From cb88b8b9eb28f5ae302cc5527b3b1c64df91e8c0 Mon Sep 17 00:00:00 2001
From: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com>
Date: Wed, 7 Feb 2024 15:11:34 +0800
Subject: [PATCH] Update inference_webui.py
---
GPT_SoVITS/inference_webui.py | 1262 ++++++++++++++++-----------------
1 file changed, 630 insertions(+), 632 deletions(-)
diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index e6ab7f7..4f89950 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -1,632 +1,630 @@
-'''
-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 os, re, logging
-import LangSegment
-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)
-import pdb
-
-if os.path.exists("./gweight.txt"):
- with open("./gweight.txt", 'r', encoding="utf-8") as file:
- gweight_data = file.read()
- gpt_path = os.environ.get(
- "gpt_path", gweight_data)
-else:
- gpt_path = os.environ.get(
- "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
-
-if os.path.exists("./sweight.txt"):
- with open("./sweight.txt", 'r', encoding="utf-8") as file:
- sweight_data = file.read()
- sovits_path = os.environ.get("sovits_path", sweight_data)
-else:
- sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
-# gpt_path = os.environ.get(
-# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-# )
-# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
-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"))
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa, torch
-from feature_extractor import cnhubert
-
-cnhubert.cnhubert_base_path = cnhubert_base_path
-
-from module.models import SynthesizerTrn
-from AR.models.t2s_lightning_module import Text2SemanticLightningModule
-from text import cleaned_text_to_sequence
-from text.cleaner import clean_text
-from time import time as ttime
-from module.mel_processing import spectrogram_torch
-from my_utils import load_audio
-from tools.i18n.i18n import I18nAuto
-
-i18n = I18nAuto()
-
-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"
-elif torch.backends.mps.is_available():
- device = "mps"
-else:
- device = "cpu"
-
-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):
- print(23333334444444,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)
-
-
-def change_sovits_weights(sovits_path):
- global vq_model, hps
- dict_s2 = torch.load(sovits_path, map_location="cpu")
- hps = dict_s2["config"]
- hps = DictToAttrRecursive(hps)
- hps.model.semantic_frame_rate = "25hz"
- vq_model = SynthesizerTrn(
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model
- )
- if ("pretrained" not in sovits_path):
- del vq_model.enc_q
- if is_half == True:
- vq_model = vq_model.half().to(device)
- else:
- vq_model = vq_model.to(device)
- vq_model.eval()
- print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
- with open("./sweight.txt", "w", encoding="utf-8") as f:
- f.write(sovits_path)
-
-
-change_sovits_weights(sovits_path)
-
-
-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("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
-
-
-change_gpt_weights(gpt_path)
-
-
-def get_spepc(hps, filename):
- audio = load_audio(filename, int(hps.data.sampling_rate))
- audio = torch.FloatTensor(audio)
- 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
-
-
-dict_language = {
- 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
-}
-
-
-def splite_en_inf(sentence, language):
- pattern = re.compile(r'[a-zA-Z ]+')
- textlist = []
- langlist = []
- pos = 0
- for match in pattern.finditer(sentence):
- start, end = match.span()
- if start > pos:
- textlist.append(sentence[pos:start])
- langlist.append(language)
- textlist.append(sentence[start:end])
- langlist.append("en")
- pos = end
- if pos < len(sentence):
- textlist.append(sentence[pos:])
- langlist.append(language)
- # Merge punctuation into previous word
- for i in range(len(textlist)-1, 0, -1):
- if re.match(r'^[\W_]+$', textlist[i]):
- textlist[i-1] += textlist[i]
- del textlist[i]
- del langlist[i]
- # Merge consecutive words with the same language tag
- i = 0
- while i < len(langlist) - 1:
- if langlist[i] == langlist[i+1]:
- textlist[i] += textlist[i+1]
- del textlist[i+1]
- del langlist[i+1]
- else:
- i += 1
-
- return textlist, langlist
-
-
-def clean_text_inf(text, language):
- phones, word2ph, norm_text = clean_text(text, language.replace("all_",""))
- phones = cleaned_text_to_sequence(phones)
- 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
-
-
-def nonen_clean_text_inf(text, language):
- if(language!="auto"):
- textlist, langlist = splite_en_inf(text, language)
- else:
- textlist=[]
- langlist=[]
- for tmp in LangSegment.getTexts(text):
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- print(textlist)
- print(langlist)
- phones_list = []
- word2ph_list = []
- norm_text_list = []
- for i in range(len(textlist)):
- lang = langlist[i]
- phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
- phones_list.append(phones)
- if lang == "zh":
- word2ph_list.append(word2ph)
- norm_text_list.append(norm_text)
- print(word2ph_list)
- phones = sum(phones_list, [])
- word2ph = sum(word2ph_list, [])
- norm_text = ' '.join(norm_text_list)
-
- return phones, word2ph, norm_text
-
-
-def nonen_get_bert_inf(text, language):
- if(language!="auto"):
- textlist, langlist = splite_en_inf(text, language)
- else:
- textlist=[]
- langlist=[]
- for tmp in LangSegment.getTexts(text):
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- print(textlist)
- print(langlist)
- bert_list = []
- for i in range(len(textlist)):
- text = textlist[i]
- lang = langlist[i]
- phones, word2ph, norm_text = clean_text_inf(text, lang)
- bert = get_bert_inf(phones, word2ph, norm_text, lang)
- bert_list.append(bert)
- bert = torch.cat(bert_list, dim=1)
-
- 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
-
-
-def get_cleaned_text_final(text,language):
- if language in {"en","all_zh","all_ja"}:
- phones, word2ph, norm_text = clean_text_inf(text, language)
- elif language in {"zh", "ja","auto"}:
- phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
- return phones, word2ph, norm_text
-
-def get_bert_final(phones, word2ph, text,language,device):
- if language == "en":
- bert = get_bert_inf(phones, word2ph, text, language)
- elif language in {"zh", "ja","auto"}:
- bert = nonen_get_bert_inf(text, language)
- elif language == "all_zh":
- bert = get_bert_feature(text, word2ph).to(device)
- else:
- bert = torch.zeros((1024, len(phones))).to(device)
- return bert
-
-def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")):
- t0 = ttime()
- prompt_language = dict_language[prompt_language]
- text_language = dict_language[text_language]
- prompt_text = prompt_text.strip("\n")
- if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
- 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("实际输入的参考文本:"), prompt_text)
- print(i18n("实际输入的目标文本:"), text)
- zero_wav = np.zeros(
- int(hps.data.sampling_rate * 0.3),
- dtype=np.float16 if is_half == True else np.float32,
- )
- 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)
- zero_wav_torch = torch.from_numpy(zero_wav)
- if is_half == True:
- wav16k = wav16k.half().to(device)
- zero_wav_torch = zero_wav_torch.half().to(device)
- else:
- wav16k = wav16k.to(device)
- zero_wav_torch = zero_wav_torch.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]
- t1 = ttime()
-
- phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
-
- 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)
- text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n")
- print(i18n("实际输入的目标文本(切句后):"), text)
- texts = text.split("\n")
- audio_opt = []
- bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
-
- for text in 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, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
- bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
- print(bert1,bert2)
- bert = torch.cat([bert1, bert2], 1)
-
- all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
- bert = bert.to(device).unsqueeze(0)
- all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
- prompt = prompt_semantic.unsqueeze(0).to(device)
- t2 = ttime()
- with torch.no_grad():
- # pred_semantic = t2s_model.model.infer(
- pred_semantic, idx = t2s_model.model.infer_panel(
- all_phoneme_ids,
- all_phoneme_len,
- prompt,
- bert,
- # prompt_phone_len=ph_offset,
- top_k=config["inference"]["top_k"],
- early_stop_num=hz * max_sec,
- )
- t3 = ttime()
- # print(pred_semantic.shape,idx)
- pred_semantic = pred_semantic[:, -idx:].unsqueeze(
- 0
- ) # .unsqueeze(0)#mq要多unsqueeze一次
- refer = get_spepc(hps, ref_wav_path) # .to(device)
- if is_half == True:
- refer = refer.half().to(device)
- else:
- refer = refer.to(device)
- # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
- audio = (
- vq_model.decode(
- pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
- )
- .detach()
- .cpu()
- .numpy()[0, 0]
- ) ###试试重建不带上prompt部分
- max_audio=np.abs(audio).max()#简单防止16bit爆音
- if max_audio>1:audio/=max_audio
- audio_opt.append(audio)
- audio_opt.append(zero_wav)
- t4 = ttime()
- print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
- yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).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]
- 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]
- return "\n".join(opts)
-
-
-def cut3(inp):
- inp = inp.strip("\n")
- return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
-
-
-def cut4(inp):
- inp = inp.strip("\n")
- return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
-
-
-# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
-def cut5(inp):
- # if not re.search(r'[^\w\s]', inp[-1]):
- # inp += '。'
- inp = inp.strip("\n")
- punds = r'[,.;?!、，。？！;：]'
- items = re.split(f'({punds})', inp)
- items = ["".join(group) for group in zip(items[::2], items[1::2])]
- opt = "\n".join(items)
- return 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 change_choices():
- SoVITS_names, GPT_names = get_weights_names()
- return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
-
-
-pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
-pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
-SoVITS_weight_root = "SoVITS_weights"
-GPT_weight_root = "GPT_weights"
-os.makedirs(SoVITS_weight_root, exist_ok=True)
-os.makedirs(GPT_weight_root, exist_ok=True)
-
-
-def get_weights_names():
- SoVITS_names = [pretrained_sovits_name]
- for name in os.listdir(SoVITS_weight_root):
- if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
- GPT_names = [pretrained_gpt_name]
- for name in os.listdir(GPT_weight_root):
- if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
- return SoVITS_names, GPT_names
-
-
-SoVITS_names, GPT_names = get_weights_names()
-
-with gr.Blocks(title="GPT-SoVITS WebUI") as app:
- gr.Markdown(
- value=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.
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.")
- )
- with gr.Group():
- gr.Markdown(value=i18n("模型切换"))
- 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)
- SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
- refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
- refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
- SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
- GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
- gr.Markdown(value=i18n("*请上传并填写参考信息"))
- 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")
- prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
- prompt_language = gr.Dropdown(
- label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
- )
- gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
- with gr.Row():
- text = gr.Textbox(label=i18n("需要合成的文本"), value="")
- text_language = gr.Dropdown(
- label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
- )
- how_to_cut = gr.Radio(
- 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,
- )
- inference_button = gr.Button(i18n("合成语音"), variant="primary")
- output = gr.Audio(label=i18n("输出的语音"))
-
- inference_button.click(
- get_tts_wav,
- [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut],
- [output],
- )
-
- 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(value=i18n("后续将支持混合语种编码文本输入。"))
-
-app.queue(concurrency_count=511, max_size=1022).launch(
- 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 os, re, logging
+import LangSegment
+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)
+import pdb
+
+if os.path.exists("./gweight.txt"):
+ with open("./gweight.txt", 'r', encoding="utf-8") as file:
+ gweight_data = file.read()
+ gpt_path = os.environ.get(
+ "gpt_path", gweight_data)
+else:
+ gpt_path = os.environ.get(
+ "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
+
+if os.path.exists("./sweight.txt"):
+ with open("./sweight.txt", 'r', encoding="utf-8") as file:
+ sweight_data = file.read()
+ sovits_path = os.environ.get("sovits_path", sweight_data)
+else:
+ sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
+# gpt_path = os.environ.get(
+# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+# )
+# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
+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"))
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa, torch
+from feature_extractor import cnhubert
+
+cnhubert.cnhubert_base_path = cnhubert_base_path
+
+from module.models import SynthesizerTrn
+from AR.models.t2s_lightning_module import Text2SemanticLightningModule
+from text import cleaned_text_to_sequence
+from text.cleaner import clean_text
+from time import time as ttime
+from module.mel_processing import spectrogram_torch
+from my_utils import load_audio
+from tools.i18n.i18n import I18nAuto
+
+i18n = I18nAuto()
+
+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"
+elif torch.backends.mps.is_available():
+ device = "mps"
+else:
+ device = "cpu"
+
+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)
+
+
+def change_sovits_weights(sovits_path):
+ global vq_model, hps
+ dict_s2 = torch.load(sovits_path, map_location="cpu")
+ hps = dict_s2["config"]
+ hps = DictToAttrRecursive(hps)
+ hps.model.semantic_frame_rate = "25hz"
+ vq_model = SynthesizerTrn(
+ hps.data.filter_length // 2 + 1,
+ hps.train.segment_size // hps.data.hop_length,
+ n_speakers=hps.data.n_speakers,
+ **hps.model
+ )
+ if ("pretrained" not in sovits_path):
+ del vq_model.enc_q
+ if is_half == True:
+ vq_model = vq_model.half().to(device)
+ else:
+ vq_model = vq_model.to(device)
+ vq_model.eval()
+ print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
+ with open("./sweight.txt", "w", encoding="utf-8") as f:
+ f.write(sovits_path)
+
+
+change_sovits_weights(sovits_path)
+
+
+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("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
+
+
+change_gpt_weights(gpt_path)
+
+
+def get_spepc(hps, filename):
+ audio = load_audio(filename, int(hps.data.sampling_rate))
+ audio = torch.FloatTensor(audio)
+ 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
+
+
+dict_language = {
+ 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
+}
+
+
+def splite_en_inf(sentence, language):
+ pattern = re.compile(r'[a-zA-Z ]+')
+ textlist = []
+ langlist = []
+ pos = 0
+ for match in pattern.finditer(sentence):
+ start, end = match.span()
+ if start > pos:
+ textlist.append(sentence[pos:start])
+ langlist.append(language)
+ textlist.append(sentence[start:end])
+ langlist.append("en")
+ pos = end
+ if pos < len(sentence):
+ textlist.append(sentence[pos:])
+ langlist.append(language)
+ # Merge punctuation into previous word
+ for i in range(len(textlist)-1, 0, -1):
+ if re.match(r'^[\W_]+$', textlist[i]):
+ textlist[i-1] += textlist[i]
+ del textlist[i]
+ del langlist[i]
+ # Merge consecutive words with the same language tag
+ i = 0
+ while i < len(langlist) - 1:
+ if langlist[i] == langlist[i+1]:
+ textlist[i] += textlist[i+1]
+ del textlist[i+1]
+ del langlist[i+1]
+ else:
+ i += 1
+
+ return textlist, langlist
+
+
+def clean_text_inf(text, language):
+ phones, word2ph, norm_text = clean_text(text, language.replace("all_",""))
+ phones = cleaned_text_to_sequence(phones)
+ 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
+
+
+def nonen_clean_text_inf(text, language):
+ if(language!="auto"):
+ textlist, langlist = splite_en_inf(text, language)
+ else:
+ textlist=[]
+ langlist=[]
+ for tmp in LangSegment.getTexts(text):
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ print(textlist)
+ print(langlist)
+ phones_list = []
+ word2ph_list = []
+ norm_text_list = []
+ for i in range(len(textlist)):
+ lang = langlist[i]
+ phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+ phones_list.append(phones)
+ if lang == "zh":
+ word2ph_list.append(word2ph)
+ norm_text_list.append(norm_text)
+ print(word2ph_list)
+ phones = sum(phones_list, [])
+ word2ph = sum(word2ph_list, [])
+ norm_text = ' '.join(norm_text_list)
+
+ return phones, word2ph, norm_text
+
+
+def nonen_get_bert_inf(text, language):
+ if(language!="auto"):
+ textlist, langlist = splite_en_inf(text, language)
+ else:
+ textlist=[]
+ langlist=[]
+ for tmp in LangSegment.getTexts(text):
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ print(textlist)
+ print(langlist)
+ bert_list = []
+ for i in range(len(textlist)):
+ text = textlist[i]
+ lang = langlist[i]
+ phones, word2ph, norm_text = clean_text_inf(text, lang)
+ bert = get_bert_inf(phones, word2ph, norm_text, lang)
+ bert_list.append(bert)
+ bert = torch.cat(bert_list, dim=1)
+
+ 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
+
+
+def get_cleaned_text_final(text,language):
+ if language in {"en","all_zh","all_ja"}:
+ phones, word2ph, norm_text = clean_text_inf(text, language)
+ elif language in {"zh", "ja","auto"}:
+ phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
+ return phones, word2ph, norm_text
+
+def get_bert_final(phones, word2ph, text,language,device):
+ if language == "en":
+ bert = get_bert_inf(phones, word2ph, text, language)
+ elif language in {"zh", "ja","auto"}:
+ bert = nonen_get_bert_inf(text, language)
+ elif language == "all_zh":
+ bert = get_bert_feature(text, word2ph).to(device)
+ else:
+ bert = torch.zeros((1024, len(phones))).to(device)
+ return bert
+
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")):
+ t0 = ttime()
+ prompt_language = dict_language[prompt_language]
+ text_language = dict_language[text_language]
+ prompt_text = prompt_text.strip("\n")
+ if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
+ 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("实际输入的参考文本:"), prompt_text)
+ print(i18n("实际输入的目标文本:"), text)
+ zero_wav = np.zeros(
+ int(hps.data.sampling_rate * 0.3),
+ dtype=np.float16 if is_half == True else np.float32,
+ )
+ 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)
+ zero_wav_torch = torch.from_numpy(zero_wav)
+ if is_half == True:
+ wav16k = wav16k.half().to(device)
+ zero_wav_torch = zero_wav_torch.half().to(device)
+ else:
+ wav16k = wav16k.to(device)
+ zero_wav_torch = zero_wav_torch.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]
+ t1 = ttime()
+
+ phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
+
+ 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)
+ text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n")
+ print(i18n("实际输入的目标文本(切句后):"), text)
+ texts = text.split("\n")
+ audio_opt = []
+ bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
+
+ for text in 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, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
+ bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
