Merge branch 'RVC-Boss:main' into main

.gitignore

@@ -7,5 +7,8 @@ runtime
 output
 logs
 reference
-SoVITS_weights
 GPT_weights
+SoVITS_weights
+TEMP
+
+

GPT_SoVITS/AR/data/data_module.py

@@ -41,7 +41,8 @@ class Text2SemanticDataModule(LightningDataModule):
         #     pad_val=self.config['data']['pad_val'])
 
     def train_dataloader(self):
-        batch_size = max(min(self.config["train"]["batch_size"],len(self._train_dataset)//4),1)#防止不保存
+        batch_size=self.config["train"]["batch_size"]//2 if self.config["train"].get("if_dpo",False)==True else self.config["train"]["batch_size"]
+        batch_size = max(min(batch_size,len(self._train_dataset)//4),1)#防止不保存
         sampler = DistributedBucketSampler(self._train_dataset, batch_size=batch_size)
         return DataLoader(
             self._train_dataset,

GPT_SoVITS/AR/models/t2s_lightning_module.py

@@ -11,7 +11,6 @@ from AR.models.t2s_model import Text2SemanticDecoder
 from AR.modules.lr_schedulers import WarmupCosineLRSchedule
 from AR.modules.optim import ScaledAdam
 
-
 class Text2SemanticLightningModule(LightningModule):
     def __init__(self, config, output_dir, is_train=True):
         super().__init__()
@@ -35,7 +34,8 @@ class Text2SemanticLightningModule(LightningModule):
     def training_step(self, batch: Dict, batch_idx: int):
         opt = self.optimizers()
         scheduler = self.lr_schedulers()
-        loss, acc = self.model.forward(
+        forward=self.model.forward if self.config["train"].get("if_dpo",False)==True else self.model.forward_old
+        loss, acc = forward(
             batch["phoneme_ids"],
             batch["phoneme_ids_len"],
             batch["semantic_ids"],

GPT_SoVITS/AR/models/t2s_model.py

@@ -337,7 +337,7 @@ class Text2SemanticDecoder(nn.Module):
 
         # AR Decoder
         y = prompts
-        prefix_len = y.shape[1]
+        
         x_len = x.shape[1]
         x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
         stop = False
@@ -353,47 +353,41 @@ class Text2SemanticDecoder(nn.Module):
             "first_infer": 1,
             "stage": 0,
         }
-        for idx in tqdm(range(1500)):
-            if cache["first_infer"] == 1:
-                y_emb = self.ar_audio_embedding(y)
-            else:
-                y_emb = torch.cat(
-                    [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], 1
-                )
-            cache["y_emb"] = y_emb
+        ###################  first step ##########################
+        if y is not None:
+            y_emb = self.ar_audio_embedding(y)
+            y_len = y_emb.shape[1]
+            prefix_len = y.shape[1]
             y_pos = self.ar_audio_position(y_emb)
-            # x 和逐渐增长的 y 一起输入给模型
-            if cache["first_infer"] == 1:
-                xy_pos = torch.concat([x, y_pos], dim=1)
-            else:
-                xy_pos = y_pos[:, -1:]
-            y_len = y_pos.shape[1]
-            ###以下3个不做缓存
-            if cache["first_infer"] == 1:
-                x_attn_mask_pad = F.pad(
+            xy_pos = torch.concat([x, y_pos], dim=1)
+            cache["y_emb"] = y_emb
+            ref_free = False
+        else:
+            y_emb = None
+            y_len = 0
+            prefix_len = 0
+            y_pos = None
+            xy_pos = x
+            y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
+            ref_free = True
+
+        x_attn_mask_pad = F.pad(
                     x_attn_mask,
                     (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
                     value=True,
                 )
-                y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
-                    torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
-                    (x_len, 0),
-                    value=False,
-                )
-                xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
-                    y.device
-                )
-            else:
-                ###最右边一列（是错的）
-                # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
-                # xy_attn_mask[:,-1]=False
-                ###最下面一行（是对的）
-                xy_attn_mask = torch.zeros(
-                    (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
-                )
-            # pdb.set_trace()
-            ###缓存重头戏
-            # print(1111,xy_pos.shape,xy_attn_mask.shape,x_len,y_len)
+        y_attn_mask = F.pad(  ###yy的右上1扩展到左边xy的0,(y,x+y)
+            torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+            (x_len, 0),
+            value=False,
+        )
+        xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
+            x.device
+        )
+        
+
+        for idx in tqdm(range(1500)):
+            
             xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache)
             logits = self.ar_predict_layer(
                 xy_dec[:, -1]
@@ -404,6 +398,10 @@ class Text2SemanticDecoder(nn.Module):
             samples = sample(
                 logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature
             )[0].unsqueeze(0)
+            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
+            # print(samples.shape)#[1,1]#第一个1是bs
+            y = torch.concat([y, samples], dim=1) 
+
             if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
                 print("use early stop num:", early_stop_num)
                 stop = True
@@ -412,13 +410,38 @@ class Text2SemanticDecoder(nn.Module):
                 # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
                 stop = True
             if stop:
-                if prompts.shape[1] == y.shape[1]:
+                # if prompts.shape[1] == y.shape[1]:
+                #     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
+                #     print("bad zero prediction")
+                if y.shape[1]==0:
                     y = torch.concat([y, torch.zeros_like(samples)], dim=1)
                     print("bad zero prediction")
                 print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
                 break
-            # 本次生成的 semantic_ids 和之前的 y 构成新的 y
-            # print(samples.shape)#[1,1]#第一个1是bs
-            y = torch.concat([y, samples], dim=1)
+            
+            ####################### update next step ###################################
             cache["first_infer"] = 0
-        return y, idx
+            if cache["y_emb"] is not None:
+                y_emb = torch.cat(
+                    [cache["y_emb"], self.ar_audio_embedding(y[:, -1:])], dim = 1
+                )
+                cache["y_emb"] = y_emb
+                y_pos = self.ar_audio_position(y_emb)
+                xy_pos = y_pos[:, -1:]
+            else:
+                y_emb = self.ar_audio_embedding(y[:, -1:])
+                cache["y_emb"] = y_emb
+                y_pos = self.ar_audio_position(y_emb)
+                xy_pos = y_pos
+            y_len = y_pos.shape[1]
+
+            ###最右边一列（是错的）
+            # xy_attn_mask=torch.ones((1, x_len+y_len), dtype=torch.bool,device=xy_pos.device)
+            # xy_attn_mask[:,-1]=False
+            ###最下面一行（是对的）
+            xy_attn_mask = torch.zeros(
+                (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device
+            )
+        if ref_free:
+            return y[:, :-1], 0
+        return y[:, :-1], idx-1

GPT_SoVITS/AR/models/utils.py

@@ -114,7 +114,8 @@ def logits_to_probs(
     top_p: Optional[int] = None,
     repetition_penalty: float = 1.0,
 ):
-    previous_tokens = previous_tokens.squeeze()
+    if previous_tokens is not None:
+        previous_tokens = previous_tokens.squeeze()
     # print(logits.shape,previous_tokens.shape)
     # pdb.set_trace()
     if previous_tokens is not None and repetition_penalty != 1.0:

GPT_SoVITS/AR/modules/patched_mha_with_cache.py

@@ -5,8 +5,8 @@ from torch.nn.functional import (
     _none_or_dtype,
     _in_projection_packed,
 )
-
-# import torch
+from torch.nn import functional as F
+import torch
 # Tensor = torch.Tensor
 # from typing import Callable, List, Optional, Tuple, Union
 
@@ -448,9 +448,11 @@ def multi_head_attention_forward_patched(
         k = k.view(bsz, num_heads, src_len, head_dim)
         v = v.view(bsz, num_heads, src_len, head_dim)
 
+        # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
         attn_output = scaled_dot_product_attention(
             q, k, v, attn_mask, dropout_p, is_causal
         )
+
         attn_output = (
             attn_output.permute(2, 0, 1, 3).contiguous().view(bsz * tgt_len, embed_dim)
         )

GPT_SoVITS/inference_webui.py

@@ -248,6 +248,10 @@ def clean_text_inf(text, language):
     formattext = ""
     language = language.replace("all_","")
     for tmp in LangSegment.getTexts(text):
+        if language == "ja":
+            if tmp["lang"] == language or tmp["lang"] == "zh":
+                formattext += tmp["text"] + " "
+            continue
         if tmp["lang"] == language:
             formattext += tmp["text"] + " "
     while "  " in formattext:
@@ -279,8 +283,6 @@ def nonen_clean_text_inf(text, language):
         for tmp in LangSegment.getTexts(text):
             langlist.append(tmp["lang"])
             textlist.append(tmp["text"])
-    print(textlist)
-    print(langlist)
     phones_list = []
     word2ph_list = []
     norm_text_list = []
@@ -365,15 +367,19 @@ def merge_short_text_in_array(texts, threshold):
             result[len(result) - 1] += text
     return result
 
-def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6):
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
+    if prompt_text is None or len(prompt_text) == 0:
+        ref_free = True
     t0 = ttime()
     prompt_language = dict_language[prompt_language]
     text_language = dict_language[text_language]
-    prompt_text = prompt_text.strip("\n")
-    if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
+    if not ref_free:
+        prompt_text = prompt_text.strip("\n")
+        if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
+        print(i18n("实际输入的参考文本:"), prompt_text)
     text = text.strip("\n")
     if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
-    print(i18n("实际输入的参考文本:"), prompt_text)
+    
     print(i18n("实际输入的目标文本:"), text)
     zero_wav = np.zeros(
         int(hps.data.sampling_rate * 0.3),
@@ -398,11 +404,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
             1, 2
         )  # .float()
         codes = vq_model.extract_latent(ssl_content)
+   
         prompt_semantic = codes[0, 0]
     t1 = ttime()
 
-    phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
-
     if (how_to_cut == i18n("凑四句一切")):
         text = cut1(text)
     elif (how_to_cut == i18n("凑50字一切")):
@@ -419,7 +424,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
     texts = text.split("\n")
     texts = merge_short_text_in_array(texts, 5)
     audio_opt = []
-    bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
+    if not ref_free:
+        phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
+        bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
 
     for text in texts:
         # 解决输入目标文本的空行导致报错的问题
@@ -429,9 +436,13 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
         print(i18n("实际输入的目标文本(每句):"), text)
         phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
         bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
-        bert = torch.cat([bert1, bert2], 1)
+        if not ref_free:
+            bert = torch.cat([bert1, bert2], 1)
+            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
+        else:
+            bert = bert2
+            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
 
-        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
         bert = bert.to(device).unsqueeze(0)
         all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
         prompt = prompt_semantic.unsqueeze(0).to(device)
@@ -441,7 +452,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
             pred_semantic, idx = t2s_model.model.infer_panel(
                 all_phoneme_ids,
                 all_phoneme_len,
-                prompt,
+                None if ref_free else prompt,
                 bert,
                 # prompt_phone_len=ph_offset,
                 top_k=top_k,
@@ -607,7 +618,10 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
         gr.Markdown(value=i18n("*请上传并填写参考信息"))
         with gr.Row():
             inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
-            prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
+            with gr.Column():
+                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
+                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT"))
+                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
             prompt_language = gr.Dropdown(
                 label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
             )
@@ -624,6 +638,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                 interactive=True,
             )
             with gr.Row():
+                gr.Markdown("gpt采样参数(无参考文本时不要太低)：")
                 top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
                 top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
                 temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
@@ -632,7 +647,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
 
         inference_button.click(
             get_tts_wav,
-            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut,top_k,top_p,temperature],
+            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free],
             [output],
         )
 
@@ -650,7 +665,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
             button3.click(cut3, [text_inp], [text_opt])
             button4.click(cut4, [text_inp], [text_opt])
             button5.click(cut5, [text_inp], [text_opt])
-        gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。"))
+        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
 
 app.queue(concurrency_count=511, max_size=1022).launch(
     server_name="0.0.0.0",

GPT_SoVITS/module/models.py

@@ -228,6 +228,7 @@ class TextEncoder(nn.Module):
         )
 
         y = self.ssl_proj(y * y_mask) * y_mask
+     
         y = self.encoder_ssl(y * y_mask, y_mask)
 
         text_mask = torch.unsqueeze(
@@ -958,11 +959,13 @@ class SynthesizerTrn(nn.Module):
 
     @torch.no_grad()
     def decode(self, codes, text, refer, noise_scale=0.5):
-        refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
-        refer_mask = torch.unsqueeze(
-            commons.sequence_mask(refer_lengths, refer.size(2)), 1
-        ).to(refer.dtype)
-        ge = self.ref_enc(refer * refer_mask, refer_mask)
+        ge = None
+        if refer is not None:
+            refer_lengths = torch.LongTensor([refer.size(2)]).to(refer.device)
+            refer_mask = torch.unsqueeze(
+                commons.sequence_mask(refer_lengths, refer.size(2)), 1
+            ).to(refer.dtype)
+            ge = self.ref_enc(refer * refer_mask, refer_mask)
 
         y_lengths = torch.LongTensor([codes.size(2) * 2]).to(codes.device)
         text_lengths = torch.LongTensor([text.size(-1)]).to(text.device)

