diff options
Diffstat (limited to 'become_yukarin/dataset/dataset.py')
| -rw-r--r-- | become_yukarin/dataset/dataset.py | 75 |
1 files changed, 39 insertions, 36 deletions
diff --git a/become_yukarin/dataset/dataset.py b/become_yukarin/dataset/dataset.py index 5ad7a80..b597bba 100644 --- a/become_yukarin/dataset/dataset.py +++ b/become_yukarin/dataset/dataset.py @@ -66,7 +66,7 @@ class SplitProcess(BaseDataProcess): class WaveFileLoadProcess(BaseDataProcess): - def __init__(self, sample_rate: int, top_db: float, pad_second: float = 0, dtype=numpy.float32): + def __init__(self, sample_rate: int, top_db: float = None, pad_second: float = 0, dtype=numpy.float32): self._sample_rate = sample_rate self._top_db = top_db self._pad_second = pad_second @@ -394,27 +394,29 @@ def create(config: DatasetConfig): data_process_train = copy.deepcopy(data_process_base) - def add_seed(): - return LambdaProcess(lambda d, test: dict(seed=numpy.random.randint(2 ** 32), **d)) + # cropping + if config.train_crop_size is not None: + def add_seed(): + return LambdaProcess(lambda d, test: dict(seed=numpy.random.randint(2 ** 32), **d)) - def padding(s): - return ChainProcess([ - LambdaProcess(lambda d, test: dict(data=d[s], seed=d['seed'])), - RandomPaddingProcess(min_size=config.train_crop_size), - ]) + def padding(s): + return ChainProcess([ + LambdaProcess(lambda d, test: dict(data=d[s], seed=d['seed'])), + RandomPaddingProcess(min_size=config.train_crop_size), + ]) - def crop(s): - return ChainProcess([ - LambdaProcess(lambda d, test: dict(data=d[s], seed=d['seed'])), - RandomCropProcess(crop_size=config.train_crop_size), - ]) + def crop(s): + return ChainProcess([ + LambdaProcess(lambda d, test: dict(data=d[s], seed=d['seed'])), + RandomCropProcess(crop_size=config.train_crop_size), + ]) - data_process_train.append(ChainProcess([ - add_seed(), - SplitProcess(dict(input=padding('input'), target=padding('target'), mask=padding('mask'))), - add_seed(), - SplitProcess(dict(input=crop('input'), target=crop('target'), mask=crop('mask'))), - ])) + data_process_train.append(ChainProcess([ + add_seed(), + SplitProcess(dict(input=padding('input'), target=padding('target'), mask=padding('mask'))), + add_seed(), + SplitProcess(dict(input=crop('input'), target=crop('target'), mask=crop('mask'))), + ])) # add noise data_process_train.append(SplitProcess(dict( @@ -432,23 +434,24 @@ def create(config: DatasetConfig): ))) data_process_test = data_process_base - data_process_test.append(SplitProcess(dict( - input=ChainProcess([ - LambdaProcess(lambda d, test: d['input']), - LastPaddingProcess(min_size=config.train_crop_size), - FirstCropProcess(crop_size=config.train_crop_size), - ]), - target=ChainProcess([ - LambdaProcess(lambda d, test: d['target']), - LastPaddingProcess(min_size=config.train_crop_size), - FirstCropProcess(crop_size=config.train_crop_size), - ]), - mask=ChainProcess([ - LambdaProcess(lambda d, test: d['mask']), - LastPaddingProcess(min_size=config.train_crop_size), - FirstCropProcess(crop_size=config.train_crop_size), - ]), - ))) + if config.train_crop_size is not None: + data_process_test.append(SplitProcess(dict( + input=ChainProcess([ + LambdaProcess(lambda d, test: d['input']), + LastPaddingProcess(min_size=config.train_crop_size), + FirstCropProcess(crop_size=config.train_crop_size), + ]), + target=ChainProcess([ + LambdaProcess(lambda d, test: d['target']), + LastPaddingProcess(min_size=config.train_crop_size), + FirstCropProcess(crop_size=config.train_crop_size), + ]), + mask=ChainProcess([ + LambdaProcess(lambda d, test: d['mask']), + LastPaddingProcess(min_size=config.train_crop_size), + FirstCropProcess(crop_size=config.train_crop_size), + ]), + ))) num_test = config.num_test pairs = [ |