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fix(dml): train extract_f0_print error

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ModuleNotFoundError: No module named 'torch.privateuseone' due to new prosess
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源文雨
2025-11-21 16:52:17 +08:00
parent 7fa122045f
commit 43d19eb00e
13 changed files with 50 additions and 39 deletions
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3
infer/modules/train/extract_f0_print.py
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@@ -2,6 +2,7 @@ import os
import sys
import traceback
from pathlib import Path
import importlib.util
from dotenv import load_dotenv
@@ -38,6 +39,8 @@ f0method = sys.argv[3]
device = sys.argv[4]
is_half = sys.argv[5] == "True"
if importlib.util.find_spec("torch_directml") is not None:
import torch_directml # use side effect
class FeatureInput(object):
def __init__(self, is_half: bool, device="cpu", samplerate=16000, hop_size=160):

6
rvc/f0/f0.py
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@@ -11,14 +11,14 @@ class F0Predictor(object):
f0_min=50,
f0_max=1100,
sampling_rate=44100,
device: Optional[str] = None,
device: Optional[Union[str, torch.device]] = None,
):
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.sampling_rate = sampling_rate
if device is None:
device = "cuda:0" if torch.cuda.is_available() else "cpu"
if not device:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.device = device
def compute_f0(

6
rvc/f0/mel.py
View File

@@ -1,4 +1,4 @@
from typing import Optional
from typing import Optional, Union
import torch
import numpy as np
@@ -17,9 +17,9 @@ class MelSpectrogram(torch.nn.Module):
hop_length: int,
n_fft: Optional[int] = None,
mel_fmin: int = 0,
mel_fmax: int = None,
mel_fmax: Optional[int] = None,
clamp: float = 1e-5,
device=torch.device("cpu"),
device: Union[str, torch.device] = torch.device("cpu"),
):
super().__init__()
if n_fft is None:

2
rvc/f0/rmvpe.py
View File

@@ -1,6 +1,6 @@
from io import BytesIO
import os
from typing import Any, Optional, Union
from typing import Optional, Union
import numpy as np
import torch

2
rvc/layers/attentions.py
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@@ -12,8 +12,8 @@ class MultiHeadAttention(nn.Module):
channels: int,
out_channels: int,
n_heads: int,
window_size: int,
p_dropout: float = 0.0,
window_size: Optional[int] = None,
heads_share: bool = True,
block_length: Optional[int] = None,
proximal_bias: bool = False,

6
rvc/layers/encoders.py
View File

@@ -42,8 +42,8 @@ class Encoder(nn.Module):
hidden_channels,
hidden_channels,
n_heads,
window_size,
p_dropout=p_dropout,
window_size=window_size,
)
)
self.norm_layers_1.append(LayerNorm(hidden_channels))
@@ -121,7 +121,7 @@ class TextEncoder(nn.Module):
def __call__(
self,
phone: torch.Tensor,
pitch: torch.Tensor,
pitch: Optional[torch.Tensor],
lengths: torch.Tensor,
skip_head: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
@@ -135,7 +135,7 @@ class TextEncoder(nn.Module):
def forward(
self,
phone: torch.Tensor,
pitch: torch.Tensor,
pitch: Optional[torch.Tensor],
lengths: torch.Tensor,
skip_head: Optional[int] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:

1
rvc/layers/generators.py
View File

@@ -46,6 +46,7 @@ class Generator(torch.nn.Module):
self.resblocks = nn.ModuleList()
resblock_module = ResBlock1 if resblock == "1" else ResBlock2
ch = 0
for i in range(len(self.ups)):
ch = upsample_initial_channel // (2 ** (i + 1))
for k, d in zip(resblock_kernel_sizes, resblock_dilation_sizes):

2
rvc/layers/norms.py
View File

@@ -30,7 +30,7 @@ class WN(torch.nn.Module):
dilation_rate: int,
n_layers: int,
gin_channels: int = 0,
p_dropout: int = 0,
p_dropout: float = 0,
):
super(WN, self).__init__()
assert kernel_size % 2 == 1

9
rvc/layers/nsf.py
View File

@@ -1,4 +1,4 @@
from typing import Optional, List
from typing import Optional, List, Union
import math
import torch
@@ -83,7 +83,7 @@ class NSFGenerator(torch.nn.Module):
self.conv_pre = Conv1d(
initial_channel, upsample_initial_channel, 7, 1, padding=3
)
resblock = ResBlock1 if resblock == "1" else ResBlock2
resblockcls = ResBlock1 if resblock == "1" else ResBlock2
self.ups = nn.ModuleList()
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
@@ -114,12 +114,13 @@ class NSFGenerator(torch.nn.Module):
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
self.resblocks = nn.ModuleList()
ch = 0
for i in range(len(self.ups)):
ch: int = upsample_initial_channel // (2 ** (i + 1))
ch = upsample_initial_channel // (2 ** (i + 1))
for j, (k, d) in enumerate(
zip(resblock_kernel_sizes, resblock_dilation_sizes)
):
self.resblocks.append(resblock(ch, k, d))
self.resblocks.append(resblockcls(ch, k, d))
self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
self.ups.apply(call_weight_data_normal_if_Conv)

