def __init__(
self,
optimizer: Optimizer, # optimizer for training
criterion: nn.Module, # loss function
trainset_list: list, # list of training dataset
validset: SpectrogramDataset, # validation dataset
num_workers: int, # number of threads
device: str, # device - cuda or cpu
print_every: int, # number of timesteps to save result after
save_result_every: int, # nimber of timesteps to save result after
checkpoint_every: int, # number of timesteps to checkpoint after
teacher_forcing_step:
float = 0.2, # step of teacher forcing ratio decrease per epoch.
min_teacher_forcing_ratio:
float = 0.8, # minimum value of teacher forcing ratio
architecture: str = 'las' # architecture to train - las, transformer
) -> None:
self.num_workers = num_workers
self.optimizer = optimizer
self.criterion = criterion
self.trainset_list = trainset_list
self.validset = validset
self.print_every = print_every
self.save_result_every = save_result_every
self.checkpoint_every = checkpoint_every
self.device = device
self.teacher_forcing_step = teacher_forcing_step
self.min_teacher_forcing_ratio = min_teacher_forcing_ratio
self.metric = CharacterErrorRate(id2char, EOS_token)
self.architecture = architecture.lower()
def __init__(self,
optimizer,
criterion,
trainset_list,
validset,
high_plateau_lr,
low_plateau_lr,
exp_decay_period,
num_workers,
device,
decay_threshold,
print_every,
save_result_every,
checkpoint_every,
teacher_forcing_step=0.0,
min_teacher_forcing_ratio=0.7):
self.num_workers = num_workers
self.optimizer = optimizer
self.criterion = criterion
self.trainset_list = trainset_list
self.validset = validset
self.high_plateau_lr = high_plateau_lr
self.low_plateau_lr = low_plateau_lr
self.exp_decay_period = exp_decay_period
self.print_every = print_every
self.save_result_every = save_result_every
self.checkpoint_every = checkpoint_every
self.decay_threshold = decay_threshold
self.device = device
self.teacher_forcing_step = teacher_forcing_step
self.min_teacher_forcing_ratio = min_teacher_forcing_ratio
self.metric = CharacterErrorRate(id2char, EOS_token)
def __init__(self, metric: str = 'char'):
self.target_list = list()
self.predict_list = list()
if metric == 'char':
self.metric = CharacterErrorRate(id2char, EOS_token)
elif metric == 'word':
self.metric = WordErrorRate(id2char, EOS_token)
else:
raise ValueError("Unsupported metric : {0}".format(metric))
def __init__(self, vocab, metric: str = 'char'):
self.target_list = list()
self.predict_list = list()
self.vocab = vocab
if metric == 'char':
self.metric = CharacterErrorRate(vocab)
elif metric == 'word':
self.metric = WordErrorRate(vocab)
else:
raise ValueError("Unsupported metric : {0}".format(metric))
def __init__(
self,
optimizer: Optimizer, # optimizer for training
criterion: nn.Module, # loss function
trainset_list: list, # list of training dataset
validset: SpectrogramDataset, # validation dataset
num_workers: int, # number of threads
device: torch.device, # device - cuda or cpu
print_every: int, # number of timesteps to save result after
save_result_every: int, # nimber of timesteps to save result after
checkpoint_every: int, # number of timesteps to checkpoint after
teacher_forcing_step:
float = 0.2, # step of teacher forcing ratio decrease per epoch.
min_teacher_forcing_ratio:
float = 0.8, # minimum value of teacher forcing ratio
architecture: str = 'las', # model to train - las, transformer
vocab: Vocabulary = None, # vocabulary object
joint_ctc_attention:
bool = False, # flag indication whether joint CTC-Attention or not
) -> None:
self.num_workers = num_workers
self.optimizer = optimizer
self.criterion = criterion
self.trainset_list = trainset_list
self.validset = validset
self.print_every = print_every
self.save_result_every = save_result_every
self.checkpoint_every = checkpoint_every
self.device = device
self.teacher_forcing_step = teacher_forcing_step
self.min_teacher_forcing_ratio = min_teacher_forcing_ratio
self.metric = CharacterErrorRate(vocab)
self.architecture = architecture.lower()
self.vocab = vocab
self.joint_ctc_attention = joint_ctc_attention
if self.joint_ctc_attention:
self.log_format = "step: {:4d}/{:4d}, loss: {:.6f}, ctc_loss: {:.6f}, ce_loss: {:.6f}, " \
"cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h, lr: {:.6f}"
else:
self.log_format = "step: {:4d}/{:4d}, loss: {:.6f}, " \
"cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h, lr: {:.6f}"
if self.architecture in ('rnnt', 'conformer'):
self.rnnt_log_format = "step: {:4d}/{:4d}, loss: {:.6f}, " \
"elapsed: {:.2f}s {:.2f}m {:.2f}h, lr: {:.6f}"
def __init__(
self,
optimizer: Optimizer, # optimizer for training
criterion: nn.Module, # loss function
trainset_list: list, # list of training dataset
validset: SpectrogramDataset, # validation dataset
high_plateau_lr: float, # high plateau learning rate
low_plateau_lr: float, # low plateau learning rate
exp_decay_period: int, # exponential decay learning rate period
num_workers: int, # number of threads
device: str, # device - cuda or cpu
decay_threshold:
float, # criteria by which exp learning ratedecay is started
print_every: int, # number of timesteps to save result after
save_result_every: int, # nimber of timesteps to save result after
checkpoint_every: int, # number of timesteps to checkpoint after
teacher_forcing_step:
float = 0.2, # step of teacher forcing ratio decrease per epoch.
min_teacher_forcing_ratio:
float = 0.8, # minimum value of teacher forcing ratio
architecture:
str = 'seq2seq' # architecture to train - seq2seq, transformer
) -> None:
self.num_workers = num_workers
self.optimizer = optimizer
self.criterion = criterion
self.trainset_list = trainset_list
self.validset = validset
self.high_plateau_lr = high_plateau_lr
self.low_plateau_lr = low_plateau_lr
self.exp_decay_period = exp_decay_period
self.print_every = print_every
self.save_result_every = save_result_every
self.checkpoint_every = checkpoint_every
self.decay_threshold = decay_threshold
self.device = device
self.teacher_forcing_step = teacher_forcing_step
self.min_teacher_forcing_ratio = min_teacher_forcing_ratio
self.metric = CharacterErrorRate(id2char, EOS_token)
self.architecture = architecture.lower()
def __init__(self):
self.target_list = list()
self.predict_list = list()
self.metric = CharacterErrorRate(id2char, EOS_token)
def __init__(self):
self.target_list = list()
self.hypothesis_list = list()
self.metric = CharacterErrorRate(id2char, EOS_token)
self.language_model = None # load_language_model('../data/weight_file/epoch1.pt', 'cuda')
def __init__(self):
self.target_list = list()
self.hypothesis_list = list()
self.metric = CharacterErrorRate(id2char, EOS_token)
self.language_model = load_language_model('lm_path', 'cuda')
