Update inference_webui.py

To implement recursive construction while retaining the characteristics of the original dictionary, we can slightly modify the DictToAttrRecursive class. This allows each object to retain its characteristics as a dictionary while accessing keys and values as attributes.

GPT_SoVITS/inference_webui.py

@@ -1,34 +1,22 @@
 import os
-gpt_path=os.environ.get("gpt_path","pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
-sovits_path=os.environ.get("sovits_path","pretrained_models/s2G488k.pth")
-cnhubert_base_path=os.environ.get("cnhubert_base_path","pretrained_models/chinese-hubert-base")
-bert_path=os.environ.get("bert_path","pretrained_models/chinese-roberta-wwm-ext-large")
-infer_ttswebui=os.environ.get("infer_ttswebui",9872)
-infer_ttswebui=int(infer_ttswebui)
+gpt_path=os.environ.get("gpt_path","./pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
+sovits_path=os.environ.get("sovits_path","./pretrained_models/s2G488k.pth")
+cnhubert_base_path=os.environ.get("cnhubert_base_path","./pretrained_models/chinese-hubert-base")
+bert_path=os.environ.get("bert_path","./pretrained_models/chinese-roberta-wwm-ext-large")
 if("_CUDA_VISIBLE_DEVICES"in os.environ):
     os.environ["CUDA_VISIBLE_DEVICES"]=os.environ["_CUDA_VISIBLE_DEVICES"]
 is_half=eval(os.environ.get("is_half","True"))
 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
+import numpy as np
+import librosa,torch
 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 text.cleaner import clean_text
 from time import time as ttime
 from module.mel_processing import spectrogram_torch
 from my_utils import load_audio
@@ -58,14 +46,33 @@ def get_bert_feature(text, word2ph):
 n_semantic = 1024
 dict_s2=torch.load(sovits_path,map_location="cpu")
 hps=dict_s2["config"]
-class DictToAttrRecursive:
+
+class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
+        super().__init__(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)
+                value = DictToAttrRecursive(value)
+            self[key] = value
+            setattr(self, key, value)
+
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
 
 hps = DictToAttrRecursive(hps)
 hps.model.semantic_frame_rate="25hz"
@@ -130,9 +137,9 @@ def get_tts_wav(ref_wav_path,prompt_text,prompt_language,text,text_language):
     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)
+        if(prompt_language=="zh"):bert1 = get_bert_feature(norm_text1, word2ph1)
         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)
+        if(text_language=="zh"):bert2 = get_bert_feature(norm_text2, word2ph2)
         else:bert2 = torch.zeros((1024, len(phones2))).to(bert1)
         bert = torch.cat([bert1, bert2], 1)
 
@@ -234,14 +241,14 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
         with gr.Row():
             inp_ref = gr.Audio(label="请上传参考音频", type="filepath")
             prompt_text= gr.Textbox(label="参考音频的文本",value="")
-            prompt_language= gr.Dropdown(label="参考音频的语种",choices=["中文","英文","日文"],value="中文")
+            prompt_language= gr.Dropdown(label="参考音频的语种",choices=["中文","英文","日文"])
         gr.Markdown(
             value=
                 "*请填写需要合成的目标文本"
         )
         with gr.Row():
             text=gr.Textbox(label="需要合成的文本",value="")
-            text_language = gr.Dropdown(label="需要合成的语种", choices=["中文", "英文", "日文"],value="中文")
+            text_language = gr.Dropdown(label="需要合成的语种", choices=["中文", "英文", "日文"])
             inference_button=gr.Button("合成语音", variant="primary")
             output = gr.Audio(label="输出的语音")
         inference_button.click(get_tts_wav, [inp_ref, prompt_text,prompt_language, text,text_language], [output])
@@ -267,6 +274,6 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
 app.queue(concurrency_count=511, max_size=1022).launch(
     server_name="0.0.0.0",
     inbrowser=True,
-    server_port=infer_ttswebui,
+    server_port=9872,
     quiet=True,
 )