-
Notifications
You must be signed in to change notification settings - Fork 35
/
rvc_for_realtime.py
166 lines (141 loc) · 6.31 KB
/
rvc_for_realtime.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
import os
import logging
from lib import BASE_MODELS_DIR, config
from lib.model_utils import change_rms, load_hubert
from pitch_extraction import FeatureExtractor
from lib.utils import gc_collect, get_filenames
logger = logging.getLogger(__name__)
import fairseq
import numpy as np
import torch
import torch.nn.functional as F
if config.dml == True:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
return res
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
# config.device=torch.device("cpu")########强制cpu测试
# config.is_half=False########强制cpu测试
class RVC(FeatureExtractor):
def __init__(self, model_path, config, onnx=False, device=None):
cpt = torch.load(model_path, map_location="cpu")
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
version = cpt.get("version", "v1")
self.sid = 0
if version == "v1":
if if_f0 == 1:
from lib.infer_pack.models import SynthesizerTrnMs256NSFsid
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
else:
from lib.infer_pack.models import SynthesizerTrnMs256NSFsid_nono
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
elif version == "v2":
if if_f0 == 1:
from lib.infer_pack.models import SynthesizerTrnMs768NSFsid
net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
else:
from lib.infer_pack.models import SynthesizerTrnMs768NSFsid_nono
net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
del net_g.enc_q
net_g.load_state_dict(cpt["weight"], strict=False)
net_g.eval().to(device if device else config.device)
if config.is_half:
net_g = net_g.half()
else:
net_g = net_g.float()
hubert_model = load_hubert(config)
model_name = os.path.basename(model_path).split(".")[0]
index_files = get_filenames(root=os.path.join(BASE_MODELS_DIR,"RVC"),folder=".index",exts=["index"],name_filters=[model_name])
file_index = index_files.pop() if len(index_files) else ""
self.cpt = cpt
self.net_g = net_g
self.hubert_model = hubert_model
self.model_name = model_name
self.index, self.big_npy = self.load_index(file_index)
self.tgt_sr = tgt_sr
self.if_f0 = if_f0
self.version = version
super().__init__(tgt_sr, config, onnx) # initiate Feature Extraction
def __del__(self):
super().__del__()
del self.cpt, self.net_g, self.hubert_model, self.index, self.big_npy
gc_collect()
# def process_input(self, x: np.ndarray, **kwargs) -> np.ndarray:
def vc(self, x: np.ndarray, **kwargs) -> np.ndarray:
index_rate = kwargs.pop("index_rate",.5)
protect = kwargs.pop("protect",.5)
rms_mix_rate = kwargs.pop("rms_mix_rate",1.)
feats = torch.from_numpy(x.copy())
feats = feats.view(1, -1)
if config.is_half:
feats = feats.half()
else:
feats = feats.float()
feats = feats.to(self.device)
with torch.no_grad():
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
inputs = {
"source": feats,
"padding_mask": padding_mask,
"output_layer": 9 if self.version == "v1" else 12,
}
logits = self.hubert_model.extract_features(**inputs)
feats = (
self.hubert_model.final_proj(logits[0]) if self.version == "v1" else logits[0]
)
if protect < 0.5 and self.if_f0:
feats0 = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
if self.index is not None and self.big_npy is not None and index_rate != 0:
npy = feats[0].cpu().numpy()
if self.is_half:
npy = npy.astype("float16")
score, ix = self.index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if self.is_half:
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)
if self.if_f0 == 1:
pitch, pitchf = self.get_f0(x, **kwargs)
p_len = min(feats.shape[1], pitch.shape[0])
pitch = pitch[:p_len]
pitchf = pitchf[:p_len]
pitch = torch.from_numpy(pitch).to(self.device).unsqueeze(0)
pitchf = torch.from_numpy(pitchf).to(self.device).unsqueeze(0)
if protect < 0.5:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect
pitchff = pitchff.unsqueeze(-1)
feats = feats * pitchff + feats0 * (1 - pitchff)
del pitchff
else:
pitch, pitchf = None, None
p_len = feats.shape[1]
p_len = torch.LongTensor([p_len]).to(self.device)
sid = torch.LongTensor([self.sid]).to(self.device)
with torch.no_grad():
if self.is_half: feats = feats.to(torch.half)
if self.if_f0 == 1:
# print("process_output",feats,p_len,pitch,pitchf)
# print(12222222222,feats.dtype,pitch.dtype,pitchf.dtype,sid.dtype,self.is_half)
infered_audio = (
self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0].data
)
else:
infered_audio = (
self.net_g.infer(feats, p_len, sid)[0][0, 0].data
)
audio_opt = infered_audio.cpu().float().numpy()
if rms_mix_rate < 1.:
audio_opt = change_rms(x, self.sr, audio_opt, self.tgt_sr, rms_mix_rate)
del feats, p_len, sid, pitch, pitchf, infered_audio
return audio_opt