VoiceConversionWebUI / trainset_preprocess_pipeline.py
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import numpy as np,ffmpeg,os,traceback
from slicer import Slicer
slicer = Slicer(
sr=40000,
db_threshold=-32,
min_length=800,
win_l=400,
win_s=20,
max_silence_kept=150
)
def p0_load_audio(file, sr):#str-ing
try:
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except ffmpeg.Error as e:
raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
def p1_trim_audio(slicer,audio):return slicer.slice(audio)
def p2_avg_cut(audio,sr,per=3.7,overlap=0.3,tail=4):
i = 0
audios=[]
while (1):
start = int(sr * (per - overlap) * i)
i += 1
if (len(audio[start:]) > tail * sr):
audios.append(audio[start:start + int(per * sr)])
else:
audios.append(audio[start:])
break
return audios
def p2b_get_vol(audio):return np.square(audio).mean()
def p3_norm(audio,alpha=0.8,maxx=0.95):return audio / np.abs(audio).max() * (maxx * alpha) + (1-alpha) * audio
def pipeline(inp_root,sr1=40000,sr2=16000,if_trim=True,if_avg_cut=True,if_norm=True,save_root1=None,save_root2=None):
if(save_root1==None and save_root2==None):return "No save root."
name2vol={}
infos=[]
names=[]
for name in os.listdir(inp_root):
try:
inp_path=os.path.join(inp_root,name)
audio=p0_load_audio(inp_path)
except:
infos.append("%s\t%s"%(name,traceback.format_exc()))
continue
if(if_trim==True):res1s=p1_trim_audio(audio)
else:res1s=[audio]
for i0,res1 in res1s:
if(if_avg_cut==True):res2=p2_avg_cut(res1)
else:res2=[res1]