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Retrieval-based-Voice-Conversion-WebUI
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optimize: config

This commit is contained in:
源文雨 2023-08-27 00:35:39 +08:00
parent 80bf0cb8c8
commit 8c36dc2cc7
5 changed files with 144 additions and 146 deletions
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4
app.py
View File

@ -9,7 +9,7 @@ import logging
from fairseq import checkpoint_utils
from vc_infer_pipeline import VC
import traceback
from config import Config
from config import defaultconfig as config
from lib.infer_pack.models import (
SynthesizerTrnMs256NSFsid,
SynthesizerTrnMs256NSFsid_nono,
@ -26,8 +26,6 @@ logging.getLogger("matplotlib").setLevel(logging.WARNING)
i18n = I18nAuto()
i18n.print()
config = Config()
weight_root = "weights"
weight_uvr5_root = "uvr5_weights"
index_root = "logs"

2
config.py
View File

@ -198,3 +198,5 @@ class Config:
except:
pass
return x_pad, x_query, x_center, x_max
defaultconfig = Config()

3
infer-web.py
View File

@ -22,7 +22,7 @@ import faiss
import ffmpeg
import gradio as gr
import soundfile as sf
from config import Config
from config import defaultconfig as config
import fairseq
from i18n import I18nAuto
from lib.infer_pack.models import (
@ -53,7 +53,6 @@ warnings.filterwarnings("ignore")
torch.manual_seed(114514)
config = Config()
if config.dml == True:
def forward_dml(ctx, x, scale):

3
rvc_for_realtime.py
View File

@ -13,11 +13,10 @@ import scipy.signal as signal
now_dir = os.getcwd()
sys.path.append(now_dir)
from config import Config
from config import defaultconfig as config
from multiprocessing import Manager as M
mm = M()
config = Config()
if config.dml == True:
def forward_dml(ctx, x, scale):