GPT_SoVITS/prepare_datasets/1-get-text.py

@@ -33,13 +33,13 @@ 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))
-
+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))
 
 
 txt_path = "%s/2-name2text-%s.txt" % (opt_dir, i_part)

GPT_SoVITS/prepare_datasets/2-get-hubert-wav32k.py

@@ -35,7 +35,8 @@ 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%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))
 

GPT_SoVITS/process_ckpt.py

@@ -1,11 +1,18 @@
 import traceback
 from collections import OrderedDict
-
+from time import time as ttime
+import shutil,os
 import torch
 from tools.i18n.i18n import I18nAuto
 
 i18n = I18nAuto()
 
+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.pth"%(ttime())
+    torch.save(fea,tmp_path)
+    shutil.move(tmp_path,"%s/%s"%(dir,name))
 
 def savee(ckpt, name, epoch, steps, hps):
     try:
@@ -17,7 +24,8 @@ def savee(ckpt, name, epoch, steps, hps):
             opt["weight"][key] = ckpt[key].half()
         opt["config"] = hps
         opt["info"] = "%sepoch_%siteration" % (epoch, steps)
-        torch.save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
+        # torch.save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
+        my_save(opt, "%s/%s.pth" % (hps.save_weight_dir, name))
         return "Success."
     except:
         return traceback.format_exc()

GPT_SoVITS/s1_train.py

@@ -24,6 +24,14 @@ torch.set_float32_matmul_precision("high")
 from AR.utils import get_newest_ckpt
 
 from collections import OrderedDict
+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.pth"%(ttime())
+    torch.save(fea,tmp_path)
+    shutil.move(tmp_path,"%s/%s"%(dir,name))
 
 
 class my_model_ckpt(ModelCheckpoint):
@@ -70,7 +78,8 @@ class my_model_ckpt(ModelCheckpoint):
                         to_save_od["weight"][key] = dictt[key].half()
                     to_save_od["config"] = self.config
                     to_save_od["info"] = "GPT-e%s" % (trainer.current_epoch + 1)
-                    torch.save(
+                    # torch.save(
+                    my_save(
                         to_save_od,
                         "%s/%s-e%s.ckpt"
                         % (

GPT_SoVITS/text/english.py

@@ -169,9 +169,9 @@ def read_dict_new():
                 line = line.strip()
                 word_split = line.split(" ")
                 word = word_split[0]
-                if word not in g2p_dict:
-                    g2p_dict[word] = []
-                    g2p_dict[word].append(word_split[1:])
+                #if word not in g2p_dict:
+                g2p_dict[word] = []
+                g2p_dict[word].append(word_split[1:])
 
             line_index = line_index + 1
             line = f.readline()

GPT_SoVITS/text/tone_sandhi.py

@@ -672,6 +672,7 @@ class ToneSandhi:
                 and i + 1 < len(seg)
                 and seg[i - 1][0] == seg[i + 1][0]
                 and seg[i - 1][1] == "v"
+                and seg[i + 1][1] == "v"
             ):
                 new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
             else:

GPT_SoVITS/utils.py

@@ -64,6 +64,14 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
     )
     return model, optimizer, learning_rate, iteration
 
+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.pth"%(ttime())
+    torch.save(fea,tmp_path)
+    shutil.move(tmp_path,"%s/%s"%(dir,name))
 
 def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
     logger.info(
@@ -75,7 +83,8 @@ def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path)
         state_dict = model.module.state_dict()
     else:
         state_dict = model.state_dict()
-    torch.save(
+    # torch.save(
+    my_save(
         {
             "model": state_dict,
             "iteration": iteration,

colab_webui.ipynb

@@ -82,7 +82,7 @@
       "source": [
         "# @title launch WebUI 启动WebUI\n",
         "!/usr/local/bin/pip install ipykernel\n",
-        "!sed -i '9s/False/True/' /content/GPT-SoVITS/config.py\n",
+        "!sed -i '10s/False/True/' /content/GPT-SoVITS/config.py\n",
         "%cd /content/GPT-SoVITS/\n",
         "!/usr/local/bin/python  webui.py"
       ],

docs/cn/Changelog_CN.md

@@ -113,12 +113,21 @@
 
 2-DPO Loss实验性训练选项开启，通过构造负样本训练缓解GPT重复漏字问题。推理界面公开几个推理参数。 https://github.com/RVC-Boss/GPT-SoVITS/pull/457
 
+### 20240214更新
+
+1-训练支持中文实验名（原来会报错）
+
+2-DPO训练改为可勾选选项而非必须。如勾选batch size自动减半。修复推理界面新参数不传参的问题。
+
+### 20240216更新
+
+1-支持无参考文本输入
+
+2-修复中文文本前端bug https://github.com/RVC-Boss/GPT-SoVITS/issues/475
 
 todolist：
 
 1-中文多音字推理优化
 
-2-训练支持中文实验名（原来会报错）
-
 
 

webui.py

@@ -266,7 +266,7 @@ def close1Ba():
     return "已终止SoVITS训练",{"__type__":"update","visible":True},{"__type__":"update","visible":False}
 
 p_train_GPT=None
-def open1Bb(batch_size,total_epoch,exp_name,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
+def open1Bb(batch_size,total_epoch,exp_name,if_dpo,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers,pretrained_s1):
     global p_train_GPT
     if(p_train_GPT==None):
         with open("GPT_SoVITS/configs/s1longer.yaml")as f:
@@ -283,6 +283,7 @@ def open1Bb(batch_size,total_epoch,exp_name,if_save_latest,if_save_every_weights
         data["train"]["save_every_n_epoch"]=save_every_epoch
         data["train"]["if_save_every_weights"]=if_save_every_weights
         data["train"]["if_save_latest"]=if_save_latest
+        data["train"]["if_dpo"]=if_dpo
         data["train"]["half_weights_save_dir"]=GPT_weight_root
         data["train"]["exp_name"]=exp_name
         data["train_semantic_path"]="%s/6-name2semantic.tsv"%s1_dir
@@ -807,6 +808,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                 with gr.Row():
                     batch_size1Bb = gr.Slider(minimum=1,maximum=40,step=1,label=i18n("每张显卡的batch_size"),value=default_batch_size,interactive=True)
                     total_epoch1Bb = gr.Slider(minimum=2,maximum=50,step=1,label=i18n("总训练轮数total_epoch"),value=15,interactive=True)
+                    if_dpo = gr.Checkbox(label=i18n("是否开启dpo训练选项(实验性)"), value=False, interactive=True, show_label=True)
                     if_save_latest1Bb = gr.Checkbox(label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"), value=True, interactive=True, show_label=True)
                     if_save_every_weights1Bb = gr.Checkbox(label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"), value=True, interactive=True, show_label=True)
                     save_every_epoch1Bb = gr.Slider(minimum=1,maximum=50,step=1,label=i18n("保存频率save_every_epoch"),value=5,interactive=True)
@@ -817,7 +819,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
                     info1Bb=gr.Textbox(label=i18n("GPT训练进程输出信息"))
             button1Ba_open.click(open1Ba, [batch_size,total_epoch,exp_name,text_low_lr_rate,if_save_latest,if_save_every_weights,save_every_epoch,gpu_numbers1Ba,pretrained_s2G,pretrained_s2D], [info1Ba,button1Ba_open,button1Ba_close])
             button1Ba_close.click(close1Ba, [], [info1Ba,button1Ba_open,button1Ba_close])
-            button1Bb_open.click(open1Bb, [batch_size1Bb,total_epoch1Bb,exp_name,if_save_latest1Bb,if_save_every_weights1Bb,save_every_epoch1Bb,gpu_numbers1Bb,pretrained_s1],   [info1Bb,button1Bb_open,button1Bb_close])
+            button1Bb_open.click(open1Bb, [batch_size1Bb,total_epoch1Bb,exp_name,if_dpo,if_save_latest1Bb,if_save_every_weights1Bb,save_every_epoch1Bb,gpu_numbers1Bb,pretrained_s1],   [info1Bb,button1Bb_open,button1Bb_close])
             button1Bb_close.click(close1Bb, [], [info1Bb,button1Bb_open,button1Bb_close])
             with gr.TabItem(i18n("1C-推理")):
                 gr.Markdown(value=i18n("选择训练完存放在SoVITS_weights和GPT_weights下的模型。默认的一个是底模，体验5秒Zero Shot TTS用。"))