MockingBird/ppg_extractor/encoder/embedding.py
Vega b617a87ee4
Init ppg extractor and ppg2mel (#375)
* Init  ppg extractor and ppg2mel

* add preprocess and training

* FIx known issues

* Update __init__.py

Allow to gen audio

* Fix length issue

* Fix bug of preparing fid

* Fix sample issues

* Add UI usage of PPG-vc
2022-03-03 23:38:12 +08:00

167 lines
4.7 KiB
Python

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Shigeki Karita
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Positonal Encoding Module."""
import math
import torch
def _pre_hook(
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
"""Perform pre-hook in load_state_dict for backward compatibility.
Note:
We saved self.pe until v.0.5.2 but we have omitted it later.
Therefore, we remove the item "pe" from `state_dict` for backward compatibility.
"""
k = prefix + "pe"
if k in state_dict:
state_dict.pop(k)
class PositionalEncoding(torch.nn.Module):
"""Positional encoding.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
:param reverse: whether to reverse the input position
"""
def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.reverse = reverse
self.xscale = math.sqrt(self.d_model)
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
self._register_load_state_dict_pre_hook(_pre_hook)
def extend_pe(self, x):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= x.size(1):
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = torch.zeros(x.size(1), self.d_model)
if self.reverse:
position = torch.arange(
x.size(1) - 1, -1, -1.0, dtype=torch.float32
).unsqueeze(1)
else:
position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.d_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor):
"""Add positional encoding.
Args:
x (torch.Tensor): Input. Its shape is (batch, time, ...)
Returns:
torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
"""
self.extend_pe(x)
x = x * self.xscale + self.pe[:, : x.size(1)]
return self.dropout(x)
class ScaledPositionalEncoding(PositionalEncoding):
"""Scaled positional encoding module.
See also: Sec. 3.2 https://arxiv.org/pdf/1809.08895.pdf
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Initialize class.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
self.alpha = torch.nn.Parameter(torch.tensor(1.0))
def reset_parameters(self):
"""Reset parameters."""
self.alpha.data = torch.tensor(1.0)
def forward(self, x):
"""Add positional encoding.
Args:
x (torch.Tensor): Input. Its shape is (batch, time, ...)
Returns:
torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
"""
self.extend_pe(x)
x = x + self.alpha * self.pe[:, : x.size(1)]
return self.dropout(x)
class RelPositionalEncoding(PositionalEncoding):
"""Relitive positional encoding module.
See : Appendix B in https://arxiv.org/abs/1901.02860
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Initialize class.
:param int d_model: embedding dim
:param float dropout_rate: dropout rate
:param int max_len: maximum input length
"""
super().__init__(d_model, dropout_rate, max_len, reverse=True)
def forward(self, x):
"""Compute positional encoding.
Args:
x (torch.Tensor): Input. Its shape is (batch, time, ...)
Returns:
torch.Tensor: x. Its shape is (batch, time, ...)
torch.Tensor: pos_emb. Its shape is (1, time, ...)
"""
self.extend_pe(x)
x = x * self.xscale
pos_emb = self.pe[:, : x.size(1)]
return self.dropout(x), self.dropout(pos_emb)