修复了t2s模型无prompt输入时的bug GPT_SoVITS/AR/models/t2s_model.py

增加一些新特性，并修复了一些bug   GPT_SoVITS/TTS_infer_pack/TTS.py
	优化网页布局   GPT_SoVITS/inference_webui.py

GPT_SoVITS/AR/models/t2s_model.py

@@ -549,7 +549,6 @@ class Text2SemanticDecoder(nn.Module):
         y_list = [None]*y.shape[0]
         batch_idx_map = list(range(y.shape[0]))
         idx_list = [None]*y.shape[0]
-        cache_y_emb = y_emb
         for idx in tqdm(range(1500)):
             if idx == 0:
                 xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask)
@@ -589,8 +588,6 @@ class Text2SemanticDecoder(nn.Module):
             if reserved_idx_of_batch_for_y is not None:
                 # index = torch.LongTensor(batch_idx_map).to(y.device)
                 y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
-                if cache_y_emb is not None:
-                    cache_y_emb = torch.index_select(cache_y_emb, dim=0, index=reserved_idx_of_batch_for_y)
                 if k_cache is not None :
                     for i in range(len(k_cache)):
                         k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
@@ -617,8 +614,8 @@ class Text2SemanticDecoder(nn.Module):
 
             ####################### update next step ###################################
             y_emb = self.ar_audio_embedding(y[:, -1:])
-            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx]
+            xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to( dtype= y_emb.dtype,device=y_emb.device)
-
+            
         if (None in idx_list):
             for i in range(x.shape[0]):
                 if idx_list[i] is None:

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -1,8 +1,7 @@
 import os, sys
-
-import ffmpeg
 now_dir = os.getcwd()
 sys.path.append(now_dir)
+import ffmpeg
 import os
 from typing import Generator, List, Union
 import numpy as np
@@ -164,6 +163,9 @@ class TTS:
             "bert_features":None,
             "norm_text":None,
         }
+        
+        
+        self.stop_flag:bool = False
 
     def _init_models(self,):
         self.init_t2s_weights(self.configs.t2s_weights_path)
@@ -310,7 +312,7 @@ class TTS:
         batch = torch.stack(padded_sequences)
         return batch
     
-    def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75):
+    def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75, split_bucket:bool=True):
         
         _data:list = []
         index_and_len_list = []
@@ -318,30 +320,35 @@ class TTS:
             norm_text_len = len(item["norm_text"])
             index_and_len_list.append([idx, norm_text_len])
 
-        index_and_len_list.sort(key=lambda x: x[1])
-        # index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)]
-        index_and_len_list = np.array(index_and_len_list, dtype=np.int64)
-        
-        # for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list):
-        
         batch_index_list = []
-        batch_index_list_len = 0
+        if split_bucket:
-        pos = 0
+            index_and_len_list.sort(key=lambda x: x[1])
-        while pos <index_and_len_list.shape[0]:
+            index_and_len_list = np.array(index_and_len_list, dtype=np.int64)            
-            # batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
+            
-            pos_end = min(pos+batch_size,index_and_len_list.shape[0])
+            batch_index_list_len = 0
-            while pos < pos_end:
+            pos = 0
-                batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
+            while pos <index_and_len_list.shape[0]:
-                score=batch[(pos_end-pos)//2]/batch.mean()
+                # batch_index_list.append(index_and_len_list[pos:min(pos+batch_size,len(index_and_len_list))])
-                if (score>=threshold) or (pos_end-pos==1):
+                pos_end = min(pos+batch_size,index_and_len_list.shape[0])
-                    batch_index=index_and_len_list[pos:pos_end, 0].tolist()
+                while pos < pos_end:
-                    batch_index_list_len += len(batch_index)
+                    batch=index_and_len_list[pos:pos_end, 1].astype(np.float32)
-                    batch_index_list.append(batch_index)
+                    score=batch[(pos_end-pos)//2]/batch.mean()
-                    pos = pos_end
+                    if (score>=threshold) or (pos_end-pos==1):
-                    break
+                        batch_index=index_and_len_list[pos:pos_end, 0].tolist()
-                pos_end=pos_end-1
+                        batch_index_list_len += len(batch_index)
-        
+                        batch_index_list.append(batch_index)
-        assert batch_index_list_len == len(data)
+                        pos = pos_end
+                        break
+                    pos_end=pos_end-1
+            
+            assert batch_index_list_len == len(data)
+            
+        else:
+            for i in range(len(data)):
+                if i%batch_size == 0:
+                    batch_index_list.append([])
+                batch_index_list[-1].append(i)
+
                 
         for batch_idx, index_list in enumerate(batch_index_list):
             item_list = [data[idx] for idx in index_list]
@@ -399,7 +406,8 @@ class TTS:
                 _data[index] = data[i][j]
         return _data
 
-
+    def stop(self,):
+        self.stop_flag = True
         
     
     def run(self, inputs:dict):
@@ -409,22 +417,26 @@ class TTS:
         Args:
             inputs (dict): 
                 {
-                    "text": "",
+                    "text": "",               # str. text to be synthesized
-                    "text_lang: "",
+                    "text_lang: "",           # str. language of the text to be synthesized
-                    "ref_audio_path": "",
+                    "ref_audio_path": "",     # str. reference audio path
-                    "prompt_text": "",
+                    "prompt_text": "",        # str. prompt text for the reference audio
-                    "prompt_lang": "",
+                    "prompt_lang": "",        # str. language of the prompt text for the reference audio
-                    "top_k": 5,
+                    "top_k": 5,               # int. top k sampling
-                    "top_p": 0.9,
+                    "top_p": 0.9,             # float. top p sampling
-                    "temperature": 0.6,
+                    "temperature": 0.6,       # float. temperature for sampling
-                    "text_split_method": "",
+                    "text_split_method": "",  # str. text split method, see text_segmentaion_method.py for details.
-                    "batch_size": 1,
+                    "batch_size": 1,          # int. batch size for inference
-                    "batch_threshold": 0.75,
+                    "batch_threshold": 0.75,  # float. threshold for batch splitting.
-                    "speed_factor":1.0,
+                    "split_bucket: True,      # bool. whether to split the batch into multiple buckets.
+                    "return_fragment": False, # bool. step by step return the audio fragment.
+                    "speed_factor":1.0,       # float. control the speed of the synthesized audio.
                 }
         returns:
             tulpe[int, np.ndarray]: sampling rate and audio data.
         """
+        self.stop_flag:bool = False
+        
         text:str = inputs.get("text", "")
         text_lang:str = inputs.get("text_lang", "")
         ref_audio_path:str = inputs.get("ref_audio_path", "")
@@ -437,7 +449,20 @@ class TTS:
         batch_size = inputs.get("batch_size", 1)
         batch_threshold = inputs.get("batch_threshold", 0.75)
         speed_factor = inputs.get("speed_factor", 1.0)
+        split_bucket = inputs.get("split_bucket", True)
+        return_fragment = inputs.get("return_fragment", False)
         
+        if return_fragment:
+            split_bucket = False
+            print(i18n("分段返回模式已开启"))
+            if split_bucket:
+                split_bucket = False
+                print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
+            
+        if split_bucket:
+            print(i18n("分桶处理模式已开启"))
+
+    
         no_prompt_text = False
         if prompt_text in [None, ""]:
             no_prompt_text = True
@@ -481,7 +506,9 @@ class TTS:
         data, batch_index_list = self.to_batch(data, 
                              prompt_data=self.prompt_cache if not no_prompt_text else None, 
                              batch_size=batch_size, 
-                             threshold=batch_threshold)
+                             threshold=batch_threshold,
+                             split_bucket=split_bucket
+                             )
         t2 = ttime()
         zero_wav = torch.zeros(
                         int(self.configs.sampling_rate * 0.3),
@@ -557,27 +584,57 @@ class TTS:
                     audio_fragment.cpu().numpy()
                 )  ###试试重建不带上prompt部分
             
-            audio.append(batch_audio_fragment)
-            # audio.append(zero_wav)
             t5 = ttime()
             t_45 += t5 - t4
+            if return_fragment:
+                print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t4 - t3, t5 - t4))
+                yield self.audio_postprocess(batch_audio_fragment, 
+                                                self.configs.sampling_rate, 
+                                                batch_index_list, 
+                                                speed_factor, 
+                                                split_bucket)
+            else:
+                audio.append(batch_audio_fragment)
+                
+            if self.stop_flag:
+                yield self.configs.sampling_rate, (zero_wav.cpu().numpy()).astype(np.int16) 
+                return
+
+        if not return_fragment:
+            print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
+            yield self.audio_postprocess(audio, 
+                                            self.configs.sampling_rate, 
+                                            batch_index_list, 
+                                            speed_factor, 
+                                            split_bucket)          
+
+       
+       
+    def audio_postprocess(self, 
+                          audio:np.ndarray, 
+                          sr:int, 
+                          batch_index_list:list=None, 
+                          speed_factor:float=1.0, 
+                          split_bucket:bool=True)->tuple[int, np.ndarray]:
+        if split_bucket:
+            audio = self.recovery_order(audio, batch_index_list)
+        else:
+            audio = [item for batch in audio for item in batch]
+            
             
-        audio = self.recovery_order(audio, batch_index_list)
-        print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45))
-        
         audio = np.concatenate(audio, 0)
         audio = (audio * 32768).astype(np.int16) 
         
         try:
             if speed_factor != 1.0:
-                audio = speed_change(audio, speed=speed_factor, sr=int(self.configs.sampling_rate))
+                audio = speed_change(audio, speed=speed_factor, sr=int(sr))
         except Exception as e:
             print(f"Failed to change speed of audio: \n{e}")
         
-        yield self.configs.sampling_rate, audio
+        return sr, audio
-       
+            
-       
+        
-       
+        
        
 def speed_change(input_audio:np.ndarray, speed:float, sr:int):
     # 将 NumPy 数组转换为原始 PCM 流

GPT_SoVITS/inference_webui.py

@@ -6,8 +6,11 @@
 全部按英文识别
 全部按日文识别
 '''
-import os, re, logging
+import os, sys
+now_dir = os.getcwd()
+sys.path.append(now_dir)
 
+import os, re, logging
 logging.getLogger("markdown_it").setLevel(logging.ERROR)
 logging.getLogger("urllib3").setLevel(logging.ERROR)
 logging.getLogger("httpcore").setLevel(logging.ERROR)
@@ -18,10 +21,7 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
 import pdb
 import torch
 # modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py
-import os, sys
 
-now_dir = os.getcwd()
-sys.path.append(now_dir)
 
 infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
 infer_ttswebui = int(infer_ttswebui)
@@ -34,6 +34,7 @@ import gradio as gr
 from TTS_infer_pack.TTS import TTS, TTS_Config
 from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5
 from tools.i18n.i18n import I18nAuto
+from TTS_infer_pack.text_segmentation_method import get_method
 i18n = I18nAuto()
 
 os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。
@@ -68,19 +69,28 @@ tts_pipline = TTS(tts_config)
 gpt_path = tts_config.t2s_weights_path
 sovits_path = tts_config.vits_weights_path
 
-def inference(text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, top_p, temperature, text_split_method, batch_size, speed_factor):
+def inference(text, text_lang, 
+              ref_audio_path, prompt_text, 
+              prompt_lang, top_k, 
+              top_p, temperature, 
+              text_split_method, batch_size, 
+              speed_factor, ref_text_free,
+              split_bucket
+              ):
     inputs={
         "text": text,
         "text_lang": dict_language[text_lang],
         "ref_audio_path": ref_audio_path,
-        "prompt_text": prompt_text,
+        "prompt_text": prompt_text if not ref_text_free else "",
         "prompt_lang": dict_language[prompt_lang],
         "top_k": top_k,
         "top_p": top_p,
         "temperature": temperature,
         "text_split_method": cut_method[text_split_method],
         "batch_size":int(batch_size),
-        "speed_factor":float(speed_factor)
+        "speed_factor":float(speed_factor),
+        "split_bucket":split_bucket,
+        "return_fragment":False,
     }
     yield next(tts_pipline.run(inputs))
 
