Cool_Tools/Retrieval-based-Voice-Conversion-WebUI
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7 changed files with 91 additions and 48 deletions
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7
extract_feature_print.py
View File

@ -64,8 +64,11 @@ def readwave(wav_path, normalize=False):
# HuBERT model
printt("load model(s) from {}".format(model_path))
# if hubert model is exist
if (os.access(model_path, os.F_OK) == False):
printt("Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" % model_path)
if os.access(model_path, os.F_OK) == False:
printt(
"Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main"
% model_path
)
exit(0)
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[model_path],

20
gui.py
View File

@ -469,21 +469,21 @@ class GUI:
self.flag_vc = False
def set_values(self, values):
if(len(values["pth_path"].strip()) == 0):
sg.popup(i18n('请选择pth文件'))
if len(values["pth_path"].strip()) == 0:
sg.popup(i18n("请选择pth文件"))
return False
if(len(values["index_path"].strip()) == 0):
sg.popup(i18n('请选择index文件'))
if len(values["index_path"].strip()) == 0:
sg.popup(i18n("请选择index文件"))
return False
pattern = re.compile("[^\x00-\x7F]+")
if(pattern.findall(values["hubert_path"])):
sg.popup(i18n('hubert模型路径不可包含中文'))
if pattern.findall(values["hubert_path"]):
sg.popup(i18n("hubert模型路径不可包含中文"))
return False
if(pattern.findall(values["pth_path"])):
sg.popup(i18n('pth文件路径不可包含中文'))
if pattern.findall(values["pth_path"]):
sg.popup(i18n("pth文件路径不可包含中文"))
return False
if(pattern.findall(values["index_path"])):
sg.popup(i18n('index文件路径不可包含中文'))
if pattern.findall(values["index_path"]):
sg.popup(i18n("index文件路径不可包含中文"))
return False
self.set_devices(values["sg_input_device"], values["sg_output_device"])
self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")

97
infer-web.py
View File

@ -1,9 +1,10 @@
import os
import shutil
import sys
now_dir = os.getcwd()
sys.path.append(now_dir)
import traceback,pdb
import traceback, pdb
import warnings
import numpy as np
@ -396,7 +397,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
# 一个选项卡全局只能有一个音色
def get_vc(sid,to_return_protect0,to_return_protect1):
def get_vc(sid, to_return_protect0, to_return_protect1):
global n_spk, tgt_sr, net_g, vc, cpt, version
if sid == "" or sid == []:
global hubert_model
@ -434,11 +435,23 @@ def get_vc(sid,to_return_protect0,to_return_protect1):
tgt_sr = cpt["config"][-1]
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
if_f0 = cpt.get("f0", 1)
if(if_f0==0):
to_return_protect0=to_return_protect1={"visible": False, "value": 0.5, "__type__": "update"}
if if_f0 == 0:
to_return_protect0 = to_return_protect1 = {
"visible": False,
"value": 0.5,
"__type__": "update",
}
else:
to_return_protect0 ={"visible": True, "value": to_return_protect0, "__type__": "update"}
to_return_protect1 ={"visible": True, "value": to_return_protect1, "__type__": "update"}
to_return_protect0 = {
"visible": True,
"value": to_return_protect0,
"__type__": "update",
}
to_return_protect1 = {
"visible": True,
"value": to_return_protect1,
"__type__": "update",
}
version = cpt.get("version", "v1")
if version == "v1":
if if_f0 == 1:
@ -459,7 +472,11 @@ def get_vc(sid,to_return_protect0,to_return_protect1):
net_g = net_g.float()
vc = VC(tgt_sr, config)
n_spk = cpt["config"][-3]
return {"visible": True, "maximum": n_spk, "__type__": "update"},to_return_protect0,to_return_protect1
return (
{"visible": True, "maximum": n_spk, "__type__": "update"},
to_return_protect0,
to_return_protect1,
)
def change_choices():
@ -665,8 +682,13 @@ def change_sr2(sr2, if_f0_3, version19):
def change_version19(sr2, if_f0_3, version19):
path_str = "" if version19 == "v1" else "_v2"
if(sr2=="32k"and version19=="v1"):sr2="40k"
to_return_sr2= {"choices": ["40k","48k"], "__type__": "update"} if version19=="v1"else {"choices": ["32k","40k","48k"], "__type__": "update"}
if sr2 == "32k" and version19 == "v1":
sr2 = "40k"
to_return_sr2 = (
{"choices": ["40k", "48k"], "__type__": "update"}
if version19 == "v1"
else {"choices": ["32k", "40k", "48k"], "__type__": "update"}
)
f0_str = "f0" if if_f0_3 else ""
if_pretrained_generator_exist = os.access(
"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
@ -691,7 +713,7 @@ def change_version19(sr2, if_f0_3, version19):
"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
if if_pretrained_discriminator_exist
else "",
to_return_sr2
to_return_sr2,
)
@ -893,14 +915,24 @@ def train_index(exp_dir1, version19):
big_npy_idx = np.arange(big_npy.shape[0])
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if(big_npy.shape[0]>2e5):
# if(1):
infos.append("Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0])
if big_npy.shape[0] > 2e5:
# if(1):
infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
yield "\n".join(infos)
try:
big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * config.n_cpu,
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
except:
info=traceback.format_exc()
info = traceback.format_exc()
print(info)
infos.append(info)
yield "\n".join(infos)
@ -1147,15 +1179,25 @@ def train1key(
np.random.shuffle(big_npy_idx)
big_npy = big_npy[big_npy_idx]
if(big_npy.shape[0]>2e5):
# if(1):
info="Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0]
if big_npy.shape[0] > 2e5:
# if(1):
info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
print(info)
yield get_info_str(info)
try:
big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_
big_npy = (
MiniBatchKMeans(
n_clusters=10000,
verbose=True,
batch_size=256 * config.n_cpu,
compute_labels=False,
init="random",
)
.fit(big_npy)
.cluster_centers_
)
except:
info=traceback.format_exc()
info = traceback.format_exc()
print(info)
yield get_info_str(info)
@ -1207,11 +1249,10 @@ def change_info_(ckpt_path):
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
def export_onnx(ModelPath, ExportedPath):
cpt = torch.load(ModelPath, map_location="cpu")
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
vec_channels = 256 if cpt.get("version","v1")=="v1"else 768
vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
test_phone = torch.rand(1, 200, vec_channels) # hidden unit
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
@ -1223,7 +1264,7 @@ def export_onnx(ModelPath, ExportedPath):
device = "cpu" # 导出时设备（不影响使用模型）
net_g = SynthesizerTrnMsNSFsidM(
*cpt["config"], is_half=False,version=cpt.get("version","v1")
*cpt["config"], is_half=False, version=cpt.get("version", "v1")
) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
net_g.load_state_dict(cpt["weight"], strict=False)
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
@ -1504,8 +1545,8 @@ with gr.Blocks() as app:
)
sid0.change(
fn=get_vc,
inputs=[sid0,protect0,protect1],
outputs=[spk_item,protect0,protect1],
inputs=[sid0, protect0, protect1],
outputs=[spk_item, protect0, protect1],
)
with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
with gr.Group():
@ -1604,7 +1645,7 @@ with gr.Blocks() as app:
maximum=config.n_cpu,
step=1,
label=i18n("提取音高和处理数据使用的CPU进程数"),
value=int(np.ceil(config.n_cpu/1.5)),
value=int(np.ceil(config.n_cpu / 1.5)),
interactive=True,
)
with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
@ -1722,7 +1763,7 @@ with gr.Blocks() as app:
version19.change(
change_version19,
[sr2, if_f0_3, version19],
[pretrained_G14, pretrained_D15,sr2],
[pretrained_G14, pretrained_D15, sr2],
)
if_f0_3.change(
change_f0,
@ -1915,7 +1956,7 @@ with gr.Blocks() as app:
[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
info7,
)
with gr.TabItem(i18n("Onnx导出")):
with gr.Row():
ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)

