From 6d95b559e8544810640219acc973276e5d43de03 Mon Sep 17 00:00:00 2001
From: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com>
Date: Thu, 30 Apr 2026 15:01:11 +0800
Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0cuda=20graph=E6=94=AF?=
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增加cuda graph支持，普通推理模式推理速度原地翻倍，效果不变。1
---
GPT_SoVITS/inference_webui.py | 2786 +++++++++++++++++----------------
1 file changed, 1433 insertions(+), 1353 deletions(-)
diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index a361ed58..91c00307 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -1,1353 +1,1433 @@
-"""
-按中英混合识别
-按日英混合识别
-多语种启动切分识别语种
-全部按中文识别
-全部按英文识别
-全部按日文识别
-"""
-import psutil
-import os
-
-def set_high_priority():
- """把当前 Python 进程设为 HIGH_PRIORITY_CLASS"""
- if os.name != "nt":
- return # 仅 Windows 有效
- p = psutil.Process(os.getpid())
- try:
- p.nice(psutil.HIGH_PRIORITY_CLASS)
- print("已将进程优先级设为 High")
- except psutil.AccessDenied:
- print("权限不足，无法修改优先级（请用管理员运行）")
-set_high_priority()
-import json
-import logging
-import os
-import re
-import sys
-import traceback
-import warnings
-
-import torch
-import torchaudio
-from text.LangSegmenter import LangSegmenter
-
-logging.getLogger("markdown_it").setLevel(logging.ERROR)
-logging.getLogger("urllib3").setLevel(logging.ERROR)
-logging.getLogger("httpcore").setLevel(logging.ERROR)
-logging.getLogger("httpx").setLevel(logging.ERROR)
-logging.getLogger("asyncio").setLevel(logging.ERROR)
-logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
-logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
-logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-version = model_version = os.environ.get("version", "v2")
-
-from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path
-
-SoVITS_names, GPT_names = get_weights_names()
-from config import pretrained_sovits_name
-
-path_sovits_v3 = pretrained_sovits_name["v3"]
-path_sovits_v4 = pretrained_sovits_name["v4"]
-is_exist_s2gv3 = os.path.exists(path_sovits_v3)
-is_exist_s2gv4 = os.path.exists(path_sovits_v4)
-
-if os.path.exists("./weight.json"):
- pass
-else:
- with open("./weight.json", "w", encoding="utf-8") as file:
- json.dump({"GPT": {}, "SoVITS": {}}, file)
-
-with open("./weight.json", "r", encoding="utf-8") as file:
- weight_data = file.read()
- weight_data = json.loads(weight_data)
- gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, GPT_names[-1]))
- sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, SoVITS_names[0]))
- if isinstance(gpt_path, list):
- gpt_path = gpt_path[0]
- if isinstance(sovits_path, list):
- sovits_path = sovits_path[0]
-
-# print(2333333)
-# print(os.environ["gpt_path"])
-# print(gpt_path)
-# print(GPT_names)
-# print(weight_data)
-# print(weight_data.get("GPT", {}))
-# print(version)###GPT version里没有s2的v2pro
-# print(weight_data.get("GPT", {}).get(version, GPT_names[-1]))
-
-cnhubert_base_path = os.environ.get("cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base")
-bert_path = os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
-infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
-infer_ttswebui = int(infer_ttswebui)
-is_share = os.environ.get("is_share", "False")
-is_share = eval(is_share)
-if "_CUDA_VISIBLE_DEVICES" in os.environ:
- os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
-is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
-# is_half=False
-punctuation = set(["!", "?", "…", ",", ".", "-", " "])
-import gradio as gr
-import librosa
-import numpy as np
-from feature_extractor import cnhubert
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-
-cnhubert.cnhubert_base_path = cnhubert_base_path
-
-import random
-
-from GPT_SoVITS.module.models import Generator, SynthesizerTrn, SynthesizerTrnV3
-
-
-def set_seed(seed):
- if seed == -1:
- seed = random.randint(0, 1000000)
- seed = int(seed)
- random.seed(seed)
- os.environ["PYTHONHASHSEED"] = str(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
-
-
-# set_seed(42)
-
-from time import time as ttime
-
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from peft import LoraConfig, get_peft_model
-from text import cleaned_text_to_sequence
-from text.cleaner import clean_text
-
-from tools.assets import css, js, top_html
-from tools.i18n.i18n import I18nAuto, scan_language_list
-
-language = os.environ.get("language", "Auto")
-language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
-i18n = I18nAuto(language=language)
-
-# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
-
-if torch.cuda.is_available():
- device = "cuda"
-else:
- device = "cpu"
-
-dict_language_v1 = {
- i18n("中文"): "all_zh", # 全部按中文识别
- i18n("英文"): "en", # 全部按英文识别#######不变
- i18n("日文"): "all_ja", # 全部按日文识别
- i18n("中英混合"): "zh", # 按中英混合识别####不变
- i18n("日英混合"): "ja", # 按日英混合识别####不变
- i18n("多语种混合"): "auto", # 多语种启动切分识别语种
-}
-dict_language_v2 = {
- i18n("中文"): "all_zh", # 全部按中文识别
- i18n("英文"): "en", # 全部按英文识别#######不变
- i18n("日文"): "all_ja", # 全部按日文识别
- i18n("粤语"): "all_yue", # 全部按中文识别
- i18n("韩文"): "all_ko", # 全部按韩文识别
- i18n("中英混合"): "zh", # 按中英混合识别####不变
- i18n("日英混合"): "ja", # 按日英混合识别####不变
- i18n("粤英混合"): "yue", # 按粤英混合识别####不变
- i18n("韩英混合"): "ko", # 按韩英混合识别####不变
- i18n("多语种混合"): "auto", # 多语种启动切分识别语种
- i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
-}
-dict_language = dict_language_v1 if version == "v1" else dict_language_v2
-
-tokenizer = AutoTokenizer.from_pretrained(bert_path)
-bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
-if is_half == True:
- bert_model = bert_model.half().to(device)
-else:
- bert_model = bert_model.to(device)
-
-
