mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
Reformat scripts
This commit is contained in:
laubonghaudoi
parent
165882d64f
commit
c18c9a9eed
1
.gitignore
vendored
1
.gitignore
vendored
@ -18,3 +18,4 @@ TEMP
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weight.json
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ffmpeg*
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ffprobe*
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zoengjyutgaai*
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@ -1,6 +1,17 @@
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# -*- coding: utf-8 -*-
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import os
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import os.path
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import shutil
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import traceback
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from time import time as ttime
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import torch
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from text.cleaner import clean_text
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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from tqdm import tqdm
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from tools.my_utils import clean_path
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inp_text = os.environ.get("inp_text")
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inp_wav_dir = os.environ.get("inp_wav_dir")
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@ -8,20 +19,11 @@ exp_name = os.environ.get("exp_name")
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i_part = os.environ.get("i_part")
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all_parts = os.environ.get("all_parts")
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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opt_dir = os.environ.get("opt_dir")
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bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
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import torch
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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version = os.environ.get('version', None)
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import sys, numpy as np, traceback, pdb
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import os.path
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from glob import glob
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from tqdm import tqdm
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from text.cleaner import clean_text
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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from tools.my_utils import clean_path
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# inp_text=sys.argv[1]
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# inp_wav_dir=sys.argv[2]
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@ -32,17 +34,14 @@ from tools.my_utils import clean_path
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# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
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# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
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from time import time as ttime
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import shutil
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def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
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dir=os.path.dirname(path)
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name=os.path.basename(path)
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def my_save(fea, path): # fix issue: torch.save doesn't support chinese path
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dir = os.path.dirname(path)
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name = os.path.basename(path)
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# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
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tmp_path="%s%s.pth"%(ttime(),i_part)
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torch.save(fea,tmp_path)
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shutil.move(tmp_path,"%s/%s"%(dir,name))
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tmp_path = "%s%s.pth" % (ttime(), i_part)
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torch.save(fea, tmp_path)
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shutil.move(tmp_path, "%s/%s" % (dir, name))
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txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)
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@ -56,11 +55,13 @@ if os.path.exists(txt_path) == False:
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# device = "mps"
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else:
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device = "cpu"
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if os.path.exists(bert_pretrained_dir):...
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else:raise FileNotFoundError(bert_pretrained_dir)
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if os.path.exists(bert_pretrained_dir):
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...
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else:
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raise FileNotFoundError(bert_pretrained_dir)
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tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if is_half == True:
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if is_half:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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@ -86,7 +87,7 @@ if os.path.exists(txt_path) == False:
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def process(data, res):
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for name, text, lan in data:
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try:
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name=clean_path(name)
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name = clean_path(name)
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name = os.path.basename(name)
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print(name)
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phones, word2ph, norm_text = clean_text(
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@ -126,7 +127,7 @@ if os.path.exists(txt_path) == False:
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"YUE": "yue",
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"Yue": "yue",
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}
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for line in lines[int(i_part) :: int(all_parts)]:
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for line in tqdm(lines[int(i_part):: int(all_parts)]):
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try:
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wav_name, spk_name, language, text = line.split("|")
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# todo.append([name,text,"zh"])
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@ -1,25 +1,40 @@
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# -*- coding: utf-8 -*-
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"""
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Step 2 of data preparation: Extract HuBERT features from the audio files,
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and resample the audio to 32kHz and saving it.
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"""
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import sys,os
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inp_text= os.environ.get("inp_text")
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inp_wav_dir= os.environ.get("inp_wav_dir")
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exp_name= os.environ.get("exp_name")
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i_part= os.environ.get("i_part")
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all_parts= os.environ.get("all_parts")
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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from feature_extractor import cnhubert
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opt_dir= os.environ.get("opt_dir")
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cnhubert.cnhubert_base_path= os.environ.get("cnhubert_base_dir")
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import os
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import shutil
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import sys
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import traceback
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from time import time as ttime
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import librosa
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import numpy as np
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import torch
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from feature_extractor import cnhubert
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from scipy.io import wavfile
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from tqdm import tqdm
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from tools.my_utils import clean_path, load_audio
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inp_text = os.environ.get("inp_text")
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inp_wav_dir = os.environ.get("inp_wav_dir")
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exp_name = os.environ.get("exp_name")
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i_part = os.environ.get("i_part")
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all_parts = os.environ.get("all_parts")
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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opt_dir = os.environ.get("opt_dir")
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cnhubert.cnhubert_base_path = os.environ.get("cnhubert_base_dir")
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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import pdb,traceback,numpy as np,logging
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from scipy.io import wavfile
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import librosa
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from tools.my_utils import load_audio,clean_path
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# from config import cnhubert_base_path
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# cnhubert.cnhubert_base_path=cnhubert_base_path
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@ -32,92 +47,112 @@ from tools.my_utils import load_audio,clean_path
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# cnhubert.cnhubert_base_path=sys.argv[7]
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# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
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from time import time as ttime
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import shutil
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def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
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dir=os.path.dirname(path)
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name=os.path.basename(path)
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def my_save(fea, path): # fix issue: torch.save doesn't support chinese path
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dir = os.path.dirname(path)
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name = os.path.basename(path)
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# tmp_path="%s/%s%s.pth"%(dir,ttime(),i_part)
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tmp_path="%s%s.pth"%(ttime(),i_part)
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torch.save(fea,tmp_path)
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shutil.move(tmp_path,"%s/%s"%(dir,name))
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tmp_path = f"{ttime()}{i_part}.pth"
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torch.save(fea, tmp_path)
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shutil.move(tmp_path, f"{dir}/{name}")
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hubert_dir="%s/4-cnhubert"%(opt_dir)
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wav32dir="%s/5-wav32k"%(opt_dir)
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os.makedirs(opt_dir,exist_ok=True)
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os.makedirs(hubert_dir,exist_ok=True)
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os.makedirs(wav32dir,exist_ok=True)
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maxx=0.95
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alpha=0.5
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hubert_dir = "%s/4-cnhubert" % (opt_dir)
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wav32dir = "%s/5-wav32k" % (opt_dir)
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os.makedirs(opt_dir, exist_ok=True)
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os.makedirs(hubert_dir, exist_ok=True)
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os.makedirs(wav32dir, exist_ok=True)
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maxx = 0.95
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alpha = 0.5
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if torch.cuda.is_available():
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device = "cuda:0"
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# elif torch.backends.mps.is_available():
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# device = "mps"
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else:
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device = "cpu"
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model=cnhubert.get_model()
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model = cnhubert.get_model()
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# is_half=False
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if(is_half==True):
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model=model.half().to(device)
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if (is_half == True):
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model = model.half().to(device)
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else:
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model = model.to(device)
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nan_fails=[]
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def name2go(wav_name,wav_path):
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hubert_path="%s/%s.pt"%(hubert_dir,wav_name)
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if(os.path.exists(hubert_path)):return
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tmp_audio = load_audio(wav_path, 32000)
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tmp_max = np.abs(tmp_audio).max()
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if tmp_max > 2.2:
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print("%s-filtered,%s" % (wav_name, tmp_max))
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nan_fails = []
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def name2go(wav_name, wav_path):
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"""
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Extract HuBERT features from the audio files, and resample the audio to 32kHz and saving it.
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"""
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# Skip if the file already exists
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hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
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if (os.path.exists(hubert_path)):
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return
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tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * tmp_audio
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tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha*1145.14)) + ((1 - alpha)*1145.14) * tmp_audio
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# Load the audio file in 32kHz sampling rate
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tmp_audio = load_audio(wav_path, 32000)
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# Check the maximum amplitude of the audio file
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tmp_max = np.abs(tmp_audio).max()
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# Skip if the maximum amplitude is too high (volume is too loud)
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if tmp_max > 2.2:
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print(f"{wav_name}-filtered,{tmp_max}")
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return
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# Normalize the audio
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tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + ((1 - alpha) * 32768) * tmp_audio
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tmp_audio32b = (tmp_audio / tmp_max * (maxx * alpha * 1145.14)) + ((1 - alpha) * 1145.14) * tmp_audio
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tmp_audio = librosa.resample(
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tmp_audio32b, orig_sr=32000, target_sr=16000
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)#不是重采样问题
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) # 不是重采样问题
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tensor_wav16 = torch.from_numpy(tmp_audio)
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if (is_half == True):
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tensor_wav16=tensor_wav16.half().to(device)
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# if half-precision is enabled, convert the tensor to half-precision
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if is_half:
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tensor_wav16 = tensor_wav16.half().to(device)
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else:
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tensor_wav16 = tensor_wav16.to(device)
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ssl=model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1,2).cpu()#torch.Size([1, 768, 215])
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if np.isnan(ssl.detach().numpy()).sum()!= 0:
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nan_fails.append((wav_name,wav_path))
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print("nan filtered:%s"%wav_name)
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# Extract HuBERT features from the audio file
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ssl = model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"].transpose(1, 2).cpu() # torch.Size([1, 768, 215])
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if np.isnan(ssl.detach().numpy()).sum() != 0:
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nan_fails.append((wav_name, wav_path))
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print(f"nan filtered:{wav_name}")
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return
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wavfile.write(
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"%s/%s"%(wav32dir,wav_name),
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f"{wav32dir}/{wav_name}",
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32000,
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tmp_audio32.astype("int16"),
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)
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my_save(ssl,hubert_path)
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my_save(ssl, hubert_path)
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with open(inp_text,"r",encoding="utf8")as f:
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lines=f.read().strip("\n").split("\n")
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for line in lines[int(i_part)::int(all_parts)]:
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with open(inp_text, "r", encoding="utf8")as f:
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lines = f.read().strip("\n").split("\n")
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for line in tqdm(lines[int(i_part)::int(all_parts)]):
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try:
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# wav_name,text=line.split("\t")
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wav_name, spk_name, language, text = line.split("|")
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wav_name=clean_path(wav_name)
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if (inp_wav_dir != "" and inp_wav_dir != None):
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wav_name = clean_path(wav_name)
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if (inp_wav_dir != "" and inp_wav_dir is not None):
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wav_name = os.path.basename(wav_name)
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wav_path = "%s/%s"%(inp_wav_dir, wav_name)
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wav_path = f"{inp_wav_dir}/{wav_name}"
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else:
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wav_path=wav_name
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wav_path = wav_name
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wav_name = os.path.basename(wav_name)
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name2go(wav_name,wav_path)
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name2go(wav_name, wav_path)
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except:
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print(line,traceback.format_exc())
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print(line, traceback.format_exc())
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if(len(nan_fails)>0 and is_half==True):
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is_half=False
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model=model.float()
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if (len(nan_fails) > 0 and is_half):
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is_half = False
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model = model.float()
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for wav in nan_fails:
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try:
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name2go(wav[0],wav[1])
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name2go(wav[0], wav[1])
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except:
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print(wav_name,traceback.format_exc())
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print(wav_name, traceback.format_exc())
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@ -1,17 +1,27 @@
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import logging
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import os
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import sys
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import traceback
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import torch
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import utils
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from tools.my_utils import clean_path
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inp_text = os.environ.get("inp_text")
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exp_name = os.environ.get("exp_name")
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i_part = os.environ.get("i_part")
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all_parts = os.environ.get("all_parts")
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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opt_dir = os.environ.get("opt_dir")
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pretrained_s2G = os.environ.get("pretrained_s2G")
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s2config_path = os.environ.get("s2config_path")
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if os.path.exists(pretrained_s2G):...
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else:raise FileNotFoundError(pretrained_s2G)
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if os.path.exists(pretrained_s2G):
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...
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else:
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raise FileNotFoundError(pretrained_s2G)
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# version=os.environ.get("version","v2")
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size = os.path.getsize(pretrained_s2G)
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if size < 82978 * 1024:
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@ -24,24 +34,14 @@ elif size < 700 * 1024 * 1024:
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version = "v2"
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else:
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version = "v3"
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import torch
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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import math, traceback
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import multiprocessing
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import sys, pdb
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from random import shuffle
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import torch.multiprocessing as mp
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from glob import glob
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from tqdm import tqdm
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import logging, librosa, utils
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if version!="v3":
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if version != "v3":
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from module.models import SynthesizerTrn
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else:
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from module.models import SynthesizerTrnV3 as SynthesizerTrn
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from tools.my_utils import clean_path
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logging.getLogger("numba").setLevel(logging.WARNING)
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# from config import pretrained_s2G
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@ -102,12 +102,12 @@ if os.path.exists(semantic_path) == False:
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lines = f.read().strip("\n").split("\n")
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lines1 = []
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for line in lines[int(i_part) :: int(all_parts)]:
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for line in lines[int(i_part):: int(all_parts)]:
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# print(line)
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try:
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# wav_name,text=line.split("\t")
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wav_name, spk_name, language, text = line.split("|")
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wav_name=clean_path(wav_name)
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wav_name = clean_path(wav_name)
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wav_name = os.path.basename(wav_name)
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# name2go(name,lines1)
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name2go(wav_name, lines1)
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