mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 09:10:25 +08:00
optimize(rmvpe): move deepunet&e2e into rvc
This commit is contained in:
源文雨
@@ -1,9 +1,10 @@
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from io import BufferedWriter, BytesIO
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from pathlib import Path
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from typing import Dict, Tuple
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import os
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import numpy as np
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import av
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import os
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from av.audio.resampler import AudioResampler
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video_format_dict: Dict[str, str] = {
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@@ -44,10 +45,8 @@ def load_audio(file: str, sr: int) -> np.ndarray:
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resampler = AudioResampler(format="fltp", layout="mono", rate=sr)
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# Estimated maximum total number of samples to pre-allocate the array
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audio_duration_sec: float = (
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container.duration / 1_000_000
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) # AV stores length in microseconds by default
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estimated_total_samples = int(audio_duration_sec * sr + 0.5)
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# AV stores length in microseconds by default
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estimated_total_samples = int(container.duration * sr // 1_000_000)
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decoded_audio = np.zeros(estimated_total_samples + 1, dtype=np.float32)
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offset = 0
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@@ -55,7 +54,7 @@ def load_audio(file: str, sr: int) -> np.ndarray:
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frame.pts = None # Clear presentation timestamp to avoid resampling issues
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resampled_frames = resampler.resample(frame)
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for resampled_frame in resampled_frames:
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frame_data = np.array(resampled_frame.to_ndarray()).flatten()
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frame_data = resampled_frame.to_ndarray()[0]
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end_index = offset + len(frame_data)
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# Check if decoded_audio has enough space, and resize if necessary
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@@ -18,269 +18,13 @@ except Exception: # pylint: disable=broad-exception-caught
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pass
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import torch.nn as nn
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import torch.nn.functional as F
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from librosa.util import normalize, pad_center, tiny
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from scipy.signal import get_window
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import logging
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logger = logging.getLogger(__name__)
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from rvc.f0.mel import MelSpectrogram
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from time import time as ttime
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class BiGRU(nn.Module):
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def __init__(self, input_features, hidden_features, num_layers):
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super(BiGRU, self).__init__()
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self.gru = nn.GRU(
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input_features,
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hidden_features,
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num_layers=num_layers,
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batch_first=True,
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bidirectional=True,
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)
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def forward(self, x):
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return self.gru(x)[0]
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class ConvBlockRes(nn.Module):
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def __init__(self, in_channels, out_channels, momentum=0.01):
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super(ConvBlockRes, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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nn.Conv2d(
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in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1),
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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# self.shortcut:Optional[nn.Module] = None
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if in_channels != out_channels:
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self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
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def forward(self, x: torch.Tensor):
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if not hasattr(self, "shortcut"):
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return self.conv(x) + x
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else:
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return self.conv(x) + self.shortcut(x)
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class Encoder(nn.Module):
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def __init__(
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self,
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in_channels,
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in_size,
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n_encoders,
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kernel_size,
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n_blocks,
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out_channels=16,
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momentum=0.01,
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):
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super(Encoder, self).__init__()
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self.n_encoders = n_encoders
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self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
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self.layers = nn.ModuleList()
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self.latent_channels = []
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for i in range(self.n_encoders):
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self.layers.append(
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ResEncoderBlock(
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in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
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)
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)
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self.latent_channels.append([out_channels, in_size])
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in_channels = out_channels
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out_channels *= 2
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in_size //= 2
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self.out_size = in_size
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self.out_channel = out_channels
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def forward(self, x: torch.Tensor):
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concat_tensors: List[torch.Tensor] = []
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x = self.bn(x)
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for i, layer in enumerate(self.layers):
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t, x = layer(x)
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concat_tensors.append(t)
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return x, concat_tensors
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class ResEncoderBlock(nn.Module):
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def __init__(
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self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
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):
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super(ResEncoderBlock, self).__init__()
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self.n_blocks = n_blocks
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self.conv = nn.ModuleList()
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self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
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for i in range(n_blocks - 1):
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self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
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self.kernel_size = kernel_size
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if self.kernel_size is not None:
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self.pool = nn.AvgPool2d(kernel_size=kernel_size)
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def forward(self, x):
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for i, conv in enumerate(self.conv):
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x = conv(x)
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if self.kernel_size is not None:
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return x, self.pool(x)
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else:
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return x
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class Intermediate(nn.Module): #
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def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
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super(Intermediate, self).__init__()
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self.n_inters = n_inters