6
rvc/layers/residuals.py
View File

@@ -20,7 +20,7 @@ class ResBlock1(torch.nn.Module):
self,
channels: int,
kernel_size: int = 3,
dilation: List[int] = (1, 3, 5),
dilation: List[int] = [1, 3, 5],
):
super(ResBlock1, self).__init__()
@@ -117,7 +117,7 @@ class ResBlock2(torch.nn.Module):
self,
channels: int,
kernel_size=3,
dilation: List[int] = (1, 3),
dilation: List[int] = [1, 3],
):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList()
@@ -182,7 +182,7 @@ class ResidualCouplingLayer(nn.Module):
kernel_size: int,
dilation_rate: int,
n_layers: int,
p_dropout: int = 0,
p_dropout: float = 0,
gin_channels: int = 0,
mean_only: bool = False,
):

32
rvc/layers/synthesizers.py
View File

@@ -34,7 +34,7 @@ class SynthesizerTrnMsNSFsid(nn.Module):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr: Optional[Union[str, int]],
sr: Union[str, int],
encoder_dim: int,
use_f0: bool,
):
@@ -143,7 +143,7 @@ class SynthesizerTrnMsNSFsid(nn.Module):
torch.nn.utils.remove_weight_norm(self.enc_q)
return self
@torch.jit.ignore
@torch.jit.ignore()
def forward(
self,
phone: torch.Tensor,
@@ -155,18 +155,20 @@ class SynthesizerTrnMsNSFsid(nn.Module):
pitchf: Optional[torch.Tensor] = None,
): # 这里ds是id，[bs,1]
# print(1,pitch.shape)#[bs,t]
g = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
embg = self.emb_g(ds).unsqueeze(-1) # [b, 256, 1]##1是t，广播的
m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
z_p = self.flow(z, y_mask, g=g)
z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=embg)
z_p = self.flow(z, y_mask, g=embg)
z_slice, ids_slice = rand_slice_segments_on_last_dim(
z, y_lengths, self.segment_size
)
if pitchf is not None:
if pitchf is not None and isinstance(self.dec, NSFGenerator):
pitchf = slice_on_last_dim(pitchf, ids_slice, self.segment_size)
o = self.dec(z_slice, pitchf, g=g)
o = self.dec(z_slice, pitchf, g=embg) # type: ignore
elif isinstance(self.dec, Generator):
o = self.dec(z_slice, g=embg)
else:
o = self.dec(z_slice, g=g)
raise KeyError(f"unknown dec type: {type(self.dec).__name__}")
return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
@torch.jit.export
@@ -201,15 +203,17 @@ class SynthesizerTrnMsNSFsid(nn.Module):
z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
z = self.flow(z_p, x_mask, g=g, reverse=True)
del z_p, m_p, logs_p
if pitchf is not None:
o = self.dec(
if pitchf is not None and isinstance(self.dec, NSFGenerator):
o = self.dec(
z * x_mask,
pitchf,
g=g,
n_res=return_length2,
)
else:
elif isinstance(self.dec, Generator):
o = self.dec(z * x_mask, g=g, n_res=return_length2)
else:
raise KeyError(f"unknown dec type: {type(self.dec).__name__}")
del x_mask, z
return o # , x_mask, (z, z_p, m_p, logs_p)
@@ -326,7 +330,7 @@ class SynthesizerTrnMs256NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr=None,
sr: Union[str, int],
):
super().__init__(
spec_channels,
@@ -346,6 +350,7 @@ class SynthesizerTrnMs256NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes,
spk_embed_dim,
gin_channels,
sr,
256,
False,
)
@@ -371,7 +376,7 @@ class SynthesizerTrnMs768NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr=None,
sr: Union[str, int],
):
super().__init__(
spec_channels,
@@ -391,6 +396,7 @@ class SynthesizerTrnMs768NSFsid_nono(SynthesizerTrnMsNSFsid):
upsample_kernel_sizes,
spk_embed_dim,
gin_channels,
sr,
768,
False,
)

10
rvc/layers/utils.py
View File

@@ -1,4 +1,4 @@
from typing import List, Optional, Tuple, Iterator
from typing import List, Optional, Tuple, Iterator, Union
import torch
@@ -17,7 +17,7 @@ def get_padding(kernel_size: int, dilation=1) -> int:
def slice_on_last_dim(
x: torch.Tensor,
start_indices: List[int],
start_indices: Union[List[int], torch.Tensor],
segment_size=4,
) -> torch.Tensor:
new_shape = [*x.shape]
@@ -32,9 +32,9 @@ def slice_on_last_dim(
def rand_slice_segments_on_last_dim(
x: torch.Tensor,
x_lengths: int = None,
x_lengths: Optional[Union[int, torch.Tensor]] = None,
segment_size=4,
) -> Tuple[torch.Tensor, List[int]]:
) -> Tuple[torch.Tensor, Union[List[int], torch.Tensor]]:
b, _, t = x.size()
if x_lengths is None:
x_lengths = t
@@ -58,7 +58,7 @@ def activate_add_tanh_sigmoid_multiply(
def sequence_mask(
length: torch.Tensor,
max_length: Optional[int] = None,
) -> torch.BoolTensor:
):
if max_length is None:
max_length = int(length.max())
x = torch.arange(max_length, dtype=length.dtype, device=length.device)

4
rvc/onnx/synthesizer.py
View File

@@ -1,4 +1,4 @@
from typing import List, Optional, Union
from typing import List, Union
import torch
@@ -25,7 +25,7 @@ class SynthesizerTrnMsNSFsid(SynthesizerBase):
upsample_kernel_sizes: List[int],
spk_embed_dim: int,
gin_channels: int,
sr: Optional[Union[str, int]],
sr: Union[str, int],
encoder_dim: int,
):
super().__init__(