278
trainset_preprocess_pipeline_print.py
View File

@ -1,139 +1,139 @@
import sys, os, multiprocessing
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(sys.argv)
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
import numpy as np, os, traceback
from lib.slicer2 import Slicer
import librosa, traceback
from scipy.io import wavfile
import multiprocessing
from lib.audio import load_audio
mutex = multiprocessing.Lock()
f = open("%s/preprocess.log" % exp_dir, "a+")
def println(strr):
mutex.acquire()
print(strr)
f.write("%s\n" % strr)
f.flush()
mutex.release()
class PreProcess:
def __init__(self, sr, exp_dir):
self.slicer = Slicer(
sr=sr,
threshold=-42,
min_length=1500,
min_interval=400,
hop_size=15,
max_sil_kept=500,
)
self.sr = sr
self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
self.per = 3.0
self.overlap = 0.3
self.tail = self.per + self.overlap
self.max = 0.9
self.alpha = 0.75
self.exp_dir = exp_dir
self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
os.makedirs(self.exp_dir, exist_ok=True)
os.makedirs(self.gt_wavs_dir, exist_ok=True)
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))
return
tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
1 - self.alpha
) * tmp_audio
wavfile.write(
"%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
self.sr,
tmp_audio.astype(np.float32),
)
tmp_audio = librosa.resample(
tmp_audio, orig_sr=self.sr, target_sr=16000
) # , res_type="soxr_vhq"
wavfile.write(
"%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
16000,
tmp_audio.astype(np.float32),
)
def pipeline(self, path, idx0):
try:
audio = load_audio(path, self.sr)
# zero phased digital filter cause pre-ringing noise...
# audio = signal.filtfilt(self.bh, self.ah, audio)
audio = signal.lfilter(self.bh, self.ah, audio)
idx1 = 0
for audio in self.slicer.slice(audio):
i = 0
while 1:
start = int(self.sr * (self.per - self.overlap) * i)
i += 1
if len(audio[start:]) > self.tail * self.sr:
tmp_audio = audio[start : start + int(self.per * self.sr)]
self.norm_write(tmp_audio, idx0, idx1)
idx1 += 1
else:
tmp_audio = audio[start:]
idx1 += 1
break
self.norm_write(tmp_audio, idx0, idx1)
println("%s->Suc." % path)
except:
println("%s->%s" % (path, traceback.format_exc()))
def pipeline_mp(self, infos):
for path, idx0 in infos:
self.pipeline(path, idx0)
def pipeline_mp_inp_dir(self, inp_root, n_p):
try:
infos = [
("%s/%s" % (inp_root, name), idx)
for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
]
if noparallel:
for i in range(n_p):
self.pipeline_mp(infos[i::n_p])
else:
ps = []
for i in range(n_p):
p = multiprocessing.Process(
target=self.pipeline_mp, args=(infos[i::n_p],)
)
ps.append(p)
p.start()
for i in range(n_p):
ps[i].join()
except:
println("Fail. %s" % traceback.format_exc())
def preprocess_trainset(inp_root, sr, n_p, exp_dir):
pp = PreProcess(sr, exp_dir)
println("start preprocess")
println(sys.argv)
pp.pipeline_mp_inp_dir(inp_root, n_p)
println("end preprocess")
if __name__ == "__main__":
preprocess_trainset(inp_root, sr, n_p, exp_dir)
import sys, os, multiprocessing
from scipy import signal
now_dir = os.getcwd()
sys.path.append(now_dir)
print(sys.argv)
inp_root = sys.argv[1]
sr = int(sys.argv[2])
n_p = int(sys.argv[3])
exp_dir = sys.argv[4]
noparallel = sys.argv[5] == "True"
import numpy as np, os, traceback
from lib.slicer2 import Slicer
import librosa, traceback
from scipy.io import wavfile
import multiprocessing
from lib.audio import load_audio
mutex = multiprocessing.Lock()
f = open("%s/preprocess.log" % exp_dir, "a+")
def println(strr):
mutex.acquire()
print(strr)
f.write("%s\n" % strr)
f.flush()
mutex.release()
class PreProcess:
def __init__(self, sr, exp_dir):
self.slicer = Slicer(
sr=sr,
threshold=-42,
min_length=1500,
min_interval=400,
hop_size=15,
max_sil_kept=500,
)
self.sr = sr
self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
self.per = 3.0
self.overlap = 0.3
self.tail = self.per + self.overlap
self.max = 0.9
self.alpha = 0.75
self.exp_dir = exp_dir
self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
os.makedirs(self.exp_dir, exist_ok=True)
os.makedirs(self.gt_wavs_dir, exist_ok=True)
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))
return
tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
1 - self.alpha
) * tmp_audio
wavfile.write(
"%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
self.sr,
tmp_audio.astype(np.float32),
)
tmp_audio = librosa.resample(
tmp_audio, orig_sr=self.sr, target_sr=16000
) # , res_type="soxr_vhq"
wavfile.write(
"%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
16000,
tmp_audio.astype(np.float32),
)
def pipeline(self, path, idx0):
try:
audio = load_audio(path, self.sr)
# zero phased digital filter cause pre-ringing noise...
# audio = signal.filtfilt(self.bh, self.ah, audio)
audio = signal.lfilter(self.bh, self.ah, audio)
idx1 = 0
for audio in self.slicer.slice(audio):
i = 0
while 1:
start = int(self.sr * (self.per - self.overlap) * i)
i += 1
if len(audio[start:]) > self.tail * self.sr:
tmp_audio = audio[start : start + int(self.per * self.sr)]
self.norm_write(tmp_audio, idx0, idx1)
idx1 += 1
else:
tmp_audio = audio[start:]
idx1 += 1
break
self.norm_write(tmp_audio, idx0, idx1)
println("%s->Suc." % path)
except:
println("%s->%s" % (path, traceback.format_exc()))
def pipeline_mp(self, infos):
for path, idx0 in infos:
self.pipeline(path, idx0)
def pipeline_mp_inp_dir(self, inp_root, n_p):
try:
infos = [
("%s/%s" % (inp_root, name), idx)
for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
]
if noparallel:
for i in range(n_p):
self.pipeline_mp(infos[i::n_p])
else:
ps = []
for i in range(n_p):
p = multiprocessing.Process(
target=self.pipeline_mp, args=(infos[i::n_p],)
)
ps.append(p)
p.start()
for i in range(n_p):
ps[i].join()
except:
println("Fail. %s" % traceback.format_exc())
def preprocess_trainset(inp_root, sr, n_p, exp_dir):
pp = PreProcess(sr, exp_dir)
println("start preprocess")
println(sys.argv)
pp.pipeline_mp_inp_dir(inp_root, n_p)
println("end preprocess")
if __name__ == "__main__":
preprocess_trainset(inp_root, sr, n_p, exp_dir)