mirror of https://github.com/coqui-ai/TTS.git
tests update
This commit is contained in:
Eren Golge
parent
14c9e9cde9
commit
b087c0b5ec
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@ -73,7 +73,7 @@ class L1LossMaskedTests(unittest.TestCase):
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dummy_length = (T.ones(4) * 8).long()
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output = layer(dummy_input, dummy_target, dummy_length)
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assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
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dummy_input = T.ones(4, 8, 128).float()
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dummy_target = T.zeros(4, 8, 128).float()
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dummy_length = (T.arange(5, 9)).long()
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@ -5,7 +5,7 @@ import numpy as np
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from torch.utils.data import DataLoader
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from TTS.utils.generic_utils import load_config
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from TTS.datasets.LJSpeech import LJSpeechDataset
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from TTS.datasets.TWEB import TWEBDataset
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# from TTS.datasets.TWEB import TWEBDataset
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file_path = os.path.dirname(os.path.realpath(__file__))
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@ -19,8 +19,8 @@ class TestLJSpeechDataset(unittest.TestCase):
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self.max_loader_iter = 4
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def test_loader(self):
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dataset = LJSpeechDataset(os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
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os.path.join(c.data_path_LJSpeech, 'wavs'),
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dataset = LJSpeechDataset(os.path.join(c.data_path, 'metadata.csv'),
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os.path.join(c.data_path, 'wavs'),
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c.r,
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c.sample_rate,
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c.text_cleaner,
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@ -59,8 +59,8 @@ class TestLJSpeechDataset(unittest.TestCase):
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assert mel_input.shape[2] == c.num_mels
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def test_padding(self):
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dataset = LJSpeechDataset(os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
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os.path.join(c.data_path_LJSpeech, 'wavs'),
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dataset = LJSpeechDataset(os.path.join(c.data_path, 'metadata.csv'),
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os.path.join(c.data_path, 'wavs'),
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1,
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c.sample_rate,
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c.text_cleaner,
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@ -144,134 +144,134 @@ class TestLJSpeechDataset(unittest.TestCase):
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assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
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class TestTWEBDataset(unittest.TestCase):
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# class TestTWEBDataset(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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super(TestTWEBDataset, self).__init__(*args, **kwargs)
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self.max_loader_iter = 4
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# def __init__(self, *args, **kwargs):
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# super(TestTWEBDataset, self).__init__(*args, **kwargs)
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# self.max_loader_iter = 4
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def test_loader(self):
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dataset = TWEBDataset(os.path.join(c.data_path_TWEB, 'transcript.txt'),
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os.path.join(c.data_path_TWEB, 'wavs'),
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c.r,
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c.sample_rate,
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c.text_cleaner,
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c.num_mels,
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c.min_level_db,
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c.frame_shift_ms,
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c.frame_length_ms,
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c.preemphasis,
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c.ref_level_db,
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c.num_freq,
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c.power
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)
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# def test_loader(self):
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# dataset = TWEBDataset(os.path.join(c.data_path_TWEB, 'transcript.txt'),
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# os.path.join(c.data_path_TWEB, 'wavs'),
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# c.r,
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# c.sample_rate,
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# c.text_cleaner,
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# c.num_mels,
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# c.min_level_db,
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# c.frame_shift_ms,
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# c.frame_length_ms,
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# c.preemphasis,
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# c.ref_level_db,
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# c.num_freq,
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# c.power
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# )
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dataloader = DataLoader(dataset, batch_size=2,
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shuffle=True, collate_fn=dataset.collate_fn,
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drop_last=True, num_workers=c.num_loader_workers)
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# dataloader = DataLoader(dataset, batch_size=2,
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# shuffle=True, collate_fn=dataset.collate_fn,
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# drop_last=True, 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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# 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 linear_input.shape[0] == c.batch_size
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assert mel_input.shape[0] == c.batch_size
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assert mel_input.shape[2] == c.num_mels
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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 linear_input.shape[0] == c.batch_size
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# assert mel_input.shape[0] == c.batch_size
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# assert mel_input.shape[2] == c.num_mels
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def test_padding(self):
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dataset = TWEBDataset(os.path.join(c.data_path_TWEB, 'transcript.txt'),
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os.path.join(c.data_path_TWEB, 'wavs'),
