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import traceback
import logging
logger = logging.getLogger(__name__)
import numpy as np
import soundfile as sf
import torch
from io import BytesIO
from infer.lib.audio import load_audio, wav2
from infer.lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
SynthesizerTrnMs768NSFsid,
SynthesizerTrnMs768NSFsid_nono,
)
from infer.modules.vc.pipeline import Pipeline
from infer.modules.vc.utils import *
class VC:
def __init__(self, config):
self.n_spk = None
self.tgt_sr = None
self.net_g = None
self.pipeline = None
self.cpt = None
self.version = None
self.if_f0 = None
self.version = None
self.hubert_model = None
self.config = config
def get_vc(self, sid, *to_return_protect):
logger.info("Get sid: " + sid)
to_return_protect0 = {
"visible": self.if_f0 != 0,
"value": to_return_protect[0]
if self.if_f0 != 0 and to_return_protect
else 0.5,
"__type__": "update",
}
to_return_protect1 = {
"visible": self.if_f0 != 0,
"value": to_return_protect[1]
if self.if_f0 != 0 and to_return_protect
else 0.33,
"__type__": "update",
}
if not sid:
if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
logger.info("Clean model cache")
del (
self.net_g,
self.n_spk,
self.vc,
self.hubert_model,
self.tgt_sr,
) # ,cpt
self.hubert_model = (
self.net_g
) = self.n_spk = self.vc = self.hubert_model = self.tgt_sr = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
###楼下不这么折腾清理不干净
self.if_f0 = self.cpt.get("f0", 1)
self.version = self.cpt.get("version", "v1")
if self.version == "v1":
if self.if_f0 == 1:
self.net_g = SynthesizerTrnMs256NSFsid(
*self.cpt["config"], is_half=self.config.is_half
)
else:
self.net_g = SynthesizerTrnMs256NSFsid_nono(*self.cpt["config"])
elif self.version == "v2":
if self.if_f0 == 1:
self.net_g = SynthesizerTrnMs768NSFsid(
*self.cpt["config"], is_half=self.config.is_half
)
else:
self.net_g = SynthesizerTrnMs768NSFsid_nono(*self.cpt["config"])
del self.net_g, self.cpt
if torch.cuda.is_available():
torch.cuda.empty_cache()
return (
{"visible": False, "__type__": "update"},
{
"visible": True,
"value": to_return_protect0,
"__type__": "update",
},
{
"visible": True,
"value": to_return_protect1,
"__type__": "update",
},
"",
"",
)
person = f'{os.getenv("weight_root")}/{sid}'
logger.info(f"Loading: {person}")
self.cpt = torch.load(person, map_location="cpu")
self.tgt_sr = self.cpt["config"][-1]
self.cpt["config"][-3] = self.cpt["weight"]["emb_g.weight"].shape[0] # n_spk
self.if_f0 = self.cpt.get("f0", 1)
self.version = self.cpt.get("version", "v1")
synthesizer_class = {
("v1", 1): SynthesizerTrnMs256NSFsid,
("v1", 0): SynthesizerTrnMs256NSFsid_nono,
("v2", 1): SynthesizerTrnMs768NSFsid,
("v2", 0): SynthesizerTrnMs768NSFsid_nono,
}
self.net_g = synthesizer_class.get(
(self.version, self.if_f0), SynthesizerTrnMs256NSFsid
)(*self.cpt["config"], is_half=self.config.is_half)
del self.net_g.enc_q
self.net_g.load_state_dict(self.cpt["weight"], strict=False)
self.net_g.eval().to(self.config.device)
if self.config.is_half:
self.net_g = self.net_g.half()
else:
self.net_g = self.net_g.float()
self.pipeline = Pipeline(self.tgt_sr, self.config)
n_spk = self.cpt["config"][-3]
index = {"value": get_index_path_from_model(sid), "__type__": "update"}
logger.info("Select index: " + index["value"])
return (
(
{"visible": True, "maximum": n_spk, "__type__": "update"},
to_return_protect0,
to_return_protect1,
index,
index,
)
if to_return_protect
else {"visible": True, "maximum": n_spk, "__type__": "update"}
)
def vc_single(
self,
sid,
input_audio_path,
f0_up_key,
f0_file,
f0_method,
file_index,
file_index2,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
):
if input_audio_path is None:
return "You need to upload an audio", None
f0_up_key = int(f0_up_key)
try:
audio = load_audio(input_audio_path, 16000)
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1:
audio /= audio_max
times = [0, 0, 0]
if self.hubert_model is None:
self.hubert_model = load_hubert(self.config)
file_index = (
(
file_index.strip(" ")
.strip('"')
.strip("\n")
.strip('"')
.strip(" ")
.replace("trained", "added")
)
if file_index != ""
else file_index2
) # 防止小白写错，自动帮他替换掉
audio_opt = self.pipeline.pipeline(
self.hubert_model,
self.net_g,
sid,
audio,
input_audio_path,
times,
f0_up_key,
f0_method,
file_index,
index_rate,
self.if_f0,
filter_radius,
self.tgt_sr,
resample_sr,
rms_mix_rate,
self.version,
protect,
f0_file,
)
if self.tgt_sr != resample_sr >= 16000:
self.tgt_sr = resample_sr
index_info = (
"Index:\n%s." % file_index
if os.path.exists(file_index)
else "Index not used."
)
return (
"Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
% (index_info, *times),
(self.tgt_sr, audio_opt),
)
except:
info = traceback.format_exc()
logger.warn(info)
return info, (None, None)
def vc_multi(
self,
sid,
dir_path,
opt_root,
paths,
f0_up_key,
f0_method,
file_index,
file_index2,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
format1,
):
try:
dir_path = (
dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
) # 防止小白拷路径头尾带了空格和"和回车
opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
os.makedirs(opt_root, exist_ok=True)
try:
if dir_path != "":
paths = [
os.path.join(dir_path, name) for name in os.listdir(dir_path)
]
else:
paths = [path.name for path in paths]
except:
traceback.print_exc()
paths = [path.name for path in paths]
infos = []
for path in paths:
info, opt = self.vc_single(
sid,
path,
f0_up_key,
None,
f0_method,
file_index,
file_index2,
# file_big_npy,
index_rate,
filter_radius,
resample_sr,
rms_mix_rate,
protect,
)
if "Success" in info:
try:
tgt_sr, audio_opt = opt
if format1 in ["wav", "flac"]:
sf.write(
"%s/%s.%s"
% (opt_root, os.path.basename(path), format1),
audio_opt,
tgt_sr,
)
else:
path = "%s/%s.%s" % (opt_root, os.path.basename(path), format1)
with BytesIO() as wavf:
sf.write(
wavf,
audio_opt,
tgt_sr,
format="wav"
)
wavf.seek(0, 0)
with open(path, "wb") as outf:
wav2(wavf, outf, format1)
except:
info += traceback.format_exc()
infos.append("%s->%s" % (os.path.basename(path), info))
yield "\n".join(infos)
yield "\n".join(infos)
except:
yield traceback.format_exc()
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