-def get_bert_feature(text, word2ph):
- with torch.no_grad():
- inputs = tokenizer(text, return_tensors="pt")
- for i in inputs:
- inputs[i] = inputs[i].to(device)
- res = bert_model(**inputs, output_hidden_states=True)
- res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
- assert len(word2ph) == len(text)
- phone_level_feature = []
- for i in range(len(word2ph)):
- repeat_feature = res[i].repeat(word2ph[i], 1)
- phone_level_feature.append(repeat_feature)
- phone_level_feature = torch.cat(phone_level_feature, dim=0)
- return phone_level_feature.T
-
-
-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")
-
-
-ssl_model = cnhubert.get_model()
-if is_half == True:
- ssl_model = ssl_model.half().to(device)
-else:
- ssl_model = ssl_model.to(device)
-
-
-###todo:put them to process_ckpt and modify my_save func (save sovits weights), gpt save weights use my_save in process_ckpt
-# symbol_version-model_version-if_lora_v3
-from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
-
-v3v4set = {"v3", "v4"}
-
-
-def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
- if "！" in sovits_path or "!" in sovits_path:
- sovits_path = name2sovits_path[sovits_path]
- global vq_model, hps, version, model_version, dict_language, if_lora_v3
- version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
- print(sovits_path, version, model_version, if_lora_v3)
- is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
- path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
- if if_lora_v3 == True and is_exist == False:
- info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重")
- gr.Warning(info)
- raise FileExistsError(info)
- dict_language = dict_language_v1 if version == "v1" else dict_language_v2
- if prompt_language is not None and text_language is not None:
- if prompt_language in list(dict_language.keys()):
- prompt_text_update, prompt_language_update = (
- {"__type__": "update"},
- {"__type__": "update", "value": prompt_language},
- )
- else:
- prompt_text_update = {"__type__": "update", "value": ""}
- prompt_language_update = {"__type__": "update", "value": i18n("中文")}
- if text_language in list(dict_language.keys()):
- text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
- else:
- text_update = {"__type__": "update", "value": ""}
- text_language_update = {"__type__": "update", "value": i18n("中文")}
- if model_version in v3v4set:
- visible_sample_steps = True
- visible_inp_refs = False
- else:
- visible_sample_steps = False
- visible_inp_refs = True
- 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("模型加载中，请等待"), "interactive": False},
- )
-
- dict_s2 = load_sovits_new(sovits_path)
- hps = dict_s2["config"]
- hps = DictToAttrRecursive(hps)
- hps.model.semantic_frame_rate = "25hz"
- if "enc_p.text_embedding.weight" not in dict_s2["weight"]:
- hps.model.version = "v2" # v3model,v2sybomls
- elif dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322:
- hps.model.version = "v1"
- else:
- hps.model.version = "v2"
- version = hps.model.version
- # print("sovits版本:",hps.model.version)
- if model_version not in v3v4set:
- if "Pro" not in model_version:
- model_version = version
- else:
- hps.model.version = model_version
- 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,
- )
- else:
- hps.model.version = model_version
- vq_model = SynthesizerTrnV3(
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model,
- )
- if "pretrained" not in sovits_path:
- try:
- del vq_model.enc_q
- except:
- pass
- if is_half == True:
- vq_model = vq_model.half().to(device)
- else:
- vq_model = vq_model.to(device)
- vq_model.eval()
- if if_lora_v3 == False:
- print("loading sovits_%s" % model_version, vq_model.load_state_dict(dict_s2["weight"], strict=False))
- else:
- 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):
- if "！" in gpt_path or "!" in gpt_path:
- gpt_path = name2gpt_path[gpt_path]
- global hz, max_sec, t2s_model, config
- hz = 50
- dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
- 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 clean_hifigan_model():
- global hifigan_model
- if hifigan_model:
- hifigan_model = hifigan_model.cpu()
- hifigan_model = None
- try:
- torch.cuda.empty_cache()
- except:
- pass
-
-
-def clean_bigvgan_model():
- global bigvgan_model
- if bigvgan_model:
- bigvgan_model = bigvgan_model.cpu()
- bigvgan_model = None
- try:
- torch.cuda.empty_cache()
- except:
- pass
-
-
-def clean_sv_cn_model():
- global sv_cn_model
- if sv_cn_model:
- sv_cn_model.embedding_model = sv_cn_model.embedding_model.cpu()
- sv_cn_model = None
- try:
- torch.cuda.empty_cache()
- except:
- pass
-
-
-def init_bigvgan():
- global bigvgan_model, hifigan_model, sv_cn_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()
- clean_hifigan_model()
- clean_sv_cn_model()
- 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, sv_cn_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",
- weights_only=False,
- )
- print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
- clean_bigvgan_model()
- clean_sv_cn_model()
- if is_half == True:
- hifigan_model = hifigan_model.half().to(device)
- else:
- hifigan_model = hifigan_model.to(device)
-
-
-from sv import SV
-
-
-def init_sv_cn():
- global hifigan_model, bigvgan_model, sv_cn_model
- sv_cn_model = SV(device, is_half)
- clean_bigvgan_model()
- clean_hifigan_model()
-
-
-bigvgan_model = hifigan_model = sv_cn_model = None
-if model_version == "v3":
- init_bigvgan()
-if model_version == "v4":
- init_hifigan()
-if model_version in {"v2Pro", "v2ProPlus"}:
- init_sv_cn()
-
-resample_transform_dict = {}
-
-
