diff --git a/api_v4.py b/api_v4.py
new file mode 100644
index 00000000..be5e0931
--- /dev/null
+++ b/api_v4.py
@@ -0,0 +1,1211 @@
+"""
+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 logging
+import traceback
+import warnings
+
+import torchaudio
+
+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)
+
+import json
+import os
+import re
+import sys
+import io
+
+import torch
+from text.LangSegmenter import LangSegmenter
+
+try:
+ import gradio.analytics as analytics
+
+ analytics.version_check = lambda: None
+except:
+ ...
+version = model_version = os.environ.get("version", "v2")
+path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth"
+path_sovits_v4 = "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth"
+is_exist_s2gv3 = os.path.exists(path_sovits_v3)
+is_exist_s2gv4 = os.path.exists(path_sovits_v4)
+pretrained_sovits_name = [
+ "GPT_SoVITS/pretrained_models/s2G488k.pth",
+ "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
+ "GPT_SoVITS/pretrained_models/s2Gv3.pth",
+ "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
+]
+pretrained_gpt_name = [
+ "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+ "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+ "GPT_SoVITS/pretrained_models/s1v3.ckpt",
+ "GPT_SoVITS/pretrained_models/s1v3.ckpt",
+]
+
+
+_ = [[], []]
+for i in range(4):
+ if os.path.exists(pretrained_gpt_name[i]):
+ _[0].append(pretrained_gpt_name[i])
+ if os.path.exists(pretrained_sovits_name[i]):
+ _[-1].append(pretrained_sovits_name[i])
+pretrained_gpt_name, pretrained_sovits_name = _
+
+
+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, pretrained_gpt_name))
+ sovits_path = os.environ.get("sovits_path", weight_data.get("SoVITS", {}).get(version, pretrained_sovits_name))
+ if isinstance(gpt_path, list):
+ gpt_path = gpt_path[0]
+ if isinstance(sovits_path, list):
+ sovits_path = sovits_path[0]
+
+# gpt_path = os.environ.get(
+# "gpt_path", pretrained_gpt_name
+# )
+# sovits_path = os.environ.get("sovits_path", pretrained_sovits_name)
+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 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 SynthesizerTrn, SynthesizerTrnV3,Generator
+
+
+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.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)
+
+resample_transform_dict = {}
+
+
+def resample(audio_tensor, sr0,sr1):
+ global resample_transform_dict
+ key="%s-%s"%(sr0,sr1)
+ 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)
+
+
+###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):
+ 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
+ if if_lora_v3 == True and is_exist == False:
+ info = "GPT_SoVITS/pretrained_models/s2Gv3.pth" + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version)
+ 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:
+ 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,
+ )
+ model_version = version
+ 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):
+ global hz, max_sec, t2s_model, config
+ hz = 50
+ dict_s1 = torch.load(gpt_path, map_location="cpu")
+ config = dict_s1["config"]
+ max_sec = config["data"]["max_sec"]
+ t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
+ t2s_model.load_state_dict(dict_s1["weight"])
+ if is_half == True:
+ t2s_model = t2s_model.half()
+ t2s_model = t2s_model.to(device)
+ t2s_model.eval()
+ # total = sum([param.nelement() for param in t2s_model.parameters()])
+ # print("Number of parameter: %.2fM" % (total / 1e6))
+ with open("./weight.json") as f:
+ data = f.read()
+ data = json.loads(data)
+ data["GPT"][version] = gpt_path
+ with open("./weight.json", "w") as f:
+ f.write(json.dumps(data))
+
+
+change_gpt_weights(gpt_path)
+os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+import torch
+
