修复了一些bug,优化了一些代码

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -1,5 +1,6 @@
 import math
 import os, sys
+import random
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 import ffmpeg
@@ -7,6 +8,7 @@ import os
 from typing import Generator, List, Union
 import numpy as np
 import torch
+import torch.nn.functional as F
 import yaml
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 
@@ -130,11 +132,11 @@ class TTS_Config:
         string = "----------------TTS Config--------------\n"
         string += "device: {}\n".format(self.device)
         string += "is_half: {}\n".format(self.is_half)
+        string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled)
         string += "bert_base_path: {}\n".format(self.bert_base_path)
         string += "t2s_weights_path: {}\n".format(self.t2s_weights_path)
         string += "vits_weights_path: {}\n".format(self.vits_weights_path)
         string += "cnhuhbert_base_path: {}\n".format(self.cnhuhbert_base_path)
-        string += "flash_attn_enabled: {}\n".format(self.flash_attn_enabled)
         string += "----------------------------------------\n"
         return string
 
@@ -184,7 +186,7 @@ class TTS:
         
     def init_cnhuhbert_weights(self, base_path: str):
         self.cnhuhbert_model = CNHubert(base_path)
-        self.cnhuhbert_model.eval()
+        self.cnhuhbert_model=self.cnhuhbert_model.eval()
         if self.configs.is_half == True:
             self.cnhuhbert_model = self.cnhuhbert_model.half()
         self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device)
@@ -194,6 +196,7 @@ class TTS:
     def init_bert_weights(self, base_path: str):
         self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path)
         self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path)
+        self.bert_model=self.bert_model.eval()
         if self.configs.is_half:
             self.bert_model = self.bert_model.half()
         self.bert_model = self.bert_model.to(self.configs.device)
@@ -226,7 +229,7 @@ class TTS:
         if self.configs.is_half:
             vits_model = vits_model.half()
         vits_model = vits_model.to(self.configs.device)
-        vits_model.eval()
+        vits_model = vits_model.eval()
         vits_model.load_state_dict(dict_s2["weight"], strict=False)
         self.vits_model = vits_model
 
@@ -244,7 +247,7 @@ class TTS:
         if self.configs.is_half:
             t2s_model = t2s_model.half()
         t2s_model = t2s_model.to(self.configs.device)
-        t2s_model.eval()
+        t2s_model = t2s_model.eval()
         self.t2s_model = t2s_model
         
     def set_ref_audio(self, ref_audio_path:str):
@@ -377,12 +380,14 @@ class TTS:
             phones_max_len = 0
             for item in item_list:
                 if prompt_data is not None:
-                    all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)
+                    all_bert_features = torch.cat([prompt_data["bert_features"], item["bert_features"]], 1)\
+                                                .to(dtype=torch.float32 if not self.configs.is_half else torch.float16)
                     all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"])
                     phones = torch.LongTensor(item["phones"])
                     # norm_text = prompt_data["norm_text"]+item["norm_text"]
                 else:
-                    all_bert_features = item["bert_features"]
+                    all_bert_features = item["bert_features"]\
+                                            .to(dtype=torch.float32 if not self.configs.is_half else torch.float16)
                     phones = torch.LongTensor(item["phones"])
                     all_phones = phones
                     # norm_text = item["norm_text"]
@@ -401,12 +406,10 @@ class TTS:
             max_len = max(bert_max_len, phones_max_len)
             # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
             all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
-            all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len)
-            all_bert_features_batch.zero_()
-
+            # all_bert_features_batch = all_bert_features_list
+            all_bert_features_batch = torch.zeros(len(item_list), 1024, max_len, dtype=torch.float32)
             for idx, item in enumerate(all_bert_features_list):
-                if item != None:
-                    all_bert_features_batch[idx, :, : item.shape[-1]] = item
+                all_bert_features_batch[idx, :, : item.shape[-1]] = item
             
             batch = {
                 "phones": phones_batch,
@@ -458,8 +461,8 @@ class TTS:
                     "prompt_text": "",        # str. prompt text for the reference audio
                     "prompt_lang": "",        # str. language of the prompt text for the reference audio
                     "top_k": 5,               # int. top k sampling
-                    "top_p": 0.9,             # float. top p sampling
-                    "temperature": 0.6,       # float. temperature for sampling
+                    "top_p": 1,             # float. top p sampling
+                    "temperature": 1,       # float. temperature for sampling
                     "text_split_method": "",  # str. text split method, see text_segmentaion_method.py for details.
                     "batch_size": 1,          # int. batch size for inference
                     "batch_threshold": 0.75,  # float. threshold for batch splitting.
@@ -477,9 +480,9 @@ class TTS:
         ref_audio_path:str = inputs.get("ref_audio_path", "")
         prompt_text:str = inputs.get("prompt_text", "")
         prompt_lang:str = inputs.get("prompt_lang", "")
-        top_k:int = inputs.get("top_k", 20)
-        top_p:float = inputs.get("top_p", 0.9)
-        temperature:float = inputs.get("temperature", 0.6)
+        top_k:int = inputs.get("top_k", 5)
+        top_p:float = inputs.get("top_p", 1)
+        temperature:float = inputs.get("temperature", 1)
         text_split_method:str = inputs.get("text_split_method", "")
         batch_size = inputs.get("batch_size", 1)
         batch_threshold = inputs.get("batch_threshold", 0.75)
@@ -497,10 +500,6 @@ class TTS:
         if split_bucket:
             print(i18n("分桶处理模式已开启"))
             
