mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-08-10 10:09:51 +08:00
1212 lines
42 KiB
Python
1212 lines
42 KiB
Python
"""
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按中英混合识别
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按日英混合识别
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多语种启动切分识别语种
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全部按中文识别
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全部按英文识别
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全部按日文识别
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"""
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import logging
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import traceback
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import warnings
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import torchaudio
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
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warnings.simplefilter(action="ignore", category=FutureWarning)
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import json
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import os
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import re
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import sys
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import io
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import torch
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from text.LangSegmenter import LangSegmenter
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try:
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import gradio.analytics as analytics
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analytics.version_check = lambda: None
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except:
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...
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version = model_version = os.environ.get("version", "v2")
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path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth"
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path_sovits_v4 = "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth"
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is_exist_s2gv3 = os.path.exists(path_sovits_v3)
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is_exist_s2gv4 = os.path.exists(path_sovits_v4)
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pretrained_sovits_name = [
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"GPT_SoVITS/pretrained_models/s2G488k.pth",
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"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
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"GPT_SoVITS/pretrained_models/s2Gv3.pth",
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"GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
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]
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pretrained_gpt_name = [
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"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
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"GPT_SoVITS/pretrained_models/s1v3.ckpt",
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"GPT_SoVITS/pretrained_models/s1v3.ckpt",
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]
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_ = [[], []]
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for i in range(4):
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if os.path.exists(pretrained_gpt_name[i]):
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_[0].append(pretrained_gpt_name[i])
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if os.path.exists(pretrained_sovits_name[i]):
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_[-1].append(pretrained_sovits_name[i])
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pretrained_gpt_name, pretrained_sovits_name = _
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if os.path.exists("./weight.json"):
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pass
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else:
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with open("./weight.json", "w", encoding="utf-8") as file:
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json.dump({"GPT": {}, "SoVITS": {}}, file)
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with open("./weight.json", "r", encoding="utf-8") as file:
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weight_data = file.read()
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weight_data = json.loads(weight_data)
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gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, pretrained_gpt_name))
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sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name))
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if isinstance(gpt_path, list):
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gpt_path = gpt_path[0]
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if isinstance(sovits_path, list):
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sovits_path = sovits_path[0]
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# gpt_path = os.environ.get(
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# "gpt_path", pretrained_gpt_name
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# )
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# sovits_path = os.environ.get("sovits_path", pretrained_sovits_name)
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cnhubert_base_path = os.environ.get("cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base")
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bert_path = os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
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infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
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infer_ttswebui = int(infer_ttswebui)
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is_share = os.environ.get("is_share", "False")
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is_share = eval(is_share)
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
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# is_half=False
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punctuation = set(["!", "?", "…", ",", ".", "-", " "])
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import librosa
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import numpy as np
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from feature_extractor import cnhubert
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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cnhubert.cnhubert_base_path = cnhubert_base_path
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import random
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from GPT_SoVITS.module.models import SynthesizerTrn, SynthesizerTrnV3,Generator
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def set_seed(seed):
