more code refactor
This commit is contained in:
Blaise
@@ -1,50 +1,81 @@
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import os,torch,sys
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import os, torch, sys
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from subprocess import Popen
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from config import text_path,wav_dir,n_card,n_process_per_card,exp_name,n_parts,exp_dir
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os.makedirs("%s/logs_s1"%exp_dir,exist_ok=True)
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os.makedirs("%s/logs_s2"%exp_dir,exist_ok=True)
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from config import (
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text_path,
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wav_dir,
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n_card,
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exp_name,
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n_parts,
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exp_dir,
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)
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os.makedirs("%s/logs_s1" % exp_dir, exist_ok=True)
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os.makedirs("%s/logs_s2" % exp_dir, exist_ok=True)
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##############step1
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ps=[]
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ps = []
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for i_part in range(n_parts):
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cmd="python prepare/1-get-text.py %s %s %s %s %s %s"%(text_path,wav_dir,exp_name,i_part,n_parts,i_part%n_card)
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cmd = "python prepare/1-get-text.py %s %s %s %s %s %s" % (
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text_path,
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wav_dir,
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exp_name,
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i_part,
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n_parts,
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i_part % n_card,
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)
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print(cmd)
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p = Popen(cmd, shell=True)
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ps.append(p)
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for p in ps:
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p.wait()
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opt=[]
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opt = []
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for i_part in range(n_parts):
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txt_path = "%s/2-name2text-%s.txt" % (exp_dir, i_part)
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with open(txt_path,"r")as f:
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opt+=f.read().strip("\n").split("\n")
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with open(txt_path, "r") as f:
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opt += f.read().strip("\n").split("\n")
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os.remove(txt_path)
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with open("%s/2-name2text.txt"%exp_dir,"w")as f:f.write("\n".join(opt)+"\n")
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with open("%s/2-name2text.txt" % exp_dir, "w") as f:
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f.write("\n".join(opt) + "\n")
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############step2
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ps=[]
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ps = []
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for i_part in range(n_parts):
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cmd="python prepare/2-get-hubert-wav32k.py %s %s %s %s %s %s"%(text_path,wav_dir,exp_name,i_part,n_parts,i_part%n_card)
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cmd = "python prepare/2-get-hubert-wav32k.py %s %s %s %s %s %s" % (
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text_path,
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wav_dir,
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exp_name,
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i_part,
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n_parts,
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i_part % n_card,
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)
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print(cmd)
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p = Popen(cmd, shell=True)
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ps.append(p)
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for p in ps:
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p.wait()
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#############step3
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ps=[]
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ps = []
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for i_part in range(n_parts):
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cmd="python prepare/3-get-semantic.py %s %s %s %s %s"%(text_path,exp_name,i_part,n_parts,i_part%n_card)
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cmd = "python prepare/3-get-semantic.py %s %s %s %s %s" % (
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text_path,
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exp_name,
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i_part,
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n_parts,
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i_part % n_card,
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)
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print(cmd)
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p = Popen(cmd, shell=True)
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ps.append(p)
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for p in ps:
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p.wait()
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opt=["item_name semantic_audio"]
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opt = ["item_name semantic_audio"]
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for i_part in range(n_parts):
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semantic_path = "%s/6-name2semantic-%s.tsv" % (exp_dir, i_part)
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with open(semantic_path,"r")as f:
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opt+=f.read().strip("\n").split("\n")
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with open(semantic_path, "r") as f:
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opt += f.read().strip("\n").split("\n")
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os.remove(semantic_path)
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with open("%s/6-name2semantic.tsv"%exp_dir,"w")as f:f.write("\n".join(opt)+"\n")
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with open("%s/6-name2semantic.tsv" % exp_dir, "w") as f:
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f.write("\n".join(opt) + "\n")
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@@ -2,16 +2,16 @@
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import 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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os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_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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is_half=eval(os.environ.get("is_half","True"))
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import sys,numpy as np,traceback,pdb
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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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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_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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is_half = eval(os.environ.get("is_half", "True"))
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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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@@ -31,25 +31,29 @@ import numpy as np
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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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tmp_path="%s/%s%s.pth"%(dir,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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if(os.path.exists(txt_path)==False):
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bert_dir="%s/3-bert"%(opt_dir)
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os.makedirs(opt_dir,exist_ok=True)
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os.makedirs(bert_dir,exist_ok=True)
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device="cuda:0"
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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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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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if os.path.exists(txt_path) == False:
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bert_dir = "%s/3-bert" % (opt_dir)
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os.makedirs(opt_dir, exist_ok=True)
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os.makedirs(bert_dir, exist_ok=True)
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device = "cuda:0"
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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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bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if is_half == True:
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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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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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@@ -67,51 +71,55 @@ if(os.path.exists(txt_path)==False):
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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def process(data,res):
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for name,text,lan in data:
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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=os.path.basename(name)
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phones, word2ph, norm_text=clean_text(text.replace("%", '-').replace('￥', ','),lan)
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path_bert="%s/%s.pt"%(bert_dir,name)
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if (os.path.exists(path_bert) == False and lan == "zh"):
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name = os.path.basename(name)
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phones, word2ph, norm_text = clean_text(
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text.replace("%", "-").replace("￥", ","), lan
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)
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path_bert = "%s/%s.pt" % (bert_dir, name)
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if os.path.exists(path_bert) == False and lan == "zh":
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bert_feature = get_bert_feature(norm_text, word2ph)
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assert bert_feature.shape[-1] == len(phones)
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# torch.save(bert_feature, path_bert)
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my_save(bert_feature, path_bert)
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phones = " ".join(phones)
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# res.append([name,phones])
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res.append([name,phones, word2ph, norm_text])
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res.append([name, phones, word2ph, norm_text])
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except:
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print(name, text, traceback.format_exc())
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todo=[]
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res=[]
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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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todo = []
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res = []
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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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language_v1_to_language_v2={
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"ZH":"zh",
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"zh":"zh",
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"JP":"ja",
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"jp":"ja",
