diff --git a/server/.vscode/settings.json b/server/.vscode/settings.json
index 7bc5a813..514dd256 100644
--- a/server/.vscode/settings.json
+++ b/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,E722,W503"
+ "--ignore=E501,E402,E722,E741,W503"
// "--max-line-length=150",
// "--max-complexity=20"
]
diff --git a/server/voice_changer/RVC/custom_vc_infer_pipeline.py b/server/voice_changer/RVC/custom_vc_infer_pipeline.py
index 1aafdc63..133ab72e 100644
--- a/server/voice_changer/RVC/custom_vc_infer_pipeline.py
+++ b/server/voice_changer/RVC/custom_vc_infer_pipeline.py
@@ -2,10 +2,9 @@ import numpy as np
import parselmouth
import torch
import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
+from config import x_query, x_center, x_max # type:ignore
import scipy.signal as signal
import pyworld
-from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
class VC(object):
@@ -20,12 +19,13 @@ class VC(object):
self.device = device
self.is_half = is_half
- def get_f0(self, audio, p_len, f0_up_key, f0_method, inp_f0=None, silence_front=0):
+ def get_f0(self, audio, p_len, f0_up_key, f0_method, silence_front=0):
n_frames = int(len(audio) // self.window) + 1
start_frame = int(silence_front * self.sr / self.window)
real_silence_front = start_frame * self.window / self.sr
- audio = audio[int(np.round(real_silence_front * self.sr)) :]
+ silence_front_offset = int(np.round(real_silence_front * self.sr))
+ audio = audio[silence_front_offset:]
time_step = self.window / self.sr * 1000
f0_min = 50
@@ -80,18 +80,6 @@ class VC(object):
)
f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
@@ -102,107 +90,108 @@ class VC(object):
f0_coarse = np.rint(f0_mel).astype(np.int)
return f0_coarse, f0bak # 1-0
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- index,
- big_npy,
- index_rate,
- embChannels=256,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half == True:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
- if embChannels == 256:
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9, # layer 9
- }
- else:
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- }
+ # def vc(
+ # self,
+ # model,
+ # net_g,
+ # sid,
+ # audio0,
+ # pitch,
+ # pitchf,
+ # index,
+ # big_npy,
+ # index_rate,
+ # embChannels=256,
+ # ): # ,file_index,file_big_npy
+ # feats = torch.from_numpy(audio0)
+ # if self.is_half is True:
+ # feats = feats.half()
+ # else:
+ # feats = feats.float()
+ # if feats.dim() == 2: # double channels
+ # feats = feats.mean(-1)
+ # assert feats.dim() == 1, feats.dim()
+ # feats = feats.view(1, -1)
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- if embChannels == 256:
- feats = model.final_proj(logits[0])
- else:
- feats = logits[0]
+ # padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
+ # if embChannels == 256:
+ # inputs = {
+ # "source": feats.to(self.device),
+ # "padding_mask": padding_mask,
+ # "output_layer": 9, # layer 9
+ # }
+ # else:
+ # inputs = {
+ # "source": feats.to(self.device),
+ # "padding_mask": padding_mask,
+ # }
- if (
- isinstance(index, type(None)) is False
- and isinstance(big_npy, type(None)) is False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half is True:
- npy = npy.astype("float32")
- D, I = index.search(npy, 1)
- npy = big_npy[I.squeeze()]
- if self.is_half is True:
- npy = npy.astype("float16")
+ # with torch.no_grad():
+ # logits = model.extract_features(**inputs)
+ # if embChannels == 256:
+ # feats = model.final_proj(logits[0])
+ # else:
+ # feats = logits[0]
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
+ # if (
+ # isinstance(index, type(None)) is False
+ # and isinstance(big_npy, type(None)) is False
+ # and index_rate != 0
+ # ):
+ # npy = feats[0].cpu().numpy()
+ # if self.is_half is True:
+ # npy = npy.astype("float32")
+ # D, I = index.search(npy, 1)
+ # npy = big_npy[I.squeeze()]
+ # if self.is_half is True:
+ # npy = npy.astype("float16")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+ # feats = (
+ # torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+ # + (1 - index_rate) * feats
+ # )
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch is not None and pitchf is not None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
- p_len = torch.tensor([p_len], device=self.device).long()
+ # feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- with torch.no_grad():
- if pitch is not None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- else:
- if hasattr(net_g, "infer_pitchless"):
- audio1 = (
- (net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
- .data.cpu()
- .float()
- .numpy()
- .astype(np.int16)
- )
+ # p_len = audio0.shape[0] // self.window
+ # if feats.shape[1] < p_len:
+ # p_len = feats.shape[1]
+ # if pitch is not None and pitchf is not None:
+ # pitch = pitch[:, :p_len]
+ # pitchf = pitchf[:, :p_len]
+ # p_len = torch.tensor([p_len], device=self.device).long()
- del feats, p_len, padding_mask
- torch.cuda.empty_cache()
+ # with torch.no_grad():
+ # if pitch is not None:
+ # audio1 = (
+ # (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
+ # .data.cpu()
+ # .float()
+ # .numpy()
+ # .astype(np.int16)
+ # )
+ # else:
+ # if hasattr(net_g, "infer_pitchless"):
+ # audio1 = (
+ # (net_g.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768)
+ # .data.cpu()
+ # .float()
+ # .numpy()
+ # .astype(np.int16)
+ # )
+ # else:
+ # audio1 = (
+ # (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
+ # .data.cpu()
+ # .float()
+ # .numpy()
+ # .astype(np.int16)
+ # )
- return audio1
+ # del feats, p_len, padding_mask
+ # torch.cuda.empty_cache()
+
+ # return audio1
def pipeline(
self,
@@ -221,7 +210,6 @@ class VC(object):
):
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
# ピッチ検出
@@ -232,7 +220,6 @@ class VC(object):
p_len,
f0_up_key,
f0_method,
- inp_f0,
silence_front=silence_front,
)
pitch = pitch[:p_len]
@@ -242,23 +229,126 @@ class VC(object):
pitchf, device=self.device, dtype=torch.float
).unsqueeze(0)
- output = self.vc(
- embedder,
- model,
- sid,
- audio_pad,
- pitch,
- pitchf,
- index,
- big_npy,
- index_rate,
- embChannels,
- )
+ # tensor
+ feats = torch.from_numpy(audio_pad)
+ if self.is_half is True:
+ feats = feats.half()
+ else:
+ feats = feats.float()
+ if feats.dim() == 2: # double channels
+ feats = feats.mean(-1)
+ assert feats.dim() == 1, feats.dim()
+ feats = feats.view(1, -1)
+
+ # embedding
+ padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
+ if embChannels == 256:
+ inputs = {
+ "source": feats.to(self.device),
+ "padding_mask": padding_mask,
+ "output_layer": 9, # layer 9
+ }
+ else:
+ inputs = {
+ "source": feats.to(self.device),
+ "padding_mask": padding_mask,
+ }
+
+ with torch.no_grad():
+ logits = embedder.extract_features(**inputs)
+ if embChannels == 256:
+ feats = embedder.final_proj(logits[0])
+ else:
+ feats = logits[0]
+
+ # Index - feature抽出
+ if (
+ isinstance(index, type(None)) is False
+ and isinstance(big_npy, type(None)) is False
+ and index_rate != 0
+ ):
+ npy = feats[0].cpu().numpy()
+ if self.is_half is True:
+ npy = npy.astype("float32")
+ D, I = index.search(npy, 1)
+ npy = big_npy[I.squeeze()]
+ if self.is_half is True:
+ npy = npy.astype("float16")
+
+ feats = (
+ torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+ + (1 - index_rate) * feats
+ )
+
+ #
+ feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+
+ # ピッチ抽出
+ p_len = audio_pad.shape[0] // self.window
+ if feats.shape[1] < p_len:
+ p_len = feats.shape[1]
+ if pitch is not None and pitchf is not None:
+ pitch = pitch[:, :p_len]
+ pitchf = pitchf[:, :p_len]
+ p_len = torch.tensor([p_len], device=self.device).long()
+
+ # 推論実行
+ with torch.no_grad():
+ if pitch is not None:
+ audio1 = (
+ (model.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
+ .data.cpu()
+ .float()
+ .numpy()
+ .astype(np.int16)
+ )
+ else:
+ if hasattr(model, "infer_pitchless"):
+ audio1 = (
+ (model.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768)
+ .data.cpu()
+ .float()
+ .numpy()
+ .astype(np.int16)
+ )
+ else:
+ audio1 = (
+ (model.infer(feats, p_len, sid)[0][0, 0] * 32768)
+ .data.cpu()
+ .float()
+ .numpy()
+ .astype(np.int16)
+ )
+
+ del feats, p_len, padding_mask
+ torch.cuda.empty_cache()
+
if self.t_pad_tgt != 0:
offset = self.t_pad_tgt
end = -1 * self.t_pad_tgt
- output = output[offset:end]
+ audio1 = audio1[offset:end]
del pitch, pitchf, sid
torch.cuda.empty_cache()
- return output
+ return audio1
+
+ # output = self.vc(
+ # embedder,
+ # model,
+ # sid,
+ # audio_pad,
+ # pitch,
+ # pitchf,
+ # index,
+ # big_npy,
+ # index_rate,
+ # embChannels,
+ # )
+ # if self.t_pad_tgt != 0:
+ # offset = self.t_pad_tgt
+ # end = -1 * self.t_pad_tgt
+ # output = output[offset:end]
+
+ # del pitch, pitchf, sid
+ # torch.cuda.empty_cache()
+ # return output