Optimize code readability and formatted output.

api.py

@@ -105,11 +105,6 @@ RESP: 无
 import argparse
 import os
 import sys
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-sys.path.append("%s/GPT_SoVITS" % (now_dir))
-
 import signal
 import LangSegment
 from time import time as ttime
@@ -130,35 +125,7 @@ from text.cleaner import clean_text
 from module.mel_processing import spectrogram_torch
 from my_utils import load_audio
 import config as global_config
-
+import logging
-g_config = global_config.Config()
-
-# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
-
-parser = argparse.ArgumentParser(description="GPT-SoVITS api")
-
-parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
-parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
-
-parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
-parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
-parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
-
-parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
-parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
-parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
-parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
-parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
-# bool值的用法为 `python ./api.py -fp ...`
-# 此时 full_precision==True, half_precision==False
-
-parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
-parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
-
-args = parser.parse_args()
-
-sovits_path = args.sovits_path
-gpt_path = args.gpt_path
 
 
 class DefaultRefer:
@@ -171,50 +138,6 @@ class DefaultRefer:
         return is_full(self.path, self.text, self.language)
 
 
-default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
-
-device = args.device
-port = args.port
-host = args.bind_addr
-
-if sovits_path == "":
-    sovits_path = g_config.pretrained_sovits_path
-    print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
-if gpt_path == "":
-    gpt_path = g_config.pretrained_gpt_path
-    print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")
-
-# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
-if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
-    default_refer.path, default_refer.text, default_refer.language = "", "", ""
-    print("[INFO] 未指定默认参考音频")
-else:
-    print(f"[INFO] 默认参考音频路径: {default_refer.path}")
-    print(f"[INFO] 默认参考音频文本: {default_refer.text}")
-    print(f"[INFO] 默认参考音频语种: {default_refer.language}")
-
-is_half = g_config.is_half
-if args.full_precision:
-    is_half = False
-if args.half_precision:
-    is_half = True
-if args.full_precision and args.half_precision:
-    is_half = g_config.is_half  # 炒饭fallback
-
-print(f"[INFO] 半精: {is_half}")
-
-cnhubert_base_path = args.hubert_path
-bert_path = args.bert_path
-
-cnhubert.cnhubert_base_path = cnhubert_base_path
-tokenizer = AutoTokenizer.from_pretrained(bert_path)
-bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
-if is_half:
-    bert_model = bert_model.half().to(device)
-else:
-    bert_model = bert_model.to(device)
-
-
 def is_empty(*items):  # 任意一项不为空返回False
     for item in items:
         if item is not None and item != "":
@@ -228,6 +151,7 @@ def is_full(*items):  # 任意一项为空返回False
             return False
     return True
 
+
 def change_sovits_weights(sovits_path):
     global vq_model, hps
     dict_s2 = torch.load(sovits_path, map_location="cpu")
@@ -247,9 +171,7 @@ def change_sovits_weights(sovits_path):
     else:
         vq_model = vq_model.to(device)
     vq_model.eval()
-    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+    vq_model.load_state_dict(dict_s2["weight"], strict=False)
-    with open("./sweight.txt", "w", encoding="utf-8") as f:
-        f.write(sovits_path)
 
 
 def change_gpt_weights(gpt_path):
@@ -265,8 +187,7 @@ def change_gpt_weights(gpt_path):
     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))
+    logger.info("Number of parameter: %.2fM" % (total / 1e6))
-    with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
 
 
 def get_bert_feature(text, word2ph):
@@ -344,8 +265,8 @@ def get_phones_and_bert(text,language):
                     # 因无法区别中日文汉字,以用户输入为准
                     langlist.append(language)
                 textlist.append(tmp["text"])
-        print(textlist)
+        # logger.info(textlist)
-        print(langlist)
+        # logger.info(langlist)
         phones_list = []
         bert_list = []
         norm_text_list = []
@@ -363,11 +284,6 @@ def get_phones_and_bert(text,language):
     return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
 
 
-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():
@@ -378,39 +294,6 @@ class DictToAttrRecursive:
                 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:
-    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:
-    vq_model = vq_model.half().to(device)
-else:
-    vq_model = vq_model.to(device)
-vq_model.eval()
-print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
-hz = 50
-max_sec = config['data']['max_sec']
-t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
-t2s_model.load_state_dict(dict_s1["weight"])
-if is_half:
-    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))
-
-
 def get_spepc(hps, filename):
     audio = load_audio(filename, int(hps.data.sampling_rate))
     audio = torch.FloatTensor(audio)
@@ -421,22 +304,6 @@ def get_spepc(hps, filename):
     return spec
 
 
-dict_language = {
-    "中文": "all_zh",
-    "英文": "en",
-    "日文": "all_ja",
-    "中英混合": "zh",
-    "日英混合": "ja",
-    "多语种混合": "auto",    #多语种启动切分识别语种
-    "all_zh": "all_zh",
-    "en": "en",
-    "all_ja": "all_ja",
-    "zh": "zh",
-    "ja": "ja",
-    "auto": "auto",
-}
-
-
 def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
     t0 = ttime()
     prompt_text = prompt_text.strip("\n")
@@ -498,7 +365,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
         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))
+    # logger.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
     yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
 
