Update app.py

app.py

@@ -1,307 +1,313 @@
-import os
-from huggingface_hub import snapshot_download
-snapshot_download(repo_id="None1145/GPT-SoVITS-Lappland-the-Decadenza", cache_dir="./Models")
-snapshot_download(repo_id="None1145/GPT-SoVITS-Base", cache_dir="./PretrainedModels")
-cnhubert_base_path = "PretrainedModels/chinese-hubert-base"
-bert_path = "PretrainedModels/chinese-roberta-wwm-ext-large"
-
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import sys,torch,numpy as np
-from pathlib import Path
-import os,pdb,utils,librosa,math,traceback,requests,argparse,torch,multiprocessing,pandas as pd,torch.multiprocessing as mp,soundfile
-# torch.backends.cuda.sdp_kernel("flash")
-# torch.backends.cuda.enable_flash_sdp(True)
-# torch.backends.cuda.enable_mem_efficient_sdp(True)  # Not avaliable if torch version is lower than 2.0
-# torch.backends.cuda.enable_math_sdp(True)
-from random import shuffle
-from AR.utils import get_newest_ckpt
-from glob import glob
-from tqdm import tqdm
-from feature_extractor import cnhubert
-cnhubert.cnhubert_base_path=cnhubert_base_path
-from io import BytesIO
-from module.models import SynthesizerTrn
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from AR.utils.io import load_yaml_config
-from text import cleaned_text_to_sequence
-from text.cleaner import text_to_sequence, clean_text
-from time import time as ttime
-from module.mel_processing import spectrogram_torch
-from my_utils import load_audio
-import re
-
-import logging
-logging.getLogger('httpx').setLevel(logging.WARNING)
-logging.getLogger('httpcore').setLevel(logging.WARNING)
-logging.getLogger('multipart').setLevel(logging.WARNING)
-
-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):
-    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
-
-
-def load_model(sovits_path, gpt_path):
-    n_semantic = 1024
-    dict_s2 = torch.load(sovits_path, map_location="cpu")
-    hps = dict_s2["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"
-    dict_s1 = torch.load(gpt_path, map_location="cpu")
-    config = dict_s1["config"]
-    ssl_model = cnhubert.get_model()
-    if (is_half == True):
-        ssl_model = ssl_model.half().to(device)
-    else:
-        ssl_model = ssl_model.to(device)
-
-    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)
-    hz = 50
-    max_sec = config['data']['max_sec']
-    # t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
-    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()])
-    print("Number of parameter: %.2fM" % (total / 1e6))
-    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=torch.FloatTensor(audio)
-    audio_norm = 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, text, text_language):
-        t0 = ttime()
-        prompt_text=prompt_text.strip("\n")
-        prompt_language,text=prompt_language,text.strip("\n")
-        print(text)
-        if len(text) > 50:
-            return f"Error: Text is too long, ({len(text)}>50)", None
-        with torch.no_grad():
-            wav16k, sr = librosa.load(ref_wav_path, sr=16000)  # 派蒙
-            wav16k = torch.from_numpy(wav16k)
-            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(int(hps.data.sampling_rate*0.3),dtype=np.float16 if is_half==True else np.float32)
-        for text in texts:
-            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)
-            else: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)
-            else: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()
-            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()
-            # print(pred_semantic.shape,idx)
-            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
-            audio_opt.append(audio)
-            audio_opt.append(zero_wav)
-            t4 = ttime()
-        print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
-        return "Success", (hps.data.sampling_rate,(np.concatenate(audio_opt,0)*32768).astype(np.int16))
-    return tts_fn
-
-
-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 = {}
-models_folder_path = "./Models/None1145"
-folder_names = [name for name in os.listdir(models_folder_path) if os.path.isdir(os.path.join(models_folder_path, name))]
-for folder_name in folder_names:
-    speaker = folder_name[11:]
-    models_info[speaker] = {}
-    models_info[speaker]["title"] = speaker
-    pattern = re.compile(r"s(\d+)\.pth$")
-    max_value = -1
-    max_file = None
-    sovits_path = f"{models_folder_path}/{folder_name}/SoVITS_weights"
-    for filename in os.listdir(sovits_path):
-        match = pattern.search(filename)
-        if match:
-            value = int(match.group(1))
-            if value > max_value:
-                max_value = value
-                max_file = filename
-    models_info[speaker]["sovits_weight"] = f"{sovits_path}/{max_file}"
-    pattern = re.compile(r"e(\d+)\.ckpt$")
-    max_value = -1
-    max_file = None
-    gpt_path = f"{models_folder_path}/{folder_name}/GPT_weights"
-    for filename in os.listdir(gpt_path):
-        match = pattern.search(filename)
-        if match:
-            value = int(match.group(1))
-            if value > max_value:
-                max_value = value
-                max_file = filename
-    models_info[speaker]["gpt_weight"] = f"{gpt_path}/{max_file}"
-    data_path = f"{models_folder_path}/{folder_name}/Data"
-    models_info[speaker]["transcript"] = {}
-    with open(f"{data_path}/{speaker}.list", "r", encoding="utf-8") as f:
-        for line in f.read().split("\n"):
-            wav = f"{models_folder_path}/{folder_name}/Data/{line.split("|")[0].split("/")[1]}"
-            text = line.split("|")[3]
-            models_info[speaker]["transcript"][text] = wav
-            models_info[speaker]["example_reference"] = text
-for speaker in models_info:
-    speaker_info = models_info[speaker]
-    title = speaker_info["title"]
-    sovits_weight = speaker_info["sovits_weight"]
-    gpt_weight = speaker_info["gpt_weight"]
-    vq_model, ssl_model, t2s_model, hps, config, hz, max_sec = load_model(sovits_weight, gpt_weight)
-    models.append(
-        (
-            speaker,
-            title,
-            speaker_info["transcript"],
-            speaker_info["example_reference"],
-            create_tts_fn(
-                vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
-            )
-        )
-    )
-
-with gr.Blocks() as app:
-    with gr.Tabs():
-        for (name, title, transcript, 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,
-                            choices=list(transcript.keys())
-                        )
-                        inp_ref_audio = gr.Audio(
-                            label="Reference Audio",
-                            type="filepath",
-                            interactive=False,
-                            value=transcript[example_reference]
-                        )
-                        transcripts_state = gr.State(value=transcript)
-                        prompt_text.change(
-                            fn=change_reference_audio,
-                            inputs=[prompt_text, transcripts_state],
-                            outputs=[inp_ref_audio]
-                        )
-                        prompt_language = gr.State(value="zh")
-                    with gr.Column():
-                        text = gr.Textbox(label="Input Text", value="你好。")
-                        text_language = gr.Dropdown(
-                            label="Language",
-                            choices=["zh", "en", "ja"],
-                            value="ja"
-                        )
-                        inference_button = gr.Button("Generate", variant="primary")
-                        om = gr.Textbox(label="Output Message")
-                        output = gr.Audio(label="Output Audio")
-                        inference_button.click(
-                            fn=tts_fn,
-                            inputs=[inp_ref_audio, prompt_text, prompt_language, text, text_language],
-                            outputs=[om, output]
-                        )
-
+import os
+from huggingface_hub import snapshot_download
+print("Models...")
+snapshot_download(repo_id="None1145/GPT-SoVITS-Lappland-the-Decadenza", cache_dir="./Models")
+print("Models!!!")
+print("PretrainedModels...")
+snapshot_download(repo_id="None1145/GPT-SoVITS-Base", cache_dir="./PretrainedModels")
+print("PretrainedModels!!!")
+cnhubert_base_path = "PretrainedModels/chinese-hubert-base"
+bert_path = "PretrainedModels/chinese-roberta-wwm-ext-large"
+
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import sys,torch,numpy as np
+from pathlib import Path
+import os,pdb,utils,librosa,math,traceback,requests,argparse,torch,multiprocessing,pandas as pd,torch.multiprocessing as mp,soundfile
+# torch.backends.cuda.sdp_kernel("flash")
+# torch.backends.cuda.enable_flash_sdp(True)
+# torch.backends.cuda.enable_mem_efficient_sdp(True)  # Not avaliable if torch version is lower than 2.0
+# torch.backends.cuda.enable_math_sdp(True)
+from random import shuffle
+from AR.utils import get_newest_ckpt
+from glob import glob
+from tqdm import tqdm
+from feature_extractor import cnhubert
+cnhubert.cnhubert_base_path=cnhubert_base_path
+from io import BytesIO
+from module.models import SynthesizerTrn
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from AR.utils.io import load_yaml_config
+from text import cleaned_text_to_sequence
+from text.cleaner import text_to_sequence, clean_text
+from time import time as ttime
+from module.mel_processing import spectrogram_torch
+from my_utils import load_audio
+import re
+
+import logging
+logging.getLogger('httpx').setLevel(logging.WARNING)
+logging.getLogger('httpcore').setLevel(logging.WARNING)
+logging.getLogger('multipart').setLevel(logging.WARNING)
+
+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):
+    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
+
+
+def load_model(sovits_path, gpt_path):
+    n_semantic = 1024
+    dict_s2 = torch.load(sovits_path, map_location="cpu")
+    hps = dict_s2["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"
+    dict_s1 = torch.load(gpt_path, map_location="cpu")
+    config = dict_s1["config"]
+    ssl_model = cnhubert.get_model()
+    if (is_half == True):
+        ssl_model = ssl_model.half().to(device)
+    else:
+        ssl_model = ssl_model.to(device)
+
+    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)
+    hz = 50
+    max_sec = config['data']['max_sec']
+    # t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
+    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()])
+    print("Number of parameter: %.2fM" % (total / 1e6))
+    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=torch.FloatTensor(audio)
+    audio_norm = 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, text, text_language):
+        t0 = ttime()
