diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py
index cc79817fc..39e1c9c33 100644
--- a/GPT_SoVITS/inference_webui.py
+++ b/GPT_SoVITS/inference_webui.py
@@ -1,661 +1,858 @@
-'''
-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
-import torch
-
-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")) and torch.cuda.is_available()
-punctuation = set(['!', '?', '…', ',', '.', '-'," "])
-import gradio as gr
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-import numpy as np
-import librosa
-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 tools.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"
-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 clean_text_inf(text, language):
- phones, word2ph, norm_text = clean_text(text, language)
- 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
-
-
-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_phones_and_bert(text,language):
- if language in {"en","all_zh","all_ja"}:
- language = language.replace("all_","")
- if language == "en":
- LangSegment.setfilters(["en"])
- formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
- else:
- # 因无法区别中日文汉字,以用户输入为准
- formattext = text
- while " " in formattext:
- formattext = formattext.replace(" ", " ")
- phones, word2ph, norm_text = clean_text_inf(formattext, language)
- if language == "zh":
- bert = get_bert_feature(norm_text, word2ph).to(device)
- else:
- bert = torch.zeros(
- (1024, len(phones)),
- dtype=torch.float16 if is_half == True else torch.float32,
- ).to(device)
- elif language in {"zh", "ja","auto"}:
- textlist=[]
- langlist=[]
- LangSegment.setfilters(["zh","ja","en","ko"])
- if language == "auto":
- for tmp in LangSegment.getTexts(text):
- if tmp["lang"] == "ko":
- langlist.append("zh")
- textlist.append(tmp["text"])
- else:
- langlist.append(tmp["lang"])
- textlist.append(tmp["text"])
- else:
- for tmp in LangSegment.getTexts(text):
- if tmp["lang"] == "en":
- langlist.append(tmp["lang"])
- else:
- # 因无法区别中日文汉字,以用户输入为准
- langlist.append(language)
- textlist.append(tmp["text"])
- print(textlist)
- 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)
- 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)
-
- return phones,bert.to(dtype),norm_text
-
-
-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
-
-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):
- if prompt_text is None or len(prompt_text) == 0:
- ref_free = True
- 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")
- text = replace_consecutive_punctuation(text)
- 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 * 0.3),
- dtype=np.float16 if is_half == True else np.float32,
- )
- if not ref_free:
- with torch.no_grad():
- wav16k, sr = librosa.load(ref_wav_path, sr=16000)
- if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
- raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
- wav16k = torch.from_numpy(wav16k)
- 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]
- prompt = prompt_semantic.unsqueeze(0).to(device)
-
- t1 = ttime()
-
- 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 = []
- if not ref_free:
- phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
-
- 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,bert2,norm_text2=get_phones_and_bert(text, text_language)
- 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()
- with torch.no_grad():
- # pred_semantic = t2s_model.model.infer(
- 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,
- )
- 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]
- 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 = ["%s" % item for item in inp.strip(".").split(".")]
- opts = [item for item in opts if not set(item).issubset(punctuation)]
- return "\n".join(opts)
-
-
-# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
-def cut5(inp):
- inp = inp.strip("\n")
- punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
- mergeitems = []
- items = []
-
- for i, char in enumerate(inp):
- if char in punds:
- if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
- items.append(char)
- else:
- items.append(char)
- mergeitems.append("".join(items))
- items = []
- else:
- items.append(char)
-
- if items:
- mergeitems.append("".join(items))
-
- opt = [item for item in mergeitems if not set(item).issubset(punds)]
- return "\n".join(opt)
-
-
-def custom_sort_key(s):
- # 使用正则表达式提取字符串中的数字部分和非数字部分
- parts = re.split('(\d+)', s)
- # 将数字部分转换为整数，非数字部分保持不变
- parts = [int(part) if part.isdigit() else part for part in parts]
- return parts
-
-def process_text(texts):
- _text=[]
- if all(text in [None, " ", "\n",""] for text in texts):
- raise ValueError(i18n("请输入有效文本"))
- for text in texts:
- if text in [None, " ", ""]:
- pass
- else:
- _text.append(text)
- return _text
-
-
-def replace_consecutive_punctuation(text):
- punctuations = ''.join(re.escape(p) for p in punctuation)
- pattern = f'([{punctuations}])([{punctuations}])+'
- result = re.sub(pattern, r'\1', text)
- return result
-
-
-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")
- with gr.Column():
- 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。"), value=False, interactive=True, show_label=True)
- gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，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="")
- 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和语种模式"))
- 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,
- )
- with gr.Row():
- gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
- top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
- top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
- temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,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, top_k, top_p, temperature, ref_text_free],
- [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("后续将支持转音素、手工修改音素、语音合成分步执行。"))
-
-if __name__ == '__main__':
- 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
+import torch
+import shutil
+from scipy.io import wavfile
+
+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")) and torch.cuda.is_available()
+punctuation = set(['!', '?', '…', ',', '.', '-'," "])
+import gradio as gr
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+import numpy as np
+import librosa
+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 tools.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"
+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 clean_text_inf(text, language):
+ phones, word2ph, norm_text = clean_text(text, language)
+ 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
+
+
+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_phones_and_bert(text,language):
+ if language in {"en","all_zh","all_ja"}:
+ language = language.replace("all_","")
+ if language == "en":
+ LangSegment.setfilters(["en"])
+ formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+ else:
+ # 因无法区别中日文汉字,以用户输入为准
+ formattext = text
+ while " " in formattext:
+ formattext = formattext.replace(" ", " ")
+ phones, word2ph, norm_text = clean_text_inf(formattext, language)
+ if language == "zh":
+ bert = get_bert_feature(norm_text, word2ph).to(device)
+ else:
+ bert = torch.zeros(
+ (1024, len(phones)),
+ dtype=torch.float16 if is_half == True else torch.float32,
+ ).to(device)
+ elif language in {"zh", "ja","auto"}:
+ textlist=[]
+ langlist=[]
+ LangSegment.setfilters(["zh","ja","en","ko"])
+ if language == "auto":
+ for tmp in LangSegment.getTexts(text):
+ if tmp["lang"] == "ko":
+ langlist.append("zh")
+ textlist.append(tmp["text"])
+ else:
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ else:
+ for tmp in LangSegment.getTexts(text):
+ if tmp["lang"] == "en":
+ langlist.append(tmp["lang"])
+ else:
+ # 因无法区别中日文汉字,以用户输入为准
+ langlist.append(language)
+ textlist.append(tmp["text"])
+ print(textlist)
+ 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)
+ 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)
+
+ return phones,bert.to(dtype),norm_text
+
+
+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
+
+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, interval=0.3, ref_free = False):
+ if prompt_text is None or len(prompt_text) == 0:
+ ref_free = True
+ 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")
+ text = replace_consecutive_punctuation(text)
+ 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 * interval),
+ dtype=np.float16 if is_half == True else np.float32,
+ )
+ if not ref_free:
+ with torch.no_grad():
+ wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+ if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
+ raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+ wav16k = torch.from_numpy(wav16k)
+ 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]
+ prompt = prompt_semantic.unsqueeze(0).to(device)
+
+ t1 = ttime()
+
+ 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 = []
+ if not ref_free:
+ phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
+
+ 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,bert2,norm_text2=get_phones_and_bert(text, text_language)
+ 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()
+ with torch.no_grad():
+ # pred_semantic = t2s_model.model.infer(
+ 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,
+ )
+ 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
+ )
+ # 指定保存音频的文件路径
+ file_path = 'moys/temp/audio.wav'
+
+ # 调用保存音频的函数
+ save_audio(hps.data.sampling_rate, np.concatenate(audio_opt, 0), file_path)
+
+# 保存音频数据到文件
+def save_audio(sampling_rate, audio_data, file_path):
+ # 确保音频数据是16位PCM格式
+ audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
+ wavfile.write(file_path, sampling_rate, audio_data)
+
+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 = ["%s" % item for item in inp.strip(".").split(".")]
