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chore(sync): merge dev into main (#1379)
* Optimize latency (#1259) * add attribute: configs/config.py Optimize latency: tools/rvc_for_realtime.py * new file: assets/Synthesizer_inputs.pth * fix: configs/config.py fix: tools/rvc_for_realtime.py * fix bug: infer/lib/infer_pack/models.py * new file: assets/hubert_inputs.pth new file: assets/rmvpe_inputs.pth modified: configs/config.py new features: infer/lib/rmvpe.py new features: tools/jit_export/__init__.py new features: tools/jit_export/get_hubert.py new features: tools/jit_export/get_rmvpe.py new features: tools/jit_export/get_synthesizer.py optimize: tools/rvc_for_realtime.py * optimize: tools/jit_export/get_synthesizer.py fix bug: tools/jit_export/__init__.py * Fixed a bug caused by using half on the CPU: infer/lib/rmvpe.py Fixed a bug caused by using half on the CPU: tools/jit_export/__init__.py Fixed CIRCULAR IMPORT: tools/jit_export/get_rmvpe.py Fixed CIRCULAR IMPORT: tools/jit_export/get_synthesizer.py Fixed a bug caused by using half on the CPU: tools/rvc_for_realtime.py * Remove useless code: infer/lib/rmvpe.py * Delete gui_v1 copy.py * Delete .vscode/launch.json * Delete jit_export_test.py * Delete tools/rvc_for_realtime copy.py * Delete configs/config.json * Delete .gitignore * Fix exceptions caused by switching inference devices: infer/lib/rmvpe.py Fix exceptions caused by switching inference devices: tools/jit_export/__init__.py Fix exceptions caused by switching inference devices: tools/rvc_for_realtime.py * restore * replace(you can undo this commit) * remove debug_print --------- Co-authored-by: Ftps <ftpsflandre@gmail.com> * Fixed some bugs when exporting ONNX model (#1254) * fix import (#1280) * fix import * lint * 🎨 同步 locale (#1242) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Fix jit load and import issue (#1282) * fix jit model loading : infer/lib/rmvpe.py * modified: assets/hubert/.gitignore move file: assets/hubert_inputs.pth -> assets/hubert/hubert_inputs.pth modified: assets/rmvpe/.gitignore move file: assets/rmvpe_inputs.pth -> assets/rmvpe/rmvpe_inputs.pth fix import: gui_v1.py * feat(workflow): trigger on dev * feat(workflow): add close-pr on non-dev branch * Add input wav and delay time monitor for real-time gui (#1293) * feat(workflow): trigger on dev * feat(workflow): add close-pr on non-dev branch * 🎨 同步 locale (#1289) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: edit PR template * add input wav and delay time monitor --------- Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com> * Optimize latency using scripted jit (#1291) * feat(workflow): trigger on dev * feat(workflow): add close-pr on non-dev branch * 🎨 同步 locale (#1289) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: edit PR template * Optimize-latency-using-scripted: configs/config.py Optimize-latency-using-scripted: infer/lib/infer_pack/attentions.py Optimize-latency-using-scripted: infer/lib/infer_pack/commons.py Optimize-latency-using-scripted: infer/lib/infer_pack/models.py Optimize-latency-using-scripted: infer/lib/infer_pack/modules.py Optimize-latency-using-scripted: infer/lib/jit/__init__.py Optimize-latency-using-scripted: infer/lib/jit/get_hubert.py Optimize-latency-using-scripted: infer/lib/jit/get_rmvpe.py Optimize-latency-using-scripted: infer/lib/jit/get_synthesizer.py Optimize-latency-using-scripted: infer/lib/rmvpe.py Optimize-latency-using-scripted: tools/rvc_for_realtime.py * modified: infer/lib/infer_pack/models.py * fix some bug: configs/config.py fix some bug: infer/lib/infer_pack/models.py fix some bug: infer/lib/rmvpe.py * Fixed abnormal reference of logger in multiprocessing: infer/modules/train/train.py --------- Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Format code (#1298) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * 🎨 同步 locale (#1299) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: optimize actions * feat(workflow): add sync dev * feat: optimize actions * feat: optimize actions * feat: optimize actions * feat: optimize actions * feat: add jit options (#1303) Delete useless code: infer/lib/jit/get_synthesizer.py Optimized code: tools/rvc_for_realtime.py * Code refactor + re-design inference ui (#1304) * Code refacor + re-design inference ui * Fix tabname * i18n jp --------- Co-authored-by: Ftps <ftpsflandre@gmail.com> * feat: optimize actions * feat: optimize actions * Update README & en_US locale file (#1309) * critical: some bug fixes (#1322) * JIT acceleration switch does not support hot update * fix padding bug of rmvpe in torch-directml * fix padding bug of rmvpe in torch-directml * Fix STFT under torch_directml (#1330) * chore(format): run black on dev (#1318) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * chore(i18n): sync locale on dev (#1317) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * feat: allow for tta to be passed to uvr (#1361) * chore(format): run black on dev (#1373) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Added script for automatically download all needed models at install (#1366) * Delete modules.py * Add files via upload * Add files via upload * Add files via upload * Add files via upload * chore(i18n): sync locale on dev (#1377) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * chore(format): run black on dev (#1376) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * Update IPEX library (#1362) * Update IPEX library * Update ipex index * chore(format): run black on dev (#1378) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> --------- Co-authored-by: Chengjia Jiang <46401978+ChasonJiang@users.noreply.github.com> Co-authored-by: Ftps <ftpsflandre@gmail.com> Co-authored-by: shizuku_nia <102004222+ShizukuNia@users.noreply.github.com> Co-authored-by: Ftps <63702646+Tps-F@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com> Co-authored-by: yxlllc <33565655+yxlllc@users.noreply.github.com> Co-authored-by: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com> Co-authored-by: Blaise <133521603+blaise-tk@users.noreply.github.com> Co-authored-by: Rice Cake <gak141808@gmail.com> Co-authored-by: AWAS666 <33494149+AWAS666@users.noreply.github.com> Co-authored-by: Dmitry <nda2911@yandex.ru> Co-authored-by: Disty0 <47277141+Disty0@users.noreply.github.com>
