mirror of
https://github.com/wassname/TTS.git
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Batch update after data-loss
This commit is contained in:
Eren Golge
+407
-208
@@ -1,50 +1,49 @@
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import os
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import unittest
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import shutil
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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.utils.audio import AudioProcessor
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from TTS.datasets import LJSpeech, Kusal
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from TTS.datasets import TTSDataset, TTSDatasetCached, TTSDatasetMemory
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from TTS.datasets.preprocess import ljspeech, tts_cache
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file_path = os.path.dirname(os.path.realpath(__file__))
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OUTPATH = os.path.join(file_path, "outputs/loader_tests/")
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os.makedirs(OUTPATH, exist_ok=True)
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c = load_config(os.path.join(file_path, 'test_config.json'))
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ok_kusal = os.path.exists(c.data_path_Kusal)
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ok_ljspeech = os.path.exists(c.data_path_LJSpeech)
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ok_ljspeech = os.path.exists(c.data_path)
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class TestLJSpeechDataset(unittest.TestCase):
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class TestTTSDataset(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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super(TestLJSpeechDataset, self).__init__(*args, **kwargs)
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super(TestTTSDataset, self).__init__(*args, **kwargs)
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self.max_loader_iter = 4
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self.ap = AudioProcessor(
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sample_rate=c.sample_rate,
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num_mels=c.num_mels,
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min_level_db=c.min_level_db,
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frame_shift_ms=c.frame_shift_ms,
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frame_length_ms=c.frame_length_ms,
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ref_level_db=c.ref_level_db,
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num_freq=c.num_freq,
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power=c.power,
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preemphasis=c.preemphasis)
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self.ap = AudioProcessor(**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,
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'metadata.csv',
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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=bgs,
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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=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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dataset = LJSpeech.MyDataset(
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os.path.join(c.data_path_LJSpeech),
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os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
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c.r,
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c.text_cleaner,
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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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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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@@ -63,29 +62,158 @@ class TestLJSpeechDataset(unittest.TestCase):
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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 linear_input.shape[2] == self.ap.num_freq
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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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assert mel_input.shape[2] == c.audio['num_mels']
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# check normalization ranges
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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 = LJSpeech.MyDataset(
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os.path.join(c.data_path_LJSpeech),
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os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
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c.r,
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c.text_cleaner,
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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, dataset = self._create_dataloader(2, c.r, 16)
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last_length = 0
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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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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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avg_length = mel_lengths.numpy().mean()