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self.layers = nn.ModuleList()
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self.layers.append(
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ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
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)
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for i in range(self.n_inters - 1):
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self.layers.append(
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ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
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)
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def forward(self, x):
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for i, layer in enumerate(self.layers):
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x = layer(x)
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return x
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class ResDecoderBlock(nn.Module):
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def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
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super(ResDecoderBlock, self).__init__()
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out_padding = (0, 1) if stride == (1, 2) else (1, 1)
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self.n_blocks = n_blocks
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self.conv1 = nn.Sequential(
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nn.ConvTranspose2d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(3, 3),
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stride=stride,
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padding=(1, 1),
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output_padding=out_padding,
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bias=False,
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),
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nn.BatchNorm2d(out_channels, momentum=momentum),
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nn.ReLU(),
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)
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self.conv2 = nn.ModuleList()
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self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
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for i in range(n_blocks - 1):
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self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
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def forward(self, x, concat_tensor):
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x = self.conv1(x)
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x = torch.cat((x, concat_tensor), dim=1)
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for i, conv2 in enumerate(self.conv2):
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x = conv2(x)
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return x
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class Decoder(nn.Module):
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def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
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super(Decoder, self).__init__()
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self.layers = nn.ModuleList()
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self.n_decoders = n_decoders
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for i in range(self.n_decoders):
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out_channels = in_channels // 2
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self.layers.append(
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ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
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)
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in_channels = out_channels
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def forward(self, x: torch.Tensor, concat_tensors: List[torch.Tensor]):
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for i, layer in enumerate(self.layers):
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x = layer(x, concat_tensors[-1 - i])
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return x
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class DeepUnet(nn.Module):
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def __init__(
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self,
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kernel_size,
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n_blocks,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(DeepUnet, self).__init__()
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self.encoder = Encoder(
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in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
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)
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self.intermediate = Intermediate(
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self.encoder.out_channel // 2,
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self.encoder.out_channel,
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inter_layers,
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n_blocks,
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)
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self.decoder = Decoder(
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self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, concat_tensors = self.encoder(x)
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x = self.intermediate(x)
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x = self.decoder(x, concat_tensors)
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return x
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class E2E(nn.Module):
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def __init__(
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self,
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n_blocks,
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n_gru,
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kernel_size,
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en_de_layers=5,
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inter_layers=4,
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in_channels=1,
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en_out_channels=16,
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):
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super(E2E, self).__init__()
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self.unet = DeepUnet(
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kernel_size,
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n_blocks,
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en_de_layers,
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inter_layers,
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in_channels,
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en_out_channels,
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)
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self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
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if n_gru:
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self.fc = nn.Sequential(
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BiGRU(3 * 128, 256, n_gru),
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nn.Linear(512, 360),
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nn.Dropout(0.25),
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nn.Sigmoid(),
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)
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else:
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self.fc = nn.Sequential(
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nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
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)
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def forward(self, mel):
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# print(mel.shape)
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mel = mel.transpose(-1, -2).unsqueeze(1)
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x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
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x = self.fc(x)
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# print(x.shape)
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return x
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from rvc.f0.e2e import E2E
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class RMVPE:
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@@ -442,27 +186,3 @@ class RMVPE:
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# t4 = ttime()
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# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
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return devided
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if __name__ == "__main__":
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import librosa
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import soundfile as sf
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audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio.transpose(1, 0))
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audio_bak = audio.copy()
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if sampling_rate != 16000:
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audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
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model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
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thred = 0.03 # 0.01
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device = "cuda" if torch.cuda.is_available() else "cpu"
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rmvpe = RMVPE(model_path, is_half=False, device=device)
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t0 = ttime()
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f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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# f0 = rmvpe.infer_from_audio(audio, thred=thred)
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t1 = ttime()
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logger.info("%s %.2f", f0.shape, t1 - t0)
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