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@@ -1,5 +1,6 @@
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import copy
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import math
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from typing import Optional
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import numpy as np
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import torch
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@@ -22,11 +23,11 @@ class Encoder(nn.Module):
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window_size=10,
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**kwargs
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):
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super().__init__()
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super(Encoder, self).__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.n_layers = int(n_layers)
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.window_size = window_size
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@@ -61,14 +62,17 @@ class Encoder(nn.Module):
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def forward(self, x, x_mask):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.attn_layers[i](x, x, attn_mask)
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zippep = zip(
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self.attn_layers, self.norm_layers_1, self.ffn_layers, self.norm_layers_2
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)
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for attn_layers, norm_layers_1, ffn_layers, norm_layers_2 in zippep:
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y = attn_layers(x, x, attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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x = norm_layers_1(x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = ffn_layers(x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = norm_layers_2(x + y)
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x = x * x_mask
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return x
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@@ -86,7 +90,7 @@ class Decoder(nn.Module):
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proximal_init=True,
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**kwargs
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):
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super().__init__()
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super(Decoder, self).__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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@@ -172,7 +176,7 @@ class MultiHeadAttention(nn.Module):
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proximal_bias=False,
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proximal_init=False,
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):
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super().__init__()
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super(MultiHeadAttention, self).__init__()
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assert channels % n_heads == 0
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self.channels = channels
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@@ -213,19 +217,28 @@ class MultiHeadAttention(nn.Module):
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self.conv_k.weight.copy_(self.conv_q.weight)
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self.conv_k.bias.copy_(self.conv_q.bias)
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def forward(self, x, c, attn_mask=None):
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def forward(
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self, x: torch.Tensor, c: torch.Tensor, attn_mask: Optional[torch.Tensor] = None
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):
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q = self.conv_q(x)
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k = self.conv_k(c)
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v = self.conv_v(c)
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x, self.attn = self.attention(q, k, v, mask=attn_mask)
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x, _ = self.attention(q, k, v, mask=attn_mask)
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x = self.conv_o(x)
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return x
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def attention(self, query, key, value, mask=None):
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def attention(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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):
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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s, t_t = (*key.size(), query.size(2))
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b, d, t_s = key.size()
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t_t = query.size(2)
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query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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@@ -292,16 +305,17 @@ class MultiHeadAttention(nn.Module):
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
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return ret
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def _get_relative_embeddings(self, relative_embeddings, length):
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def _get_relative_embeddings(self, relative_embeddings, length: int):
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max_relative_position = 2 * self.window_size + 1
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# Pad first before slice to avoid using cond ops.
