mirror of https://github.com/coqui-ai/TTS.git
232 lines
9.5 KiB
Python
232 lines
9.5 KiB
Python
import os
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import shutil
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import unittest
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from tests import get_tests_output_path
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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from TTS.tts.datasets import TTSDataset
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from TTS.tts.datasets.formatters import ljspeech
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from TTS.utils.audio import AudioProcessor
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# pylint: disable=unused-variable
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OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
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os.makedirs(OUTPATH, exist_ok=True)
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# create a dummy config for testing data loaders.
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c = BaseTTSConfig(text_cleaner="english_cleaners", num_loader_workers=0, batch_size=2)
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c.r = 5
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c.data_path = "tests/data/ljspeech/"
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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().__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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items = ljspeech(c.data_path, "metadata.csv")
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# add a default language because now the TTSDataset expect a language
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language = ""
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items = [[*item, language] for item in items]
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dataset = TTSDataset(
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r,
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c.text_cleaner,
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compute_linear_spec=True,
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return_wav=True,
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ap=self.ap,
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meta_data=items,
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characters=c.characters,
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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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)
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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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)
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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["text"]
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text_lengths = data["text_lengths"]
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speaker_name = data["speaker_names"]
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linear_input = data["linear"]
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mel_input = data["mel"]
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mel_lengths = data["mel_lengths"]
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stop_target = data["stop_targets"]
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item_idx = data["item_idxs"]
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wavs = data["waveform"]
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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, " !! Negative values in text_input: {}".format(check_count)
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assert isinstance(speaker_name[0], str)
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assert linear_input.shape[0] == c.batch_size
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assert linear_input.shape[2] == self.ap.fft_size // 2 + 1
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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 (
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wavs.shape[1] == mel_input.shape[1] * c.audio.hop_length
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), f"wavs.shape: {wavs.shape[1]}, mel_input.shape: {mel_input.shape[1] * c.audio.hop_length}"
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# make sure that the computed mels and the waveform match and correctly computed
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mel_new = self.ap.melspectrogram(wavs[0].squeeze().numpy())
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ignore_seg = -(1 + c.audio.win_length // c.audio.hop_length)
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mel_diff = (mel_new[:, : mel_input.shape[1]] - mel_input[0].T.numpy())[:, 0:ignore_seg]
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assert abs(mel_diff.sum()) < 1e-5
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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 # pylint: disable=invalid-unary-operand-type
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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["text"]
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text_lengths = data["text_lengths"]
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speaker_name = data["speaker_names"]
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linear_input = data["linear"]
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mel_input = data["mel"]
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mel_lengths = data["mel_lengths"]
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stop_target = data["stop_targets"]
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item_idx = data["item_idxs"]
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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_and_filter_items()
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is_items_reordered = False
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for idx, item in enumerate(dataloader.dataset.items):
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if item != frames[idx]:
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is_items_reordered = True
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break
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assert is_items_reordered
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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["text"]
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text_lengths = data["text_lengths"]
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speaker_name = data["speaker_names"]
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linear_input = data["linear"]
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mel_input = data["mel"]
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mel_lengths = data["mel_lengths"]
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stop_target = data["stop_targets"]
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item_idx = data["item_idxs"]
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# check mel_spec consistency
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wav = np.asarray(self.ap.load_wav(item_idx[0]), dtype=np.float32)
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mel = self.ap.melspectrogram(wav).astype("float32")
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mel = torch.FloatTensor(mel).contiguous()
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mel_dl = mel_input[0]
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# NOTE: Below needs to check == 0 but due to an unknown reason
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# there is a slight difference between two matrices.
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# TODO: Check this assert cond more in detail.
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assert abs(mel.T - mel_dl).max() < 1e-5, abs(mel.T - mel_dl).max()
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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_melspectrogram(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["text"]
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text_lengths = data["text_lengths"]
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speaker_name = data["speaker_names"]
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linear_input = data["linear"]
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mel_input = data["mel"]
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mel_lengths = data["mel_lengths"]
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stop_target = data["stop_targets"]
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item_idx = data["item_idxs"]
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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, mel_lengths[1] - 1] == 1
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assert stop_target[1, mel_lengths[1] :].sum() == stop_target.shape[1] - mel_lengths[1]
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assert len(mel_lengths.shape) == 1
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# check batch zero-frame conditions (zero-frame disabled)
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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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