@@ -121,7 +131,9 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(
         value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
     )
-    with gr.Group():
+    
+    with gr.Column():
+        # with gr.Group():
         gr.Markdown(value=i18n("模型切换"))
         with gr.Row():
             GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True)
@@ -130,61 +142,88 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
             refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
             SoVITS_dropdown.change(tts_pipline.init_vits_weights, [SoVITS_dropdown], [])
             GPT_dropdown.change(tts_pipline.init_t2s_weights, [GPT_dropdown], [])
-        gr.Markdown(value=i18n("*请上传并填写参考信息"))
+    
-        with gr.Row():
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown(value=i18n("*请上传并填写参考信息"))
             inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
-            with gr.Column():
+            prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=2)
-                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)
+            with gr.Row():
-                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
+                prompt_language = gr.Dropdown(
-                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
+                    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")
-            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")
+                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(value=i18n("*请填写需要合成的目标文本和语种模式"))
+                    gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
-        with gr.Row():
+    
-            text = gr.Textbox(label=i18n("需要合成的文本"), value="")
+        with gr.Column():
+            gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
+            text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=16, max_lines=16)
             text_language = gr.Dropdown(
                 label=i18n("需要合成的语种"), 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")
             )
-            how_to_cut = gr.Radio(
+
-                label=i18n("怎么切"),
+        
-                choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+    with gr.Group():
-                value=i18n("凑四句一切"),
+        gr.Markdown(value=i18n("推理设置"))
-                interactive=True,
+        with gr.Row():
-            )
+
-            with gr.Row():
+            with gr.Column():
-                gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
                 batch_size = gr.Slider(minimum=1,maximum=20,step=1,label=i18n("batch_size"),value=1,interactive=True)
                 speed_factor = gr.Slider(minimum=0.25,maximum=4,step=0.05,label="speed_factor",value=1.0,interactive=True)
                 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)
-            inference_button = gr.Button(i18n("合成语音"), variant="primary")
+            with gr.Column():
-            output = gr.Audio(label=i18n("输出的语音"))
+                how_to_cut = gr.Radio(
-
+                    label=i18n("怎么切"),
+                    choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
+                    value=i18n("凑四句一切"),
+                    interactive=True,
+                )
+                split_bucket = gr.Checkbox(label=i18n("数据分桶(可能会降低一点计算量,选就对了)"), value=True, interactive=True, show_label=True)
+            # with gr.Column():
+                output = gr.Audio(label=i18n("输出的语音"))
+                with gr.Row():
+                    inference_button = gr.Button(i18n("合成语音"), variant="primary")
+                    stop_infer = gr.Button(i18n("终止合成"), variant="primary")
+                
         
-
-
         inference_button.click(
             inference,
-            [text,text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size, speed_factor],
+            [
+                text,text_language, inp_ref, 
+                prompt_text, prompt_language, 
+                top_k, top_p, temperature, 
+                how_to_cut, batch_size, 
+                speed_factor, ref_text_free,
+                split_bucket
+             ],
             [output],
         )
+        stop_infer.click(tts_pipline.stop, [], [])
 
+    with gr.Group():
         gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
         with gr.Row():
-            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
+            text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="", lines=4)
-            button1 = gr.Button(i18n("凑四句一切"), variant="primary")
+            with gr.Column():
-            button2 = gr.Button(i18n("凑50字一切"), variant="primary")
+                _how_to_cut = gr.Radio(
-            button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
+                            label=i18n("怎么切"),
-            button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
+                            choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
-            button5 = gr.Button(i18n("按标点符号切"), variant="primary")
+                            value=i18n("凑四句一切"),
-            text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
+                            interactive=True,
-            button1.click(cut1, [text_inp], [text_opt])
+                        )
-            button2.click(cut2, [text_inp], [text_opt])
+                cut_text= gr.Button(i18n("切分"), variant="primary")
-            button3.click(cut3, [text_inp], [text_opt])
+            
-            button4.click(cut4, [text_inp], [text_opt])
+            def to_cut(text_inp, how_to_cut):
-            button5.click(cut5, [text_inp], [text_opt])
+                if len(text_inp.strip()) == 0 or text_inp==[]:
+                    return ""
+                method = get_method(cut_method[how_to_cut])
+                return method(text_inp)
+        
+            text_opt = gr.Textbox(label=i18n("切分后文本"), value="", lines=4)
+            cut_text.click(to_cut, [text_inp, _how_to_cut], [text_opt])
         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(