Update app.py

app.py

@@ -1,384 +1,384 @@
-import os
-import sys
-# to avoid the modified user.pth file
-cnhubert_base_path = "GPT_SoVITS\pretrained_models\chinese-hubert-base"
-bert_path = "GPT_SoVITS\pretrained_models\chinese-roberta-wwm-ext-large"
-os.environ["version"] = 'v2'
-now_dir = os.getcwd()
-sys.path.insert(0, now_dir)
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-from pathlib import Path
-import os,librosa,torch, audiosegment
-from scipy.io.wavfile import write as wavwrite
-from GPT_SoVITS.feature_extractor import cnhubert
-cnhubert.cnhubert_base_path=cnhubert_base_path
-from GPT_SoVITS.module.models import SynthesizerTrn
-from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from GPT_SoVITS.text import cleaned_text_to_sequence
-from GPT_SoVITS.text.cleaner import clean_text
-from time import time as ttime
-from GPT_SoVITS.module.mel_processing import spectrogram_torch
-import tempfile
-from tools.my_utils import load_audio
-import os
-import json
-
-################ End strange import and user.pth modification ################
-
-# import pyopenjtalk
-# cwd = os.getcwd()
-# if os.path.exists(os.path.join(cwd,'user.dic')):
-#     pyopenjtalk.update_global_jtalk_with_user_dict(os.path.join(cwd, 'user.dic'))
-
-
-import logging
-logging.getLogger('httpx').setLevel(logging.WARNING)
-logging.getLogger('httpcore').setLevel(logging.WARNING)
-logging.getLogger('multipart').setLevel(logging.WARNING)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-#device = "cpu"
-is_half = False
-
-tokenizer = AutoTokenizer.from_pretrained(bert_path)
-bert_model=AutoModelForMaskedLM.from_pretrained(bert_path)
-if(is_half==True):bert_model=bert_model.half().to(device)
-else:bert_model=bert_model.to(device)
-# bert_model=bert_model.to(device)
-def get_bert_feature(text, word2ph): # Bert(不是HuBERT的特征计算)
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors="pt")
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)#####输入是long不用管精度问题，精度随bert_model
-        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)
-    # if(is_half==True):phone_level_feature=phone_level_feature.half()
-    return phone_level_feature.T
-
-loaded_sovits_model = [] # [(path, dict, model)]
-loaded_gpt_model = []
-ssl_model = cnhubert.get_model()
-if (is_half == True):
-    ssl_model = ssl_model.half().to(device)
-else:
-    ssl_model = ssl_model.to(device)
-
-
-def load_model(sovits_path, gpt_path):
-    global ssl_model
-    global loaded_sovits_model
-    global loaded_gpt_model
-    vq_model = None
-    t2s_model = None
-    dict_s2 = None
-    dict_s1 = None
-    hps = None
-    for path, dict_s2_, model in loaded_sovits_model:
-        if path == sovits_path:
-            vq_model = model
-            dict_s2 = dict_s2_
-            break
-    for path, dict_s1_, model in loaded_gpt_model:
-        if path == gpt_path:
-            t2s_model = model
-            dict_s1 = dict_s1_
-            break
-    
-    if dict_s2 is None:
-        dict_s2 = torch.load(sovits_path, map_location="cpu")
-    hps = dict_s2["config"]
-
-    if dict_s1 is None:
-        dict_s1 = torch.load(gpt_path, map_location="cpu")
-    config = dict_s1["config"]
-    class DictToAttrRecursive:
-        def __init__(self, input_dict):
-            for key, value in input_dict.items():
-                if isinstance(value, dict):
-                    # 如果值是字典，递归调用构造函数
-                    setattr(self, key, DictToAttrRecursive(value))
-                else:
-                    setattr(self, key, value)
-
-    hps = DictToAttrRecursive(hps)
-    hps.model.semantic_frame_rate = "25hz"
-
-
-    if not vq_model:
-        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)
-        else:
-            vq_model = vq_model.to(device)
-        vq_model.eval()
-        vq_model.load_state_dict(dict_s2["weight"], strict=False)
-        loaded_sovits_model.append((sovits_path, dict_s2, vq_model))
-    hz = 50
-    max_sec = config['data']['max_sec']
-    if not t2s_model:
-        t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
-        t2s_model.load_state_dict(dict_s1["weight"])
-        if (is_half == True): t2s_model = t2s_model.half()
-        t2s_model = t2s_model.to(device)
-        t2s_model.eval()
-        total = sum([param.nelement() for param in t2s_model.parameters()])