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"JA":"ja",
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"ja":"ja",
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"EN":"en",
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"en":"en",
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"En":"en",
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language_v1_to_language_v2 = {
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"ZH": "zh",
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"zh": "zh",
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"JP": "ja",
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"jp": "ja",
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"JA": "ja",
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"ja": "ja",
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"EN": "en",
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"en": "en",
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"En": "en",
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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 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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wav_name, spk_name, language, text = line.split("|")
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# todo.append([name,text,"zh"])
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todo.append([wav_name,text,language_v1_to_language_v2.get(language,language)])
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todo.append(
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[wav_name, text, language_v1_to_language_v2.get(language, language)]
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)
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except:
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print(line,traceback.format_exc())
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process(todo,res)
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opt=[]
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for name,phones, word2ph, norm_text in res:
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opt.append("%s\t%s\t%s\t%s"%(name,phones, word2ph, norm_text))
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with open(txt_path,"w",encoding="utf8")as f:
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f.write("\n".join(opt)+"\n")
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print(line, traceback.format_exc())
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process(todo, res)
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opt = []
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for name, phones, word2ph, norm_text in res:
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opt.append("%s\t%s\t%s\t%s" % (name, phones, word2ph, norm_text))
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with open(txt_path, "w", encoding="utf8") as f:
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f.write("\n".join(opt) + "\n")
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@@ -1,20 +1,23 @@
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# -*- coding: utf-8 -*-
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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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os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_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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is_half=eval(os.environ.get("is_half","True"))
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import sys, os
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import pdb,traceback,numpy as np,logging
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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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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_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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is_half = eval(os.environ.get("is_half", "True"))
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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,torch
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import librosa, torch
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from my_utils import load_audio
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@@ -32,63 +35,75 @@ from my_utils import load_audio
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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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tmp_path="%s/%s%s.pth"%(dir,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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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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device="cuda:0"
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model=cnhubert.get_model()
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if(is_half==True):
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model=model.half().to(device)
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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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torch.save(fea, tmp_path)
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shutil.move(tmp_path, "%s/%s" % (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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device = "cuda:0"
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model = cnhubert.get_model()
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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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def name2go(wav_name):
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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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wav_path="%s/%s"%(inp_wav_dir,wav_name)
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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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wav_path = "%s/%s" % (inp_wav_dir, wav_name)
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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-%s-%s-filtered" % (idx0, idx1, tmp_max))
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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_audio = librosa.resample(
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tmp_audio32, orig_sr=32000, target_sr=16000
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)
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tmp_audio32 = (tmp_audio / tmp_max * (maxx * alpha * 32768)) + (
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(1 - alpha) * 32768
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) * tmp_audio
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tmp_audio = librosa.resample(tmp_audio32, orig_sr=32000, target_sr=16000)
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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 is_half == True:
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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:return
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ssl = (
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model.model(tensor_wav16.unsqueeze(0))["last_hidden_state"]
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.transpose(1, 2)
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.cpu()
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) # torch.Size([1, 768, 215])
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if np.isnan(ssl.detach().numpy()).sum() != 0:
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return
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wavfile.write(
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"%s/%s"%(wav32dir,wav_name),
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"%s/%s" % (wav32dir, wav_name),
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32000,
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tmp_audio32.astype("int16"),
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)
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# torch.save(ssl,hubert_path )
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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:
|
||||
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=os.path.basename(wav_name)
|
||||
wav_name = os.path.basename(wav_name)
|
||||
name2go(wav_name)
|
||||
except:
|
||||
print(line,traceback.format_exc())
|
||||
print(line, traceback.format_exc())
|
||||
|
||||
@@ -1,24 +1,27 @@
|
||||
import os
|
||||
inp_text= os.environ.get("inp_text")
|
||||
exp_name= os.environ.get("exp_name")
|
||||
i_part= os.environ.get("i_part")
|
||||
all_parts= os.environ.get("all_parts")
|
||||
os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
|
||||
opt_dir= os.environ.get("opt_dir")
|
||||
pretrained_s2G= os.environ.get("pretrained_s2G")
|
||||
s2config_path= os.environ.get("s2config_path")
|
||||
is_half=eval(os.environ.get("is_half","True"))
|
||||
import math,traceback
|
||||
|
||||
inp_text = os.environ.get("inp_text")
|
||||
exp_name = os.environ.get("exp_name")
|
||||
i_part = os.environ.get("i_part")
|
||||
all_parts = os.environ.get("all_parts")
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
|
||||
opt_dir = os.environ.get("opt_dir")
|
||||
pretrained_s2G = os.environ.get("pretrained_s2G")
|
||||
s2config_path = os.environ.get("s2config_path")
|
||||
is_half = eval(os.environ.get("is_half", "True"))
|
||||
import math, traceback
|
||||
import multiprocessing
|
||||
import sys,pdb
|
||||
import sys, pdb
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
from random import shuffle
|
||||
import torch.multiprocessing as mp
|
||||
from glob import glob
|
||||
from tqdm import tqdm
|
||||
import logging,librosa,utils,torch
|
||||
import logging, librosa, utils, torch
|
||||
from module.models import SynthesizerTrn
|
||||
|
||||
logging.getLogger("numba").setLevel(logging.WARNING)
|
||||
# from config import pretrained_s2G
|
||||
|
||||
@@ -30,52 +33,58 @@ logging.getLogger("numba").setLevel(logging.WARNING)
|
||||
# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
|
||||
|
||||
|
||||
hubert_dir="%s/4-cnhubert"%(opt_dir)
|
||||
semantic_path="%s/6-name2semantic-%s.tsv"%(opt_dir,i_part)
|
||||
if(os.path.exists(semantic_path)==False):
|
||||
os.makedirs(opt_dir,exist_ok=True)
|
||||
hubert_dir = "%s/4-cnhubert" % (opt_dir)
|
||||
semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
|
||||
if os.path.exists(semantic_path) == False:
|
||||
os.makedirs(opt_dir, exist_ok=True)
|
||||
|
||||
device="cuda:0"
|
||||
device = "cuda:0"
|
||||
hps = utils.get_hparams_from_file(s2config_path)
|
||||
vq_model = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**hps.model)
|
||||
if(is_half==True):
|
||||
vq_model=vq_model.half().to(device)
|
||||
**hps.model
|
||||
)
|
||||
if is_half == True:
|
||||
vq_model = vq_model.half().to(device)
|
||||
else:
|
||||
vq_model = vq_model.to(device)
|
||||
vq_model.eval()
|
||||
# utils.load_checkpoint(utils.latest_checkpoint_path(hps.s2_ckpt_dir, "G_*.pth"), vq_model, None, True)
|
||||
# utils.load_checkpoint(pretrained_s2G, vq_model, None, True)
|
||||
print(vq_model.load_state_dict(torch.load(pretrained_s2G,map_location="cpu")["weight"], strict=False))
|
||||
print(
|
||||
vq_model.load_state_dict(
|
||||
torch.load(pretrained_s2G, map_location="cpu")["weight"], strict=False
|
||||
)
|
||||
)
|
||||
|
||||
def name2go(wav_name,lines):
|
||||
def name2go(wav_name, lines):
|
||||
hubert_path = "%s/%s.pt" % (hubert_dir, wav_name)
|
||||
if(os.path.exists(hubert_path)==False):return
|
||||
if os.path.exists(hubert_path) == False:
|
||||
return
|
||||
ssl_content = torch.load(hubert_path, map_location="cpu")
|
||||
if(is_half==True):
|
||||
ssl_content=ssl_content.half().to(device)
|
||||
if is_half == True:
|
||||
ssl_content = ssl_content.half().to(device)
|
||||
else:
|
||||
ssl_content = ssl_content.to(device)
|
||||
codes = vq_model.extract_latent(ssl_content)
|
||||
semantic = " ".join([str(i) for i in codes[0, 0, :].tolist()])
|
||||
lines.append("%s\t%s"%(wav_name,semantic))
|
||||
lines.append("%s\t%s" % (wav_name, semantic))
|
||||
|
||||
with open(inp_text,"r",encoding="utf8")as f:
|
||||
lines=f.read().strip("\n").split("\n")
|
||||
with open(inp_text, "r", encoding="utf8") as f:
|
||||
lines = f.read().strip("\n").split("\n")
|
||||
|
||||
lines1=[]
|
||||
for line in lines[int(i_part)::int(all_parts)]:
|
||||
lines1 = []
|
||||
for line in lines[int(i_part) :: int(all_parts)]:
|
||||
# print(line)
|
||||
try:
|
||||
# wav_name,text=line.split("\t")
|
||||
wav_name, spk_name, language, text = line.split("|")
|
||||
wav_name=os.path.basename(wav_name)
|
||||
wav_name = os.path.basename(wav_name)
|
||||
# name2go(name,lines1)
|
||||
name2go(wav_name,lines1)
|
||||
name2go(wav_name, lines1)
|
||||
except:
|
||||
print(line,traceback.format_exc())
|
||||
with open(semantic_path,"w",encoding="utf8")as f:f.write("\n".join(lines1))
|
||||
|
||||
print(line, traceback.format_exc())
|
||||
with open(semantic_path, "w", encoding="utf8") as f:
|
||||
f.write("\n".join(lines1))
|
||||
|
||||
Reference in New Issue
Block a user