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assert avg_length >= last_length
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dataloader.dataset.sort_items()
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assert frames[0] != dataloader.dataset.items[0]
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frames = dataset.frames
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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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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.wav')
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shutil.copy(item_idx[0], OUTPATH + '/mel_target_dataloader.wav')
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# check linear-spec
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linear_spec = linear_input[0].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.wav')
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shutil.copy(item_idx[0], OUTPATH + '/linear_target_dataloader.wav')
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# check the last time step to be zero padded
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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 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] == linear_input[0].shape[0]
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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 linear_input[idx, -1].sum() == 0
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assert linear_input[idx, -2].sum() != 0, linear_input
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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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assert mel_lengths[idx] == linear_input[idx].shape[0]
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# check the second itme in the batch
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assert linear_input[1 - idx, -1].sum() == 0
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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 (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 = TTSDatasetCached.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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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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@@ -102,32 +230,21 @@ class TestLJSpeechDataset(unittest.TestCase):
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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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dataloader.dataset.sort_frames()
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assert frames[0] != dataloader.dataset.frames[0]
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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_padding(self):
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def test_batch_group_shuffle(self):
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if ok_ljspeech:
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dataset = LJSpeech.MyDataset(
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os.path.join(c.data_path_LJSpeech),
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os.path.join(c.data_path_LJSpeech, 'metadata.csv'),
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1,
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c.text_cleaner,
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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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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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@@ -139,11 +256,51 @@ class TestLJSpeechDataset(unittest.TestCase):
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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[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,
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OUTPATH + '/mel_inv_dataloader_cache.wav')
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shutil.copy(item_idx[0], OUTPATH + '/mel_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 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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@@ -151,14 +308,7 @@ class TestLJSpeechDataset(unittest.TestCase):
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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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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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@@ -178,8 +328,6 @@ class TestLJSpeechDataset(unittest.TestCase):
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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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@@ -188,151 +336,202 @@ class TestLJSpeechDataset(unittest.TestCase):
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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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class TestKusalDataset(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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super(TestKusalDataset, self).__init__(*args, **kwargs)
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self.max_loader_iter = 4
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self.ap = AudioProcessor(
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sample_rate=c.sample_rate,
