70 lines
3.0 KiB
Python
70 lines
3.0 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Tuple
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import torch.nn as nn
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import torch
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from torch.nn import functional as F
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from cosyvoice.utils.mask import make_pad_mask
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class InterpolateRegulator(nn.Module):
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def __init__(
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self,
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channels: int,
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sampling_ratios: Tuple,
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out_channels: int = None,
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groups: int = 1,
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):
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super().__init__()
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self.sampling_ratios = sampling_ratios
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out_channels = out_channels or channels
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model = nn.ModuleList([])
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if len(sampling_ratios) > 0:
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for _ in sampling_ratios:
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module = nn.Conv1d(channels, channels, 3, 1, 1)
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norm = nn.GroupNorm(groups, channels)
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act = nn.Mish()
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model.extend([module, norm, act])
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model.append(
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nn.Conv1d(channels, out_channels, 1, 1)
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)
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self.model = nn.Sequential(*model)
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def forward(self, x, ylens=None):
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# x in (B, T, D)
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mask = (~make_pad_mask(ylens)).to(x).unsqueeze(-1)
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x = F.interpolate(x.transpose(1, 2).contiguous(), size=ylens.max(), mode='linear')
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out = self.model(x).transpose(1, 2).contiguous()
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olens = ylens
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return out * mask, olens
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def inference(self, x1, x2, mel_len1, mel_len2, input_frame_rate=50):
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# in inference mode, interploate prompt token and token(head/mid/tail) seprately, so we can get a clear separation point of mel
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# x in (B, T, D)
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if x2.shape[1] > 40:
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x2_head = F.interpolate(x2[:, :20].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
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x2_mid = F.interpolate(x2[:, 20:-20].transpose(1, 2).contiguous(), size=mel_len2 - int(20 / input_frame_rate * 22050 / 256) * 2,
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mode='linear')
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x2_tail = F.interpolate(x2[:, -20:].transpose(1, 2).contiguous(), size=int(20 / input_frame_rate * 22050 / 256), mode='linear')
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x2 = torch.concat([x2_head, x2_mid, x2_tail], dim=2)
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else:
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x2 = F.interpolate(x2.transpose(1, 2).contiguous(), size=mel_len2, mode='linear')
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if x1.shape[1] != 0:
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x1 = F.interpolate(x1.transpose(1, 2).contiguous(), size=mel_len1, mode='linear')
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x = torch.concat([x1, x2], dim=2)
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else:
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x = x2
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out = self.model(x).transpose(1, 2).contiguous()
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return out, mel_len1 + mel_len2
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