+now_dir = os.getcwd()
+
+
+def init_bigvgan():
+ global bigvgan_model,hifigan_model
+ from BigVGAN import bigvgan
+
+ bigvgan_model = bigvgan.BigVGAN.from_pretrained(
+ "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
+ use_cuda_kernel=False,
+ ) # if True, RuntimeError: Ninja is required to load C++ extensions
+ # remove weight norm in the model and set to eval mode
+ bigvgan_model.remove_weight_norm()
+ bigvgan_model = bigvgan_model.eval()
+ if hifigan_model:
+ hifigan_model=hifigan_model.cpu()
+ hifigan_model=None
+ try:torch.cuda.empty_cache()
+ except:pass
+ if is_half == True:
+ bigvgan_model = bigvgan_model.half().to(device)
+ else:
+ bigvgan_model = bigvgan_model.to(device)
+
+def init_hifigan():
+ global hifigan_model,bigvgan_model
+ hifigan_model = Generator(
+ initial_channel=100,
+ resblock="1",
+ resblock_kernel_sizes=[3, 7, 11],
+ resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+ upsample_rates=[10, 6, 2, 2, 2],
+ upsample_initial_channel=512,
+ upsample_kernel_sizes=[20, 12, 4, 4, 4],
+ gin_channels=0, is_bias=True
+ )
+ hifigan_model.eval()
+ hifigan_model.remove_weight_norm()
+ state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu")
+ print("loading vocoder",hifigan_model.load_state_dict(state_dict_g))
+ if bigvgan_model:
+ bigvgan_model=bigvgan_model.cpu()
+ bigvgan_model=None
+ try:torch.cuda.empty_cache()
+ except:pass
+ if is_half == True:
+ hifigan_model = hifigan_model.half().to(device)
+ else:
+ hifigan_model = hifigan_model.to(device)
+
+bigvgan_model=hifigan_model=None
+if model_version=="v3":
+ init_bigvgan()
+if model_version=="v4":
+ init_hifigan()
+
+
+def get_spepc(hps, filename):
+ # audio = load_audio(filename, int(hps.data.sampling_rate))
+ audio, sampling_rate = librosa.load(filename, sr=int(hps.data.sampling_rate))
+ audio = torch.FloatTensor(audio)
+ maxx = audio.abs().max()
+ if maxx > 1:
+ audio /= min(2, maxx)
+ audio_norm = audio
+ audio_norm = audio_norm.unsqueeze(0)
+ spec = spectrogram_torch(
+ audio_norm,
+ hps.data.filter_length,
+ hps.data.sampling_rate,
+ hps.data.hop_length,
+ hps.data.win_length,
+ center=False,
+ )
+ return spec
+
+
+def clean_text_inf(text, language, version):
+ language = language.replace("all_", "")
+ phones, word2ph, norm_text = clean_text(text, language, version)
+ phones = cleaned_text_to_sequence(phones, version)
+ return phones, word2ph, norm_text
+
+
+dtype = torch.float16 if is_half == True else torch.float32
+
+
+def get_bert_inf(phones, word2ph, norm_text, language):
+ language = language.replace("all_", "")
+ if language == "zh":
+ bert = get_bert_feature(norm_text, word2ph).to(device) # .to(dtype)
+ else:
+ bert = torch.zeros(
+ (1024, len(phones)),
+ dtype=torch.float16 if is_half == True else torch.float32,
+ ).to(device)
+
+ return bert
+
+
+splits = {
+ "，",
+ "。",
+ "？",
+ "！",
+ ",",
+ ".",
+ "?",
+ "!",
+ "~",
+ ":",
+ "：",
+ "—",
+ "…",
+}
+
+
+def get_first(text):
+ pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
+ text = re.split(pattern, text)[0].strip()
+ return text
+
+
+from text import chinese
+
+
+def get_phones_and_bert(text, language, version, final=False):
+ if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
+ formattext = text
+ while " " in formattext:
+ formattext = formattext.replace(" ", " ")
+ if language == "all_zh":
+ if re.search(r"[A-Za-z]", formattext):
+ formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
+ formattext = chinese.mix_text_normalize(formattext)
+ return get_phones_and_bert(formattext, "zh", version)
+ else:
+ phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
+ bert = get_bert_feature(norm_text, word2ph).to(device)
+ elif language == "all_yue" and re.search(r"[A-Za-z]", formattext):
+ formattext = re.sub(r"[a-z]", lambda x: x.group(0).upper(), formattext)
+ formattext = chinese.mix_text_normalize(formattext)
+ return get_phones_and_bert(formattext, "yue", version)
+ else:
+ phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
+ bert = torch.zeros(
+ (1024, len(phones)),
+ dtype=torch.float16 if is_half == True else torch.float32,
+ ).to(device)
+ elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
+ textlist = []
+ langlist = []
+ if language == "auto":
+ for tmp in LangSegmenter.getTexts(text):
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ elif language == "auto_yue":
+ for tmp in LangSegmenter.getTexts(text):
+ if tmp["lang"] == "zh":
+ tmp["lang"] = "yue"
+ langlist.append(tmp["lang"])
+ textlist.append(tmp["text"])
+ else:
+ for tmp in LangSegmenter.getTexts(text):
+ if tmp["lang"] == "en":
+ langlist.append(tmp["lang"])
+ else:
+ # 因无法区别中日韩文汉字,以用户输入为准
+ langlist.append(language)
+ textlist.append(tmp["text"])
+ print(textlist)
+ print(langlist)
+ phones_list = []
+ bert_list = []
+ norm_text_list = []
+ for i in range(len(textlist)):
+ lang = langlist[i]
+ phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
+ bert = get_bert_inf(phones, word2ph, norm_text, lang)
+ phones_list.append(phones)
+ norm_text_list.append(norm_text)
+ bert_list.append(bert)
+ bert = torch.cat(bert_list, dim=1)
+ phones = sum(phones_list, [])
+ norm_text = "".join(norm_text_list)
+
+ if not final and len(phones) < 6:
+ return get_phones_and_bert("." + text, language, version, final=True)
+
+ return phones, bert.to(dtype), norm_text
+
+
+from module.mel_processing import mel_spectrogram_torch, spectrogram_torch
+
+spec_min = -12
+spec_max = 2
+
+
+def norm_spec(x):
+ return (x - spec_min) / (spec_max - spec_min) * 2 - 1
+
+
+def denorm_spec(x):
+ return (x + 1) / 2 * (spec_max - spec_min) + spec_min
+
+
+mel_fn = lambda x: mel_spectrogram_torch(
+ x,
+ **{
+ "n_fft": 1024,
+ "win_size": 1024,
+ "hop_size": 256,
+ "num_mels": 100,
+ "sampling_rate": 24000,
+ "fmin": 0,
+ "fmax": None,
+ "center": False,
+ },
+)
+mel_fn_v4 = lambda x: mel_spectrogram_torch(
+ x,
+ **{
+ "n_fft": 1280,
+ "win_size": 1280,
+ "hop_size": 320,
+ "num_mels": 100,
+ "sampling_rate": 32000,
+ "fmin": 0,
+ "fmax": None,
+ "center": False,
+ },
+)
+
+
+def merge_short_text_in_array(texts, threshold):
+ if (len(texts)) < 2:
+ return texts
+ result = []
+ text = ""
+ for ele in texts:
+ text += ele
+ if len(text) >= threshold:
+ result.append(text)
+ text = ""
+ if len(text) > 0:
+ if len(result) == 0:
+ result.append(text)
+ else:
+ result[len(result) - 1] += text
+ return result
+
+
+sr_model = None
+
+
+def audio_sr(audio, sr):
+ global sr_model
+ if sr_model == None:
+ from tools.audio_sr import AP_BWE
+
+ try:
+ sr_model = AP_BWE(device, DictToAttrRecursive)
+ except FileNotFoundError:
+ return audio.cpu().detach().numpy(), sr
+ return sr_model(audio, sr)
+
+
+##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
+# cache_tokens={}#暂未实现清理机制
+cache = {}
+
+
+def get_tts_wav(
+ ref_wav_path,
+ prompt_text,
+ prompt_language,
+ text,
+ text_language,
+ how_to_cut=i18n("不切"),
+ top_k=20,
+ top_p=0.6,
+ temperature=0.6,
+ speed=1,
+ sample_steps=8,
+ if_sr=False,
+ media_type="ogg",
+ pause_second=0.3,
+ ref_free=False,
+ if_freeze=False,
+ inp_refs=None,
+):
+ #top_k = int(top_k)
+ global cache
+ t = []
+ if prompt_text is None or len(prompt_text) == 0:
+ ref_free = True
+ if model_version in v3v4set:
+ ref_free = False # s2v3暂不支持ref_free
+ else:
+ if_sr = False
+ t0 = ttime()
+
+ #prompt_language = dict_language[prompt_language]
+ #text_language = dict_language[text_language]
+
+ if not ref_free:
+ prompt_text = prompt_text.strip("\n")
+ if prompt_text[-1] not in splits:
+ prompt_text += "。" if prompt_language != "en" else "."