+ opts = [item for item in opts if not set(item).issubset(punctuation)]
+ return "\n".join(opts)
+
+
+# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
+def cut5(inp):
+ inp = inp.strip("\n")
+ punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
+ mergeitems = []
+ items = []
+
+ for i, char in enumerate(inp):
+ if char in punds:
+ if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
+ items.append(char)
+ else:
+ items.append(char)
+ mergeitems.append("".join(items))
+ items = []
+ else:
+ items.append(char)
+
+ if items:
+ mergeitems.append("".join(items))
+
+ opt = [item for item in mergeitems if not set(item).issubset(punds)]
+ return "\n".join(opt)
+
+
+def custom_sort_key(s):
+ # 使用正则表达式提取字符串中的数字部分和非数字部分
+ parts = re.split('(\d+)', s)
+ # 将数字部分转换为整数，非数字部分保持不变
+ parts = [int(part) if part.isdigit() else part for part in parts]
+ return parts
+
+def process_text(texts):
+ _text=[]
+ if all(text in [None, " ", "\n",""] for text in texts):
+ raise ValueError(i18n("请输入有效文本"))
+ for text in texts:
+ if text in [None, " ", ""]:
+ pass
+ else:
+ _text.append(text)
+ return _text
+
+
+def replace_consecutive_punctuation(text):
+ punctuations = ''.join(re.escape(p) for p in punctuation)
+ pattern = f'([{punctuations}])([{punctuations}])+'
+ result = re.sub(pattern, r'\1', text)
+ return result
+
+
+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
+
+
+def save_model_config(GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
+ config_dir = "moys"
+ config_dir1 = r"moys\audio"
+ if not os.path.exists(config_dir):
+ os.makedirs(config_dir)
+
+ # 复制参考音频文件到配置目录
+ copy_ref_audio_path = os.path.join(config_dir1, os.path.basename(inp_ref))
+ shutil.copy(inp_ref, copy_ref_audio_path)
+
+ gpt_model_name = os.path.basename(GPT_dropdown).split('-')[0]
+ config_file_path = os.path.join(config_dir, f"{gpt_model_name}.txt")
+
+ with open(config_file_path, 'w', encoding='utf-8') as f:
+ f.write(f"GPT_model_path={GPT_dropdown}\n")
+ f.write(f"SoVITS_model_path={SoVITS_dropdown}\n")
+ f.write(f"ref_audio_path={copy_ref_audio_path}\n") # 修改写入的路径为复制文件的路径
+ f.write(f"ref_text={prompt_text}\n")
+ f.write(f"ref_audio_language={prompt_language}\n")
+
+ return f"Configuration saved to {config_file_path}"
+
+def load_model_config(config_file_name):
+ config_dir = "moys"
+ # 因为 config_file_name 现在是字符串，我们直接使用它来构造文件路径
+ config_file_path = os.path.join(config_dir, config_file_name)
+
+ with open(config_file_path, 'r', encoding='utf-8') as f:
+ lines = f.readlines()
+
+ config = {}
+ for line in lines:
+ key, value = line.strip().split('=')
+ config[key] = value
+
+ # 返回一个包含所有组件期望值的字典
+ return (
+ config["GPT_model_path"],
+ config["SoVITS_model_path"],
+ config["ref_audio_path"],
+ config["ref_text"],
+ config["ref_audio_language"]
+ )
+def refresh_config_files():
+ # 获取最新的配置文件列表
+ config_files = get_config_files()
+ # 创建一个新的文件名列表，只包含文件名
+ config_file_names = [os.path.basename(path) for path in config_files]
+
+ # 返回一个更新的配置，告诉 Gradio 更新下拉菜单的选项
+ return {"choices": config_file_names, "__type__": "update"}
+
+
+
+ # 辅助函数，用于处理 load_model_config 函数的输出
+def handle_load_model_config(config_file_name, GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language):
+ # 调用原始函数获取配置
+ config = load_model_config(config_file_name)
+
+ # 更新组件的值
+ GPT_dropdown.update(value=config.get("GPT_model_path"))
+ SoVITS_dropdown.update(value=config.get("SoVITS_model_path"))
+ inp_ref.update(value=config.get("ref_audio_path"))
+ prompt_text.update(value=config.get("ref_text"))
+ prompt_language.update(value=config.get("ref_audio_language"))
+
+def get_config_files():
+ config_dir = "moys"
+ if not os.path.exists(config_dir):
+ return []
+
+ return [os.path.join(config_dir, f) for f in os.listdir(config_dir) if f.endswith('.txt')]
+
+def echo(input_text):
+ # 直接返回输入的文本
+ return input_text
+
+
+def find_latest_wav(source_dir, dest_dir):
+ # 确保目标文件夹存在
+ if not os.path.exists(dest_dir):
+ os.makedirs(dest_dir)
+
+ # 初始化找到的wav文件路径
+ wav_file_path = None
+
+ # 遍历源文件夹
+ for root, dirs, files in os.walk(source_dir):
+ for file in files:
+ if file.lower().endswith('.wav'):
+ wav_file_path = os.path.join(root, file)
+ # 找到第一个wav文件就退出循环
+ break
+ if wav_file_path:
+ break # 确保找到文件后不再继续遍历
+
+ # 如果找到了wav文件，复制到目标文件夹
+ if wav_file_path:
+ base_name = os.path.basename(wav_file_path)
+ file_name, file_ext = os.path.splitext(base_name)
+ dest_file_path = os.path.join(dest_dir, base_name)
+
+ # 检查目标文件夹中是否存在同名文件，并添加后缀以避免覆盖
+ counter = 1
+ while os.path.exists(dest_file_path):
+ new_name = f"{file_name}({counter}){file_ext}"
+ dest_file_path = os.path.join(dest_dir, new_name)
+ counter += 1
+ # 复制文件
+ shutil.copy2(wav_file_path, dest_file_path)
+ print(f"Copied WAV file to {dest_file_path}")
+ return dest_file_path # 返回复制的文件路径
+ else:
+ print("No WAV files found.")
