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on
Zero
Running
on
Zero
| from typing import Tuple | |
| import torch | |
| import torch.nn as nn | |
| from torch.nn import functional as F | |
| from modules.commons import sequence_mask | |
| import numpy as np | |
| # f0_bin = 256 | |
| f0_max = 1100.0 | |
| f0_min = 50.0 | |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) | |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) | |
| def f0_to_coarse(f0, f0_bin): | |
| f0_mel = 1127 * (1 + f0 / 700).log() | |
| a = (f0_bin - 2) / (f0_mel_max - f0_mel_min) | |
| b = f0_mel_min * a - 1. | |
| f0_mel = torch.where(f0_mel > 0, f0_mel * a - b, f0_mel) | |
| # torch.clip_(f0_mel, min=1., max=float(f0_bin - 1)) | |
| f0_coarse = torch.round(f0_mel).long() | |
| f0_coarse = f0_coarse * (f0_coarse > 0) | |
| f0_coarse = f0_coarse + ((f0_coarse < 1) * 1) | |
| f0_coarse = f0_coarse * (f0_coarse < f0_bin) | |
| f0_coarse = f0_coarse + ((f0_coarse >= f0_bin) * (f0_bin - 1)) | |
| return f0_coarse | |
| class InterpolateRegulator(nn.Module): | |
| def __init__( | |
| self, | |
| channels: int, | |
| sampling_ratios: Tuple, | |
| is_discrete: bool = False, | |
| in_channels: int = None, # only applies to continuous input | |
| codebook_size: int = 1024, # for discrete only | |
| out_channels: int = None, | |
| groups: int = 1, | |
| 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: | |
| self.interpolate = True | |
| 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]) | |
| else: | |
| self.interpolate = False | |
| model.append( | |
| nn.Conv1d(channels, out_channels, 1, 1) if channels != out_channels else nn.Identity() | |
| ) | |
| 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)) | |
| 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 | |
| if not is_discrete: | |
| self.content_in_proj = nn.Linear(in_channels, channels) | |
| def forward(self, x, ylens=None, f0=None): | |
| if self.is_discrete: | |
| if len(x.size()) == 2: | |
| x = self.embedding(x) | |
| else: | |
| x = self.embedding(x[:, 0]) | |
| else: | |
| x = self.content_in_proj(x) | |
| # x in (B, T, D) | |
| if self.interpolate: | |
| mask = sequence_mask(ylens).unsqueeze(-1) | |
| x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='nearest') | |
| else: | |
| x = x.transpose(1, 2).contiguous() | |
| mask = None | |
| # mask = mask[:, :x.size(2), :] | |
| # ylens = ylens.clamp(max=x.size(2)).long() | |
| 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) | |
| quantized_f0 = f0_to_coarse(f0, self.n_f0_bins) | |
| quantized_f0 = quantized_f0.clamp(0, self.n_f0_bins - 1).long() | |
| f0_emb = self.f0_embedding(quantized_f0) | |
| 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() | |
| out = out * mask if mask is not None else out | |
| olens = ylens | |
| return out, olens | |