-        loaded_gpt_model.append((gpt_path, dict_s1, t2s_model))
-    return vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
-
-
-def get_spepc(hps, filename):
-    audio=load_audio(filename,int(hps.data.sampling_rate)) 
-    audio = audio / np.max(np.abs(audio))
-    audio=torch.FloatTensor(audio)
-    audio_norm = audio
-    # audio_norm = audio / torch.max(torch.abs(audio))
-    audio_norm = audio_norm.unsqueeze(0)
-    spec = spectrogram_torch(audio_norm, hps.data.filter_length,hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,center=False)
-    return spec
-
-def create_tts_fn(vq_model, ssl_model, t2s_model, hps, config, hz, max_sec):
-    def tts_fn(ref_wav_path, prompt_text, prompt_language, target_phone, text_language, target_text = None):
-        t0 = ttime()
-        prompt_text=prompt_text.strip()
-        prompt_language=prompt_language
-        with torch.no_grad():
-            wav16k, sr = librosa.load(ref_wav_path, sr=16000, mono=False)
-            direction = np.array([1,1])
-            if wav16k.ndim == 2:
-                power = np.sum(np.abs(wav16k) ** 2, axis=1)
-                direction = power / np.sum(power)
-                wav16k = (wav16k[0] + wav16k[1]) / 2
-            # 
-            # maxx=0.95
-            # tmp_max = np.abs(wav16k).max()
-            # alpha=0.5
-            # wav16k = (wav16k / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * wav16k
-            #在这里归一化
-            #print(max(np.abs(wav16k)))
-            #wav16k = wav16k / np.max(np.abs(wav16k))
-            #print(max(np.abs(wav16k)))
-            # 添加0.3s的静音
-            wav16k = np.concatenate([wav16k, np.zeros(int(hps.data.sampling_rate * 0.3)),])
-            wav16k = torch.from_numpy(wav16k)
-            wav16k = wav16k.float()
-            if(is_half==True):wav16k=wav16k.half().to(device)
-            else:wav16k=wav16k.to(device)
-            ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)#.float()
-            codes = vq_model.extract_latent(ssl_content)
-            prompt_semantic = codes[0, 0]
-        t1 = ttime()
-        phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
-        phones1=cleaned_text_to_sequence(phones1)
-        #texts=text.split("\n")
-        audio_opt = []
-        zero_wav=np.zeros((2, int(hps.data.sampling_rate*0.3)),dtype=np.float16 if is_half==True else np.float32)
-        phones = get_phone_from_str_list(target_phone, text_language)
-        for phones2 in phones:
-            if(len(phones2) == 0):
-                continue
-            if(len(phones2) == 1 and phones2[0] == ""):
-                continue
-            #phones2, word2ph2, norm_text2 = clean_text(text, text_language)
-            phones2 = cleaned_text_to_sequence(phones2)
-            #if(prompt_language=="zh"):bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
-            bert1 = torch.zeros((1024, len(phones1)),dtype=torch.float16 if is_half==True else torch.float32).to(device)
-            #if(text_language=="zh"):bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
-            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
-            bert = torch.cat([bert1, bert2], 1)
-
-            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)
-            t2 = ttime()
-            idx = 0
-            cnt = 0
-            while idx == 0 and cnt < 2:
-                with torch.no_grad():
-                    # pred_semantic = t2s_model.model.infer
-                    pred_semantic,idx = t2s_model.model.infer_panel(
-                        all_phoneme_ids,
-                        all_phoneme_len,
-                        prompt,
-                        bert,
-                        # prompt_phone_len=ph_offset,
-                        top_k=config['inference']['top_k'],
-                        early_stop_num=hz * max_sec)
-                t3 = ttime()
-                cnt+=1
-            if idx == 0:
-                return "Error: Generation failure: bad zero prediction.", None
-            pred_semantic = pred_semantic[:,-idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
-            refer = get_spepc(hps, ref_wav_path)#.to(device)
-            if(is_half==True):refer=refer.half().to(device)
-            else:refer=refer.to(device)
-            # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
-            audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer).detach().cpu().numpy()[0, 0]###试试重建不带上prompt部分
-            # direction乘上，变双通道
-            # 强制0.5
-            direction = np.array([1, 1])
-            audio = np.expand_dims(audio, 0) * direction[:, np.newaxis]
-            audio_opt.append(audio)
-            audio_opt.append(zero_wav)
-            t4 = ttime()
-
-        audio = (hps.data.sampling_rate,(np.concatenate(audio_opt, axis=1)*32768).astype(np.int16).T)
-        prefix_1 = prompt_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_")
-        prefix_2 = target_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_")
-        filename = tempfile.mktemp(suffix=".wav",prefix=f"{prefix_1}_{prefix_2}_")
-        #audiosegment.from_numpy_array(audio[1].T, framerate=audio[0]).export(filename, format="WAV")
-        wavwrite(filename, audio[0], audio[1])
-        return "Success", audio, filename
-    return tts_fn
-
-
-def get_str_list_from_phone(text, text_language):
-    # raw文本过g2p得到音素列表，再转成字符串
-    # 注意，这里的text是一个段落，可能包含多个句子
-    # 段落间\n分割，音素间空格分割
-    print(text)
-    texts=text.split("\n")
-    phone_list = []
-    for text in texts:
-        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
-        phone_list.append(" ".join(phones2))
-    return "\n".join(phone_list)
-
-def get_phone_from_str_list(str_list:str, language:str = 'ja'):
-    # 从音素字符串中得到音素列表
-    # 注意，这里的text是一个段落，可能包含多个句子
-    # 段落间\n分割，音素间空格分割
-    sentences = str_list.split("\n")
-    phones = []
-    for sentence in sentences:
-        phones.append(sentence.split(" "))
-    return phones
-
-splits={"，","。","？","！",",",".","?","!","~",":","：","—","…",}#不考虑省略号
-def split(todo_text):
-    todo_text = todo_text.replace("……", "。").replace("——", "，")
-    if (todo_text[-1] not in splits): todo_text += "。"
-    i_split_head = i_split_tail = 0
-    len_text = len(todo_text)
-    todo_texts = []
-    while (1):
-        if (i_split_head >= len_text): break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
-        if (todo_text[i_split_head] in splits):
-            i_split_head += 1
-            todo_texts.append(todo_text[i_split_tail:i_split_head])
-            i_split_tail = i_split_head
-        else:
-            i_split_head += 1
-    return todo_texts
-
-
-def change_reference_audio(prompt_text, transcripts):
-    return transcripts[prompt_text]
-
-
-models = []
-models_info = json.load(open("./models/models_info.json", "r", encoding="utf-8")) 
-
-
-
-for i, info in models_info.items():
-    title = info['title']
-    cover = info['cover']
-    gpt_weight = info['gpt_weight']
-    sovits_weight = info['sovits_weight']
-    example_reference = info['example_reference']
-    transcripts = {}
-    transcript_path = info["transcript_path"]
-    path = os.path.dirname(transcript_path)
-    with open(transcript_path, 'r', encoding='utf-8') as file:
-        for line in file:
-            line = line.strip().replace("\\", "/")
-            items = line.split("|")
-            wav,t = items[0], items[-1]
-            wav = os.path.basename(wav)
-            transcripts[t] = os.path.join(os.path.join(path,"reference_audio"), wav)
-
-    vq_model, ssl_model, t2s_model, hps, config, hz, max_sec = load_model(sovits_weight, gpt_weight)
-
-
-    models.append(
-        (
-            i,
-            title,
-            cover,
-            transcripts,
-            example_reference,
-            create_tts_fn(
-                vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
-            )
-        )
-    )
-with gr.Blocks() as app:
-    gr.Markdown(
-        "# <center> GPT-SoVITS Demo\n"
-    )
-    with gr.Tabs():
-        for (name, title, cover, transcripts, example_reference, tts_fn) in models:
-            with gr.TabItem(name):
-                with gr.Row():
-                    gr.Markdown(
-                        '<div align="center">'
-                        f'<a><strong>{title}</strong></a>'
-                        '</div>')
-                with gr.Row():
-                    with gr.Column():
-                        prompt_text = gr.Dropdown(
-                            label="Transcript of the Reference Audio",
-                            value=example_reference if example_reference in transcripts else list(transcripts.keys())[0],
