sound_extraction/model/LASSNet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from .text_encoder import Text_Encoder
from .resunet_film import UNetRes_FiLM

class LASSNet(nn.Module):
    def __init__(self, device='cuda'):
        super(LASSNet, self).__init__()
        self.text_embedder = Text_Encoder(device)
        self.UNet = UNetRes_FiLM(channels=1, cond_embedding_dim=256)

    def forward(self, x, caption):
        # x: (Batch, 1, T, 128))
        input_ids, attns_mask = self.text_embedder.tokenize(caption)
        
        cond_vec = self.text_embedder(input_ids, attns_mask)[0]
        dec_cond_vec = cond_vec

        mask = self.UNet(x, cond_vec, dec_cond_vec)
        mask = torch.sigmoid(mask)
        return mask

    def get_tokenizer(self):
        return self.text_embedder.tokenizer