def __init__(self, args, task):
super().__init__(args, task)
dictionary = task.target_dictionary
self.scorer = wer.Scorer(dictionary, wer_output_filter=task.args.wer_output_filter)
self.decoder_for_validation = SimpleGreedyDecoder(dictionary, for_validation=True)
self.num_updates = -1
self.epoch = 0
def build_model(self, args):
model = super().build_model(args)
# build the greedy decoder for validation with WER
from espresso.tools.simple_greedy_decoder import SimpleGreedyDecoder
self.decoder_for_validation = SimpleGreedyDecoder(
[model], self.target_dictionary, for_validation=True,
)
return model
def __init__(self, args, task):
super().__init__(args, task)
dictionary = task.target_dictionary
self.scorer = wer.Scorer(dictionary, wer_output_filter=task.args.wer_output_filter)
self.decoder_for_validation = SimpleGreedyDecoder(dictionary, for_validation=True)
self.num_updates = -1
self.epoch = 0
self.unigram_tensor = None
if args.smoothing_type == 'unigram':
self.unigram_tensor = torch.cuda.FloatTensor(dictionary.count).unsqueeze(-1) \
if torch.cuda.is_available() and not args.cpu \
else torch.FloatTensor(dictionary.count).unsqueeze(-1)
self.unigram_tensor += args.unigram_pseudo_count # for further backoff
self.unigram_tensor.div_(self.unigram_tensor.sum())
def build_model(self, cfg: DictConfig, from_checkpoint=False):
model = super().build_model(cfg, from_checkpoint)
# build a greedy decoder for validation with WER
if self.cfg.criterion_name == "transducer_loss": # a transducer model
from espresso.tools.transducer_greedy_decoder import TransducerGreedyDecoder
self.decoder_for_validation = TransducerGreedyDecoder(
[model],
self.target_dictionary,
max_num_expansions_per_step=self.cfg.max_num_expansions_per_step,
)
elif self.cfg.criterion_name == "ctc": # a ctc model
raise NotImplementedError
else: # assume it is an attention-based encoder-decoder model
from espresso.tools.simple_greedy_decoder import SimpleGreedyDecoder
self.decoder_for_validation = SimpleGreedyDecoder(
[model],
self.target_dictionary,
for_validation=True,
)
return model