-                            choices=list(transcripts.keys())
-                        )
-                        inp_ref_audio = gr.Audio(
-                            label="Reference Audio",
-                            type="filepath",
-                            interactive=False,
-                            value=transcripts[example_reference] if example_reference in transcripts else list(transcripts.values())[0]
-                        )
-                        transcripts_state = gr.State(value=transcripts)
-                        prompt_text.change(
-                            fn=change_reference_audio,
-                            inputs=[prompt_text, transcripts_state],
-                            outputs=[inp_ref_audio]
-                        )
-                        prompt_language = gr.State(value="ja")
-                    with gr.Column():
-                        text = gr.Textbox(label="Input Text", value="私はお兄ちゃんのだいだいだーいすきな妹なんだから、言うことなんでも聞いてくれますよね！")
-                        text_language = gr.Dropdown(
-                            label="Language",
-                            choices=["ja"],
-                            value="ja"
-                        )
-                        clean_button = gr.Button("Clean Text", variant="primary")
-                        inference_button = gr.Button("Generate", variant="primary")
-                        cleaned_text = gr.Textbox(label="Cleaned Text")
-                        output = gr.Audio(label="Output Audio")
-                        output_file = gr.File(label="Output Audio File")
-                        om = gr.Textbox(label="Output Message")
-                        clean_button.click(
-                            fn=get_str_list_from_phone,
-                            inputs=[text, text_language],
-                            outputs=[cleaned_text]
-                        )
-                        inference_button.click(
-                            fn=tts_fn,
-                            inputs=[inp_ref_audio, prompt_text, prompt_language, cleaned_text, text_language, text],
-                            outputs=[om, output, output_file]
-                        )
-
+import os
+import sys
+# to avoid the modified user.pth file
+cnhubert_base_path = "GPT_SoVITS\pretrained_models\chinese-hubert-base"
+bert_path = "GPT_SoVITS\pretrained_models\chinese-roberta-wwm-ext-large"
+os.environ["version"] = 'v2'
+now_dir = os.getcwd()
+sys.path.insert(0, now_dir)
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+from pathlib import Path
+import os,librosa,torch
+from scipy.io.wavfile import write as wavwrite
+from GPT_SoVITS.feature_extractor import cnhubert
+cnhubert.cnhubert_base_path=cnhubert_base_path
+from GPT_SoVITS.module.models import SynthesizerTrn
+from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from GPT_SoVITS.text import cleaned_text_to_sequence
+from GPT_SoVITS.text.cleaner import clean_text
+from time import time as ttime
+from GPT_SoVITS.module.mel_processing import spectrogram_torch
+import tempfile
+from tools.my_utils import load_audio
+import os
+import json
+
+################ End strange import and user.pth modification ################
+
+# import pyopenjtalk
+# cwd = os.getcwd()
+# if os.path.exists(os.path.join(cwd,'user.dic')):
+#     pyopenjtalk.update_global_jtalk_with_user_dict(os.path.join(cwd, 'user.dic'))
+
+
+import logging
+logging.getLogger('httpx').setLevel(logging.WARNING)
+logging.getLogger('httpcore').setLevel(logging.WARNING)
+logging.getLogger('multipart').setLevel(logging.WARNING)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#device = "cpu"
+is_half = False
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model=AutoModelForMaskedLM.from_pretrained(bert_path)
+if(is_half==True):bert_model=bert_model.half().to(device)
+else:bert_model=bert_model.to(device)
+# bert_model=bert_model.to(device)
+def get_bert_feature(text, word2ph): # Bert(不是HuBERT的特征计算)
+    with torch.no_grad():
+        inputs = tokenizer(text, return_tensors="pt")
+        for i in inputs:
+            inputs[i] = inputs[i].to(device)#####输入是long不用管精度问题，精度随bert_model
+        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)
+    # if(is_half==True):phone_level_feature=phone_level_feature.half()
+    return phone_level_feature.T
+
