WIP: refactoring

server/.vscode/settings.json

@@ -9,7 +9,7 @@
     "editor.formatOnSave": true // ファイル保存時に自動フォーマット
   },
   "flake8.args": [
-    "--ignore=E501,E402,W503"
+    "--ignore=E501,E402,E722,W503"
     // "--max-line-length=150",
     // "--max-complexity=20"
   ]

server/voice_changer/RVC/ModelWrapper.py

@@ -2,6 +2,7 @@ import onnxruntime
 import torch
 import numpy as np
 import json
+
 # providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
 providers = ["CPUExecutionProvider"]
 
@@ -13,8 +14,7 @@ class ModelWrapper:
         # ort_options = onnxruntime.SessionOptions()
         # ort_options.intra_op_num_threads = 8
         self.onnx_session = onnxruntime.InferenceSession(
-            self.onnx_model,
-            providers=providers
+            self.onnx_model, providers=providers
         )
         # input_info = s
         first_input_type = self.onnx_session.get_inputs()[0].type
@@ -30,8 +30,12 @@ class ModelWrapper:
             self.embChannels = metadata["embChannels"]
             self.modelType = metadata["modelType"]
             self.deprecated = False
-            self.embedder = metadata["embedder"] if "embedder" in metadata else "hubert_base"
-            print(f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}, embedder:{self.embedder}")
+            self.embedder = (
+                metadata["embedder"] if "embedder" in metadata else "hubert_base"
+            )
+            print(
+                f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}, embedder:{self.embedder}"
+            )
         except:
             self.samplingRate = 48000
             self.f0 = True
@@ -39,10 +43,18 @@ class ModelWrapper:
             self.modelType = 0
             self.deprecated = True
             self.embedder = "hubert_base"
-            print(f"[Voice Changer] ############## !!!! CAUTION !!!! ####################")
-            print(f"[Voice Changer] This onnx's version is depricated. Please regenerate onnxfile. Fallback to default")
-            print(f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}")
-            print(f"[Voice Changer] ############## !!!! CAUTION !!!! ####################")
+            print(
+                "[Voice Changer] ############## !!!! CAUTION !!!! ####################"
+            )
+            print(
+                "[Voice Changer] This onnx's version is depricated. Please regenerate onnxfile. Fallback to default"
+            )
+            print(
+                f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}"
+            )
+            print(
+                "[Voice Changer] ############## !!!! CAUTION !!!! ####################"
+            )
 
     def getSamplingRate(self):
         return self.samplingRate
@@ -63,7 +75,9 @@ class ModelWrapper:
         return self.embedder
 
     def set_providers(self, providers, provider_options=[{}]):
-        self.onnx_session.set_providers(providers=providers, provider_options=provider_options)
+        self.onnx_session.set_providers(
+            providers=providers, provider_options=provider_options
+        )
 
     def get_providers(self):
         return self.onnx_session.get_providers()
@@ -76,7 +90,8 @@ class ModelWrapper:
                     "feats": feats.cpu().numpy().astype(np.float16),
                     "p_len": p_len.cpu().numpy().astype(np.int64),
                     "sid": sid.cpu().numpy().astype(np.int64),
-                })
+                },
+            )
         else:
             audio1 = self.onnx_session.run(
                 ["audio"],
@@ -84,7 +99,8 @@ class ModelWrapper:
                     "feats": feats.cpu().numpy().astype(np.float32),
                     "p_len": p_len.cpu().numpy().astype(np.int64),
                     "sid": sid.cpu().numpy().astype(np.int64),
-                })
+                },
+            )
         return torch.tensor(np.array(audio1))
 
     def infer(self, feats, p_len, pitch, pitchf, sid):
@@ -97,7 +113,8 @@ class ModelWrapper:
                     "pitch": pitch.cpu().numpy().astype(np.int64),
                     "pitchf": pitchf.cpu().numpy().astype(np.float32),
                     "sid": sid.cpu().numpy().astype(np.int64),
-                })
+                },
+            )
         else:
             audio1 = self.onnx_session.run(
                 ["audio"],
@@ -107,6 +124,7 @@ class ModelWrapper:
                     "pitch": pitch.cpu().numpy().astype(np.int64),
                     "pitchf": pitchf.cpu().numpy().astype(np.float32),
                     "sid": sid.cpu().numpy().astype(np.int64),
-                })
+                },
+            )
 
         return torch.tensor(np.array(audio1))

server/voice_changer/RVC/export2onnx.py

@@ -1,383 +1,37 @@
-from distutils.util import strtobool
 import json
 import torch
-from torch import nn
 from onnxsim import simplify
 import onnx
 
