def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'repeat': int,
'num_cpu_cores': int,
'tgt_vocab_file': str,
'm_padding': bool,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'num_parallel_calls': int,
'shuffle_buffer': int,
'image_size': int,
'num_classes': int,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'num_audio_features': int,
'input_type': ['spectrogram', 'mfcc'],
'vocab_file': str,
'dataset_files': list,
})
def get_optional_params():
return dict(
DataLayer.get_optional_params(), **{
'pad_to': int,
'mag_power': int,
'pad_EOS': bool,
'pad_value': float,
'feature_normalize_mean': float,
'feature_normalize_std': float,
'trim': bool,
'data_min': None,
'duration_min': int,
'duration_max': int,
'mel_type': ['slaney', 'htk'],
"exp_mag": bool,
'style_input': [None, 'wav'],
'n_samples_eval': int,
'n_fft': int,
'fmax': float,
'max_normalization': bool,
'use_cache': bool,
"save_embeddings": bool,
"use_npy_wavs": bool,
"use_phonemes": bool,
"use_saved_embedding": bool,
"saved_embedding_location": str,
})
def get_optional_params():
return dict(
DataLayer.get_optional_params(), **{
'augmentation': dict,
'pad_to': int,
'max_duration': float,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'num_audio_features': int,
'input_type': ['spectrogram', 'mfcc', 'logfbank'],
'vocab_file': str,
'dataset_files': list,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
# 'content_file': str,
# 'vocab_file': str,
'shuffle': bool,
'repeat': bool,
'bptt': int,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'repeat': int,
'num_cpu_cores': int,
'tgt_vocab_file': str,
'pad_data_to_eight': bool,
'batch_in_tokens': bool,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'augmentation': dict,
'pad_to': int,
'max_duration': float,
'bpe': bool,
'autoregressive': bool,
})
def get_required_params():
return dict(
DataLayer.get_required_params(), **{
"dataset_files": list,
"dataset_location": str,
"num_audio_features": int,
"num_labels": int
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'delimiter': str,
'map_parallel_calls': int,
'prefetch_buffer_size': int,
'pad_lengths_to_eight': bool,
'pad_vocab_to_eight': bool,
'seed_file': str,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'source_file': str,
'src_vocab_file': str,
'tgt_vocab_file': str,
'max_length': int,
'shuffle': bool,
'repeat': bool,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'source_file': str,
'src_vocab_file': str,
'tgt_vocab_file': str,
'max_length': int,
'shuffle': bool,
'repeat': bool,
})
def get_required_params():
return dict(
DataLayer.get_required_params(), **{
'lm_vocab_file': str,
'shuffle': bool,
'repeat': bool,
'max_length': int,
'processed_data_folder': str,
})
def get_required_params():
return dict(
DataLayer.get_required_params(), **{
'dataset_location': str,
'mel_feature_num': None,
'vocab_file': str,
'dataset_files': list,
"minimal_vocabulary": bool,
})
def get_optional_params():
return dict(
DataLayer.get_optional_params(), **{
'pad_to': int,
'mag_power': int,
'pad_EOS': bool,
'feature_normalize_mean': float,
'feature_normalize_std': float
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'data_dir': str,
'file_pattern': str,
'src_vocab_file': str,
'batch_size': int,
'max_length': int,
'shuffle': bool,
"delimiter": str,
})
def get_required_params():
return dict(
DataLayer.get_required_params(), **{
'dataset_location': str,
'num_audio_features': None,
'output_type': ['magnitude', 'mel', 'both'],
'vocab_file': str,
'dataset_files': list,
'feature_normalize': bool,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'data_dir': str,
'file_pattern': str,
'src_vocab_file': str,
'batch_size': int,
'max_length': int,
'shuffle': bool,
"delimiter": str,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'use_targets': bool,
'delimiter': str,
'target_file': str,
'map_parallel_calls': int,
'prefetch_buffer_size': int,
'pad_lengths_to_eight': bool,
'pad_vocab_to_eight': bool,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'augmentation': dict,
'pad_to': int,
'max_duration': float,
'bpe': bool,
'autoregressive': bool,
'syn_enable': bool,
'syn_subdirs': list,
'window_size': float,
'window_stride': float,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'use_targets': bool,
'delimiter': str,
'target_file': str,
'map_parallel_calls': int,
'prefetch_buffer_size': int,
'pad_lengths_to_eight': bool,
'pad_vocab_to_eight': bool,
'shuffle_buffer_size': int,
'special_tokens_already_in_vocab': bool,
'use_start_token': bool,
})
def get_optional_params():
return dict(
DataLayer.get_optional_params(), **{
'pad_to': int,
'mag_power': int,
'pad_EOS': bool,
'pad_value': float,
'feature_normalize_mean': float,
'feature_normalize_std': float,
'trim': bool,
'data_min': None,
'duration_min': int,
'duration_max': int,
'mel_type': ['slaney', 'htk'],
"exp_mag": bool
})
def get_optional_params():
return dict(
DataLayer.get_optional_params(), **{
'data_root': str,
'rand_start': bool,
'small': bool,
'use_targets': bool,
'delimiter': str,
'map_parallel_calls': int,
'prefetch_buffer_size': int,
'pad_lengths_to_eight': bool,
'pad_vocab_to_eight': bool,
'seed_tokens': str,
'shuffle_buffer_size': int,
'processed_data_folder': str,
})
def get_optional_params():
return dict(
DataLayer.get_optional_params(), **{
'rand_start': bool,
'small': bool,
'use_targets': bool,
'delimiter': str,
'map_parallel_calls': int,
'prefetch_buffer_size': int,
'pad_lengths_to_eight': bool,
'pad_vocab_to_eight': bool,
'shuffle_buffer_size': int,
'data_root': str,
'binary': bool,
'num_classes': int,
'get_stats': bool,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'backend': ['psf', 'librosa'],
'augmentation': dict,
'pad_to': int,
'max_duration': float,
'min_duration': float,
'bpe': bool,
'autoregressive': bool,
'syn_enable': bool,
'syn_subdirs': list,
'window_size': float,
'window_stride': float,
'dither': float,
'norm_per_feature': bool,
'window': ['hanning', 'hamming', 'none'],
'num_fft': int,
'precompute_mel_basis': bool,
'sample_freq': int,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'vocab_file': str,
'bptt': int,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'data_dir': str,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'repeat': bool,
'bptt': int,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
"num_audio_features": int,
"dataset_files": list
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'augmentation': dict,
'pad_to': int,
})
def get_optional_params():
return dict(DataLayer.get_optional_params(),
**{"dataset_location": str})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
"cache_data": bool,
"augment_data": bool
})
def get_optional_params():
return dict(DataLayer.get_optional_params(), **{
'num_parallel_calls': int,
'shuffle_buffer': int,
})
def get_required_params():
return dict(DataLayer.get_required_params(), **{
'data_dir': str,
})