ok_ljspeech:
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# dataset = TTSDatasetMemory.MyDataset(
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# c.data_path_cache,
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# 'tts_metadata.csv',
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# c.r,
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# c.text_cleaner,
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# preprocessor=ljspeech,
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# ap=self.ap,
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# batch_group_size=16,
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# min_seq_len=c.min_seq_len)
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# dataloader = DataLoader(
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# dataset,
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# batch_size=2,
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# shuffle=True,
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# collate_fn=dataset.collate_fn,
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# drop_last=True,
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# num_workers=c.num_loader_workers)
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# frames = dataset.items
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# for i, data in enumerate(dataloader):
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# if i == self.max_loader_iter:
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# break
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# text_input = data[0]
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# text_lengths = data[1]
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# linear_input = data[2]
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# mel_input = data[3]
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# mel_lengths = data[4]
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# stop_target = data[5]
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# item_idx = data[6]
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# neg_values = text_input[text_input < 0]
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# check_count = len(neg_values)
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# assert check_count == 0, \
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# " !! Negative values in text_input: {}".format(check_count)
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# assert mel_input.shape[0] == c.batch_size
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# assert mel_input.shape[2] == c.audio['num_mels']
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# dataloader.dataset.sort_items()
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# assert frames[0] != dataloader.dataset.items[0]
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# def test_padding_and_spec(self):
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# if ok_ljspeech:
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# dataset = TTSDatasetMemory.MyDataset(
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# c.data_path_cache,
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# 'tts_meta_data.csv',
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# 1,
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# c.text_cleaner,
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# preprocessor=ljspeech,
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# ap=self.ap,
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# min_seq_len=c.min_seq_len)
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# # Test for batch size 1
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# dataloader = DataLoader(
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# dataset,
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# batch_size=1,
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# shuffle=False,
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# collate_fn=dataset.collate_fn,
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# drop_last=True,
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# num_workers=c.num_loader_workers)
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# for i, data in enumerate(dataloader):
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# if i == self.max_loader_iter:
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# break
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# text_input = data[0]
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# text_lengths = data[1]
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# linear_input = data[2]
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# mel_input = data[3]
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# mel_lengths = data[4]
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# stop_target = data[5]
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# item_idx = data[6]
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# # check mel_spec consistency
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# if item_idx[0].split('.')[-1] == 'npy':
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# wav = np.load(item_idx[0])
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# else:
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# wav = self.ap.load_wav(item_idx[0])
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# mel = self.ap.melspectrogram(wav)
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# mel_dl = mel_input[0].cpu().numpy()
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# assert (
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# abs(mel.T).astype("float32") - abs(mel_dl[:-1])).sum() == 0
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# # check mel-spec correctness
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# mel_spec = mel_input[0].cpu().numpy()
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# wav = self.ap.inv_mel_spectrogram(mel_spec.T)
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# self.ap.save_wav(wav, OUTPATH + '/mel_inv_dataloader_memo.wav')
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# shutil.copy(item_idx[0], OUTPATH + '/mel_target_dataloader_memo.wav')
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# # check the last time step to be zero padded
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# assert mel_input[0, -1].sum() == 0
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# assert mel_input[0, -2].sum() != 0
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# assert stop_target[0, -1] == 1
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# assert stop_target[0, -2] == 0
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# assert stop_target.sum() == 1
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# assert len(mel_lengths.shape) == 1
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# assert mel_lengths[0] == mel_input[0].shape[0]
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# # Test for batch size 2
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# dataloader = DataLoader(
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# dataset,
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# batch_size=2,
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# shuffle=False,
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# collate_fn=dataset.collate_fn,
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# drop_last=False,
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# num_workers=c.num_loader_workers)
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# for i, data in enumerate(dataloader):
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# if i == self.max_loader_iter:
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# break
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# text_input = data[0]
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# text_lengths = data[1]
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# linear_input = data[2]
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# mel_input = data[3]
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# mel_lengths = data[4]
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# stop_target = data[5]
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# item_idx = data[6]
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# if mel_lengths[0] > mel_lengths[1]:
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# idx = 0
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# else:
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# idx = 1
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# # check the first item in the batch
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# assert mel_input[idx, -1].sum() == 0
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# assert mel_input[idx, -2].sum() != 0, mel_input
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# assert stop_target[idx, -1] == 1
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# assert stop_target[idx, -2] == 0
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# assert stop_target[idx].sum() == 1
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# assert len(mel_lengths.shape) == 1
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# assert mel_lengths[idx] == mel_input[idx].shape[0]
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# # check the second itme in the batch
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# assert mel_input[1 - idx, -1].sum() == 0
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# assert stop_target[1 - idx, -1] == 1
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# assert len(mel_lengths.shape) == 1
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# # check batch conditions
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# assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
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assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
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@ -35,8 +35,8 @@ class TacotronTrainTest(unittest.TestCase):
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criterion = L1LossMasked().to(device)
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criterion_st = nn.BCELoss().to(device)
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model = Tacotron(c.embedding_size, c.audio['num_freq'], c.audio['num_mels'],
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c.r).to(device)
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model = Tacotron(32, c.embedding_size, c.audio['num_freq'], c.audio['num_mels'],
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c.r, c.memory_size).to(device)
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model.train()
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model_ref = copy.deepcopy(model)
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count = 0
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@ -32,6 +32,7 @@
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"mk": 1.0,
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"priority_freq": false,
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"num_loader_workers": 4,
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"memory_size": 5,
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"save_step": 200,
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"data_path": "/home/erogol/Data/LJSpeech-1.1/",
|
||||
|
|
|
|||
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Reference in New Issue