+ print(i18n("实际输入的参考文本:"), prompt_text)
+ text = text.strip("\n")
+ # if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
+
+ print(i18n("实际输入的目标文本:"), text)
+ zero_wav = np.zeros(
+ int(hps.data.sampling_rate * pause_second),
+ dtype=np.float16 if is_half == True else np.float32,
+ )
+ zero_wav_torch = torch.from_numpy(zero_wav)
+ if is_half == True:
+ zero_wav_torch = zero_wav_torch.half().to(device)
+ else:
+ zero_wav_torch = zero_wav_torch.to(device)
+ if not ref_free:
+ with torch.no_grad():
+ wav16k, sr = librosa.load(ref_wav_path, sr=16000)
+ if wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000:
+ raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
+ wav16k = torch.from_numpy(wav16k)
+ if is_half == True:
+ wav16k = wav16k.half().to(device)
+ else:
+ wav16k = wav16k.to(device)
+ wav16k = torch.cat([wav16k, zero_wav_torch])
+ ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
+ codes = vq_model.extract_latent(ssl_content)
+ prompt_semantic = codes[0, 0]
+ prompt = prompt_semantic.unsqueeze(0).to(device)
+
+ t1 = ttime()
+ t.append(t1 - t0)
+
+ if how_to_cut == i18n("凑四句一切"):
+ text = cut1(text)
+ elif how_to_cut == i18n("凑50字一切"):
+ text = cut2(text)
+ elif how_to_cut == i18n("按中文句号。切"):
+ text = cut3(text)
+ elif how_to_cut == i18n("按英文句号.切"):
+ text = cut4(text)
+ elif how_to_cut == i18n("按标点符号切"):
+ text = cut5(text)
+ while "\n\n" in text:
+ text = text.replace("\n\n", "\n")
+ print(i18n("实际输入的目标文本(切句后):"), text)
+ texts = text.split("\n")
+ texts = process_text(texts)
+ texts = merge_short_text_in_array(texts, 5)
+ audio_opt = []
+ ###s2v3暂不支持ref_free
+ if not ref_free:
+ phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
+
+ for i_text, text in enumerate(texts):
+ # 解决输入目标文本的空行导致报错的问题
+ if len(text.strip()) == 0:
+ continue
+ if text[-1] not in splits:
+ text += "。" if text_language != "en" else "."
+ print(i18n("实际输入的目标文本(每句):"), text)
+ phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
+ print(i18n("前端处理后的文本(每句):"), norm_text2)
+ if not ref_free:
+ bert = torch.cat([bert1, bert2], 1)
+ all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
+ else:
+ bert = bert2
+ all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
+
+ bert = bert.to(device).unsqueeze(0)
+ all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
+
+ t2 = ttime()
+ # cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
+ # print(cache.keys(),if_freeze)
+ if i_text in cache and if_freeze == True:
+ pred_semantic = cache[i_text]
+ else:
+ with torch.no_grad():
+ pred_semantic, idx = t2s_model.model.infer_panel(
+ all_phoneme_ids,
+ all_phoneme_len,
+ None if ref_free else prompt,
+ bert,
+ # prompt_phone_len=ph_offset,
+ top_k=top_k,
+ top_p=top_p,
+ temperature=temperature,
+ early_stop_num=hz * max_sec,
+ )
+ pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
+ cache[i_text] = pred_semantic
+ t3 = ttime()
+ ###v3不存在以下逻辑和inp_refs
+ if model_version not in v3v4set:
+ refers = []
+ if inp_refs:
+ for path in inp_refs:
+ try:
+ refer = get_spepc(hps, path.name).to(dtype).to(device)
+ refers.append(refer)
+ except:
+ traceback.print_exc()
+ if len(refers) == 0:
+ refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
+ audio = vq_model.decode(
+ pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed
+ )[0][0] # .cpu().detach().numpy()
+ else:
+ refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)
+ phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
+ phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
+ # print(11111111, phoneme_ids0, phoneme_ids1)
+ fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
+ ref_audio, sr = torchaudio.load(ref_wav_path)
+ ref_audio = ref_audio.to(device).float()
+ if ref_audio.shape[0] == 2:
+ ref_audio = ref_audio.mean(0).unsqueeze(0)
+ tgt_sr=24000 if model_version=="v3"else 32000
+ if sr != tgt_sr:
+ ref_audio = resample(ref_audio, sr,tgt_sr)
+ # print("ref_audio",ref_audio.abs().mean())
+ mel2 = mel_fn(ref_audio)if model_version=="v3"else mel_fn_v4(ref_audio)
+ mel2 = norm_spec(mel2)
+ T_min = min(mel2.shape[2], fea_ref.shape[2])
+ mel2 = mel2[:, :, :T_min]
+ fea_ref = fea_ref[:, :, :T_min]
+ Tref=468 if model_version=="v3"else 500
+ Tchunk=934 if model_version=="v3"else 1000
+ if T_min > Tref:
+ mel2 = mel2[:, :, -Tref:]
+ fea_ref = fea_ref[:, :, -Tref:]
+ T_min = Tref
+ chunk_len = Tchunk - T_min
+ mel2 = mel2.to(dtype)
+ fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)
+ cfm_resss = []
+ idx = 0
+ while 1:
+ fea_todo_chunk = fea_todo[:, :, idx : idx + chunk_len]
+ if fea_todo_chunk.shape[-1] == 0:
+ break
+ idx += chunk_len
+ fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
+ cfm_res = vq_model.cfm.inference(
+ fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0
+ )
+ cfm_res = cfm_res[:, :, mel2.shape[2] :]
+ mel2 = cfm_res[:, :, -T_min:]
+ fea_ref = fea_todo_chunk[:, :, -T_min:]
+ cfm_resss.append(cfm_res)
+ cfm_res = torch.cat(cfm_resss, 2)
+ cfm_res = denorm_spec(cfm_res)
+ if model_version=="v3":
+ if bigvgan_model == None:
+ init_bigvgan()
+ else:#v4
+ if hifigan_model == None:
+ init_hifigan()
+ vocoder_model=bigvgan_model if model_version=="v3"else hifigan_model
+ with torch.inference_mode():
+ wav_gen = vocoder_model(cfm_res)
+ audio = wav_gen[0][0] # .cpu().detach().numpy()
+ max_audio = torch.abs(audio).max() # 简单防止16bit爆音
+ if max_audio > 1:
+ audio = audio / max_audio
+ audio_opt.append(audio)
+ audio_opt.append(zero_wav_torch) # zero_wav
+ t4 = ttime()
+ t.extend([t2 - t1, t3 - t2, t4 - t3])
+ t1 = ttime()
+ print("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])))
+ audio_opt = torch.cat(audio_opt, 0) # np.concatenate
+ if model_version in {"v1","v2"}:opt_sr=32000
+ elif model_version=="v3":opt_sr=24000
+ else:opt_sr=48000#v4
+ if if_sr == True and opt_sr == 24000:
+ print(i18n("音频超分中"))
+ audio_opt, opt_sr = audio_sr(audio_opt.unsqueeze(0), opt_sr)
+ max_audio = np.abs(audio_opt).max()
+ if max_audio > 1:
+ audio_opt /= max_audio
+ else:
+ audio_opt = audio_opt.cpu().detach().numpy()
+
+ audio_bytes = pack_audio(io.BytesIO(), (audio_opt * 32767).astype(np.int16), sr, media_type)
+ yield audio_bytes.getvalue()
+
+def pack_audio(audio_bytes, data, rate, media_type)->io.BytesIO:
+ if media_type == "ogg":
+ audio_bytes = pack_ogg(audio_bytes, data, rate)
+ else:
+ # wav无法流式, 先暂存raw
+ audio_bytes = pack_raw(audio_bytes, data, rate)
+ return audio_bytes
+
+
+def pack_ogg(audio_bytes, data, rate):
+ # Author: AkagawaTsurunaki
+ # Issue:
+ # Stack overflow probabilistically occurs
+ # when the function `sf_writef_short` of `libsndfile_64bit.dll` is called
+ # using the Python library `soundfile`
+ # Note:
+ # This is an issue related to `libsndfile`, not this project itself.
+ # It happens when you generate a large audio tensor (about 499804 frames in my PC)
+ # and try to convert it to an ogg file.