 
@@ -521,10 +388,11 @@ def handle_change(path, text, language):
     if language != "" or language is not None:
         default_refer.language = language
 
-    print(f"[INFO] 当前默认参考音频路径: {default_refer.path}")
+    logger.info(f"当前默认参考音频路径: {default_refer.path}")
-    print(f"[INFO] 当前默认参考音频文本: {default_refer.text}")
+    logger.info(f"当前默认参考音频文本: {default_refer.text}")
-    print(f"[INFO] 当前默认参考音频语种: {default_refer.language}")
+    logger.info(f"当前默认参考音频语种: {default_refer.language}")
-    print(f"[INFO] is_ready: {default_refer.is_ready()}")
+    logger.info(f"is_ready: {default_refer.is_ready()}")
+
 
     return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
 
@@ -557,10 +425,116 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
     return StreamingResponse(wav, media_type="audio/wav")
 
 
+
+
+# --------------------------------
+# 初始化部分
+# --------------------------------
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+sys.path.append("%s/GPT_SoVITS" % (now_dir))
+
+dict_language = {
+    "中文": "all_zh",
+    "英文": "en",
+    "日文": "all_ja",
+    "中英混合": "zh",
+    "日英混合": "ja",
+    "多语种混合": "auto",    #多语种启动切分识别语种
+    "all_zh": "all_zh",
+    "en": "en",
+    "all_ja": "all_ja",
+    "zh": "zh",
+    "ja": "ja",
+    "auto": "auto",
+}
+
+# logger
+logging.config.dictConfig(uvicorn.config.LOGGING_CONFIG)
+logger = logging.getLogger('uvicorn')
+
+# 获取配置
+g_config = global_config.Config()
+
+# 获取参数
+parser = argparse.ArgumentParser(description="GPT-SoVITS api")
+
+parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
+parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
+parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
+parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
+parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
+parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
+parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
+parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
+parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
+parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
+# bool值的用法为 `python ./api.py -fp ...`
+# 此时 full_precision==True, half_precision==False
+parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
+parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
+
+args = parser.parse_args()
+sovits_path = args.sovits_path
+gpt_path = args.gpt_path
+device = args.device
+port = args.port
+host = args.bind_addr
+cnhubert_base_path = args.hubert_path
+bert_path = args.bert_path
+
+# 应用参数配置
+default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
+
+# 模型路径检查
+if sovits_path == "":
+    sovits_path = g_config.pretrained_sovits_path
+    logger.warn(f"未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
+if gpt_path == "":
+    gpt_path = g_config.pretrained_gpt_path
+    logger.warn(f"未指定GPT模型路径, fallback后当前值: {gpt_path}")
+
+# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
+if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
+    default_refer.path, default_refer.text, default_refer.language = "", "", ""
+    logger.info("未指定默认参考音频")
+else:
+    logger.info(f"默认参考音频路径: {default_refer.path}")
+    logger.info(f"默认参考音频文本: {default_refer.text}")
+    logger.info(f"默认参考音频语种: {default_refer.language}")
+
+# 获取半精度
+is_half = g_config.is_half
+if args.full_precision:
+    is_half = False
+if args.half_precision:
+    is_half = True
+if args.full_precision and args.half_precision:
+    is_half = g_config.is_half  # 炒饭fallback
+logger.info(f"半精: {is_half}")
+
+# 初始化模型
+cnhubert.cnhubert_base_path = cnhubert_base_path
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+ssl_model = cnhubert.get_model()
+if is_half:
+    bert_model = bert_model.half().to(device)
+    ssl_model = ssl_model.half().to(device)
+else:
+    bert_model = bert_model.to(device)
+    ssl_model = ssl_model.to(device)
+change_sovits_weights(sovits_path)
+change_gpt_weights(gpt_path)
+
+
+
+
+# --------------------------------
+# 接口部分
+# --------------------------------
 app = FastAPI()
 
-#clark新增-----2024-02-21
-#可在启动后动态修改模型，以此满足同一个api不同的朗读者请求
 @app.post("/set_model")
 async def set_model(request: Request):
     json_post_raw = await request.json()
@@ -568,11 +542,11 @@ async def set_model(request: Request):
     gpt_path=json_post_raw.get("gpt_model_path")
     global sovits_path
     sovits_path=json_post_raw.get("sovits_model_path")
-    print("gptpath"+gpt_path+";vitspath"+sovits_path)
+    logger.info("gptpath"+gpt_path+";vitspath"+sovits_path)
     change_sovits_weights(sovits_path)
     change_gpt_weights(gpt_path)
     return "ok"
-# 新增-----end------
+
 
 @app.post("/control")
 async def control(request: Request):