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num_mels=c.num_mels,
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min_level_db=c.min_level_db,
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frame_shift_ms=c.frame_shift_ms,
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frame_length_ms=c.frame_length_ms,
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ref_level_db=c.ref_level_db,
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num_freq=c.num_freq,
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power=c.power,
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preemphasis=c.preemphasis)
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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_kusal:
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dataset = Kusal.MyDataset(
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os.path.join(c.data_path_Kusal),
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os.path.join(c.data_path_Kusal, 'prompts.txt'),
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c.r,
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c.text_cleaner,
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ap=self.ap,
|
||||
min_seq_len=c.min_seq_len)
|
||||
# def test_loader(self):
|
||||
# if ok_ljspeech:
|
||||
# dataset = TTSDatasetMemory.MyDataset(
|
||||
# c.data_path_cache,
|
||||
# 'tts_metadata.csv',
|
||||
# c.r,
|
||||
# c.text_cleaner,
|
||||
# preprocessor=tts_cache,
|
||||
# ap=self.ap,
|
||||
# min_seq_len=c.min_seq_len)
|
||||
|
||||
dataloader = DataLoader(
|
||||
dataset,
|
||||
batch_size=2,
|
||||
shuffle=True,
|
||||
collate_fn=dataset.collate_fn,
|
||||
drop_last=True,
|
||||
num_workers=c.num_loader_workers)
|
||||
# dataloader = DataLoader(
|
||||
# dataset,
|
||||
# batch_size=2,
|
||||
# shuffle=True,
|
||||
# collate_fn=dataset.collate_fn,
|
||||
# drop_last=True,
|
||||
# num_workers=c.num_loader_workers)
|
||||
|
||||
for i, data in enumerate(dataloader):
|
||||
if i == self.max_loader_iter:
|
||||
break
|
||||
text_input = data[0]
|
||||
text_lengths = data[1]
|
||||
linear_input = data[2]
|
||||
mel_input = data[3]
|
||||
mel_lengths = data[4]
|
||||
stop_target = data[5]
|
||||
item_idx = data[6]
|
||||
# for i, data in enumerate(dataloader):
|
||||
# if i == self.max_loader_iter:
|
||||
# break
|
||||
# text_input = data[0]
|
||||
# text_lengths = data[1]
|
||||
# linear_input = data[2]
|
||||
# mel_input = data[3]
|
||||
# mel_lengths = data[4]
|
||||
# stop_target = data[5]
|
||||
# item_idx = data[6]
|
||||
|
||||
neg_values = text_input[text_input < 0]
|
||||
check_count = len(neg_values)
|
||||
assert check_count == 0, \
|
||||
" !! Negative values in text_input: {}".format(check_count)
|
||||
# TODO: more assertion here
|
||||
assert linear_input.shape[0] == c.batch_size
|
||||
assert mel_input.shape[0] == c.batch_size
|
||||
assert mel_input.shape[2] == c.num_mels
|
||||
# neg_values = text_input[text_input < 0]
|
||||
# check_count = len(neg_values)
|
||||
# assert check_count == 0, \
|
||||
# " !! Negative values in text_input: {}".format(check_count)
|
||||
# # check mel-spec shape
|
||||
# assert mel_input.shape[0] == c.batch_size
|
||||
# assert mel_input.shape[2] == c.audio['num_mels']
|
||||
# assert mel_input.max() <= self.ap.max_norm
|
||||
# # check data range
|
||||
# if self.ap.symmetric_norm:
|
||||
# assert mel_input.max() <= self.ap.max_norm
|
||||
# assert mel_input.min() >= -self.ap.max_norm
|
||||
# assert mel_input.min() < 0
|
||||
# else:
|
||||
# assert mel_input.max() <= self.ap.max_norm
|
||||
# assert mel_input.min() >= 0
|
||||
|
||||
def test_padding(self):
|
||||
if ok_kusal:
|
||||
dataset = Kusal.MyDataset(
|
||||
os.path.join(c.data_path_Kusal),
|
||||
os.path.join(c.data_path_Kusal, 'prompts.txt'),
|
||||
1,
|
||||
c.text_cleaner,
|
||||
ap=self.ap,
|
||||
min_seq_len=c.min_seq_len)
|
||||
# def test_batch_group_shuffle(self):
|
||||
# if ok_ljspeech:
|
||||
# dataset = TTSDatasetMemory.MyDataset(
|
||||
# c.data_path_cache,
|
||||
# 'tts_metadata.csv',
|
||||
# c.r,
|
||||
# c.text_cleaner,
|
||||
# preprocessor=ljspeech,
|
||||
# ap=self.ap,
|
||||
# batch_group_size=16,
|
||||
# min_seq_len=c.min_seq_len)
|
||||
|
||||
# Test for batch size 1
|
||||
dataloader = DataLoader(
|
||||
dataset,
|
||||
batch_size=1,
|
||||
shuffle=False,
|
||||
collate_fn=dataset.collate_fn,
|
||||
drop_last=True,
|
||||
num_workers=c.num_loader_workers)
|
||||
# dataloader = DataLoader(
|
||||
# dataset,
|
||||
# batch_size=2,
|
||||
# shuffle=True,
|
||||
# collate_fn=dataset.collate_fn,
|
||||
# drop_last=True,
|
||||
# num_workers=c.num_loader_workers)
|
||||
|
||||
for i, data in enumerate(dataloader):
|
||||
if i == self.max_loader_iter:
|
||||
break
|
||||
text_input = data[0]
|
||||
text_lengths = data[1]
|
||||
linear_input = data[2]
|
||||
mel_input = data[3]
|
||||
mel_lengths = data[4]
|
||||
stop_target = data[5]
|
||||
item_idx = data[6]
|
||||
# frames = dataset.items
|
||||
# for i, data in enumerate(dataloader):
|
||||
# if i == self.max_loader_iter:
|
||||
# break
|
||||
# text_input = data[0]
|
||||
# text_lengths = data[1]
|
||||
# linear_input = data[2]
|
||||
# mel_input = data[3]
|
||||
# mel_lengths = data[4]
|
||||
# stop_target = data[5]
|
||||
# item_idx = data[6]
|
||||
|
||||
# check the last time step to be zero padded
|
||||
assert mel_input[0, -1].sum() == 0
|
||||
# assert mel_input[0, -2].sum() != 0
|
