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GPT_SoVITS/prepare_datasets/1-get-text.py

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+# -*- coding: utf-8 -*-
+
+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")
+os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
+opt_dir=                            os.environ.get("opt_dir")
+bert_pretrained_dir=                os.environ.get("bert_pretrained_dir")
+is_half=eval(os.environ.get("is_half","True"))
+import sys,numpy as np,traceback,pdb
+import os.path
+from glob import glob
+from tqdm import tqdm
+from text.cleaner import clean_text
+import torch
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+
+# 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]#i_gpu
+# opt_dir="/data/docker/liujing04/gpt-vits/fine_tune_dataset/%s"%exp_name
+# bert_pretrained_dir="/data/docker/liujing04/bert-vits2/Bert-VITS2-master20231106/bert/chinese-roberta-wwm-ext-large"
+
+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)
+    torch.save(fea,tmp_path)
+    shutil.move(tmp_path,"%s/%s"%(dir,name))
+
+txt_path="%s/2-name2text-%s.txt"%(opt_dir,i_part)
+if(os.path.exists(txt_path)==False):
+    bert_dir="%s/3-bert"%(opt_dir)
+    os.makedirs(opt_dir,exist_ok=True)
+    os.makedirs(bert_dir,exist_ok=True)
+    device="cuda:0"
+    tokenizer = AutoTokenizer.from_pretrained(bert_pretrained_dir)
+    bert_model=AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
+    if (is_half == True):
+        bert_model = bert_model.half().to(device)
+    else:
+        bert_model = bert_model.to(device)
+    def get_bert_feature(text, word2ph):
+        with torch.no_grad():
+            inputs = tokenizer(text, return_tensors="pt")
+            for i in inputs:
+                inputs[i] = inputs[i].to(device)
+            res = bert_model(**inputs, output_hidden_states=True)
+            res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
+
+        assert len(word2ph) == len(text)
+        phone_level_feature = []
+        for i in range(len(word2ph)):
+            repeat_feature = res[i].repeat(word2ph[i], 1)
+            phone_level_feature.append(repeat_feature)
+
+        phone_level_feature = torch.cat(phone_level_feature, dim=0)
+
+        return phone_level_feature.T
+    def process(data,res):
+        for name,text,lan in data:
+            try:
+                name=os.path.basename(name)
+                phones, word2ph, norm_text=clean_text(text.replace("%", '-').replace('￥', ','),lan)
+                path_bert="%s/%s.pt"%(bert_dir,name)
+                if (os.path.exists(path_bert) == False and lan == "zh"):
+                    bert_feature = get_bert_feature(norm_text, word2ph)
+                    assert bert_feature.shape[-1] == len(phones)
+                    # torch.save(bert_feature, path_bert)
+                    my_save(bert_feature, path_bert)
+                phones = " ".join(phones)
+                # res.append([name,phones])
+                res.append([name,phones, word2ph, norm_text])
+            except:
+                print(name, text, traceback.format_exc())
+
+    todo=[]
+    res=[]
+    with open(inp_text,"r",encoding="utf8")as f:
+        lines=f.read().strip("\n").split("\n")
+
+    language_v1_to_language_v2={
+        "ZH":"zh"
+    }
+    for line in lines[int(i_part)::int(all_parts)]:
+        try:
+            wav_name,spk_name,language,text=line.split("|")
+            # todo.append([name,text,"zh"])
+            todo.append([wav_name,text,language_v1_to_language_v2.get(language,language)])
+        except:
+            print(line,traceback.format_exc())
+
+    process(todo,res)
+    opt=[]
+    for name,phones, word2ph, norm_text in res:
+        opt.append("%s\t%s\t%s\t%s"%(name,phones, word2ph, norm_text))
+    with open(txt_path,"w",encoding="utf8")as f:
+        f.write("\n".join(opt)+"\n")
+