-        # if vits_batched_inference:
-        #     print(i18n("VITS批量推理模式已开启"))
-        # else:
-        #     print(i18n("VITS单句推理模式已开启"))
     
         no_prompt_text = False
         if prompt_text in [None, ""]:
@@ -547,7 +546,7 @@ class TTS:
                              )
         t2 = ttime()
         
-        
+        print("############ 推理 ############")
         ###### inference ######
         t_34 = 0.0
         t_45 = 0.0
@@ -601,6 +600,10 @@ class TTS:
             # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)]
             # pred_semantic_len = torch.LongTensor([item.shape[0] for item in pred_semantic_list]).to(self.configs.device)
             # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0)
+            # max_len = 0
+            # for i in range(0, len(batch_phones)):
+            #     max_len = max(max_len, batch_phones[i].shape[-1])
+            # batch_phones = self.batch_sequences(batch_phones, axis=0, pad_value=0, max_length=max_len)
             # batch_phones = batch_phones.to(self.configs.device)
             # batch_audio_fragment = (self.vits_model.batched_decode(
             #         pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spepc
@@ -654,7 +657,12 @@ class TTS:
                                             self.configs.sampling_rate, 
                                             batch_index_list, 
                                             speed_factor, 
-                                            split_bucket)          
+                                            split_bucket)         
+            
+        try:
+            torch.cuda.empty_cache()
+        except:
+            pass 
 
        
        

GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py

@@ -1,5 +1,7 @@
 
 import os, sys
+
+from tqdm import tqdm
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 
@@ -12,9 +14,9 @@ from text import cleaned_text_to_sequence
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 from TTS_infer_pack.text_segmentation_method import split_big_text, splits, get_method as get_seg_method
 
-# from tools.i18n.i18n import I18nAuto
+from tools.i18n.i18n import I18nAuto
 
-# i18n = I18nAuto()
+i18n = I18nAuto()
 
 def get_first(text:str) -> str:
     pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
@@ -51,9 +53,11 @@ class TextPreprocessor:
         self.device = device
         
     def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]:
+        print(i18n("############ 切分文本 ############"))
         texts = self.pre_seg_text(text, lang, text_split_method)
         result = []
-        for text in texts:
+        print(i18n("############ 提取文本Bert特征 ############"))
+        for text in tqdm(texts):
             phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang)
             res={
                 "phones": phones,
@@ -67,14 +71,16 @@ class TextPreprocessor:
         text = text.strip("\n")
         if (text[0] not in splits and len(get_first(text)) < 4): 
             text = "。" + text if lang != "en" else "." + text
-        # print(i18n("实际输入的目标文本:"), text)
+        print(i18n("实际输入的目标文本:"))
+        print(text)
         
         seg_method = get_seg_method(text_split_method)
         text = seg_method(text)
         
         while "\n\n" in text:
             text = text.replace("\n\n", "\n")
-        # print(i18n("实际输入的目标文本(切句后):"), text)
+        print(i18n("实际输入的目标文本(切句后):"))
+        print(text)
         _texts = text.split("\n")
         _texts = merge_short_text_in_array(_texts, 5)
         texts = []
@@ -105,7 +111,7 @@ class TextPreprocessor:
         textlist=[]
         langlist=[]
         if language in ["auto", "zh", "ja"]:
-            # LangSegment.setfilters(["zh","ja","en","ko"])
+            LangSegment.setfilters(["zh","ja","en","ko"])
             for tmp in LangSegment.getTexts(text):
                 if tmp["lang"] == "ko":
                     langlist.append("zh")
@@ -116,7 +122,7 @@ class TextPreprocessor:
                     langlist.append(language if language!="auto" else tmp["lang"])
                 textlist.append(tmp["text"])
         elif language == "en":
-            # LangSegment.setfilters(["en"])
+            LangSegment.setfilters(["en"])
             formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
             while "  " in formattext:
                 formattext = formattext.replace("  ", " ")
@@ -153,7 +159,7 @@ class TextPreprocessor:
         # phones = sum(phones_list, [])
         norm_text = ''.join(norm_text_list)
 
-        return phones, bert_feature, norm_text
+        return phones_list, bert_feature, norm_text
 
 
     def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor: