1b45e3a304f9d2479a836b82d889f0e19183faf6,src/plugin.py,EmbeddingRegularizer,__init__,#EmbeddingRegularizer#,8

Before Change

    """ Perform word embedding regularization training for ASR"""
    def __init__(self, tokenizer, dec_dim, enable, src, distance, weight, fuse, temperature, 
                 freeze=True, fuse_normalize=False, dropout=0.0):
        super().__init__()
        self.enable = enable
        if enable:
            pretrained_emb = torch.FloatTensor(load_embedding(tokenizer,src))
            //pretrained_emb = nn.functional.normalize(pretrained_emb,dim=-1) // ToDo : Check impact on old version

After Change

            vocab_size, emb_dim = pretrained_emb.shape
            self.dim = emb_dim

            if bert is not None:
                if not isinstance(bert, (tuple, list)) or len(bert) != 2:
                    raise ValueError("`bert` should be a tuple/list of config and fine-tuned model "
                                     "such as (\"bert-base-uncased\", \"fine-tuned-model.pth\").")
                self.emb_table = BertEmbeddingPredictor(
                    bert[0], text_encoder, bert[1])
            else:
                self.emb_table = nn.Embedding.from_pretrained(
                    pretrained_emb, freeze=freeze, padding_idx=0)

            self.emb_net = nn.Sequential(nn.Linear(dec_dim, (emb_dim+dec_dim)//2),
                                         nn.ReLU(),
                                         nn.Linear((emb_dim+dec_dim)//2, emb_dim))
            self.weight = weight

Instances