def build_model(self, model_cfg: FairseqDataclass):
model = super().build_model(model_cfg)
actualized_cfg = getattr(model, "cfg", None)
if actualized_cfg is not None:
# if "w2v_args" in actualized_cfg:
if hasattr(actualized_cfg, "w2v_args"):
model_cfg.w2v_args = actualized_cfg.w2v_args
return model
def build_model(self, model_cfg: FairseqDataclass):
model = super().build_model(model_cfg)
if self.cfg.eval_wer and self.cfg.autoregressive:
self.sequence_generator = self.build_generator(
[model],
self.cfg.eval_wer_config,
)
if self.cfg.eval_wer_tokenizer:
self.tokenizer = encoders.build_tokenizer(self.cfg.eval_wer_tokenizer)
else:
self.tokenizer = None
actualized_cfg = getattr(model, "cfg", None)
if actualized_cfg is not None:
if "w2v_args" in actualized_cfg:
model_cfg.w2v_args = actualized_cfg.w2v_args
return model