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if seed == -1:
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seed = random.randint(0, 1000000)
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seed = int(seed)
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random.seed(seed)
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os.environ["PYTHONHASHSEED"] = str(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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# set_seed(42)
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from time import time as ttime
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from peft import LoraConfig, get_peft_model
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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 tools.i18n.i18n import I18nAuto, scan_language_list
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language = os.environ.get("language", "Auto")
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language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
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i18n = I18nAuto(language=language)
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# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
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if torch.cuda.is_available():
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device = "cuda"
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else:
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device = "cpu"
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dict_language_v1 = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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}
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dict_language_v2 = {
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i18n("中文"): "all_zh", # 全部按中文识别
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i18n("英文"): "en", # 全部按英文识别#######不变
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i18n("日文"): "all_ja", # 全部按日文识别
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i18n("粤语"): "all_yue", # 全部按中文识别
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i18n("韩文"): "all_ko", # 全部按韩文识别
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i18n("中英混合"): "zh", # 按中英混合识别####不变
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i18n("日英混合"): "ja", # 按日英混合识别####不变
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i18n("粤英混合"): "yue", # 按粤英混合识别####不变
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i18n("韩英混合"): "ko", # 按韩英混合识别####不变
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i18n("多语种混合"): "auto", # 多语种启动切分识别语种
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i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
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}
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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if is_half == True:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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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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ssl_model = cnhubert.get_model()
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if is_half == True:
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ssl_model = ssl_model.half().to(device)
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else:
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ssl_model = ssl_model.to(device)
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resample_transform_dict = {}
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def resample(audio_tensor, sr0,sr1):
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global resample_transform_dict
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key="%s-%s"%(sr0,sr1)
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if key not in resample_transform_dict:
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resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
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return resample_transform_dict[key](audio_tensor)
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###todo:put them to process_ckpt and modify my_save func (save sovits weights), gpt save weights use my_save in process_ckpt
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# symbol_version-model_version-if_lora_v3
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from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
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v3v4set={"v3","v4"}
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def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
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global vq_model, hps, version, model_version, dict_language, if_lora_v3
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version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
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print(sovits_path,version, model_version, if_lora_v3)
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is_exist=is_exist_s2gv3 if model_version=="v3"else is_exist_s2gv4
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if if_lora_v3 == True and is_exist == False:
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info = "GPT_SoVITS/pretrained_models/s2Gv3.pth" + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version)
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raise FileExistsError(info)
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dict_language = dict_language_v1 if version == "v1" else dict_language_v2
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if prompt_language is not None and text_language is not None:
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if prompt_language in list(dict_language.keys()):
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prompt_text_update, prompt_language_update = (
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{"__type__": "update"},
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{"__type__": "update", "value": prompt_language},
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)
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else:
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prompt_text_update = {"__type__": "update", "value": ""}
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prompt_language_update = {"__type__": "update", "value": i18n("中文")}
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if text_language in list(dict_language.keys()):
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text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
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else:
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text_update = {"__type__": "update", "value": ""}
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text_language_update = {"__type__": "update", "value": i18n("中文")}
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if model_version in v3v4set:
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visible_sample_steps = True
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visible_inp_refs = False
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else:
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visible_sample_steps = False
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visible_inp_refs = True
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yield (
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{"__type__": "update", "choices": list(dict_language.keys())},
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{"__type__": "update", "choices": list(dict_language.keys())},
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prompt_text_update,
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prompt_language_update,
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text_update,
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text_language_update,
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{"__type__": "update", "visible": visible_sample_steps, "value": 32 if model_version=="v3"else 8,"choices":[4, 8, 16, 32,64,128]if model_version=="v3"else [4, 8, 16, 32]},
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{"__type__": "update", "visible": visible_inp_refs},
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{"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False},
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{"__type__": "update", "visible": True if model_version =="v3" else False},
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{"__type__": "update", "value": i18n("模型加载中，请等待"), "interactive": False},
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)
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dict_s2 = load_sovits_new(sovits_path)
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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if "enc_p.text_embedding.weight" not in dict_s2["weight"]:
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hps.model.version = "v2" # v3model,v2sybomls
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elif dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322:
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hps.model.version = "v1"
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else:
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hps.model.version = "v2"
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version = hps.model.version
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# print("sovits版本:",hps.model.version)
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if model_version not in v3v4set:
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model,
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)
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model_version = version
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else:
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hps.model.version=model_version
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vq_model = SynthesizerTrnV3(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
|
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n_speakers=hps.data.n_speakers,
|
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**hps.model,
|
||
)
|
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if "pretrained" not in sovits_path:
|
||
try:
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||
del vq_model.enc_q
|
||
except:
|
||
pass
|
||
if is_half == True:
|
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vq_model = vq_model.half().to(device)
|
||
else:
|
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vq_model = vq_model.to(device)
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vq_model.eval()
|
||
if if_lora_v3 == False:
|
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print("loading sovits_%s" % model_version, vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
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else:
|
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path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
|
||
print(
|
||
"loading sovits_%spretrained_G"%model_version,
|
||
vq_model.load_state_dict(load_sovits_new(path_sovits)["weight"], strict=False),
|
||
)
|
||
lora_rank = dict_s2["lora_rank"]
|
||
lora_config = LoraConfig(
|
||
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
|
||
r=lora_rank,
|
||
lora_alpha=lora_rank,
|
||
init_lora_weights=True,
|
||
)
|
||
vq_model.cfm = get_peft_model(vq_model.cfm, lora_config)
|
||
print("loading sovits_%s_lora%s" % (model_version,lora_rank))
|
||
vq_model.load_state_dict(dict_s2["weight"], strict=False)
|
||
vq_model.cfm = vq_model.cfm.merge_and_unload()
|
||
# torch.save(vq_model.state_dict(),"merge_win.pth")
|
||
vq_model.eval()
|
||
|
||
yield (
|
||
{"__type__": "update", "choices": list(dict_language.keys())},
|
||
{"__type__": "update", "choices": list(dict_language.keys())},
|
||
prompt_text_update,
|
||
prompt_language_update,
|
||
text_update,
|
||
text_language_update,
|
||
{"__type__": "update", "visible": visible_sample_steps, "value":32 if model_version=="v3"else 8,"choices":[4, 8, 16, 32,64,128]if model_version=="v3"else [4, 8, 16, 32]},
|
||
{"__type__": "update", "visible": visible_inp_refs},
|
||
{"__type__": "update", "value": False, "interactive": True if model_version not in v3v4set else False},
|
||
{"__type__": "update", "visible": True if model_version =="v3" else False},
|
||
{"__type__": "update", "value": i18n("合成语音"), "interactive": True},
|
||
)
|
||
with open("./weight.json") as f:
|
||
data = f.read()
|
||
data = json.loads(data)
|
||
data["SoVITS"][version] = sovits_path
|
||
with open("./weight.json", "w") as f:
|
||
f.write(json.dumps(data))
|
||
|
||
|
||
try:
|
||
next(change_sovits_weights(sovits_path))
|
||
except:
|
||
pass
|
||
|
||
|
||
def change_gpt_weights(gpt_path):
|
||
global hz, max_sec, t2s_model, config
|
||
hz = 50
|
||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||
config = dict_s1["config"]
|
||
max_sec = config["data"]["max_sec"]
|
||
t2s_model = Text2SemanticLightningModule(config, "****", 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))
|
||
with open("./weight.json") as f:
|
||
data = f.read()
|
||
data = json.loads(data)
|
||
data["GPT"][version] = gpt_path
|
||
with open("./weight.json", "w") as f:
|
||
f.write(json.dumps(data))
|
||
|
||
|
||
change_gpt_weights(gpt_path)
|
||
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
|
||
import torch
|
||
|
||
now_dir = os.getcwd()
|
||
|
||
|
||
def init_bigvgan():
|
||
global bigvgan_model,hifigan_model
|
||
from 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()
|
||
bigvgan_model = bigvgan_model.eval()
|
||
if hifigan_model:
|
||
hifigan_model=hifigan_model.cpu()
|
||
hifigan_model=None
|
||
try:torch.cuda.empty_cache()
|
||
except:pass
|
||
if is_half == True:
|
||
bigvgan_model = bigvgan_model.half().to(device)
|
||
else:
|
||
bigvgan_model = bigvgan_model.to(device)
|
||
|
||
def init_hifigan():
|
||
global hifigan_model,bigvgan_model
|
||
hifigan_model = Generator(
|
||
initial_channel=100,
|
||
resblock="1",
|
||
resblock_kernel_sizes=[3, 7, 11],
|
||
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
|
||
upsample_rates=[10, 6, 2, 2, 2],
|
||
upsample_initial_channel=512,
|
||
upsample_kernel_sizes=[20, 12, 4, 4, 4],
|
||
gin_channels=0, is_bias=True
|
||
)
|
||
hifigan_model.eval()
|
||
hifigan_model.remove_weight_norm()
|
||
state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu")
|
||
print("loading vocoder",hifigan_model.load_state_dict(state_dict_g))
|
||
if bigvgan_model:
|
||
bigvgan_model=bigvgan_model.cpu()
|
||
bigvgan_model=None
|
||
try:torch.cuda.empty_cache()
|
||
except:pass
|
||
if is_half == True:
|
||
hifigan_model = hifigan_model.half().to(device)
|
||
else:
|
||
hifigan_model = hifigan_model.to(device)
|
||
|
||
bigvgan_model=hifigan_model=None
|
||
if model_version=="v3":
|
||
init_bigvgan()
|
||
if model_version=="v4":
|
||
init_hifigan()
|
||
|
||
|
||
def get_spepc(hps, filename):
|
||
# audio = load_audio(filename, int(hps.data.sampling_rate))
|
||
audio, sampling_rate = librosa.load(filename, sr=int(hps.data.sampling_rate))
|
||
audio = torch.FloatTensor(audio)
|
||
maxx = audio.abs().max()
|
||
if maxx > 1:
|
||
audio /= min(2, maxx)
|
||
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 clean_text_inf(text, language, version):
|
||
language = language.replace("all_", "")
|
||
phones, word2ph, norm_text = clean_text(text, language, version)
|
||
phones = cleaned_text_to_sequence(phones, version)
|
||
return phones, word2ph, norm_text
|
||
|
||
|
||
dtype = torch.float16 if is_half == True else torch.float32
|
||
|
||
|
||
def get_bert_inf(phones, word2ph, norm_text, language):
|
||
language = language.replace("all_", "")
|
||
if language == "zh":
|
||
bert = get_bert_feature(norm_text, word2ph).to(device) # .to(dtype)
|
||
else:
|
||
bert = torch.zeros(
|
||
(1024, len(phones)),
|
||
dtype=torch.float16 if is_half == True else torch.float32,
|
||
).to(device)
|
||
|
||
return bert
|
||
|
||
|
||
splits = {
|
||
"，",
|
||
"。",
|
||
"？",
|
||
"！",
|
||
",",
|
||
".",
|
||
"?",
|
||
"!",
|
||
"~",
|
||
":",
|
||
"：",
|
||
"—",
|
||
"…",
|
||
}
|
||
|
||
|
||
def get_first(text):
|
||
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
|
||
text = re.split(pattern, text)[0].strip()
|
||
return text
|
||
|
||
|
||
from 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
|
||
while " " in formattext:
|
||
formattext = formattext.replace(" ", " ")
|
||
if language == "all_zh":
|
||
if re.search(r"[A-Za-z]", formattext):
|
||
formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
|
||
formattext = chinese.mix_text_normalize(formattext)
|
||
return get_phones_and_bert(formattext, "zh", version)
|
||
else:
|
||
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
|
||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||
elif language == "all_yue" and re.search(r"[A-Za-z]", formattext):
|
||
formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
|
||
formattext = chinese.mix_text_normalize(formattext)
|
||
return get_phones_and_bert(formattext, "yue", version)
|
||
else:
|
||
phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
|
||
bert = torch.zeros(
|
||
(1024, len(phones)),
|
||
dtype=torch.float16 if is_half == True else torch.float32,
|
||
).to(device)
|
||
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
|
||
textlist = []
|
||
langlist = []
|
||
if language == "auto":
|
||
for tmp in LangSegmenter.getTexts(text):
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
elif language == "auto_yue":
|
||
for tmp in LangSegmenter.getTexts(text):
|
||
if tmp["lang"] == "zh":
|
||
tmp["lang"] = "yue"
|
||
langlist.append(tmp["lang"])
|
||
textlist.append(tmp["text"])
|
||
else:
|
||
for tmp in LangSegmenter.getTexts(text):
|
||
if tmp["lang"] == "en":
|
||
langlist.append(tmp["lang"])
|
||
else:
|
||
# 因无法区别中日韩文汉字,以用户输入为准
|
||
langlist.append(language)
|
||
textlist.append(tmp["text"])
|
||
print(textlist)
|
||
print(langlist)
|
||
phones_list = []
|
||
bert_list = []
|
||
norm_text_list = []
|
||
for i in range(len(textlist)):
|
||
lang = langlist[i]
|
||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
|
||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||
phones_list.append(phones)
|
||
norm_text_list.append(norm_text)
|
||
bert_list.append(bert)
|
||
bert = torch.cat(bert_list, dim=1)
|
||
phones = sum(phones_list, [])
|
||
norm_text = "".join(norm_text_list)
|
||
|
||
if not final and len(phones) < 6:
|
||
return get_phones_and_bert("." + text, language, version, final=True)
|
||
|
||
return phones, bert.to(dtype), norm_text
|
||
|
||
|
||
from module.mel_processing import mel_spectrogram_torch, spectrogram_torch
|
||
|
||
spec_min = -12
|
||
spec_max = 2
|
||
|
||
|
||
def norm_spec(x):
|
||
return (x - spec_min) / (spec_max - spec_min) * 2 - 1
|
||
|
||
|
||
def denorm_spec(x):
|
||
return (x + 1) / 2 * (spec_max - spec_min) + spec_min
|
||
|
||
|
||
mel_fn = lambda x: mel_spectrogram_torch(
|
||
x,
|
||
**{
|
||
"n_fft": 1024,
|
||
"win_size": 1024,
|
||
"hop_size": 256,
|
||
"num_mels": 100,
|
||
"sampling_rate": 24000,
|
||
"fmin": 0,
|
||
"fmax": None,
|
||
"center": False,
|
||
},
|
||
)
|
||
mel_fn_v4 = lambda x: mel_spectrogram_torch(
|
||
x,
|
||
**{
|
||
"n_fft": 1280,
|
||
"win_size": 1280,
|
||
"hop_size": 320,
|
||
"num_mels": 100,
|
||
"sampling_rate": 32000,
|
||
"fmin": 0,
|
||
"fmax": None,
|
||
"center": False,
|
||
},
|
||
)
|
||
|
||
|
||
def merge_short_text_in_array(texts, threshold):
|
||
if (len(texts)) < 2:
|
||
return texts
|
||
result = []
|
||
text = ""
|
||
for ele in texts:
|
||
text += ele
|
||
if len(text) >= threshold:
|
||
result.append(text)
|
||
text = ""
|
||
if len(text) > 0:
|
||
if len(result) == 0:
|
||
result.append(text)
|
||
else:
|
||
result[len(result) - 1] += text
|
||
return result
|
||
|
||
|
||
sr_model = None
|
||
|
||
|
||
def audio_sr(audio, sr):
|
||
global sr_model
|
||
if sr_model == None:
|
||
from tools.audio_sr import AP_BWE
|
||
|
||
try:
|
||
sr_model = AP_BWE(device, DictToAttrRecursive)
|
||
except FileNotFoundError:
|
||
return audio.cpu().detach().numpy(), sr
|
||
return sr_model(audio, sr)
|
||
|
||
|
||
##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
|
||
# cache_tokens={}#暂未实现清理机制
|
||
cache = {}
|
||
|
||
|
||
def get_tts_wav(
|
||
ref_wav_path,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
how_to_cut=i18n("不切"),
|
||
top_k=20,
|
||
top_p=0.6,
|
||
temperature=0.6,
|
||
speed=1,
|
||
sample_steps=8,
|
||
if_sr=False,
|
||
media_type="ogg",
|
||
pause_second=0.3,
|
||
ref_free=False,
|
||
if_freeze=False,
|
||
inp_refs=None,
|
||
):
|
||
#top_k = int(top_k)
|
||
global cache
|
||
t = []
|
||
if prompt_text is None or len(prompt_text) == 0:
|
||
ref_free = True
|
||
if model_version in v3v4set:
|
||
ref_free = False # s2v3暂不支持ref_free
|
||
else:
|
||
if_sr = False
|
||
t0 = ttime()
|
||
|
||
#prompt_language = dict_language[prompt_language]
|
||
#text_language = dict_language[text_language]
|
||
|
||
if not ref_free:
|
||
prompt_text = prompt_text.strip("\n")
|
||
if prompt_text[-1] not in splits:
|
||
prompt_text += "。" if prompt_language != "en" else "."
|
||
print(i18n("实际输入的参考文本:"), prompt_text)
|
||
text = text.strip("\n")
|
||
# if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
|
||
|
||
print(i18n("实际输入的目标文本:"), text)
|
||
zero_wav = np.zeros(
|
||
int(hps.data.sampling_rate * pause_second),
|
||
dtype=np.float16 if is_half == True else np.float32,
|
||
)
|
||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||
if is_half == True:
|
||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||
else:
|
||
zero_wav_torch = zero_wav_torch.to(device)
|
||
if not ref_free:
|
||
with torch.no_grad():
|
||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||
if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000:
|
||
raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
|
||
wav16k = torch.from_numpy(wav16k)
|
||
if is_half == True:
|
||
wav16k = wav16k.half().to(device)
|
||
else:
|
||
wav16k = wav16k.to(device)
|
||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||
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]
|
||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||
|
||
t1 = ttime()
|
||
t.append(t1 - t0)
|
||
|
||
if how_to_cut == i18n("凑四句一切"):
|
||
text = cut1(text)
|
||
elif how_to_cut == i18n("凑50字一切"):
|
||
text = cut2(text)
|
||
elif how_to_cut == i18n("按中文句号。切"):
|
||
text = cut3(text)
|
||
elif how_to_cut == i18n("按英文句号.切"):
|
||
text = cut4(text)
|
||
elif how_to_cut == i18n("按标点符号切"):
|
||
text = cut5(text)
|
||
while "\n\n" in text:
|
||
text = text.replace("\n\n", "\n")
|
||
print(i18n("实际输入的目标文本(切句后):"), text)
|
||
texts = text.split("\n")
|
||
texts = process_text(texts)
|
||
texts = merge_short_text_in_array(texts, 5)
|
||
audio_opt = []
|
||
###s2v3暂不支持ref_free
|
||
if not ref_free:
|
||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
|
||
|
||
for i_text, text in enumerate(texts):
|
||
# 解决输入目标文本的空行导致报错的问题
|
||
if len(text.strip()) == 0:
|
||
continue
|
||
if text[-1] not in splits:
|
||
text += "。" if text_language != "en" else "."
