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# -*- coding: utf-8 -*-
import sys
import os
inp_text = os.environ.get("inp_text")
inp_wav_dir = os.environ.get("inp_wav_dir")
exp_name = os.environ.get("exp_name")
i_part = os.environ.get("i_part")
all_parts = os.environ.get("all_parts")
if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
from feature_extractor import cnhubert
opt_dir = os.environ.get("opt_dir")
cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
import torch
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
import traceback
import numpy as np
from scipy.io import wavfile
import librosa
now_dir = os.getcwd()
sys.path.append(now_dir)
from tools.my_utils import load_audio, clean_path
# from config import cnhubert_base_path
# cnhubert.cnhubert_base_path=cnhubert_base_path
# inp_text=sys.argv[1]
# inp_wav_dir=sys.argv[2]
# exp_name=sys.argv[3]
# i_part=sys.argv[4]
# all_parts=sys.argv[5]
# os.environ["CUDA_VISIBLE_DEVICES"]=sys.argv[6]
# cnhubert.cnhubert_base_path=sys.argv[7]
# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
from time import time as ttime
import shutil
def my_save(fea, path): #####fix issue: torch.save doesn't support chinese path
dir = os.path.dirname(path)
name = os.path.basename(path)
# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
tmp_path = "%s%s.pth" % (ttime(), i_part)
torch.save(fea, tmp_path)
shutil.move(tmp_path, "%s/%s" % (dir, name))
hubert_dir = "%s/4-cnhubert" % (opt_dir)
wav32dir = "%s/5-wav32k" % (opt_dir)
os.makedirs(opt_dir, exist_ok=True)
os.makedirs(hubert_dir, exist_ok=True)
os.makedirs(wav32dir, exist_ok=True)
maxx = 0.95
alpha = 0.5
if torch.cuda.is_available():
device = "cuda:0"
# elif torch.backends.mps.is_available():
# device = "mps"
else:
device = "cpu"
model = cnhubert.get_model()
# is_half=False
if is_half == True:
model = model.half().to(device)
else:
model = model.to(device)
nan_fails = []
def name2go(wav_name, wav_path):
hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
if os.path.exists(hubert_path):
return
tmp_audio = load_audio(wav_path, 32000)
tmp_max = np.abs(tmp_audio).max()
if tmp_max > 2.2:
print("%s-filtered,%s" % (wav_name, tmp_max))
return
tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
tmp_audio = librosa.resample(tmp_audio32b, orig_sr=32000, target_sr=16000) # 不是重采样问题
tensor_wav16 = torch.from_numpy(tmp_audio)
if is_half == True:
tensor_wav16 = tensor_wav16.half().to(device)
else:
tensor_wav16 = tensor_wav16.to(device)
ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu() # torch.Size([1, 768, 215])
if np.isnan(ssl.detach().numpy()).sum() != 0:
nan_fails.append((wav_name, wav_path))
print("nan filtered:%s" % wav_name)
return
wavfile.write(
"%s/%s" % (wav32dir, wav_name),
32000,
tmp_audio32.astype("int16"),
)
my_save(ssl, hubert_path)
with open(inp_text, "r", encoding="utf8") as f:
lines = f.read().strip("\n").split("\n")
for line in lines[int(i_part) :: int(all_parts)]:
try:
# wav_name,text=line.split("\t")
wav_name, spk_name, language, text = line.split("|")
wav_name = clean_path(wav_name)
if inp_wav_dir != "" and inp_wav_dir != None:
wav_name = os.path.basename(wav_name)
wav_path = "%s/%s" % (inp_wav_dir, wav_name)
else:
wav_path = wav_name
wav_name = os.path.basename(wav_name)
name2go(wav_name, wav_path)
except:
print(line, traceback.format_exc())
if len(nan_fails) > 0 and is_half == True:
is_half = False
model = model.float()
for wav in nan_fails:
try:
name2go(wav[0], wav[1])
except:
print(wav_name, traceback.format_exc())
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