mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
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
This commit is contained in:
Spr_Aachen
parent
9da7e17efe
commit
28effc2d44
117
api.py
117
api.py
@ -150,7 +150,7 @@ sys.path.append(now_dir)
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sys.path.append("%s/GPT_SoVITS" % (now_dir))
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import signal
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from text.LangSegmenter import LangSegmenter
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from GPT_SoVITS.text.LangSegmenter import LangSegmenter
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from time import time as ttime
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import torch, torchaudio
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import librosa
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@ -160,14 +160,14 @@ from fastapi.responses import StreamingResponse, JSONResponse
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import uvicorn
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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from feature_extractor import cnhubert
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from GPT_SoVITS.feature_extractor import cnhubert
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from io import BytesIO
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from module.models import SynthesizerTrn, SynthesizerTrnV3
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from GPT_SoVITS.module.models import SynthesizerTrn, SynthesizerTrnV3
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from peft import LoraConfig, PeftModel, get_peft_model
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from module.mel_processing import spectrogram_torch
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from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from GPT_SoVITS.text import cleaned_text_to_sequence
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from GPT_SoVITS.text.cleaner import clean_text
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from GPT_SoVITS.module.mel_processing import spectrogram_torch
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from tools.my_utils import load_audio
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import config as global_config
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import logging
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@ -176,9 +176,9 @@ import subprocess
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class DefaultRefer:
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def __init__(self, path, text, language):
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self.path = args.default_refer_path
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self.text = args.default_refer_text
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self.language = args.default_refer_language
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self.path = path
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self.text = text
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self.language = language
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def is_ready(self) -> bool:
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return is_full(self.path, self.text, self.language)
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@ -200,7 +200,7 @@ def is_full(*items): # 任意一项为空返回False
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def init_bigvgan():
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global bigvgan_model
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from BigVGAN import bigvgan
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from GPT_SoVITS.BigVGAN import bigvgan
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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
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# remove weight norm in the model and set to eval mode
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bigvgan_model.remove_weight_norm()
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@ -221,7 +221,7 @@ def resample(audio_tensor, sr0):
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return resample_transform_dict[sr0](audio_tensor)
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from module.mel_processing import spectrogram_torch,mel_spectrogram_torch
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from GPT_SoVITS.module.mel_processing import spectrogram_torch,mel_spectrogram_torch
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spec_min = -12
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spec_max = 2
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def norm_spec(x):
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@ -240,6 +240,34 @@ mel_fn=lambda x: mel_spectrogram_torch(x, **{
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})
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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sr_model=None
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def audio_sr(audio,sr):
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global sr_model
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@ -270,7 +298,7 @@ class Sovits:
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self.vq_model = vq_model
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self.hps = hps
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from process_ckpt import get_sovits_version_from_path_fast,load_sovits_new
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from GPT_SoVITS.process_ckpt import get_sovits_version_from_path_fast,load_sovits_new
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def get_sovits_weights(sovits_path):
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path_sovits_v3="GPT_SoVITS/pretrained_models/s2Gv3.pth"
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is_exist_s2gv3=os.path.exists(path_sovits_v3)
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@ -340,6 +368,7 @@ def get_sovits_weights(sovits_path):
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sovits = Sovits(vq_model, hps)
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return sovits
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class Gpt:
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def __init__(self, max_sec, t2s_model):
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self.max_sec = max_sec
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@ -363,6 +392,7 @@ def get_gpt_weights(gpt_path):
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gpt = Gpt(max_sec, t2s_model)
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return gpt
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def change_gpt_sovits_weights(gpt_path,sovits_path):
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try:
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gpt = get_gpt_weights(gpt_path)
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@ -410,7 +440,8 @@ def get_bert_inf(phones, word2ph, norm_text, language):
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return bert
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from text import chinese
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from GPT_SoVITS.text import chinese
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def get_phones_and_bert(text,language,version,final=False):
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if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
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formattext = text
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@ -475,36 +506,8 @@ def get_phones_and_bert(text,language,version,final=False):
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return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def get_spepc(hps, filename):
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audio,_ = librosa.load(filename, int(hps.data.sampling_rate))
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audio,_ = librosa.load(filename, sr=int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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maxx=audio.abs().max()
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if(maxx>1):
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@ -934,15 +937,23 @@ parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, h
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args = parser.parse_args()
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sovits_path = args.sovits_path
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gpt_path = args.gpt_path
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default_refer_path = args.default_refer_path
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default_refer_text = args.default_refer_text
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default_refer_language = args.default_refer_language
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device = args.device
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port = args.port
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host = args.bind_addr
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full_precision = args.full_precision
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half_precision = args.half_precision
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stream_mode = args.stream_mode
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media_type = args.media_type
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sub_type = args.sub_type
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default_cut_punc = args.cut_punc
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cnhubert_base_path = args.hubert_path
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bert_path = args.bert_path
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default_cut_punc = args.cut_punc
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# 应用参数配置
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default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
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default_refer = DefaultRefer(default_refer_path, default_refer_text, default_refer_language)
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# 模型路径检查
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if sovits_path == "":
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@ -963,24 +974,24 @@ else:
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# 获取半精度
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is_half = g_config.is_half
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if args.full_precision:
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if full_precision:
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is_half = False
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if args.half_precision:
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if half_precision:
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is_half = True
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if args.full_precision and args.half_precision:
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if full_precision and half_precision:
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is_half = g_config.is_half # 炒饭fallback
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logger.info(f"半精: {is_half}")
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# 流式返回模式
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if args.stream_mode.lower() in ["normal","n"]:
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if stream_mode.lower() in ["normal","n"]:
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stream_mode = "normal"
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logger.info("流式返回已开启")
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else:
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stream_mode = "close"
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# 音频编码格式
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if args.media_type.lower() in ["aac","ogg"]:
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media_type = args.media_type.lower()
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if media_type.lower() in ["aac","ogg"]:
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media_type = media_type.lower()
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elif stream_mode == "close":
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media_type = "wav"
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else:
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@ -988,7 +999,7 @@ else:
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logger.info(f"编码格式: {media_type}")
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# 音频数据类型
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if args.sub_type.lower() == 'int32':
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if sub_type.lower() == 'int32':
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is_int32 = True
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logger.info(f"数据类型: int32")
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else:
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@ -1102,4 +1113,4 @@ async def tts_endpoint(
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if __name__ == "__main__":
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uvicorn.run(app, host=host, port=port, workers=1)
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uvicorn.run(app, host=host, port=port, workers=1)
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