|
||
print(i18n("实际输入的目标文本(每句):"), text)
|
||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
|
||
print(i18n("前端处理后的文本(每句):"), norm_text2)
|
||
if not ref_free:
|
||
bert = torch.cat([bert1, bert2], 1)
|
||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||
else:
|
||
bert = bert2
|
||
all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||
|
||
bert = bert.to(device).unsqueeze(0)
|
||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||
|
||
t2 = ttime()
|
||
# cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
|
||
# print(cache.keys(),if_freeze)
|
||
if i_text in cache and if_freeze == True:
|
||
pred_semantic = cache[i_text]
|
||
else:
|
||
with torch.no_grad():
|
||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||
all_phoneme_ids,
|
||
all_phoneme_len,
|
||
None if ref_free else prompt,
|
||
bert,
|
||
# prompt_phone_len=ph_offset,
|
||
top_k=top_k,
|
||
top_p=top_p,
|
||
temperature=temperature,
|
||
early_stop_num=hz * max_sec,
|
||
)
|
||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
|
||
cache[i_text] = pred_semantic
|
||
t3 = ttime()
|
||
###v3不存在以下逻辑和inp_refs
|
||
if model_version not in v3v4set:
|
||
refers = []
|
||
if inp_refs:
|
||
for path in inp_refs:
|
||
try:
|
||
refer = get_spepc(hps, path.name).to(dtype).to(device)
|
||
refers.append(refer)
|
||
except:
|
||
traceback.print_exc()
|
||
if len(refers) == 0:
|
||
refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
|
||
audio = vq_model.decode(
|
||
pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
|
||
)[0][0] # .cpu().detach().numpy()
|
||
else:
|
||
refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)
|
||
phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
|
||
phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
|
||
# print(11111111, phoneme_ids0, phoneme_ids1)
|
||
fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
|
||
ref_audio, sr = torchaudio.load(ref_wav_path)
|
||
ref_audio = ref_audio.to(device).float()
|
||
if ref_audio.shape[0] == 2:
|
||
ref_audio = ref_audio.mean(0).unsqueeze(0)
|
||
tgt_sr=24000 if model_version=="v3"else 32000
|
||
if sr != tgt_sr:
|
||
ref_audio = resample(ref_audio, sr,tgt_sr)
|
||
# print("ref_audio",ref_audio.abs().mean())
|
||
mel2 = mel_fn(ref_audio)if model_version=="v3"else mel_fn_v4(ref_audio)
|
||
mel2 = norm_spec(mel2)
|
||
T_min = min(mel2.shape[2], fea_ref.shape[2])
|
||
mel2 = mel2[:, :, :T_min]
|
||
fea_ref = fea_ref[:, :, :T_min]
|
||
Tref=468 if model_version=="v3"else 500
|
||
Tchunk=934 if model_version=="v3"else 1000
|
||
if T_min > Tref:
|
||
mel2 = mel2[:, :, -Tref:]
|
||
fea_ref = fea_ref[:, :, -Tref:]
|
||
T_min = Tref
|
||
chunk_len = Tchunk - T_min
|
||
mel2 = mel2.to(dtype)
|
||
fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)
|
||
cfm_resss = []
|
||
idx = 0
|
||
while 1:
|
||
fea_todo_chunk = fea_todo[:, :, idx : idx + chunk_len]
|
||
if fea_todo_chunk.shape[-1] == 0:
|
||
break
|
||
idx += chunk_len
|
||
fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
|
||
cfm_res = vq_model.cfm.inference(
|
||
fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0
|
||
)
|
||
cfm_res = cfm_res[:, :, mel2.shape[2] :]
|
||
mel2 = cfm_res[:, :, -T_min:]
|
||
fea_ref = fea_todo_chunk[:, :, -T_min:]
|
||
cfm_resss.append(cfm_res)
|
||
cfm_res = torch.cat(cfm_resss, 2)
|
||
cfm_res = denorm_spec(cfm_res)
|
||
if model_version=="v3":
|
||
if bigvgan_model == None:
|
||
init_bigvgan()
|
||
else:#v4
|
||
if hifigan_model == None:
|
||
init_hifigan()
|
||
vocoder_model=bigvgan_model if model_version=="v3"else hifigan_model
|
||
with torch.inference_mode():
|
||
wav_gen = vocoder_model(cfm_res)
|
||
audio = wav_gen[0][0] # .cpu().detach().numpy()
|
||
max_audio = torch.abs(audio).max() # 简单防止16bit爆音
|
||
if max_audio > 1:
|
||
audio = audio / max_audio
|
||
audio_opt.append(audio)
|
||
audio_opt.append(zero_wav_torch) # zero_wav
|
||
t4 = ttime()
|
||
t.extend([t2 - t1, t3 - t2, t4 - t3])
|
||
t1 = ttime()
|
||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])))
|
||
audio_opt = torch.cat(audio_opt, 0) # np.concatenate
|
||
if model_version in {"v1","v2"}:opt_sr=32000
|
||
elif model_version=="v3":opt_sr=24000
|
||
else:opt_sr=48000#v4
|
||
if if_sr == True and opt_sr == 24000:
|
||
print(i18n("音频超分中"))
|
||
audio_opt, opt_sr = audio_sr(audio_opt.unsqueeze(0), opt_sr)
|
||
max_audio = np.abs(audio_opt).max()
|
||
if max_audio > 1:
|
||
audio_opt /= max_audio
|
||
else:
|
||
audio_opt = audio_opt.cpu().detach().numpy()
|
||
|
||
audio_bytes = pack_audio(io.BytesIO(), (audio_opt * 32767).astype(np.int16), sr, media_type)
|
||
yield audio_bytes.getvalue()
|
||
|
||
def pack_audio(audio_bytes, data, rate, media_type)->io.BytesIO:
|
||
if media_type == "ogg":
|
||
audio_bytes = pack_ogg(audio_bytes, data, rate)
|
||
else:
|
||
# wav无法流式, 先暂存raw
|
||
audio_bytes = pack_raw(audio_bytes, data, rate)
|
||
return audio_bytes
|
||
|
||
|
||
def pack_ogg(audio_bytes, data, rate):
|
||
# Author: AkagawaTsurunaki
|
||
# Issue:
|
||
# Stack overflow probabilistically occurs
|
||
# when the function `sf_writef_short` of `libsndfile_64bit.dll` is called
|
||
# using the Python library `soundfile`
|
||
# Note:
|
||
# This is an issue related to `libsndfile`, not this project itself.
|
||
# It happens when you generate a large audio tensor (about 499804 frames in my PC)
|
||
# and try to convert it to an ogg file.
|
||
# Related:
|
||
# https://github.com/RVC-Boss/GPT-SoVITS/issues/1199
|
||
# https://github.com/libsndfile/libsndfile/issues/1023
|
||
# https://github.com/bastibe/python-soundfile/issues/396
|
||
# Suggestion:
|
||
# Or split the whole audio data into smaller audio segment to avoid stack overflow?
|
||
|
||
def handle_pack_ogg():
|
||
import soundfile as sf
|
||
with sf.SoundFile(audio_bytes, mode="w", samplerate=rate, channels=1, format="ogg") as audio_file:
|
||
audio_file.write(data)
|
||
|
||
import threading
|
||
|
||
# See: https://docs.python.org/3/library/threading.html
|
||
# The stack size of this thread is at least 32768
|
||
# If stack overflow error still occurs, just modify the `stack_size`.
|
||
# stack_size = n * 4096, where n should be a positive integer.
|
||
# Here we chose n = 4096.
|
||
stack_size = 4096 * 4096
|
||
try:
|
||
threading.stack_size(stack_size)
|
||
pack_ogg_thread = threading.Thread(target=handle_pack_ogg)
|
||
pack_ogg_thread.start()
|
||
pack_ogg_thread.join()
|
||
except RuntimeError as e:
|
||
# If changing the thread stack size is unsupported, a RuntimeError is raised.
|
||
print("RuntimeError: {}".format(e))
|
||
print("Changing the thread stack size is unsupported.")
|
||
except ValueError as e:
|
||
# If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified.
|
||
print("ValueError: {}".format(e))
|
||
print("The specified stack size is invalid.")
|
||
|
||
return audio_bytes
|
||
|
||
def pack_raw(audio_bytes, data, rate):
|
||
audio_bytes.write(data.tobytes())
|
||
return audio_bytes
|
||
|
||
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 cut1(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
split_idx = list(range(0, len(inps), 4))
|
||
split_idx[-1] = None
|
||
if len(split_idx) > 1:
|
||
opts = []
|
||
for idx in range(len(split_idx) - 1):
|
||
opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
|
||
else:
|
||
opts = [inp]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut2(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
if len(inps) < 2:
|
||
return inp
|
||
opts = []
|
||
summ = 0
|
||
tmp_str = ""
|
||
for i in range(len(inps)):
|
||
summ += len(inps[i])
|
||
tmp_str += inps[i]
|
||
if summ > 50:
|
||
summ = 0
|
||
opts.append(tmp_str)
|
||
tmp_str = ""
|
||
if tmp_str != "":
|
||
opts.append(tmp_str)
|
||
# print(opts)
|
||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||
opts[-2] = opts[-2] + opts[-1]
|
||
opts = opts[:-1]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut3(inp):
|
||
inp = inp.strip("\n")
|
||
opts = ["%s" % item for item in inp.strip("。").split("。")]
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut4(inp):
|
||
inp = inp.strip("\n")
|
||
opts = re.split(r"(?<!\d)\.(?!\d)", inp.strip("."))