+loaded_sovits_model = [] # [(path, dict, model)]
+loaded_gpt_model = []
+ssl_model = cnhubert.get_model()
+if (is_half == True):
+    ssl_model = ssl_model.half().to(device)
+else:
+    ssl_model = ssl_model.to(device)
+
+
+def load_model(sovits_path, gpt_path):
+    global ssl_model
+    global loaded_sovits_model
+    global loaded_gpt_model
+    vq_model = None
+    t2s_model = None
+    dict_s2 = None
+    dict_s1 = None
+    hps = None
+    for path, dict_s2_, model in loaded_sovits_model:
+        if path == sovits_path:
+            vq_model = model
+            dict_s2 = dict_s2_
+            break
+    for path, dict_s1_, model in loaded_gpt_model:
+        if path == gpt_path:
+            t2s_model = model
+            dict_s1 = dict_s1_
+            break
+    
+    if dict_s2 is None:
+        dict_s2 = torch.load(sovits_path, map_location="cpu")
+    hps = dict_s2["config"]
+
+    if dict_s1 is None:
+        dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    class DictToAttrRecursive:
+        def __init__(self, input_dict):
+            for key, value in input_dict.items():
+                if isinstance(value, dict):
+                    # 如果值是字典，递归调用构造函数
+                    setattr(self, key, DictToAttrRecursive(value))
+                else:
+                    setattr(self, key, value)
+
+    hps = DictToAttrRecursive(hps)
+    hps.model.semantic_frame_rate = "25hz"
+
+
+    if not vq_model:
+        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)
+        else:
+            vq_model = vq_model.to(device)
+        vq_model.eval()
+        vq_model.load_state_dict(dict_s2["weight"], strict=False)
+        loaded_sovits_model.append((sovits_path, dict_s2, vq_model))
+    hz = 50
+    max_sec = config['data']['max_sec']
+    if not t2s_model:
+        t2s_model = Text2SemanticLightningModule(config, "ojbk", is_train=False)
+        t2s_model.load_state_dict(dict_s1["weight"])
+        if (is_half == True): t2s_model = t2s_model.half()
+        t2s_model = t2s_model.to(device)
+        t2s_model.eval()
+        total = sum([param.nelement() for param in t2s_model.parameters()])
+        loaded_gpt_model.append((gpt_path, dict_s1, t2s_model))
+    return vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
+
+
+def get_spepc(hps, filename):
+    audio=load_audio(filename,int(hps.data.sampling_rate)) 
+    audio = audio / np.max(np.abs(audio))
+    audio=torch.FloatTensor(audio)
+    audio_norm = audio
+    # audio_norm = audio / torch.max(torch.abs(audio))
+    audio_norm = audio_norm.unsqueeze(0)
+    spec = spectrogram_torch(audio_norm, hps.data.filter_length,hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,center=False)
+    return spec
+
+def create_tts_fn(vq_model, ssl_model, t2s_model, hps, config, hz, max_sec):
+    def tts_fn(ref_wav_path, prompt_text, prompt_language, target_phone, text_language, target_text = None):
+        t0 = ttime()
+        prompt_text=prompt_text.strip()
+        prompt_language=prompt_language
+        with torch.no_grad():
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000, mono=False)
+            direction = np.array([1,1])
+            if wav16k.ndim == 2:
+                power = np.sum(np.abs(wav16k) ** 2, axis=1)
+                direction = power / np.sum(power)
+                wav16k = (wav16k[0] + wav16k[1]) / 2
+            # 
+            # maxx=0.95
+            # tmp_max = np.abs(wav16k).max()
+            # alpha=0.5
+            # wav16k = (wav16k / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * wav16k
+            #在这里归一化
+            #print(max(np.abs(wav16k)))
+            #wav16k = wav16k / np.max(np.abs(wav16k))
+            #print(max(np.abs(wav16k)))
+            # 添加0.3s的静音
+            wav16k = np.concatenate([wav16k, np.zeros(int(hps.data.sampling_rate * 0.3)),])
+            wav16k = torch.from_numpy(wav16k)
+            wav16k = wav16k.float()
+            if(is_half==True):wav16k=wav16k.half().to(device)
+            else:wav16k=wav16k.to(device)
+            ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)#.float()
+            codes = vq_model.extract_latent(ssl_content)
+            prompt_semantic = codes[0, 0]
+        t1 = ttime()
+        phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
+        phones1=cleaned_text_to_sequence(phones1)