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pad_length = max(length - (self.window_size + 1), 0)
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pad_length: int = max(length - (self.window_size + 1), 0)
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slice_start_position = max((self.window_size + 1) - length, 0)
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slice_end_position = slice_start_position + 2 * length - 1
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if pad_length > 0:
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padded_relative_embeddings = F.pad(
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relative_embeddings,
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commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
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# commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
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[0, 0, pad_length, pad_length, 0, 0],
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)
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else:
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padded_relative_embeddings = relative_embeddings
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@@ -317,12 +331,18 @@ class MultiHeadAttention(nn.Module):
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"""
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batch, heads, length, _ = x.size()
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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
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x = F.pad(
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x,
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# commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])
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[0, 1, 0, 0, 0, 0, 0, 0],
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)
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# Concat extra elements so to add up to shape (len+1, 2*len-1).
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x_flat = x.view([batch, heads, length * 2 * length])
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x_flat = F.pad(
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x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
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x_flat,
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# commons.convert_pad_shape([[0, 0], [0, 0], [0, int(length) - 1]])
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[0, int(length) - 1, 0, 0, 0, 0],
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)
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# Reshape and slice out the padded elements.
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@@ -339,15 +359,21 @@ class MultiHeadAttention(nn.Module):
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batch, heads, length, _ = x.size()
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# padd along column
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x = F.pad(
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x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
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x,
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# commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, int(length) - 1]])
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[0, int(length) - 1, 0, 0, 0, 0, 0, 0],
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)
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x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
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x_flat = x.view([batch, heads, int(length**2) + int(length * (length - 1))])
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# add 0's in the beginning that will skew the elements after reshape
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x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
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x_flat = F.pad(
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x_flat,
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# commons.convert_pad_shape([[0, 0], [0, 0], [int(length), 0]])
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[length, 0, 0, 0, 0, 0],
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)
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x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
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return x_final
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def _attention_bias_proximal(self, length):
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def _attention_bias_proximal(self, length: int):
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"""Bias for self-attention to encourage attention to close positions.
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Args:
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length: an integer scalar.
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@@ -367,10 +393,10 @@ class FFN(nn.Module):
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filter_channels,
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kernel_size,
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p_dropout=0.0,
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activation=None,
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activation: str = None,
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causal=False,
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):
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super().__init__()
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super(FFN, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.filter_channels = filter_channels
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@@ -378,40 +404,56 @@ class FFN(nn.Module):
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self.p_dropout = p_dropout
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self.activation = activation
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self.causal = causal
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if causal:
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self.padding = self._causal_padding
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else:
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self.padding = self._same_padding
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self.is_activation = True if activation == "gelu" else False
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# if causal:
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# self.padding = self._causal_padding
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# else:
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# self.padding = self._same_padding
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
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self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
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self.drop = nn.Dropout(p_dropout)
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def forward(self, x, x_mask):
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x = self.conv_1(self.padding(x * x_mask))
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if self.activation == "gelu":
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def padding(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
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if self.causal:
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padding = self._causal_padding(x * x_mask)
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else:
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padding = self._same_padding(x * x_mask)
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return padding
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def forward(self, x: torch.Tensor, x_mask: torch.Tensor):
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x = self.conv_1(self.padding(x, x_mask))
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if self.is_activation:
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x = x * torch.sigmoid(1.702 * x)
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else:
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x = torch.relu(x)
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x = self.drop(x)
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x = self.conv_2(self.padding(x * x_mask))
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x = self.conv_2(self.padding(x, x_mask))
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return x * x_mask
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def _causal_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = self.kernel_size - 1
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pad_r = 0
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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pad_l: int = self.kernel_size - 1
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pad_r: int = 0
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# padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(
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x,
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# commons.convert_pad_shape(padding)
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[pad_l, pad_r, 0, 0, 0, 0],
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)
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return x
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def _same_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l = (self.kernel_size - 1) // 2
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pad_r = self.kernel_size // 2
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padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(x, commons.convert_pad_shape(padding))
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pad_l: int = (self.kernel_size - 1) // 2
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pad_r: int = self.kernel_size // 2
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# padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(
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x,
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# commons.convert_pad_shape(padding)
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[pad_l, pad_r, 0, 0, 0, 0],
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)
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return x
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