Correct imports and parameter handling for api.py

- Fix import errors
- Resolve parameter assignment issue for DefaultRefer class
- Add missing key argument to librosa.load() call
- Complement parameter passing in initialization section

api.py

@@ -150,7 +150,7 @@ sys.path.append(now_dir)
 sys.path.append("%s/GPT_SoVITS" % (now_dir))
 
 import signal
-from text.LangSegmenter import LangSegmenter
+from GPT_SoVITS.text.LangSegmenter import LangSegmenter
 from time import time as ttime
 import torch, torchaudio
 import librosa
@@ -160,14 +160,14 @@ from fastapi.responses import StreamingResponse, JSONResponse
 import uvicorn
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 import numpy as np
-from feature_extractor import cnhubert
+from GPT_SoVITS.feature_extractor import cnhubert
 from io import BytesIO
-from module.models import SynthesizerTrn, SynthesizerTrnV3
+from GPT_SoVITS.module.models import SynthesizerTrn, SynthesizerTrnV3
 from peft import LoraConfig, PeftModel, get_peft_model
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from text import cleaned_text_to_sequence
+from GPT_SoVITS.text import cleaned_text_to_sequence
-from text.cleaner import clean_text
+from GPT_SoVITS.text.cleaner import clean_text
-from module.mel_processing import spectrogram_torch
+from GPT_SoVITS.module.mel_processing import spectrogram_torch
 from tools.my_utils import load_audio
 import config as global_config
 import logging
@@ -176,9 +176,9 @@ import subprocess
 
 class DefaultRefer:
     def __init__(self, path, text, language):
-        self.path = args.default_refer_path
+        self.path = path
-        self.text = args.default_refer_text
+        self.text = text
-        self.language = args.default_refer_language
+        self.language = language
 
     def is_ready(self) -> bool:
         return is_full(self.path, self.text, self.language)
@@ -200,7 +200,7 @@ def is_full(*items):  # 任意一项为空返回False
 
 def init_bigvgan():
     global bigvgan_model
-    from BigVGAN import bigvgan
+    from GPT_SoVITS.BigVGAN import bigvgan
     bigvgan_model = bigvgan.BigVGAN.from_pretrained("%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), use_cuda_kernel=False)  # if True, RuntimeError: Ninja is required to load C++ extensions
     # remove weight norm in the model and set to eval mode
     bigvgan_model.remove_weight_norm()
@@ -221,7 +221,7 @@ def resample(audio_tensor, sr0):
     return resample_transform_dict[sr0](audio_tensor)
 
 
-from module.mel_processing import spectrogram_torch,mel_spectrogram_torch
+from GPT_SoVITS.module.mel_processing import spectrogram_torch,mel_spectrogram_torch
 spec_min = -12
 spec_max = 2
 def norm_spec(x):
@@ -240,6 +240,34 @@ mel_fn=lambda x: mel_spectrogram_torch(x, **{
 })
 
 
+class DictToAttrRecursive(dict):
+    def __init__(self, input_dict):
+        super().__init__(input_dict)
+        for key, value in input_dict.items():
+            if isinstance(value, dict):
+                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")
+
+
 sr_model=None
 def audio_sr(audio,sr):
     global sr_model
@@ -270,7 +298,7 @@ class Sovits:
         self.vq_model = vq_model
         self.hps = hps
 
-from process_ckpt import get_sovits_version_from_path_fast,load_sovits_new
+from GPT_SoVITS.process_ckpt import get_sovits_version_from_path_fast,load_sovits_new
 def get_sovits_weights(sovits_path):
     path_sovits_v3="GPT_SoVITS/pretrained_models/s2Gv3.pth"
     is_exist_s2gv3=os.path.exists(path_sovits_v3)
@@ -340,6 +368,7 @@ def get_sovits_weights(sovits_path):
     sovits = Sovits(vq_model, hps)
     return sovits
 
+
 class Gpt:
     def __init__(self, max_sec, t2s_model):
         self.max_sec = max_sec
@@ -363,6 +392,7 @@ def get_gpt_weights(gpt_path):
     gpt = Gpt(max_sec, t2s_model)
     return gpt
 
+
 def change_gpt_sovits_weights(gpt_path,sovits_path):
     try:
         gpt = get_gpt_weights(gpt_path)
@@ -410,7 +440,8 @@ def get_bert_inf(phones, word2ph, norm_text, language):
 
     return bert
 
-from text import chinese
+
+from GPT_SoVITS.text import chinese
 def get_phones_and_bert(text,language,version,final=False):
     if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
         formattext = text
@@ -475,36 +506,8 @@ def get_phones_and_bert(text,language,version,final=False):
     return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
 
 
-class DictToAttrRecursive(dict):
-    def __init__(self, input_dict):
-        super().__init__(input_dict)
-        for key, value in input_dict.items():
-            if isinstance(value, dict):
-                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")
-
-
 def get_spepc(hps, filename):
-    audio,_ = librosa.load(filename, int(hps.data.sampling_rate))
+    audio,_ = librosa.load(filename, sr=int(hps.data.sampling_rate))
     audio = torch.FloatTensor(audio)
     maxx=audio.abs().max()
     if(maxx>1):
@@ -934,15 +937,23 @@ parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, h
 args = parser.parse_args()
 sovits_path = args.sovits_path
 gpt_path = args.gpt_path
+default_refer_path = args.default_refer_path
+default_refer_text = args.default_refer_text
+default_refer_language = args.default_refer_language
 device = args.device
 port = args.port
 host = args.bind_addr
+full_precision = args.full_precision
+half_precision = args.half_precision
+stream_mode = args.stream_mode
+media_type = args.media_type
+sub_type = args.sub_type
+default_cut_punc = args.cut_punc
 cnhubert_base_path = args.hubert_path
 bert_path = args.bert_path
-default_cut_punc = args.cut_punc
 
 # 应用参数配置
-default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
+default_refer = DefaultRefer(default_refer_path, default_refer_text, default_refer_language)
 
 # 模型路径检查
 if sovits_path == "":
@@ -963,24 +974,24 @@ else:
 
 # 获取半精度
 is_half = g_config.is_half
-if args.full_precision:
+if full_precision:
     is_half = False
-if args.half_precision:
+if half_precision:
     is_half = True
-if args.full_precision and args.half_precision:
+if full_precision and half_precision:
     is_half = g_config.is_half  # 炒饭fallback
 logger.info(f"半精: {is_half}")
 
 # 流式返回模式
-if args.stream_mode.lower() in ["normal","n"]:
+if stream_mode.lower() in ["normal","n"]:
     stream_mode = "normal"
     logger.info("流式返回已开启")
 else:
     stream_mode = "close"
 
 # 音频编码格式
-if args.media_type.lower() in ["aac","ogg"]:
+if media_type.lower() in ["aac","ogg"]:
-    media_type = args.media_type.lower()
+    media_type = media_type.lower()
 elif stream_mode == "close":
     media_type = "wav"
 else:
@@ -988,7 +999,7 @@ else:
 logger.info(f"编码格式: {media_type}")
 
 # 音频数据类型
-if args.sub_type.lower() == 'int32':
+if sub_type.lower() == 'int32':
     is_int32 = True
     logger.info(f"数据类型: int32")
 else: