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from typing import Tuple
import torch
import torch.nn as nn
from torch.nn import functional as F
from modules.commons import sequence_mask
class InterpolateRegulator(nn.Module):
def __init__(
self,
channels: int,
sampling_ratios: Tuple,
is_discrete: bool = False,
codebook_size: int = 1024, # for discrete only
out_channels: int = None,
groups: int = 1,
token_dropout_prob: float = 0.5, # randomly drop out input tokens
token_dropout_range: float = 0.5, # randomly drop out input tokens
n_codebooks: int = 1, # number of codebooks
quantizer_dropout: float = 0.0, # dropout for quantizer
f0_condition: bool = False,
n_f0_bins: int = 512,
):
super().__init__()
self.sampling_ratios = sampling_ratios
out_channels = out_channels or channels
model = nn.ModuleList([])
if len(sampling_ratios) > 0:
for _ in sampling_ratios:
module = nn.Conv1d(channels, channels, 3, 1, 1)
norm = nn.GroupNorm(groups, channels)
act = nn.Mish()
model.extend([module, norm, act])
model.append(
nn.Conv1d(channels, out_channels, 1, 1)
)
self.model = nn.Sequential(*model)
self.embedding = nn.Embedding(codebook_size, channels)
self.is_discrete = is_discrete
self.mask_token = nn.Parameter(torch.zeros(1, channels))
self.n_codebooks = n_codebooks
if n_codebooks > 1:
self.extra_codebooks = nn.ModuleList([
nn.Embedding(codebook_size, channels) for _ in range(n_codebooks - 1)
])
self.token_dropout_prob = token_dropout_prob
self.token_dropout_range = token_dropout_range
self.quantizer_dropout = quantizer_dropout
if f0_condition:
self.f0_embedding = nn.Embedding(n_f0_bins, channels)
self.f0_condition = f0_condition
self.n_f0_bins = n_f0_bins
self.f0_bins = torch.arange(2, 1024, 1024 // n_f0_bins)
self.f0_mask = nn.Parameter(torch.zeros(1, channels))
else:
self.f0_condition = False
def forward(self, x, ylens=None, n_quantizers=None, f0=None):
# apply token drop
if self.training:
n_quantizers = torch.ones((x.shape[0],)) * self.n_codebooks
dropout = torch.randint(1, self.n_codebooks + 1, (x.shape[0],))
n_dropout = int(x.shape[0] * self.quantizer_dropout)
n_quantizers[:n_dropout] = dropout[:n_dropout]
n_quantizers = n_quantizers.to(x.device)
# decide whether to drop for each sample in batch
else:
n_quantizers = torch.ones((x.shape[0],), device=x.device) * (self.n_codebooks if n_quantizers is None else n_quantizers)
if self.is_discrete:
if self.n_codebooks > 1:
assert len(x.size()) == 3
x_emb = self.embedding(x[:, 0])
for i, emb in enumerate(self.extra_codebooks):
x_emb = x_emb + (n_quantizers > i+1)[..., None, None] * emb(x[:, i+1])
x = x_emb
elif self.n_codebooks == 1:
if len(x.size()) == 2:
x = self.embedding(x)
else:
x = self.embedding(x[:, 0])
# x in (B, T, D)
mask = sequence_mask(ylens).unsqueeze(-1)
x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
if self.f0_condition:
if f0 is None:
x = x + self.f0_mask.unsqueeze(-1)
else:
quantized_f0 = torch.bucketize(f0, self.f0_bins.to(f0.device)) # (N, T)
if self.training:
drop_f0 = torch.rand(quantized_f0.size(0)).to(f0.device) < self.quantizer_dropout
else:
drop_f0 = torch.zeros(quantized_f0.size(0)).to(f0.device).bool()
f0_emb = self.f0_embedding(quantized_f0)
f0_emb[drop_f0] = self.f0_mask
f0_emb = F.interpolate(f0_emb.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest')
x = x + f0_emb
out = self.model(x).transpose(1, 2).contiguous()
olens = ylens
return out * mask, olens
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