-from infer_pack.models import TextEncoder256, GeneratorNSF, PosteriorEncoder, ResidualCouplingBlock, Generator
-from .models import TextEncoder
+from voice_changer.RVC.onnx.SynthesizerTrnMs256NSFsid_ONNX import (
+    SynthesizerTrnMs256NSFsid_ONNX,
+)
+from voice_changer.RVC.onnx.SynthesizerTrnMs256NSFsid_nono_ONNX import (
+    SynthesizerTrnMs256NSFsid_nono_ONNX,
+)
+from voice_changer.RVC.onnx.SynthesizerTrnMsNSFsidNono_webui_ONNX import (
+    SynthesizerTrnMsNSFsidNono_webui_ONNX,
+)
+from voice_changer.RVC.onnx.SynthesizerTrnMsNSFsid_webui_ONNX import (
+    SynthesizerTrnMsNSFsid_webui_ONNX,
+)
 from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
 
 
-class SynthesizerTrnMs256NSFsid_ONNX(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        **kwargs
-    ):
-
-        super().__init__()
-        if (type(sr) == type("strr")):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels, sr=sr, is_half=kwargs["is_half"]
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        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)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMs256NSFsid_nono_ONNX(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr=None,
-        **kwargs
-    ):
-
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder256(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout, f0=False
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def forward(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        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)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMsNSFsid_webui_ONNX(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        emb_channels,
-        sr,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        self.emb_channels = emb_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            emb_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-        )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        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)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
-class SynthesizerTrnMsNSFsidNono_webui_ONNX(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        emb_channels,
-        sr=None,
-        **kwargs
-    ):
-        super().__init__()
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        self.emb_channels = emb_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        self.enc_p = TextEncoder(
-            inter_channels,
-            hidden_channels,
-            filter_channels,
-            emb_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            f0=False,
-        )
-        self.dec = Generator(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def forward(self, phone, phone_lengths, sid, max_len=None):
-        g = self.emb_g(sid).unsqueeze(-1)
-        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
-        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)
-        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
-        return o, x_mask, (z, z_p, m_p, logs_p)
-
-
 def export2onnx(input_model, output_model, output_model_simple, is_half, metadata):
-
     cpt = torch.load(input_model, map_location="cpu")
     if is_half:
         dev = torch.device("cuda", index=0)
     else:
         dev = torch.device("cpu")
 
-    if metadata["f0"] == True and metadata["modelType"] == RVC_MODEL_TYPE_RVC:
+    if metadata["f0"] is True and metadata["modelType"] == RVC_MODEL_TYPE_RVC:
         net_g_onnx = SynthesizerTrnMs256NSFsid_ONNX(*cpt["config"], is_half=is_half)
-    elif metadata["f0"] == True and metadata["modelType"] == RVC_MODEL_TYPE_WEBUI:
+    elif metadata["f0"] is True and metadata["modelType"] == RVC_MODEL_TYPE_WEBUI:
         net_g_onnx = SynthesizerTrnMsNSFsid_webui_ONNX(**cpt["params"], is_half=is_half)
-    elif metadata["f0"] == False and metadata["modelType"] == RVC_MODEL_TYPE_RVC:
+    elif metadata["f0"] is False and metadata["modelType"] == RVC_MODEL_TYPE_RVC:
         net_g_onnx = SynthesizerTrnMs256NSFsid_nono_ONNX(*cpt["config"])
-    elif metadata["f0"] == False and metadata["modelType"] == RVC_MODEL_TYPE_WEBUI:
+    elif metadata["f0"] is False and metadata["modelType"] == RVC_MODEL_TYPE_WEBUI:
         net_g_onnx = SynthesizerTrnMsNSFsidNono_webui_ONNX(**cpt["params"])
 
     net_g_onnx.eval().to(dev)
@@ -392,31 +46,45 @@ def export2onnx(input_model, output_model, output_model_simple, is_half, metadat
     p_len = torch.LongTensor([2192]).to(dev)
     sid = torch.LongTensor([0]).to(dev)
 
-    if metadata["f0"] == True:
+    if metadata["f0"] is True:
         pitch = torch.zeros(1, 2192, dtype=torch.int64).to(dev)
         pitchf = torch.FloatTensor(1, 2192).to(dev)
         input_names = ["feats", "p_len", "pitch", "pitchf", "sid"]
-        inputs = (feats, p_len, pitch, pitchf, sid,)
+        inputs = (
+            feats,
+            p_len,
+            pitch,
+            pitchf,
+            sid,
+        )
 
     else:
         input_names = ["feats", "p_len", "sid"]
-        inputs = (feats, p_len, sid,)
+        inputs = (
+            feats,
+            p_len,
+            sid,
+        )
 