+        #texts=text.split("\n")
+        audio_opt = []
+        zero_wav=np.zeros((2, int(hps.data.sampling_rate*0.3)),dtype=np.float16 if is_half==True else np.float32)
+        phones = get_phone_from_str_list(target_phone, text_language)
+        for phones2 in phones:
+            if(len(phones2) == 0):
+                continue
+            if(len(phones2) == 1 and phones2[0] == ""):
+                continue
+            #phones2, word2ph2, norm_text2 = clean_text(text, text_language)
+            phones2 = cleaned_text_to_sequence(phones2)
+            #if(prompt_language=="zh"):bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
+            bert1 = torch.zeros((1024, len(phones1)),dtype=torch.float16 if is_half==True else torch.float32).to(device)
+            #if(text_language=="zh"):bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
+            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
+            bert = torch.cat([bert1, bert2], 1)
+
+            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)
+            t2 = ttime()
+            idx = 0
+            cnt = 0
+            while idx == 0 and cnt < 2:
+                with torch.no_grad():
+                    # pred_semantic = t2s_model.model.infer
+                    pred_semantic,idx = t2s_model.model.infer_panel(
+                        all_phoneme_ids,
+                        all_phoneme_len,
+                        prompt,
+                        bert,
+                        # prompt_phone_len=ph_offset,
+                        top_k=config['inference']['top_k'],
+                        early_stop_num=hz * max_sec)
+                t3 = ttime()
+                cnt+=1
+            if idx == 0:
+                return "Error: Generation failure: bad zero prediction.", None
+            pred_semantic = pred_semantic[:,-idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
+            refer = get_spepc(hps, ref_wav_path)#.to(device)
+            if(is_half==True):refer=refer.half().to(device)
+            else:refer=refer.to(device)
+            # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
+            audio = vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer).detach().cpu().numpy()[0, 0]###试试重建不带上prompt部分
+            # direction乘上，变双通道
+            # 强制0.5
+            direction = np.array([1, 1])
+            audio = np.expand_dims(audio, 0) * direction[:, np.newaxis]
+            audio_opt.append(audio)
+            audio_opt.append(zero_wav)
+            t4 = ttime()
+
+        audio = (hps.data.sampling_rate,(np.concatenate(audio_opt, axis=1)*32768).astype(np.int16).T)
+        prefix_1 = prompt_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_")
+        prefix_2 = target_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_")
+        filename = tempfile.mktemp(suffix=".wav",prefix=f"{prefix_1}_{prefix_2}_")
+        #audiosegment.from_numpy_array(audio[1].T, framerate=audio[0]).export(filename, format="WAV")
+        wavwrite(filename, audio[0], audio[1])
+        return "Success", audio, filename
+    return tts_fn
+
+
+def get_str_list_from_phone(text, text_language):
+    # raw文本过g2p得到音素列表，再转成字符串
+    # 注意，这里的text是一个段落，可能包含多个句子
+    # 段落间\n分割，音素间空格分割
+    print(text)
+    texts=text.split("\n")
+    phone_list = []
+    for text in texts:
+        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
+        phone_list.append(" ".join(phones2))
+    return "\n".join(phone_list)
+
+def get_phone_from_str_list(str_list:str, language:str = 'ja'):
+    # 从音素字符串中得到音素列表
+    # 注意，这里的text是一个段落，可能包含多个句子
+    # 段落间\n分割，音素间空格分割
+    sentences = str_list.split("\n")
+    phones = []
+    for sentence in sentences:
+        phones.append(sentence.split(" "))
+    return phones
+
+splits={"，","。","？","！",",",".","?","!","~",":","：","—","…",}#不考虑省略号
+def split(todo_text):
+    todo_text = todo_text.replace("……", "。").replace("——", "，")
+    if (todo_text[-1] not in splits): todo_text += "。"
+    i_split_head = i_split_tail = 0
+    len_text = len(todo_text)
+    todo_texts = []
+    while (1):
+        if (i_split_head >= len_text): break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+        if (todo_text[i_split_head] in splits):
+            i_split_head += 1
+            todo_texts.append(todo_text[i_split_tail:i_split_head])