-def resample(audio_tensor, sr0, sr1, device):
- global resample_transform_dict
- key = "%s-%s-%s" % (sr0, sr1, str(device))
- if key not in resample_transform_dict:
- resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
- return resample_transform_dict[key](audio_tensor)
-
-
-def get_spepc(hps, filename, dtype, device, is_v2pro=False):
- # 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)
-
- sr1 = int(hps.data.sampling_rate)
- audio, sr0 = torchaudio.load(filename)
- if sr0 != sr1:
- audio = audio.to(device)
- if audio.shape[0] == 2:
- audio = audio.mean(0).unsqueeze(0)
- audio = resample(audio, sr0, sr1, device)
- else:
- audio = audio.to(device)
- if audio.shape[0] == 2:
- audio = audio.mean(0).unsqueeze(0)
-
- maxx = audio.abs().max()
- if maxx > 1:
- audio /= min(2, maxx)
- spec = spectrogram_torch(
- audio,
- hps.data.filter_length,
- hps.data.sampling_rate,
- hps.data.hop_length,
- hps.data.win_length,
- center=False,
- )
- spec = spec.to(dtype)
- if is_v2pro == True:
- audio = resample(audio, sr1, 16000, device).to(dtype)
- return spec, audio
-
-
-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):
- text = re.sub(r' {2,}', ' ', text)
- textlist = []
- langlist = []
- if language == "all_zh":
- for tmp in LangSegmenter.getTexts(text,"zh"):
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- elif language == "all_yue":
- for tmp in LangSegmenter.getTexts(text,"zh"):
- if tmp["lang"] == "zh":
- tmp["lang"] = "yue"
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- elif language == "all_ja":
- for tmp in LangSegmenter.getTexts(text,"ja"):
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- elif language == "all_ko":
- for tmp in LangSegmenter.getTexts(text,"ko"):
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- elif language == "en":
- langlist.append("en")
- textlist.append(text)
- elif 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 langlist:
- if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"):
- textlist[-1] += tmp["text"]
- continue
- 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:
- gr.Warning(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
- 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,
- ref_free=False,
- speed=1,
- if_freeze=False,
- inp_refs=None,
- sample_steps=8,
- if_sr=False,
- pause_second=0.3,
-):
- global cache
- if ref_wav_path:
- pass
- else:
- gr.Warning(i18n("请上传参考音频"))
- if text:
- pass
- else:
- gr.Warning(i18n("请填入推理文本"))
- 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
- if model_version not in {"v3", "v4", "v2Pro", "v2ProPlus"}:
- clean_bigvgan_model()
- clean_hifigan_model()
- clean_sv_cn_model()
- 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:
- gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
- 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()
- is_v2pro = model_version in {"v2Pro", "v2ProPlus"}
- # print(23333,is_v2pro,model_version)
- ###v3不存在以下逻辑和inp_refs
- if model_version not in v3v4set:
- refers = []
- if is_v2pro:
- sv_emb = []
- if sv_cn_model == None:
- init_sv_cn()
- if inp_refs:
- for path in inp_refs:
- try: #####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
- refer, audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro)
- refers.append(refer)
- if is_v2pro:
- sv_emb.append(sv_cn_model.compute_embedding3(audio_tensor))
- except:
- traceback.print_exc()
- if len(refers) == 0:
- refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro)
- refers = [refers]
- if is_v2pro:
- sv_emb = [sv_cn_model.compute_embedding3(audio_tensor)]
- if is_v2pro:
- audio = vq_model.decode(
- pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed, sv_emb=sv_emb
- )[0][0]
- else:
- audio = vq_model.decode(
- pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
- )[0][0]
- else:
- refer, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device)
- phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
- phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
- 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, device)
- # 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", "v2Pro", "v2ProPlus"}:
- 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()
- yield opt_sr, (audio_opt * 32767).astype(np.int16)
-
-
-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"(? 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 html_center(text, label="p"):
- return f"""
- <{label} style="margin: 0; padding: 0;">{text}
-
"""
-
-
-def html_left(text, label="p"):
- return f"""
- <{label} style="margin: 0; padding: 0;">{text}
-
"""
-
-
-with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
- gr.HTML(
- top_html.format(
- i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
- + i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
- ),
- elem_classes="markdown",
- )
- with gr.Group():
- gr.Markdown(html_center(i18n("模型切换"), "h3"))
- with gr.Row():
- GPT_dropdown = gr.Dropdown(
- label=i18n("GPT模型列表"),
- choices=sorted(GPT_names, key=custom_sort_key),
- value=gpt_path,
- interactive=True,
- scale=14,
- )
- SoVITS_dropdown = gr.Dropdown(
- label=i18n("SoVITS模型列表"),
- choices=sorted(SoVITS_names, key=custom_sort_key),
- value=sovits_path,
- interactive=True,
- scale=14,
- )
- refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
- refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
- gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
- with gr.Row():
- inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath", scale=13)
- with gr.Column(scale=13):
- ref_text_free = gr.Checkbox(
- label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。")
- + i18n("v3暂不支持该模式，使用了会报错。"),
