qa_mdt/audioldm_train/modules/phoneme_encoder/encoder.py

import copy
import math
import torch
from torch import nn
from torch.nn import functional as F

import qa_mdt.audioldm_train.modules.phoneme_encoder.commons as commons
import qa_mdt.audioldm_train.modules.phoneme_encoder.attentions as attentions


class TextEncoder(nn.Module):
    def __init__(
        self,
        n_vocab,
        out_channels=192,
        hidden_channels=192,
        filter_channels=768,
        n_heads=2,
        n_layers=6,
        kernel_size=3,
        p_dropout=0.1,
    ):
        super().__init__()
        self.n_vocab = n_vocab
        self.out_channels = out_channels
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout

        self.emb = nn.Embedding(n_vocab, hidden_channels)
        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)

        self.encoder = attentions.Encoder(
            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
        )
        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(self, x, x_lengths):
        x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
        x = torch.transpose(x, 1, -1)  # [b, h, t]
        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
            x.dtype
        )

        x = self.encoder(x * x_mask, x_mask)
        stats = self.proj(x) * x_mask

        m, logs = torch.split(stats, self.out_channels, dim=1)
        return x, m, logs, x_mask