+ # Related:
+ # https://github.com/RVC-Boss/GPT-SoVITS/issues/1199
+ # https://github.com/libsndfile/libsndfile/issues/1023
+ # https://github.com/bastibe/python-soundfile/issues/396
+ # Suggestion:
+ # Or split the whole audio data into smaller audio segment to avoid stack overflow?
+
+ def handle_pack_ogg():
+ import soundfile as sf
+ with sf.SoundFile(audio_bytes, mode="w", samplerate=rate, channels=1, format="ogg") as audio_file:
+ audio_file.write(data)
+
+ import threading
+
+ # See: https://docs.python.org/3/library/threading.html
+ # The stack size of this thread is at least 32768
+ # If stack overflow error still occurs, just modify the `stack_size`.
+ # stack_size = n * 4096, where n should be a positive integer.
+ # Here we chose n = 4096.
+ stack_size = 4096 * 4096
+ try:
+ threading.stack_size(stack_size)
+ pack_ogg_thread = threading.Thread(target=handle_pack_ogg)
+ pack_ogg_thread.start()
+ pack_ogg_thread.join()
+ except RuntimeError as e:
+ # If changing the thread stack size is unsupported, a RuntimeError is raised.
+ print("RuntimeError: {}".format(e))
+ print("Changing the thread stack size is unsupported.")
+ except ValueError as e:
+ # If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified.
+ print("ValueError: {}".format(e))
+ print("The specified stack size is invalid.")
+
+ return audio_bytes
+
+def pack_raw(audio_bytes, data, rate):
+ audio_bytes.write(data.tobytes())
+ return audio_bytes
+
+def split(todo_text):
+ todo_text = todo_text.replace("……", "。").replace("——", "，")
+ if todo_text[-1] not in splits:
+ todo_text += "。"
+ i_split_head = i_split_tail = 0
+ len_text = len(todo_text)
+ todo_texts = []
+ while 1:
+ if i_split_head >= len_text:
+ break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
+ if todo_text[i_split_head] in splits:
+ i_split_head += 1
+ todo_texts.append(todo_text[i_split_tail:i_split_head])
+ i_split_tail = i_split_head
+ else:
+ i_split_head += 1
+ return todo_texts
+
+
+def cut1(inp):
+ inp = inp.strip("\n")
+ inps = split(inp)
+ split_idx = list(range(0, len(inps), 4))
+ split_idx[-1] = None
+ if len(split_idx) > 1:
+ opts = []
+ for idx in range(len(split_idx) - 1):
+ opts.append("".join(inps[split_idx[idx] : split_idx[idx + 1]]))
+ else:
+ opts = [inp]
+ opts = [item for item in opts if not set(item).issubset(punctuation)]
+ return "\n".join(opts)
+
+
+def cut2(inp):
+ inp = inp.strip("\n")
+ inps = split(inp)
+ if len(inps) < 2:
+ return inp
+ opts = []
+ summ = 0
+ tmp_str = ""
+ for i in range(len(inps)):
+ summ += len(inps[i])
+ tmp_str += inps[i]
+ if summ > 50:
+ summ = 0
+ opts.append(tmp_str)
+ tmp_str = ""
+ if tmp_str != "":
+ opts.append(tmp_str)
+ # print(opts)
+ if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
+ opts[-2] = opts[-2] + opts[-1]
+ opts = opts[:-1]
+ opts = [item for item in opts if not set(item).issubset(punctuation)]
+ return "\n".join(opts)
+
+
+def cut3(inp):
+ inp = inp.strip("\n")
+ opts = ["%s" % item for item in inp.strip("。").split("。")]
+ opts = [item for item in opts if not set(item).issubset(punctuation)]
+ return "\n".join(opts)
+
+
+def cut4(inp):
+ inp = inp.strip("\n")
+ opts = re.split(r"(? 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
+ items.append(char)
+ else:
+ items.append(char)
+ mergeitems.append("".join(items))
+ items = []
+ else:
+ items.append(char)
+
+ if items:
+ mergeitems.append("".join(items))
+
+ opt = [item for item in mergeitems if not set(item).issubset(punds)]
+ return "\n".join(opt)
+
+