+            i_split_tail = i_split_head
+        else:
+            i_split_head += 1
+    return todo_texts
+
+
+def change_reference_audio(prompt_text, transcripts):
+    return transcripts[prompt_text]
+
+
+models = []
+models_info = json.load(open("./models/models_info.json", "r", encoding="utf-8")) 
+
+
+
+for i, info in models_info.items():
+    title = info['title']
+    cover = info['cover']
+    gpt_weight = info['gpt_weight']
+    sovits_weight = info['sovits_weight']
+    example_reference = info['example_reference']
+    transcripts = {}
+    transcript_path = info["transcript_path"]
+    path = os.path.dirname(transcript_path)
+    with open(transcript_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            line = line.strip().replace("\\", "/")
+            items = line.split("|")
+            wav,t = items[0], items[-1]
+            wav = os.path.basename(wav)
+            transcripts[t] = os.path.join(os.path.join(path,"reference_audio"), wav)
+
+    vq_model, ssl_model, t2s_model, hps, config, hz, max_sec = load_model(sovits_weight, gpt_weight)
+
+
+    models.append(
+        (
+            i,
+            title,
+            cover,
+            transcripts,
+            example_reference,
+            create_tts_fn(
+                vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
+            )
+        )
+    )
+with gr.Blocks() as app:
+    gr.Markdown(
+        "# <center> GPT-SoVITS Demo\n"
+    )
+    with gr.Tabs():
+        for (name, title, cover, transcripts, example_reference, tts_fn) in models:
+            with gr.TabItem(name):
+                with gr.Row():
+                    gr.Markdown(
+                        '<div align="center">'
+                        f'<a><strong>{title}</strong></a>'
+                        '</div>')
+                with gr.Row():
+                    with gr.Column():
+                        prompt_text = gr.Dropdown(
+                            label="Transcript of the Reference Audio",
+                            value=example_reference if example_reference in transcripts else list(transcripts.keys())[0],
+                            choices=list(transcripts.keys())
+                        )
+                        inp_ref_audio = gr.Audio(
+                            label="Reference Audio",
+                            type="filepath",
+                            interactive=False,
+                            value=transcripts[example_reference] if example_reference in transcripts else list(transcripts.values())[0]
+                        )
+                        transcripts_state = gr.State(value=transcripts)
+                        prompt_text.change(
+                            fn=change_reference_audio,
+                            inputs=[prompt_text, transcripts_state],
+                            outputs=[inp_ref_audio]
+                        )
+                        prompt_language = gr.State(value="ja")
+                    with gr.Column():
+                        text = gr.Textbox(label="Input Text", value="私はお兄ちゃんのだいだいだーいすきな妹なんだから、言うことなんでも聞いてくれますよね！")
+                        text_language = gr.Dropdown(
+                            label="Language",
+                            choices=["ja"],
+                            value="ja"
+                        )
+                        clean_button = gr.Button("Clean Text", variant="primary")
+                        inference_button = gr.Button("Generate", variant="primary")
+                        cleaned_text = gr.Textbox(label="Cleaned Text")
+                        output = gr.Audio(label="Output Audio")
+                        output_file = gr.File(label="Output Audio File")
+                        om = gr.Textbox(label="Output Message")
+                        clean_button.click(
+                            fn=get_str_list_from_phone,
+                            inputs=[text, text_language],
+                            outputs=[cleaned_text]
+                        )
+                        inference_button.click(
+                            fn=tts_fn,
+                            inputs=[inp_ref_audio, prompt_text, prompt_language, cleaned_text, text_language, text],
+                            outputs=[om, output, output_file]
+                        )
+
 app.launch(share=True)