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WIP: support rvc-webui, refactoring

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wataru 2023-04-24 17:39:31 +09:00
parent cdceae9cf1
commit 86798b3896
5 changed files with 198 additions and 10 deletions
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0
client/demo/src/components/demo/components/301-2-5_ModelSelectRow copy.tsx → client/demo/src/components/demo/components/301-2-5_ModelSelectRow.tsx
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2
client/demo/src/components/demo/components/301_ModelUploaderRow.tsx
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@ -1,7 +1,7 @@
import React, { useMemo, useEffect } from "react"
import { useGuiState } from "../001_GuiStateProvider"
import { ConfigSelectRow } from "./301-1_ConfigSelectRow"
import { ModelSelectRow } from "./301-2-5_ModelSelectRow copy"
import { ModelSelectRow } from "./301-2-5_ModelSelectRow"
import { ONNXSelectRow } from "./301-2_ONNXSelectRow"
import { PyTorchSelectRow } from "./301-3_PyTorchSelectRow"
import { CorrespondenceSelectRow } from "./301-4_CorrespondenceSelectRow"

4
server/voice_changer/RVC/ModelWrapper.py
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@ -27,6 +27,7 @@ class ModelWrapper:
metadata = json.loads(modelmeta.custom_metadata_map["metadata"])
self.samplingRate = metadata["samplingRate"]
self.f0 = metadata["f0"]
self.embChannels = metadata["embChannels"]
print(f"[Voice Changer] Onnx metadata: sr:{self.samplingRate}, f0:{self.f0}")
except:
self.samplingRate = -1
@ -40,6 +41,9 @@ class ModelWrapper:
def getF0(self):
return self.f0
def getEmbChannels(self):
return self.embChannels
def set_providers(self, providers, provider_options=[{}]):
self.onnx_session.set_providers(providers=providers, provider_options=provider_options)

9
server/voice_changer/RVC/RVC.py
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@ -52,6 +52,7 @@ class ModelSlot():
embChannels: int = 256
samplingRateOnnx: int = -1
f0Onnx: bool = True
embChannelsOnnx: int = 256
@dataclass
@ -169,9 +170,6 @@ class RVC:
(2-2) rvc-webuiの、(256 or 768) x (ノーマルor pitchレス)判定 ⇒ 256, or 768 は17番目の要素で判定。, ノーマルor pitchレスはckp["f0"]で判定
'''
# print("config shape:1::::", cpt["config"], cpt["f0"])
# print("config shape:2::::", (cpt).keys)
config_len = len(cpt["config"])
if config_len == 18:
self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_RVC
@ -217,11 +215,12 @@ class RVC:
self.settings.modelSlots[slot].f0Onnx = self.next_onnx_session.getF0()
if self.settings.modelSlots[slot].samplingRate == -1: # ONNXにsampling rateが入っていない
self.settings.modelSlots[slot].samplingRate = self.settings.modelSamplingRate
self.settings.modelSlots[slot].embChannelsOnnx = self.next_onnx_session.getEmbChannels()
# ONNXがある場合は、ONNXの設定を優先
self.settings.modelSlots[slot].samplingRate = self.settings.modelSlots[slot].samplingRateOnnx
self.settings.modelSlots[slot].f0 = self.settings.modelSlots[slot].f0Onnx
self.settings.modelSlots[slot].embChannels = self.settings.modelSlots[slot].embChannelsOnnx
else:
print("[Voice Changer] Skip Loading ONNX Model...")
self.next_onnx_session = None
@ -357,6 +356,7 @@ class RVC:
f0 = self.settings.modelSlots[self.currentSlot].f0
embChannels = self.settings.modelSlots[self.currentSlot].embChannels
print("embChannels::1:", embChannels)
audio_out = vc.pipeline(self.hubert_model, self.onnx_session, sid, audio, times, f0_up_key, f0_method,
file_index, file_big_npy, index_rate, if_f0, f0_file=f0_file, silence_front=self.settings.extraConvertSize / self.settings.modelSamplingRate, f0=f0, embChannels=embChannels)
result = audio_out * np.sqrt(vol)
@ -403,7 +403,6 @@ class RVC:
f0_file = None
f0 = self.settings.modelSlots[self.currentSlot].f0
embChannels = self.settings.modelSlots[self.currentSlot].embChannels
audio_out = vc.pipeline(self.hubert_model, self.net_g, sid, audio, times, f0_up_key, f0_method,
file_index, file_big_npy, index_rate, if_f0, f0_file=f0_file, silence_front=self.settings.extraConvertSize / self.settings.modelSamplingRate, f0=f0, embChannels=embChannels)

193
server/voice_changer/RVC/export2onnx.py
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@ -6,6 +6,8 @@ from onnxsim import simplify
import onnx
from infer_pack.models import TextEncoder256, GeneratorNSF, PosteriorEncoder, ResidualCouplingBlock, Generator
from .models import TextEncoder
from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
class SynthesizerTrnMs256NSFsid_ONNX(nn.Module):
@ -182,6 +184,185 @@ class SynthesizerTrnMs256NSFsid_nono_ONNX(nn.Module):
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")
@ -190,10 +371,14 @@ def export2onnx(input_model, output_model, output_model_simple, is_half, metadat
else:
dev = torch.device("cpu")
if metadata["f0"] == True:
if metadata["f0"] == True and metadata["ModelType"] == RVC_MODEL_TYPE_RVC:
net_g_onnx = SynthesizerTrnMs256NSFsid_ONNX(*cpt["config"], is_half=is_half)
elif metadata["f0"] == False:
elif metadata["f0"] == 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:
net_g_onnx = SynthesizerTrnMs256NSFsid_nono_ONNX(*cpt["config"])
elif metadata["f0"] == False and metadata["ModelType"] == RVC_MODEL_TYPE_WEBUI:
net_g_onnx = SynthesizerTrnMsNSFsidNono_webui_ONNX(**cpt["params"])
net_g_onnx.eval().to(dev)
net_g_onnx.load_state_dict(cpt["weight"], strict=False)
@ -201,9 +386,9 @@ def export2onnx(input_model, output_model, output_model_simple, is_half, metadat
net_g_onnx = net_g_onnx.half()
if is_half:
feats = torch.HalfTensor(1, 2192, 256).to(dev)
feats = torch.HalfTensor(1, 2192, metadata["embChannels"]).to(dev)
else:
feats = torch.FloatTensor(1, 2192, 256).to(dev)
feats = torch.FloatTensor(1, 2192, metadata["embChannels"]).to(dev)
p_len = torch.LongTensor([2192]).to(dev)
sid = torch.LongTensor([0]).to(dev)