- value=False,
- interactive=True if model_version not in v3v4set else False,
- show_label=True,
- scale=1,
- )
- gr.Markdown(
- html_left(
- i18n("使用无参考文本模式时建议使用微调的GPT")
- + "
"
- + i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
- )
- )
- prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=5, max_lines=5, scale=1)
- with gr.Column(scale=14):
- prompt_language = gr.Dropdown(
- label=i18n("参考音频的语种"),
- choices=list(dict_language.keys()),
- value=i18n("中文"),
- )
- inp_refs = (
- gr.File(
- label=i18n(
- "可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
- ),
- file_count="multiple",
- )
- if model_version not in v3v4set
- else gr.File(
- label=i18n(
- "可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
- ),
- file_count="multiple",
- visible=False,
- )
- )
- sample_steps = (
- gr.Radio(
- label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
- value=32 if model_version == "v3" else 8,
- choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
- visible=True,
- )
- if model_version in v3v4set
- else gr.Radio(
- label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
- choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
- visible=False,
- value=32 if model_version == "v3" else 8,
- )
- )
- if_sr_Checkbox = gr.Checkbox(
- label=i18n("v3输出如果觉得闷可以试试开超分"),
- value=False,
- interactive=True,
- show_label=True,
- visible=False if model_version != "v3" else True,
- )
- gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"), "h3"))
- with gr.Row():
- with gr.Column(scale=13):
- text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
- with gr.Column(scale=7):
- text_language = gr.Dropdown(
- label=i18n("需要合成的语种") + i18n(".限制范围越小判别效果越好。"),
- choices=list(dict_language.keys()),
- value=i18n("中文"),
- scale=1,
- )
- how_to_cut = gr.Dropdown(
- label=i18n("怎么切"),
- choices=[
- i18n("不切"),
- i18n("凑四句一切"),
- i18n("凑50字一切"),
- i18n("按中文句号。切"),
- i18n("按英文句号.切"),
- i18n("按标点符号切"),
- ],
- value=i18n("凑四句一切"),
- interactive=True,
- scale=1,
- )
- gr.Markdown(value=html_center(i18n("语速调整，高为更快")))
- if_freeze = gr.Checkbox(
- label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"),
- value=False,
- interactive=True,
- show_label=True,
- scale=1,
- )
- with gr.Row():
- speed = gr.Slider(
- minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True, scale=1
- )
- pause_second_slider = gr.Slider(
- minimum=0.1,
- maximum=0.5,
- step=0.01,
- label=i18n("句间停顿秒数"),
- value=0.3,
- interactive=True,
- scale=1,
- )
- gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
- top_k = gr.Slider(
- minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True, scale=1
- )
- top_p = gr.Slider(
- minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True, scale=1
- )
- temperature = gr.Slider(
- minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True, scale=1
- )
- # with gr.Column():
- # gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
- # phoneme=gr.Textbox(label=i18n("音素框"), value="")
- # get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
- with gr.Row():
- inference_button = gr.Button(value=i18n("合成语音"), variant="primary", size="lg", scale=25)
- output = gr.Audio(label=i18n("输出的语音"), scale=14)
-
- inference_button.click(
- get_tts_wav,
- [
- inp_ref,
- prompt_text,
- prompt_language,
- text,
- text_language,
- how_to_cut,
- top_k,
- top_p,
- temperature,
- ref_text_free,
- speed,
- if_freeze,
- inp_refs,
- sample_steps,
- if_sr_Checkbox,
- pause_second_slider,
- ],
- [output],
- )
- SoVITS_dropdown.change(
- change_sovits_weights,
- [SoVITS_dropdown, prompt_language, text_language],
- [
- prompt_language,
- text_language,
- prompt_text,
- prompt_language,
- text,
- text_language,
- sample_steps,
- inp_refs,
- ref_text_free,
- if_sr_Checkbox,
- inference_button,
- ],
- )
- GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
-
- # gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
- # with gr.Row():
- # text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
- # button1 = gr.Button(i18n("凑四句一切"), variant="primary")
- # button2 = gr.Button(i18n("凑50字一切"), variant="primary")
- # button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
- # button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
- # button5 = gr.Button(i18n("按标点符号切"), variant="primary")
- # text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
- # button1.click(cut1, [text_inp], [text_opt])
- # button2.click(cut2, [text_inp], [text_opt])
- # button3.click(cut3, [text_inp], [text_opt])
- # button4.click(cut4, [text_inp], [text_opt])
- # button5.click(cut5, [text_inp], [text_opt])
- # gr.Markdown(html_center(i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")))
-
-if __name__ == "__main__":
- app.queue().launch( # concurrency_count=511, max_size=1022
- server_name="0.0.0.0",
- inbrowser=True,
- share=is_share,
- server_port=infer_ttswebui,
- # quiet=True,
- )
+"""
+按中英混合识别
+按日英混合识别
+多语种启动切分识别语种
+全部按中文识别
+全部按英文识别
+全部按日文识别
+"""
+import psutil
+import os
+
+def set_high_priority():
+ """把当前 Python 进程设为 HIGH_PRIORITY_CLASS"""
+ if os.name != "nt":
+ return # 仅 Windows 有效
+ p = psutil.Process(os.getpid())
+ try:
+ p.nice(psutil.HIGH_PRIORITY_CLASS)
+ print("已将进程优先级设为 High")
+ except psutil.AccessDenied:
+ print("权限不足，无法修改优先级（请用管理员运行）")
+set_high_priority()
+import json
+import logging
+import os
+import re
+import sys
+import traceback
+import warnings
+
+import torch
+import torchaudio
+from text.LangSegmenter import LangSegmenter
+
+logging.getLogger("markdown_it").setLevel(logging.ERROR)
+logging.getLogger("urllib3").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("asyncio").setLevel(logging.ERROR)
+logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
+logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
+logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
+warnings.simplefilter(action="ignore", category=FutureWarning)
+
+version = model_version = os.environ.get("version", "v2")
+
+from config import change_choices, get_weights_names, name2gpt_path, name2sovits_path
+
+SoVITS_names, GPT_names = get_weights_names()
+from config import pretrained_sovits_name
+
+path_sovits_v3 = pretrained_sovits_name["v3"]
+path_sovits_v4 = pretrained_sovits_name["v4"]
+is_exist_s2gv3 = os.path.exists(path_sovits_v3)
+is_exist_s2gv4 = os.path.exists(path_sovits_v4)
+
+if os.path.exists("./weight.json"):
+ pass
+else:
+ with open("./weight.json", "w", encoding="utf-8") as file:
+ json.dump({"GPT": {}, "SoVITS": {}}, file)
+
+with open("./weight.json", "r", encoding="utf-8") as file:
+ weight_data = file.read()
+ weight_data = json.loads(weight_data)
+ gpt_path = os.environ.get("gpt_path", weight_data.get("GPT", {}).get(version, GPT_names[-1]))
+ sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, SoVITS_names[0]))
+ if isinstance(gpt_path, list):
+ gpt_path = gpt_path[0]
+ if isinstance(sovits_path, list):
+ sovits_path = sovits_path[0]
+
+# print(2333333)
+# print(os.environ["gpt_path"])
+# print(gpt_path)
+# print(GPT_names)
+# print(weight_data)
+# print(weight_data.get("GPT", {}))
+# print(version)###GPT version里没有s2的v2pro
+# print(weight_data.get("GPT", {}).get(version, GPT_names[-1]))
+
+cnhubert_base_path = os.environ.get("cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base")
+bert_path = os.environ.get("bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large")
+infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
+infer_ttswebui = int(infer_ttswebui)
+is_share = os.environ.get("is_share", "False")
+is_share = eval(is_share)
+if "_CUDA_VISIBLE_DEVICES" in os.environ:
+ os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
+# is_half=False
+punctuation = set(["!", "?", "…", ",", ".", "-", " "])
+import gradio as gr
+import librosa
+import numpy as np
+from feature_extractor import cnhubert
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+import random
+
+from GPT_SoVITS.module.models import Generator, SynthesizerTrn, SynthesizerTrnV3
+
+
+def set_seed(seed):
+ if seed == -1:
+ seed = random.randint(0, 1000000)
+ seed = int(seed)
+ random.seed(seed)
+ os.environ["PYTHONHASHSEED"] = str(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+
+
+# set_seed(42)
+
+from time import time as ttime
+
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from peft import LoraConfig, get_peft_model
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+
+from tools.assets import css, js, top_html
+from tools.i18n.i18n import I18nAuto, scan_language_list
+
+language = os.environ.get("language", "Auto")
+language = sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
+i18n = I18nAuto(language=language)
+
+# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
+
+if torch.cuda.is_available():
+ device = "cuda"
+else:
+ device = "cpu"
+
+
+def check_cuda_graph_support():
+ if device != "cuda":
+ return False
+ try:
+ major, _ = torch.cuda.get_device_capability()
+ if major < 7:
+ print("CUDA Graph: GPU compute capability < 7.0, disabled")
+ return False
+ a = torch.randn(2, 2, device="cuda")
+ g = torch.cuda.CUDAGraph()
+ s = torch.cuda.Stream()
+ s.wait_stream(torch.cuda.current_stream())
+ with torch.cuda.stream(s):
+ b = a * 2
+ torch.cuda.current_stream().wait_stream(s)
+ out = torch.empty_like(b)
+ with torch.cuda.graph(g):
+ out.copy_(a * 2)
+ g.replay()
+ torch.cuda.synchronize()
+ del a, b, out, g, s
+ torch.cuda.empty_cache()
+ print("CUDA Graph: support check passed, auto-enabled")
+ return True
+ except Exception as e:
+ print(f"CUDA Graph: support check failed ({e}), disabled")
+ return False
+
+
+cuda_graph_supported = check_cuda_graph_support()
+
+dict_language_v1 = {
+ i18n("中文"): "all_zh", # 全部按中文识别
+ i18n("英文"): "en", # 全部按英文识别#######不变
+ i18n("日文"): "all_ja", # 全部按日文识别
+ i18n("中英混合"): "zh", # 按中英混合识别####不变
+ i18n("日英混合"): "ja", # 按日英混合识别####不变
+ i18n("多语种混合"): "auto", # 多语种启动切分识别语种
+}
+dict_language_v2 = {
+ i18n("中文"): "all_zh", # 全部按中文识别
+ i18n("英文"): "en", # 全部按英文识别#######不变
+ i18n("日文"): "all_ja", # 全部按日文识别
+ i18n("粤语"): "all_yue", # 全部按中文识别
+ i18n("韩文"): "all_ko", # 全部按韩文识别
+ i18n("中英混合"): "zh", # 按中英混合识别####不变
+ i18n("日英混合"): "ja", # 按日英混合识别####不变
+ i18n("粤英混合"): "yue", # 按粤英混合识别####不变
+ i18n("韩英混合"): "ko", # 按韩英混合识别####不变
+ i18n("多语种混合"): "auto", # 多语种启动切分识别语种
+ i18n("多语种混合(粤语)"): "auto_yue", # 多语种启动切分识别语种
+}
+dict_language = dict_language_v1 if version == "v1" else dict_language_v2
+
+tokenizer = AutoTokenizer.from_pretrained(bert_path)
+bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
+if is_half == True:
+ bert_model = bert_model.half().to(device)
+else:
+ bert_model = bert_model.to(device)
+
+
+def get_bert_feature(text, word2ph):
+ with torch.no_grad():
+ inputs = tokenizer(text, return_tensors="pt")
+ for i in inputs:
+ inputs[i] = inputs[i].to(device)
+ res = bert_model(**inputs, output_hidden_states=True)
+ res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
+ assert len(word2ph) == len(text)
+ phone_level_feature = []
+ for i in range(len(word2ph)):
+ repeat_feature = res[i].repeat(word2ph[i], 1)
+ phone_level_feature.append(repeat_feature)
+ phone_level_feature = torch.cat(phone_level_feature, dim=0)
+ return phone_level_feature.T
+
+
+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")
+
+
+ssl_model = cnhubert.get_model()
+if is_half == True:
+ ssl_model = ssl_model.half().to(device)
+else:
+ ssl_model = ssl_model.to(device)
+
+
+###todo:put them to process_ckpt and modify my_save func (save sovits weights), gpt save weights use my_save in process_ckpt
+# symbol_version-model_version-if_lora_v3
+from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
+
+v3v4set = {"v3", "v4"}
+
+
+def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
+ if "！" in sovits_path or "!" in sovits_path:
+ sovits_path = name2sovits_path[sovits_path]
+ global vq_model, hps, version, model_version, dict_language, if_lora_v3, t2s_model_cudagraph
+ t2s_model_cudagraph = None
+ version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
+ print(sovits_path, version, model_version, if_lora_v3)
+ is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
+ path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
+ if if_lora_v3 == True and is_exist == False:
+ info = path_sovits + "SoVITS %s" % model_version + i18n("底模缺失，无法加载相应 LoRA 权重")
+ gr.Warning(info)
+ raise FileExistsError(info)
+ dict_language = dict_language_v1 if version == "v1" else dict_language_v2
+ if prompt_language is not None and text_language is not None:
+ if prompt_language in list(dict_language.keys()):
+ prompt_text_update, prompt_language_update = (
+ {"__type__": "update"},
+ {"__type__": "update", "value": prompt_language},
+ )
+ else:
+ prompt_text_update = {"__type__": "update", "value": ""}
+ prompt_language_update = {"__type__": "update", "value": i18n("中文")}
+ if text_language in list(dict_language.keys()):
+ text_update, text_language_update = {"__type__": "update"}, {"__type__": "update", "value": text_language}
+ else:
+ text_update = {"__type__": "update", "value": ""}
+ text_language_update = {"__type__": "update", "value": i18n("中文")}
+ if model_version in v3v4set:
+ visible_sample_steps = True
+ visible_inp_refs = False
+ else:
+ visible_sample_steps = False
+ visible_inp_refs = True
+ 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("模型加载中，请等待"), "interactive": False},
+ )
+
+ dict_s2 = load_sovits_new(sovits_path)
+ hps = dict_s2["config"]
+ hps = DictToAttrRecursive(hps)
+ hps.model.semantic_frame_rate = "25hz"
+ if "enc_p.text_embedding.weight" not in dict_s2["weight"]:
+ hps.model.version = "v2" # v3model,v2sybomls
+ elif dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322:
+ hps.model.version = "v1"
+ else:
+ hps.model.version = "v2"
+ version = hps.model.version
+ # print("sovits版本:",hps.model.version)
+ if model_version not in v3v4set:
+ if "Pro" not in model_version:
+ model_version = version
+ else:
+ hps.model.version = model_version
+ 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,
+ )
+ else:
+ hps.model.version = model_version
+ vq_model = SynthesizerTrnV3(
+ hps.data.filter_length // 2 + 1,
+ hps.train.segment_size // hps.data.hop_length,
+ n_speakers=hps.data.n_speakers,
+ **hps.model,
+ )
+ if "pretrained" not in sovits_path:
+ try:
+ del vq_model.enc_q
+ except:
+ pass
+ if is_half == True:
+ vq_model = vq_model.half().to(device)
+ else:
+ vq_model = vq_model.to(device)
+ vq_model.eval()
+ if if_lora_v3 == False:
+ print("loading sovits_%s" % model_version, vq_model.load_state_dict(dict_s2["weight"], strict=False))
+ else:
+ 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
+
+
+t2s_model_cudagraph = None
+gpt_path_global = None
+
+
+def change_gpt_weights(gpt_path):
+ if "！" in gpt_path or "!" in gpt_path:
+ gpt_path = name2gpt_path[gpt_path]
+ global hz, max_sec, t2s_model, config, t2s_model_cudagraph, gpt_path_global
+ t2s_model_cudagraph = None
+ gpt_path_global = gpt_path
+ hz = 50
+ dict_s1 = torch.load(gpt_path, map_location="cpu", weights_only=False)
+ 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 clean_hifigan_model():
+ global hifigan_model
+ if hifigan_model:
+ hifigan_model = hifigan_model.cpu()
+ hifigan_model = None
+ try:
+ torch.cuda.empty_cache()
+ except:
+ pass
+
+
+def clean_bigvgan_model():
+ global bigvgan_model
+ if bigvgan_model:
+ bigvgan_model = bigvgan_model.cpu()
+ bigvgan_model = None
+ try:
+ torch.cuda.empty_cache()
+ except:
+ pass
+
+
+def clean_sv_cn_model():
+ global sv_cn_model
+ if sv_cn_model:
+ sv_cn_model.embedding_model = sv_cn_model.embedding_model.cpu()
+ sv_cn_model = None
+ try:
+ torch.cuda.empty_cache()
+ except:
+ pass
+
+
+def init_bigvgan():
+ global bigvgan_model, hifigan_model, sv_cn_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()
+ clean_hifigan_model()
+ clean_sv_cn_model()
+ 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, sv_cn_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",
+ weights_only=False,
+ )
+ print("loading vocoder", hifigan_model.load_state_dict(state_dict_g))
+ clean_bigvgan_model()
+ clean_sv_cn_model()
+ if is_half == True:
+ hifigan_model = hifigan_model.half().to(device)
+ else:
+ hifigan_model = hifigan_model.to(device)
+
+
+from sv import SV
+
+
+def init_sv_cn():
+ global hifigan_model, bigvgan_model, sv_cn_model
+ sv_cn_model = SV(device, is_half)
+ clean_bigvgan_model()
+ clean_hifigan_model()
+
+
+bigvgan_model = hifigan_model = sv_cn_model = None
+if model_version == "v3":
+ init_bigvgan()
+if model_version == "v4":
+ init_hifigan()
+if model_version in {"v2Pro", "v2ProPlus"}:
+ init_sv_cn()
+
+resample_transform_dict = {}
+
+
+def resample(audio_tensor, sr0, sr1, device):
+ global resample_transform_dict
+ key = "%s-%s-%s" % (sr0, sr1, str(device))
+ if key not in resample_transform_dict:
+ resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
+ return resample_transform_dict[key](audio_tensor)
+
+
+def get_spepc(hps, filename, dtype, device, is_v2pro=False):
+ # 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)
+
+ sr1 = int(hps.data.sampling_rate)
+ audio, sr0 = torchaudio.load(filename)
+ if sr0 != sr1:
+ audio = audio.to(device)
+ if audio.shape[0] == 2:
+ audio = audio.mean(0).unsqueeze(0)
+ audio = resample(audio, sr0, sr1, device)
+ else:
+ audio = audio.to(device)
+ if audio.shape[0] == 2:
+ audio = audio.mean(0).unsqueeze(0)
+
+ maxx = audio.abs().max()
+ if maxx > 1:
+ audio /= min(2, maxx)
+ spec = spectrogram_torch(
+ audio,
+ hps.data.filter_length,
+ hps.data.sampling_rate,
+ hps.data.hop_length,
+ hps.data.win_length,
+ center=False,
+ )
+ spec = spec.to(dtype)
+ if is_v2pro == True:
+ audio = resample(audio, sr1, 16000, device).to(dtype)
+ return spec, audio
+
+
+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):
+ text = re.sub(r' {2,}', ' ', text)
+ textlist = []
+ langlist = []
+ if language == "all_zh":
+ for tmp in LangSegmenter.getTexts(text,"zh"):
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ elif language == "all_yue":
+ for tmp in LangSegmenter.getTexts(text,"zh"):
+ if tmp["lang"] == "zh":
+ tmp["lang"] = "yue"
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ elif language == "all_ja":
+ for tmp in LangSegmenter.getTexts(text,"ja"):
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ elif language == "all_ko":
+ for tmp in LangSegmenter.getTexts(text,"ko"):
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ elif language == "en":
+ langlist.append("en")
+ textlist.append(text)
+ elif 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 langlist:
+ if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"):
+ textlist[-1] += tmp["text"]
+ continue
+ 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:
+ gr.Warning(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
+ 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,
+ ref_free=False,
+ speed=1,
+ if_freeze=False,
+ inp_refs=None,
+ sample_steps=8,
+ if_sr=False,
+ pause_second=0.3,
+ use_cuda_graph=False,
+):
+ global cache
+ if ref_wav_path:
+ pass
+ else:
+ gr.Warning(i18n("请上传参考音频"))
+ if text:
+ pass
+ else:
+ gr.Warning(i18n("请填入推理文本"))
+ 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
+ if model_version not in {"v3", "v4", "v2Pro", "v2ProPlus"}:
+ clean_bigvgan_model()
+ clean_hifigan_model()
+ clean_sv_cn_model()
+ 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:
+ gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
+ 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:
+ if use_cuda_graph and device == "cuda":
+ global t2s_model_cudagraph
+ if t2s_model_cudagraph is None:
+ from AR.models.t2s_model_cudagraph import CUDAGraphRunner
+ t2s_model_cudagraph = CUDAGraphRunner(
+ CUDAGraphRunner.load_decoder(gpt_path_global),
+ torch.device(device),
+ torch.float16 if is_half else torch.float32,
+ )
+ from AR.models.structs_cudagraph import T2SRequest
+ with torch.no_grad():
+ t2s_request = T2SRequest(
+ [all_phoneme_ids.squeeze(0)],
+ all_phoneme_len,
+ all_phoneme_ids.new_zeros((1, 0)) if ref_free else prompt,
+ [bert.squeeze(0)],
+ valid_length=1,
+ top_k=top_k,
+ top_p=top_p,
+ temperature=temperature,
+ early_stop_num=hz * max_sec,
+ use_cuda_graph=True,
+ )
+ t2s_result = t2s_model_cudagraph.generate(t2s_request)
+ if t2s_result.exception is not None:
+ print(t2s_result.exception)
+ print(t2s_result.traceback)
+ raise RuntimeError("CUDA Graph T2S inference failed")
+ pred_semantic = t2s_result.result[0].unsqueeze(0).unsqueeze(0)
+ cache[i_text] = pred_semantic
+ 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()
+ is_v2pro = model_version in {"v2Pro", "v2ProPlus"}
+ # print(23333,is_v2pro,model_version)
+ ###v3不存在以下逻辑和inp_refs
+ if model_version not in v3v4set:
+ refers = []
+ if is_v2pro:
+ sv_emb = []
+ if sv_cn_model == None:
+ init_sv_cn()
+ if inp_refs:
+ for path in inp_refs:
+ try: #####这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
+ refer, audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro)
+ refers.append(refer)
+ if is_v2pro:
+ sv_emb.append(sv_cn_model.compute_embedding3(audio_tensor))
+ except:
+ traceback.print_exc()
+ if len(refers) == 0:
+ refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro)
+ refers = [refers]
+ if is_v2pro:
+ sv_emb = [sv_cn_model.compute_embedding3(audio_tensor)]
+ if is_v2pro:
+ audio = vq_model.decode(
+ pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed, sv_emb=sv_emb
+ )[0][0]
+ else:
+ audio = vq_model.decode(
+ pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
+ )[0][0]
+ else:
+ refer, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device)
+ phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
+ phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
+ 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, device)
+ # 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", "v2Pro", "v2ProPlus"}:
+ 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()
+ yield opt_sr, (audio_opt * 32767).astype(np.int16)
+
+
+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"(? 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 html_center(text, label="p"):
+ return f"""
+ <{label} style="margin: 0; padding: 0;">{text}
+
"""
+
+
+def html_left(text, label="p"):
+ return f"""
+ <{label} style="margin: 0; padding: 0;">{text}
+
"""
+
+
+with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css) as app:
+ gr.HTML(
+ top_html.format(
+ i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.")
+ + i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
+ ),
+ elem_classes="markdown",
+ )
+ with gr.Group():
+ gr.Markdown(html_center(i18n("模型切换"), "h3"))
+ with gr.Row():
+ GPT_dropdown = gr.Dropdown(
+ label=i18n("GPT模型列表"),
+ choices=sorted(GPT_names, key=custom_sort_key),
+ value=gpt_path,
+ interactive=True,
+ scale=14,
+ )
+ SoVITS_dropdown = gr.Dropdown(
+ label=i18n("SoVITS模型列表"),
+ choices=sorted(SoVITS_names, key=custom_sort_key),
+ value=sovits_path,
+ interactive=True,
+ scale=14,
+ )
+ refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
+ refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+ gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
+ with gr.Row():
+ inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath", scale=13)
+ with gr.Column(scale=13):
+ ref_text_free = gr.Checkbox(
+ label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。")
+ + i18n("v3暂不支持该模式，使用了会报错。"),
+ value=False,
+ interactive=True if model_version not in v3v4set else False,
+ show_label=True,
+ scale=1,
+ )
+ gr.Markdown(
+ html_left(
+ i18n("使用无参考文本模式时建议使用微调的GPT")
+ + "
"
+ + i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
+ )
+ )
+ prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=5, max_lines=5, scale=1)
+ with gr.Column(scale=14):
+ prompt_language = gr.Dropdown(
+ label=i18n("参考音频的语种"),
+ choices=list(dict_language.keys()),
+ value=i18n("中文"),
+ )
+ inp_refs = (
+ gr.File(
+ label=i18n(
+ "可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
+ ),
+ file_count="multiple",
+ )
+ if model_version not in v3v4set
+ else gr.File(
+ label=i18n(
+ "可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
+ ),
+ file_count="multiple",
+ visible=False,
+ )
+ )
+ sample_steps = (
+ gr.Radio(
+ label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
+ value=32 if model_version == "v3" else 8,
+ choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
+ visible=True,
+ )
+ if model_version in v3v4set
+ else gr.Radio(
+ label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
+ choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
+ visible=False,
+ value=32 if model_version == "v3" else 8,
+ )
+ )
+ if_sr_Checkbox = gr.Checkbox(
+ label=i18n("v3输出如果觉得闷可以试试开超分"),
+ value=False,
+ interactive=True,
+ show_label=True,
+ visible=False if model_version != "v3" else True,
+ )
+ gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"), "h3"))
+ with gr.Row():
+ with gr.Column(scale=13):
+ text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
+ with gr.Column(scale=7):
+ text_language = gr.Dropdown(
+ label=i18n("需要合成的语种") + i18n(".限制范围越小判别效果越好。"),
+ choices=list(dict_language.keys()),
+ value=i18n("中文"),
+ scale=1,
+ )
+ how_to_cut = gr.Dropdown(
+ label=i18n("怎么切"),
+ choices=[
+ i18n("不切"),
+ i18n("凑四句一切"),
+ i18n("凑50字一切"),
+ i18n("按中文句号。切"),
+ i18n("按英文句号.切"),
+ i18n("按标点符号切"),
+ ],
+ value=i18n("凑四句一切"),
+ interactive=True,
+ scale=1,
+ )
+ gr.Markdown(value=html_center(i18n("语速调整，高为更快")))
+ if_freeze = gr.Checkbox(
+ label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"),
+ value=False,
+ interactive=True,
+ show_label=True,
+ scale=1,
+ )
+ with gr.Row():
+ speed = gr.Slider(
+ minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True, scale=1
+ )
+ pause_second_slider = gr.Slider(
+ minimum=0.1,
+ maximum=0.5,
+ step=0.01,
+ label=i18n("句间停顿秒数"),
+ value=0.3,
+ interactive=True,
+ scale=1,
+ )
+ gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
+ top_k = gr.Slider(
+ minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True, scale=1
+ )
+ top_p = gr.Slider(
+ minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True, scale=1
+ )
+ temperature = gr.Slider(
+ minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True, scale=1
+ )
+ # with gr.Column():
+ # gr.Markdown(value=i18n("手工调整音素。当音素框不为空时使用手工音素输入推理，无视目标文本框。"))
+ # phoneme=gr.Textbox(label=i18n("音素框"), value="")
+ # get_phoneme_button = gr.Button(i18n("目标文本转音素"), variant="primary")
+ with gr.Row():
+ inference_button = gr.Button(value=i18n("合成语音"), variant="primary", size="lg", scale=25)
+ use_cuda_graph_checkbox = gr.Checkbox(
+ label="CUDA Graph " + i18n("加速"),
+ value=cuda_graph_supported,
+ interactive=True if torch.cuda.is_available() else False,
+ show_label=True,
+ scale=5,
+ visible=False,
+ )
+ output = gr.Audio(label=i18n("输出的语音"), scale=14)
+
+ inference_button.click(
+ get_tts_wav,
+ [
+ inp_ref,
+ prompt_text,
+ prompt_language,
+ text,
+ text_language,
+ how_to_cut,
+ top_k,
+ top_p,
+ temperature,
+ ref_text_free,
+ speed,
+ if_freeze,
+ inp_refs,
+ sample_steps,
+ if_sr_Checkbox,
+ pause_second_slider,
+ use_cuda_graph_checkbox,
+ ],
+ [output],
+ )
+ SoVITS_dropdown.change(
+ change_sovits_weights,
+ [SoVITS_dropdown, prompt_language, text_language],
+ [
+ prompt_language,
+ text_language,
+ prompt_text,
+ prompt_language,
+ text,
+ text_language,
+ sample_steps,
+ inp_refs,
+ ref_text_free,
+ if_sr_Checkbox,
+ inference_button,
+ ],
+ )
+ GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
+
+ # gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"))
+ # with gr.Row():
+ # text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="")
+ # button1 = gr.Button(i18n("凑四句一切"), variant="primary")
+ # button2 = gr.Button(i18n("凑50字一切"), variant="primary")
+ # button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
+ # button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
+ # button5 = gr.Button(i18n("按标点符号切"), variant="primary")
+ # text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
+ # button1.click(cut1, [text_inp], [text_opt])
+ # button2.click(cut2, [text_inp], [text_opt])
+ # button3.click(cut3, [text_inp], [text_opt])
+ # button4.click(cut4, [text_inp], [text_opt])
+ # button5.click(cut5, [text_inp], [text_opt])
+ # gr.Markdown(html_center(i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")))
+
+if __name__ == "__main__":
+ app.queue().launch( # concurrency_count=511, max_size=1022
+ server_name="0.0.0.0",
+ inbrowser=True,
+ share=is_share,
+ server_port=infer_ttswebui,
+ # quiet=True,
+ )