mirror of https://github.com/coqui-ai/TTS.git
194 lines
7.4 KiB
Python
194 lines
7.4 KiB
Python
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 utils.generic_utils import load_config
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from utils.audio import AudioProcessor
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from datasets import TTSDataset
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from datasets.preprocess import ljspeech
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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_ljspeech = os.path.exists(c.data_path)
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DATA_EXIST = True
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if not os.path.exists(c.data_path):
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DATA_EXIST = False
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print(" > Dynamic data loader test: {}".format(DATA_EXIST))
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class TestTTSDataset(unittest.TestCase):
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def __init__(self, *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(**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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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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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 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.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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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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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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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 (abs(mel.T).astype("float32")
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- abs(mel_dl[:-1])
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).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],
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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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