-    output_names = ["audio", ]
+    output_names = [
+        "audio",
+    ]
 
-    torch.onnx.export(net_g_onnx,
-                      inputs,
-                      output_model,
-                      dynamic_axes={
-                          "feats": [1],
-                          "pitch": [1],
-                          "pitchf": [1],
-                      },
-                      do_constant_folding=False,
-                      opset_version=17,
-                      verbose=False,
-                      input_names=input_names,
-                      output_names=output_names)
+    torch.onnx.export(
+        net_g_onnx,
+        inputs,
+        output_model,
+        dynamic_axes={
+            "feats": [1],
+            "pitch": [1],
+            "pitchf": [1],
+        },
+        do_constant_folding=False,
+        opset_version=17,
+        verbose=False,
+        input_names=input_names,
+        output_names=output_names,
+    )
 
     model_onnx2 = onnx.load(output_model)
     model_simp, check = simplify(model_onnx2)

server/voice_changer/RVC/models.py

@@ -1,10 +1,14 @@
 import math
 import torch
 from torch import nn
-import numpy as np
 
-from infer_pack.models import sr2sr, GeneratorNSF, PosteriorEncoder, ResidualCouplingBlock, Generator
-from infer_pack import commons, attentions
+from infer_pack.models import (  # type:ignore
+    GeneratorNSF,
+    PosteriorEncoder,
+    ResidualCouplingBlock,
+    Generator,
+)
+from infer_pack import commons, attentions  # type:ignore
 
 
 class TextEncoder(nn.Module):
@@ -31,7 +35,7 @@ class TextEncoder(nn.Module):
         self.p_dropout = p_dropout
         self.emb_phone = nn.Linear(emb_channels, hidden_channels)
         self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
+        if f0 is True:
             self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
         self.encoder = attentions.Encoder(
             hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
@@ -39,7 +43,7 @@ class TextEncoder(nn.Module):
         self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
 
     def forward(self, phone, pitch, lengths):
-        if pitch == None:
+        if pitch is None:
             x = self.emb_phone(phone)
         else:
             x = self.emb_phone(phone) + self.emb_pitch(pitch)
@@ -81,8 +85,6 @@ class SynthesizerTrnMsNSFsid(nn.Module):
         **kwargs
     ):
         super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
         self.spec_channels = spec_channels
         self.inter_channels = inter_channels
         self.hidden_channels = hidden_channels

server/voice_changer/RVC/onnx/SynthesizerTrnMs256NSFsid_ONNX.py

@@ -0,0 +1,95 @@
+from torch import nn
+from infer_pack.models import (  # type:ignore
+    TextEncoder256,
+    GeneratorNSF,
+    PosteriorEncoder,
+    ResidualCouplingBlock,
+)
+import torch
+
+
+class SynthesizerTrnMs256NSFsid_ONNX(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            sr=sr,
+            is_half=kwargs["is_half"],
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        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)
+        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)

server/voice_changer/RVC/onnx/SynthesizerTrnMs256NSFsid_nono_ONNX.py

@@ -0,0 +1,94 @@
+from torch import nn
+from infer_pack.models import (  # type:ignore
+    TextEncoder256,
+    PosteriorEncoder,
+    ResidualCouplingBlock,
+    Generator,
+)
+import torch
+
+
+class SynthesizerTrnMs256NSFsid_nono_ONNX(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr=None,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            f0=False,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def forward(self, phone, phone_lengths, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
+        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)
+        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)

server/voice_changer/RVC/onnx/SynthesizerTrnMsNSFsidNono_webui_ONNX.py

@@ -0,0 +1,97 @@
+from torch import nn
+from infer_pack.models import (  # type:ignore
+    PosteriorEncoder,
+    ResidualCouplingBlock,
+    Generator,
+)
+from voice_changer.RVC.models import TextEncoder
+import torch
+
+
+class SynthesizerTrnMsNSFsidNono_webui_ONNX(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        emb_channels,
+        sr=None,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        self.emb_channels = emb_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            emb_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            f0=False,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def forward(self, phone, phone_lengths, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
+        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)
+        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)

server/voice_changer/RVC/onnx/SynthesizerTrnMsNSFsid_webui_ONNX.py

@@ -0,0 +1,98 @@
+from torch import nn
+from infer_pack.models import (  # type:ignore
+    GeneratorNSF,
+    PosteriorEncoder,
+    ResidualCouplingBlock,
+)
+from voice_changer.RVC.models import TextEncoder
+import torch
+
+
+class SynthesizerTrnMsNSFsid_webui_ONNX(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        emb_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        self.emb_channels = emb_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            emb_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            sr=sr,
+            is_half=kwargs["is_half"],
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        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)
+        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)