+ return None # 没有找到 WAV 文件时返回 None
+
+
+
+
+def on_download_click(textq_value):
+ source_directory = r'moys/temp' # 源文件夹路径
+ destination_directory = textq_value
+ # outputs.update_value(f"开始查找最新的WAV文件...")
+
+ result = find_latest_wav(source_directory, destination_directory) # 调用函数
+ # outputs.update_value(f"已保存到: {destination_directory}")
+ return f"{result}已保存到: {destination_directory}"
+
+
+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")
+ with gr.Column():
+ 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。"), value=False, interactive=True, show_label=True)
+ gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，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="")
+ 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和语种模式"))
+ 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,
+ )
+ with gr.Row():
+ gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
+ top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
+ top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
+ temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
+ interval = gr.Slider(minimum=0,maximum=5,step=0.02,label=i18n("interval"),value=0.3,interactive=True)
+ inference_button = gr.Button(i18n("合成语音"), variant="primary")
+ output = gr.Audio(label=i18n("输出的语音"))
+
+ with gr.Row():
+ # 创建文本框和下载按钮
+ download_button = gr.Button("下载语音", variant="primary")
+ textq = gr.Textbox(label="保存的语音路径", value="")
+ outputs0 = gr.Textbox(label=i18n("保存状态"), value="", interactive=False)
+ # 将事件处理函数绑定到按钮的点击事件
+ download_button.click(
+ on_download_click,
+ inputs=[textq], # 这里确保 textq 是正确的组件引用
+ outputs=[outputs0] # 这里确保 outputs 是正确的组件引用
+ )
+
+ inference_button.click(
+ get_tts_wav,
+ [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, interval, ref_text_free],
+ [output],
+ )
+ # Add new UI elements for saving and loading configurations
+ with gr.Row():
+
+
+ # 初始加载配置文件列表
+ config_files = get_config_files()
+ # 创建一个新的列表，只包含文件名
+ config_file_names = [os.path.basename(path) for path in config_files]
+
+ # 使用文件名列表作为 Dropdown 组件的选项
+ config_dropdown = gr.Dropdown(
+ label=i18n("加载模型配置"),
+ choices=config_file_names,
+ value=config_file_names[0] if config_file_names else None
+ )
+
+ # Output textbox for displaying save confirmation
+ save_output = gr.Textbox(label=i18n("保存配置状态"), value="", interactive=False)
+
+ # 绑定刷新按钮的点击事件
+ refresh_button = gr.Button(i18n("刷新配置文件列表"), variant="primary")
+ refresh_button.click(
+ fn=refresh_config_files, # 使用新创建的 refresh_config_files 函数
+ inputs=[], # 刷新按钮不需要输入
+ outputs=[config_dropdown] # 指定输出为 config_dropdown 组件，以更新其选项
+ )
+
+ # 绑定保存按钮的点击事件
+ save_button = gr.Button(i18n("保存模型配置"), variant="primary")
+ save_button.click(
+ fn=save_model_config,
+ inputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language],
+ outputs=[save_output]
+ )
+
+ # 绑定加载按钮的点击事件
+ load_button = gr.Button(i18n("加载模型配置"), variant="primary")
+ load_button.click(
+ fn=load_model_config, # 直接使用 load_model_config 函数
+ inputs=[config_dropdown], # config_dropdown 组件本身作为输入
+ outputs=[GPT_dropdown, SoVITS_dropdown, inp_ref, prompt_text, prompt_language] # 期望更新的组件列表
+ )
+ 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")
+ button6 = gr.Button(i18n("推送"), 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])
+ button6.click(echo, [text_opt], [text])
+ gr.Markdown(value=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(concurrency_count=511, max_size=1022).launch(
+ server_name="0.0.0.0",
+ inbrowser=True,
+ share=is_share,
+ server_port=infer_ttswebui,
+ quiet=True,
+ )
diff --git a/moys/audio/audio.wav b/moys/audio/audio.wav
new file mode 100644
index 000000000..0e0215dbd
Binary files /dev/null and b/moys/audio/audio.wav differ
diff --git a/moys/temp/audio.wav b/moys/temp/audio.wav
new file mode 100644
index 000000000..0e0215dbd
Binary files /dev/null and b/moys/temp/audio.wav differ