+        prompt_text=prompt_text.strip("\n")
+        prompt_language,text=prompt_language,text.strip("\n")
+        print(text)
+        if len(text) > 50:
+            return f"Error: Text is too long, ({len(text)}>50)", None
+        with torch.no_grad():
+            wav16k, sr = librosa.load(ref_wav_path, sr=16000)  # 派蒙
+            wav16k = torch.from_numpy(wav16k)
+            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(int(hps.data.sampling_rate*0.3),dtype=np.float16 if is_half==True else np.float32)
+        for text in texts:
+            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)
+            else: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)
+            else: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()
+            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()
+            # print(pred_semantic.shape,idx)
+            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
+            audio_opt.append(audio)
+            audio_opt.append(zero_wav)
+            t4 = ttime()
+        print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+        return "Success", (hps.data.sampling_rate,(np.concatenate(audio_opt,0)*32768).astype(np.int16))
+    return tts_fn
+
+
+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 = {}
+models_folder_path = "./Models/None1145"
+folder_names = [name for name in os.listdir(models_folder_path) if os.path.isdir(os.path.join(models_folder_path, name))]
+for folder_name in folder_names:
+    speaker = folder_name[11:]
+    models_info[speaker] = {}
+    models_info[speaker]["title"] = speaker
+    pattern = re.compile(r"s(\d+)\.pth$")
+    max_value = -1
+    max_file = None
+    sovits_path = f"{models_folder_path}/{folder_name}/SoVITS_weights"
+    for filename in os.listdir(sovits_path):
+        match = pattern.search(filename)
+        if match:
+            value = int(match.group(1))
+            if value > max_value:
+                max_value = value
+                max_file = filename
+    models_info[speaker]["sovits_weight"] = f"{sovits_path}/{max_file}"
+    pattern = re.compile(r"e(\d+)\.ckpt$")
+    max_value = -1
+    max_file = None
+    gpt_path = f"{models_folder_path}/{folder_name}/GPT_weights"
+    for filename in os.listdir(gpt_path):
+        match = pattern.search(filename)
+        if match:
+            value = int(match.group(1))
+            if value > max_value:
+                max_value = value
+                max_file = filename
+    models_info[speaker]["gpt_weight"] = f"{gpt_path}/{max_file}"
+    data_path = f"{models_folder_path}/{folder_name}/Data"
+    models_info[speaker]["transcript"] = {}
+    with open(f"{data_path}/{speaker}.list", "r", encoding="utf-8") as f:
+        for line in f.read().split("\n"):
+            wav = f"{models_folder_path}/{folder_name}/Data/{line.split('|')[0].split('/')[1]}"
+            text = line.split("|")[3]
+            models_info[speaker]["transcript"][text] = wav
+            models_info[speaker]["example_reference"] = text
+    print(models_info)
+for speaker in models_info:
+    speaker_info = models_info[speaker]
+    title = speaker_info["title"]
+    sovits_weight = speaker_info["sovits_weight"]
+    gpt_weight = speaker_info["gpt_weight"]
+    vq_model, ssl_model, t2s_model, hps, config, hz, max_sec = load_model(sovits_weight, gpt_weight)
+    models.append(
+        (
+            speaker,
+            title,
+            speaker_info["transcript"],
+            speaker_info["example_reference"],
+            create_tts_fn(
+                vq_model, ssl_model, t2s_model, hps, config, hz, max_sec
+            )
+        )
+    )
+print(models)
+
+with gr.Blocks() as app:
+    with gr.Tabs():
+        for (name, title, transcript, 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,
+                            choices=list(transcript.keys())
+                        )
+                        inp_ref_audio = gr.Audio(
+                            label="Reference Audio",
+                            type="filepath",
+                            interactive=False,
+                            value=transcript[example_reference]
+                        )
+                        transcripts_state = gr.State(value=transcript)
+                        prompt_text.change(
+                            fn=change_reference_audio,
+                            inputs=[prompt_text, transcripts_state],
+                            outputs=[inp_ref_audio]
+                        )
+                        prompt_language = gr.State(value="zh")
+                    with gr.Column():
+                        text = gr.Textbox(label="Input Text", value="你好。")
+                        text_language = gr.Dropdown(
+                            label="Language",
+                            choices=["zh", "en", "ja"],
+                            value="ja"
+                        )
+                        inference_button = gr.Button("Generate", variant="primary")
+                        om = gr.Textbox(label="Output Message")
+                        output = gr.Audio(label="Output Audio")
+                        inference_button.click(
+                            fn=tts_fn,
+                            inputs=[inp_ref_audio, prompt_text, prompt_language, text, text_language],
+                            outputs=[om, output]
+                        )
+
 app.queue().launch()