1
infer_pack/onnx_inference.py
View File

@ -3,6 +3,7 @@ import librosa
import numpy as np
import soundfile
class ContentVec:
def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
print("load model(s) from {}".format(vec_path))

2
train_nsf_sim_cache_sid_load_pretrain.py
View File

@ -192,7 +192,6 @@ def run(rank, n_gpus, hps):
epoch_str = 1
global_step = 0
if hps.pretrainG != "":
if rank == 0:
logger.info("loaded pretrained %s" % (hps.pretrainG))
print(
@ -201,7 +200,6 @@ def run(rank, n_gpus, hps):
)
) ##测试不加载优化器
if hps.pretrainD != "":
if rank == 0:
logger.info("loaded pretrained %s" % (hps.pretrainD))
print(

6
trainset_preprocess_pipeline_print.py
View File

@ -53,9 +53,9 @@ class PreProcess:
os.makedirs(self.wavs16k_dir, exist_ok=True)
def norm_write(self, tmp_audio, idx0, idx1):
tmp_max=np.abs(tmp_audio).max()
if(tmp_max>2.5):
print("%s-%s-%s-filtered"%(idx0,idx1,tmp_max))
tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.5:
print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
return
tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
1 - self.alpha

6
vc_infer_pipeline.py
View File

@ -184,7 +184,7 @@ class VC(object):
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
if protect < 0.5 and pitch!=None and pitchf!=None:
if protect < 0.5 and pitch != None and pitchf != None:
feats0 = feats.clone()
if (
isinstance(index, type(None)) == False
@ -211,7 +211,7 @@ class VC(object):
)
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
if protect < 0.5 and pitch!=None and pitchf!=None:
if protect < 0.5 and pitch != None and pitchf != None:
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
0, 2, 1
)
@ -223,7 +223,7 @@ class VC(object):
pitch = pitch[:, :p_len]
pitchf = pitchf[:, :p_len]
if protect < 0.5 and pitch!=None and pitchf!=None:
if protect < 0.5 and pitch != None and pitchf != None:
pitchff = pitchf.clone()
pitchff[pitchf > 0] = 1
pitchff[pitchf < 1] = protect