+def custom_sort_key(s):
+ # 使用正则表达式提取字符串中的数字部分和非数字部分
+ parts = re.split("(\d+)", s)
+ # 将数字部分转换为整数，非数字部分保持不变
+ parts = [int(part) if part.isdigit() else part for part in parts]
+ return parts
+
+
+def process_text(texts):
+ _text = []
+ if all(text in [None, " ", "\n", ""] for text in texts):
+ raise ValueError(i18n("请输入有效文本"))
+ for text in texts:
+ if text in [None, " ", ""]:
+ pass
+ else:
+ _text.append(text)
+ return _text
+
+
+def change_choices():
+ SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+ return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {
+ "choices": sorted(GPT_names, key=custom_sort_key),
+ "__type__": "update",
+ }
+
+
+SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4"]
+GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
+for path in SoVITS_weight_root + GPT_weight_root:
+ os.makedirs(path, exist_ok=True)
+
+
+def get_weights_names(GPT_weight_root, SoVITS_weight_root):
+ SoVITS_names = [i for i in pretrained_sovits_name]
+ for path in SoVITS_weight_root:
+ for name in os.listdir(path):
+ if name.endswith(".pth"):
+ SoVITS_names.append("%s/%s" % (path, name))
+ GPT_names = [i for i in pretrained_gpt_name]
+ for path in GPT_weight_root:
+ for name in os.listdir(path):
+ if name.endswith(".ckpt"):
+ GPT_names.append("%s/%s" % (path, name))
+ return SoVITS_names, GPT_names
+
+
+SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root)
+
+
+def html_center(text, label="p"):
+ return f"""
+ <{label} style="margin: 0; padding: 0;">{text}
+
"""
+
+
+def html_left(text, label="p"):
+ return f"""
+ <{label} style="margin: 0; padding: 0;">{text}
+
"""
+
+
+from fastapi import FastAPI, Request, Query
+from fastapi.responses import StreamingResponse
+import uvicorn
+
+def handle(
+ refer_wav_path,
+ prompt_text,
+ prompt_language,
+ text,
+ text_language,
+ how_to_cut,
+ top_k,
+ top_p,
+ temperature,
+ speed,
+ sample_steps,
+ if_sr,
+ media_type,
+):
+
+ if sample_steps not in [4, 8, 16, 32]:
+ sample_steps = 32
+
+ return StreamingResponse(
+ get_tts_wav(
+ refer_wav_path,
+ prompt_text,
+ prompt_language,
+ text,
+ text_language,
+ how_to_cut,
+ top_k,
+ top_p,
+ temperature,
+ speed,
+ sample_steps,
+ if_sr,
+ media_type,
+ ),
+ media_type="audio/wav",
+ )
+app = FastAPI()
+@app.post("/")
+async def tts_endpoint(request: Request):
+ json_post_raw = await request.json()
+ return handle(
+ json_post_raw.get("refer_wav_path"),
+ json_post_raw.get("prompt_text"),
+ json_post_raw.get("prompt_language"),
+ json_post_raw.get("text"),
+ json_post_raw.get("text_language"),
+ json_post_raw.get("how_to_cut","cut1"),
+ json_post_raw.get("top_k", 15),
+ json_post_raw.get("top_p", 1.0),
+ json_post_raw.get("temperature", 1.0),
+ json_post_raw.get("speed", 1.0),
+ json_post_raw.get("sample_steps", 32),
+ json_post_raw.get("if_sr", False),
+ json_post_raw.get("media_type", "ogg"),
+ )
+
+
+@app.get("/")
+async def tts_endpoint(
+ refer_wav_path: str = None,
+ prompt_text: str = None,
+ prompt_language: str = None,
+ text: str = None,
+ text_language: str = None,
+ how_to_cut: str = "cut1",
+ top_k: int = 15,
+ top_p: float = 1.0,
+ temperature: float = 1.0,
+ speed: float = 1.0,
+ sample_steps: int = 32,
+ if_sr: bool = False,
+ media_type: str = "ogg",
+):
+ return handle(
+ refer_wav_path,
+ prompt_text,
+ prompt_language,
+ text,
+ text_language,
+ how_to_cut,
+ top_k,
+ top_p,
+ temperature,
+ speed,
+ sample_steps,
+ if_sr,
+ media_type,
+ )
+
+if __name__ == "__main__":
+ uvicorn.run(app, host="0.0.0.0", port=9880, workers=1)