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| # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from typing import Tuple | |
| import torch.nn as nn | |
| import torch | |
| from torch.nn import functional as F | |
| from cosyvoice.utils.mask import make_pad_mask | |
| class InterpolateRegulator(nn.Module): | |
| def __init__( | |
| self, | |
| channels: int, | |
| sampling_ratios: Tuple, | |
| out_channels: int = None, | |
| groups: int = 1, | |
| ): | |
| 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) | |
| def forward(self, x, ylens=None): | |
| # x in (B, T, D) | |
| mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1) | |
| x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear') | |
| out = self.model(x).transpose(1, 2).contiguous() | |
| olens = ylens | |
| return out * mask, olens | |
| def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50): | |
| # in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel | |
| # x in (B, T, D) | |
| if x2.shape[1] > 40: | |
| x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear') | |
| x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2, | |
| mode='linear') | |
| x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear') | |
| x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2) | |
| else: | |
| x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear') | |
| if x1.shape[1] != 0: | |
| x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear') | |
| x = torch.concat([x1, x2], dim=2) | |
| else: | |
| x = x2 | |
| out = self.model(x).transpose(1, 2).contiguous() | |
| return out, mel_len1 + mel_len2 | |