||||
assert linear_input[0, -1].sum() == 0
|
||||
# assert linear_input[0, -2].sum() != 0
|
||||
assert stop_target[0, -1] == 1
|
||||
assert stop_target[0, -2] == 0
|
||||
assert stop_target.sum() == 1
|
||||
assert len(mel_lengths.shape) == 1
|
||||
assert mel_lengths[0] == mel_input[0].shape[0]
|
||||
# neg_values = text_input[text_input < 0]
|
||||
# check_count = len(neg_values)
|
||||
# assert check_count == 0, \
|
||||
# " !! Negative values in text_input: {}".format(check_count)
|
||||
# assert mel_input.shape[0] == c.batch_size
|
||||
# assert mel_input.shape[2] == c.audio['num_mels']
|
||||
# dataloader.dataset.sort_items()
|
||||
# assert frames[0] != dataloader.dataset.items[0]
|
||||
|
||||
# Test for batch size 2
|
||||
dataloader = DataLoader(
|
||||
dataset,
|
||||
batch_size=2,
|
||||
shuffle=False,
|
||||
collate_fn=dataset.collate_fn,
|
||||
drop_last=False,
|
||||
num_workers=c.num_loader_workers)
|
||||
# def test_padding_and_spec(self):
|
||||
# if ok_ljspeech:
|
||||
# dataset = TTSDatasetMemory.MyDataset(
|
||||
# c.data_path_cache,
|
||||
# 'tts_meta_data.csv',
|
||||
# 1,
|
||||
# c.text_cleaner,
|
||||
# preprocessor=ljspeech,
|
||||
# ap=self.ap,
|
||||
# min_seq_len=c.min_seq_len)
|
||||
|
||||
for i, data in enumerate(dataloader):
|
||||
if i == self.max_loader_iter:
|
||||
break
|
||||
text_input = data[0]
|
||||
text_lengths = data[1]
|
||||
linear_input = data[2]
|
||||
mel_input = data[3]
|
||||
mel_lengths = data[4]
|
||||
stop_target = data[5]
|
||||
item_idx = data[6]
|
||||
# # Test for batch size 1
|
||||
# dataloader = DataLoader(
|
||||
# dataset,
|
||||
# batch_size=1,
|
||||
# shuffle=False,
|
||||
# collate_fn=dataset.collate_fn,
|
||||
# drop_last=True,
|
||||
# num_workers=c.num_loader_workers)
|
||||
|
||||
if mel_lengths[0] > mel_lengths[1]:
|
||||
idx = 0
|
||||
else:
|
||||
idx = 1
|
||||
# for i, data in enumerate(dataloader):
|
||||
# if i == self.max_loader_iter:
|
||||
# break
|
||||
# text_input = data[0]
|
||||
# text_lengths = data[1]
|
||||
# linear_input = data[2]
|
||||
# mel_input = data[3]
|
||||
# mel_lengths = data[4]
|
||||
# stop_target = data[5]
|
||||
# item_idx = data[6]
|
||||
|
||||
# check the first item in the batch
|
||||
assert mel_input[idx, -1].sum() == 0
|
||||
assert mel_input[idx, -2].sum() != 0, mel_input
|
||||
assert linear_input[idx, -1].sum() == 0
|
||||
assert linear_input[idx, -2].sum() != 0
|
||||
assert stop_target[idx, -1] == 1
|
||||
assert stop_target[idx, -2] == 0
|
||||
assert stop_target[idx].sum() == 1
|
||||
assert len(mel_lengths.shape) == 1
|
||||
assert mel_lengths[idx] == mel_input[idx].shape[0]
|
||||
# # check mel_spec consistency
|
||||
# if item_idx[0].split('.')[-1] == 'npy':
|
||||
# wav = np.load(item_idx[0])
|
||||
# else:
|
||||
# wav = self.ap.load_wav(item_idx[0])
|
||||
# mel = self.ap.melspectrogram(wav)
|
||||
# mel_dl = mel_input[0].cpu().numpy()
|
||||
# assert (
|
||||
# abs(mel.T).astype("float32") - abs(mel_dl[:-1])).sum() == 0
|
||||
|
||||
# check the second itme in the batch
|
||||
assert mel_input[1 - idx, -1].sum() == 0
|
||||
assert linear_input[1 - idx, -1].sum() == 0
|
||||
assert stop_target[1 - idx, -1] == 1
|
||||
assert len(mel_lengths.shape) == 1
|
||||
# # check mel-spec correctness
|
||||
# mel_spec = mel_input[0].cpu().numpy()
|
||||
# wav = self.ap.inv_mel_spectrogram(mel_spec.T)
|
||||
# self.ap.save_wav(wav, OUTPATH + '/mel_inv_dataloader_memo.wav')
|
||||
# shutil.copy(item_idx[0], OUTPATH + '/mel_target_dataloader_memo.wav')
|
||||
|
||||
# check batch conditions
|
||||
assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
|
||||
assert (linear_input * stop_target.unsqueeze(2)).sum() == 0
|
||||
# # check the last time step to be zero padded
|
||||
# assert mel_input[0, -1].sum() == 0
|
||||
# assert mel_input[0, -2].sum() != 0
|
||||
# assert stop_target[0, -1] == 1
|
||||
# assert stop_target[0, -2] == 0
|
||||
# assert stop_target.sum() == 1
|
||||
# assert len(mel_lengths.shape) == 1
|
||||
# assert mel_lengths[0] == mel_input[0].shape[0]
|
||||
|
||||
# # Test for batch size 2
|
||||
# dataloader = DataLoader(
|
||||
# dataset,
|
||||
# batch_size=2,
|
||||
# shuffle=False,
|
||||
# collate_fn=dataset.collate_fn,
|
||||
# drop_last=False,
|
||||
# num_workers=c.num_loader_workers)
|
||||
|
||||
# for i, data in enumerate(dataloader):
|
||||
# if i == self.max_loader_iter:
|
||||
# break
|
||||
# text_input = data[0]
|
||||
# text_lengths = data[1]
|
||||
# linear_input = data[2]
|
||||
# mel_input = data[3]
|
||||
# mel_lengths = data[4]
|
||||
# stop_target = data[5]
|
||||
# item_idx = data[6]
|
||||
|
||||
# if mel_lengths[0] > mel_lengths[1]:
|
||||
# idx = 0
|
||||
# else:
|
||||
# idx = 1
|
||||
|
||||
# # check the first item in the batch
|
||||
# assert mel_input[idx, -1].sum() == 0
|
||||
# assert mel_input[idx, -2].sum() != 0, mel_input
|
||||
# assert stop_target[idx, -1] == 1
|
||||
# assert stop_target[idx, -2] == 0
|
||||
# assert stop_target[idx].sum() == 1
|
||||
# assert len(mel_lengths.shape) == 1
|
||||
# assert mel_lengths[idx] == mel_input[idx].shape[0]
|
||||
|
||||
# # check the second itme in the batch
|
||||
# assert mel_input[1 - idx, -1].sum() == 0
|
||||
# assert stop_target[1 - idx, -1] == 1
|
||||
# assert len(mel_lengths.shape) == 1
|
||||
|
||||
# # check batch conditions
|
||||
# assert (mel_input * stop_target.unsqueeze(2)).sum() == 0
|
||||
|
||||
Reference in New Issue
Block a user