model.py

import torch
import torch.nn as nn
from .positional_encoding import PositionalEncoding


class RealtimeTTS(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.embedding = nn.Embedding(
            config.vocab_size,
            config.d_model
        )

        self.positional_encoding = PositionalEncoding(
            config.d_model,
            config.max_seq_len
        )

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=config.d_model,
            nhead=config.nhead,
            dim_feedforward=config.dim_feedforward,
            batch_first=True
        )

        self.transformer = nn.TransformerEncoder(
            encoder_layer,
            num_layers=config.num_layers
        )

        self.output_linear = nn.Linear(
            config.d_model,
            80  # mel bins
        )

    def forward(self, tokens, mel_input):
        x = self.embedding(tokens)
        x = self.positional_encoding(x)
        x = self.transformer(x)
        mel = self.output_linear(x)
        return mel