|
||
opts = [item for item in opts if not set(item).issubset(punctuation)]
|
||
return "\n".join(opts)
|
||
|
||
|
||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||
def cut5(inp):
|
||
inp = inp.strip("\n")
|
||
punds = {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", ";", "：", "…"}
|
||
mergeitems = []
|
||
items = []
|
||
|
||
for i, char in enumerate(inp):
|
||
if char in punds:
|
||
if char == "." and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
|
||
items.append(char)
|
||
else:
|
||
items.append(char)
|
||
mergeitems.append("".join(items))
|
||
items = []
|
||
else:
|
||
items.append(char)
|
||
|
||
if items:
|
||
mergeitems.append("".join(items))
|
||
|
||
opt = [item for item in mergeitems if not set(item).issubset(punds)]
|
||
return "\n".join(opt)
|
||
|
||
|
||
def custom_sort_key(s):
|
||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||
parts = re.split("(\d+)", s)
|
||
# 将数字部分转换为整数，非数字部分保持不变
|
||
parts = [int(part) if part.isdigit() else part for part in parts]
|
||
return parts
|
||
|
||
|
||
def process_text(texts):
|
||
_text = []
|
||
if all(text in [None, " ", "\n", ""] for text in texts):
|
||
raise ValueError(i18n("请输入有效文本"))
|
||
for text in texts:
|
||
if text in [None, " ", ""]:
|
||
pass
|
||
else:
|
||
_text.append(text)
|
||
return _text
|
||
|
||
|
||
def change_choices():
|
||
SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
|
||
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
|
||
"choices": sorted(GPT_names, key=custom_sort_key),
|
||
"__type__": "update",
|
||
}
|
||
|
||
|
||
SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4"]
|
||
GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
|
||
for path in SoVITS_weight_root + GPT_weight_root:
|
||
os.makedirs(path, exist_ok=True)
|
||
|
||
|
||
def get_weights_names(GPT_weight_root, SoVITS_weight_root):
|
||
SoVITS_names = [i for i in pretrained_sovits_name]
|
||
for path in SoVITS_weight_root:
|
||
for name in os.listdir(path):
|
||
if name.endswith(".pth"):
|
||
SoVITS_names.append("%s/%s" % (path, name))
|
||
GPT_names = [i for i in pretrained_gpt_name]
|
||
for path in GPT_weight_root:
|
||
for name in os.listdir(path):
|
||
if name.endswith(".ckpt"):
|
||
GPT_names.append("%s/%s" % (path, name))
|
||
return SoVITS_names, GPT_names
|
||
|
||
|
||
SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
|
||
|
||
|
||
def html_center(text, label="p"):
|
||
return f"""<div style="text-align: center; margin: 100; padding: 50;">
|
||
<{label} style="margin: 0; padding: 0;">{text}</{label}>
|
||
</div>"""
|
||
|
||
|
||
def html_left(text, label="p"):
|
||
return f"""<div style="text-align: left; margin: 0; padding: 0;">
|
||
<{label} style="margin: 0; padding: 0;">{text}</{label}>
|
||
</div>"""
|
||
|
||
|
||
from fastapi import FastAPI, Request, Query
|
||
from fastapi.responses import StreamingResponse
|
||
import uvicorn
|
||
|
||
def handle(
|
||
refer_wav_path,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
how_to_cut,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
speed,
|
||
sample_steps,
|
||
if_sr,
|
||
media_type,
|
||
):
|
||
|
||
if sample_steps not in [4, 8, 16, 32]:
|
||
sample_steps = 32
|
||
|
||
return StreamingResponse(
|
||
get_tts_wav(
|
||
refer_wav_path,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
how_to_cut,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
speed,
|
||
sample_steps,
|
||
if_sr,
|
||
media_type,
|
||
),
|
||
media_type="audio/wav",
|
||
)
|
||
app = FastAPI()
|
||
@app.post("/")
|
||
async def tts_endpoint(request: Request):
|
||
json_post_raw = await request.json()
|
||
return handle(
|
||
json_post_raw.get("refer_wav_path"),
|
||
json_post_raw.get("prompt_text"),
|
||
json_post_raw.get("prompt_language"),
|
||
json_post_raw.get("text"),
|
||
json_post_raw.get("text_language"),
|
||
json_post_raw.get("how_to_cut","cut1"),
|
||
json_post_raw.get("top_k", 15),
|
||
json_post_raw.get("top_p", 1.0),
|
||
json_post_raw.get("temperature", 1.0),
|
||
json_post_raw.get("speed", 1.0),
|
||
json_post_raw.get("sample_steps", 32),
|
||
json_post_raw.get("if_sr", False),
|
||
json_post_raw.get("media_type", "ogg"),
|
||
)
|
||
|
||
|
||
@app.get("/")
|
||
async def tts_endpoint(
|
||
refer_wav_path: str = None,
|
||
prompt_text: str = None,
|
||
prompt_language: str = None,
|
||
text: str = None,
|
||
text_language: str = None,
|
||
how_to_cut: str = "cut1",
|
||
top_k: int = 15,
|
||
top_p: float = 1.0,
|
||
temperature: float = 1.0,
|
||
speed: float = 1.0,
|
||
sample_steps: int = 32,
|
||
if_sr: bool = False,
|
||
media_type: str = "ogg",
|
||
):
|
||
return handle(
|
||
refer_wav_path,
|
||
prompt_text,
|
||
prompt_language,
|
||
text,
|
||
text_language,
|
||
how_to_cut,
|
||
top_k,
|
||
top_p,
|
||
temperature,
|
||
speed,
|
||
sample_steps,
|
||
if_sr,
|
||
media_type,
|
||
)
|
||
|
||
if __name__ == "__main__":
|
||
uvicorn.run(app, host="0.0.0.0", port=9880, workers=1)
|