+ bert = torch.cat([bert1, bert2], 1)
+
+ all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
+ bert = bert.to(device).unsqueeze(0)
+ all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+ prompt = prompt_semantic.unsqueeze(0).to(device)
+ t2 = ttime()
+ with torch.no_grad():
+ # pred_semantic = t2s_model.model.infer(
+ pred_semantic, idx = t2s_model.model.infer_panel(
+ all_phoneme_ids,
+ all_phoneme_len,
+ prompt,
+ bert,
+ # prompt_phone_len=ph_offset,
+ top_k=config["inference"]["top_k"],
+ early_stop_num=hz * max_sec,
+ )
+ t3 = ttime()
+ # print(pred_semantic.shape,idx)
+ pred_semantic = pred_semantic[:, -idx:].unsqueeze(
+ 0
+ ) # .unsqueeze(0)#mq要多unsqueeze一次
+ refer = get_spepc(hps, ref_wav_path) # .to(device)
+ if is_half == True:
+ refer = refer.half().to(device)
+ else:
+ refer = refer.to(device)
+ # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
+ audio = (
+ vq_model.decode(
+ pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer
+ )
+ .detach()
+ .cpu()
+ .numpy()[0, 0]
+ ) ###试试重建不带上prompt部分
+ max_audio=np.abs(audio).max()#简单防止16bit爆音
+ if max_audio>1:audio/=max_audio
+ audio_opt.append(audio)
+ audio_opt.append(zero_wav)
+ t4 = ttime()
+ print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+ yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).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]
+ 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]
+ return "\n".join(opts)
+
+
+def cut3(inp):
+ inp = inp.strip("\n")
+ return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
+
+
+def cut4(inp):
+ inp = inp.strip("\n")
+ return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+ # if not re.search(r'[^\w\s]', inp[-1]):
+ # inp += '。'
+ inp = inp.strip("\n")
+ punds = r'[,.;?!、，。？！;：]'
+ items = re.split(f'({punds})', inp)
+ items = ["".join(group) for group in zip(items[::2], items[1::2])]
+ opt = "\n".join(items)
+ return 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 change_choices():
+ SoVITS_names, GPT_names = get_weights_names()
+ return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
+
+
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+SoVITS_weight_root = "SoVITS_weights"
+GPT_weight_root = "GPT_weights"
+os.makedirs(SoVITS_weight_root, exist_ok=True)
+os.makedirs(GPT_weight_root, exist_ok=True)
+
+
+def get_weights_names():
+ SoVITS_names = [pretrained_sovits_name]
+ for name in os.listdir(SoVITS_weight_root):
+ if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
+ GPT_names = [pretrained_gpt_name]
+ for name in os.listdir(GPT_weight_root):
+ if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
+ return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names()
+
+with gr.Blocks(title="GPT-SoVITS WebUI") as app:
+ gr.Markdown(
+ value=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.
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.")
+ )
+ with gr.Group():
+ gr.Markdown(value=i18n("模型切换"))
+ 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)
+ SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True)
+ refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary")
+ refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
+ SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
+ GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])
+ gr.Markdown(value=i18n("*请上传并填写参考信息"))
+ 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")
+ prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="")
+ prompt_language = gr.Dropdown(
+ label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+ )
+ gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
+ with gr.Row():
+ text = gr.Textbox(label=i18n("需要合成的文本"), value="")
+ text_language = gr.Dropdown(
+ label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
+ )
+ how_to_cut = gr.Radio(
+ 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,
+ )
+ inference_button = gr.Button(i18n("合成语音"), variant="primary")
+ output = gr.Audio(label=i18n("输出的语音"))
+
+ inference_button.click(
+ get_tts_wav,
+ [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut],
+ [output],
+ )
+
+ 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(value=i18n("后续将支持混合语种编码文本输入。"))
+
+app.queue(concurrency_count=511, max_size=1022).launch(
+ server_name="0.0.0.0",
+ inbrowser=True,
+ share=is_share,
+ server_port=infer_ttswebui,
+ quiet=True,
+)