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1,
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c.sample_rate,
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c.text_cleaner,
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c.num_mels,
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c.min_level_db,
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c.frame_shift_ms,
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c.frame_length_ms,
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c.preemphasis,
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c.ref_level_db,
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c.num_freq,
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c.power
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)
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# def test_padding(self):
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# dataset = TWEBDataset(os.path.join(c.data_path_TWEB, 'transcript.txt'),
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# os.path.join(c.data_path_TWEB, 'wavs'),
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# 1,
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# c.sample_rate,
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# c.text_cleaner,
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# c.num_mels,
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# c.min_level_db,
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# c.frame_shift_ms,
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# c.frame_length_ms,
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# c.preemphasis,
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# c.ref_level_db,
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# c.num_freq,
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# c.power
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# )
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# Test for batch size 1
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dataloader = DataLoader(dataset, batch_size=1,
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shuffle=False, collate_fn=dataset.collate_fn,
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drop_last=False, num_workers=c.num_loader_workers)
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# # Test for batch size 1
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# dataloader = DataLoader(dataset, batch_size=1,
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# shuffle=False, collate_fn=dataset.collate_fn,
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# drop_last=False, 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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# 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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# 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 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, "{} -- {}".format(item_idx, i)
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assert linear_input[0, -1].sum() == 0
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assert linear_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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# # 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, "{} -- {}".format(item_idx, i)
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# assert linear_input[0, -1].sum() == 0
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# assert linear_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(dataset, batch_size=2,
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shuffle=False, collate_fn=dataset.collate_fn,
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drop_last=False, num_workers=c.num_loader_workers)
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# # Test for batch size 2
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# dataloader = DataLoader(dataset, batch_size=2,
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# shuffle=False, collate_fn=dataset.collate_fn,
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# drop_last=False, 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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# 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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# 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 linear_input[idx, -1].sum() == 0
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assert linear_input[idx, -2].sum() != 0
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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 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 linear_input[idx, -1].sum() == 0
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# assert linear_input[idx, -2].sum() != 0
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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 linear_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 the second itme in the batch
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# assert mel_input[1-idx, -1].sum() == 0
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# assert linear_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 (linear_input * stop_target.unsqueeze(2)).sum() == 0
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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 (linear_input * stop_target.unsqueeze(2)).sum() == 0
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@ -42,20 +42,21 @@ class TacotronTrainTest(unittest.TestCase):
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count += 1
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optimizer = optim.Adam(model.parameters(), lr=c.lr)
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for i in range(5):
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mel_out, linear_out, align, stop_tokens = model.forward(input, mel_spec)
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assert stop_tokens.data.max() <= 1.0
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assert stop_tokens.data.min() >= 0.0
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mel_out, linear_out, align = model.forward(input, mel_spec)
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# mel_out, linear_out, align, stop_tokens = model.forward(input, mel_spec)
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# assert stop_tokens.data.max() <= 1.0
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# assert stop_tokens.data.min() >= 0.0
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optimizer.zero_grad()
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loss = criterion(mel_out, mel_spec, mel_lengths)
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stop_loss = criterion_st(stop_tokens, stop_targets)
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loss = loss + criterion(linear_out, linear_spec, mel_lengths) + stop_loss
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# stop_loss = criterion_st(stop_tokens, stop_targets)
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loss = loss + criterion(linear_out, linear_spec, mel_lengths)
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loss.backward()
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optimizer.step()
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# check parameter changes
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count = 0
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for param, param_ref in zip(model.parameters(), model_ref.parameters()):
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# ignore pre-higway layer since it works conditional
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if count not in [141, 59]:
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if count not in [139, 59]:
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assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(count, param.shape, param, param_ref)
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count += 1
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