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https://github.com/Nighthawk42/llm-tts-factory.git
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51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
import torch
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from torch import nn
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from .spectral_ops import ISTFT
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class ISTFTHead(nn.Module):
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"""
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ISTFT Head module for predicting STFT complex coefficients.
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Args:
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dim (int): Hidden dimension of the model.
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n_fft (int): Size of Fourier transform.
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hop_length (int): The distance between neighboring sliding window frames, which should align with
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the resolution of the input features.
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padding (str, optional): Type of padding. Options are "center" or "same". Defaults to "same".
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"""
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def __init__(self, dim: int, n_fft: int, hop_length: int, padding: str = "center"):
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super().__init__()
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out_dim = n_fft + 2
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self.out = torch.nn.Linear(dim, out_dim)
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self.istft = ISTFT(n_fft=n_fft, hop_length=hop_length, win_length=n_fft, padding=padding)
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@torch.compiler.disable
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass of the ISTFTHead module.
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Args:
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x (Tensor): Input tensor of shape (B, L, H), where B is the batch size,
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L is the sequence length, and H denotes the model dimension.
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Returns:
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Tensor: Reconstructed time-domain audio signal of shape (B, T), where T is the length of the output signal.
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"""
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x = self.out(x.transpose(1,2)).transpose(1, 2)
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mag, p = x.chunk(2, dim=1)
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mag = torch.exp(mag)
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mag = torch.clip(mag, max=1e2) # safeguard to prevent excessively large magnitudes
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# wrapping happens here. These two lines produce real and imaginary value
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x = torch.cos(p)
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y = torch.sin(p)
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# recalculating phase here does not produce anything new
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# only costs time
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# phase = torch.atan2(y, x)
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# S = mag * torch.exp(phase * 1j)
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# better directly produce the complex value
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S = mag * (x + 1j * y)
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audio = self.istft(S)
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return audio
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