mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-06 22:50:00 +08:00
Merge branch 'main' of https://github.com/RVC-Boss/GPT-SoVITS
This commit is contained in:
lalala-233
commit
372a2ff89a
@ -1,480 +1,13 @@
|
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import os,re,logging
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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|
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if os.path.exists("./gweight.txt"):
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with open("./gweight.txt", 'r',encoding="utf-8") as file:
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gweight_data = file.read()
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gpt_path = os.environ.get(
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"gpt_path", gweight_data)
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else:
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gpt_path = os.environ.get(
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"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
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if os.path.exists("./sweight.txt"):
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with open("./sweight.txt", 'r',encoding="utf-8") as file:
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sweight_data = file.read()
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sovits_path = os.environ.get("sovits_path", sweight_data)
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else:
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sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
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# gpt_path = os.environ.get(
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# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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# )
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# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
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cnhubert_base_path = os.environ.get(
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"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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)
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bert_path = os.environ.get(
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"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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)
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infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
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infer_ttswebui = int(infer_ttswebui)
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is_share = os.environ.get("is_share", "False")
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is_share=eval(is_share)
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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is_half = eval(os.environ.get("is_half", "True"))
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import gradio as gr
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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import librosa,torch
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from feature_extractor import cnhubert
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cnhubert.cnhubert_base_path=cnhubert_base_path
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import sys
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from PyQt5.QtCore import QEvent
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from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton, QTextEdit
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from PyQt5.QtWidgets import QGridLayout, QVBoxLayout, QWidget, QFileDialog, QStatusBar, QComboBox
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import soundfile as sf
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from module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from time import time as ttime
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto()
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os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。
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if torch.cuda.is_available():
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device = "cuda"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "cpu"
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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if is_half == True:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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return phone_level_feature.T
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class DictToAttrRecursive(dict):
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def __init__(self, input_dict):
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super().__init__(input_dict)
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for key, value in input_dict.items():
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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self[key] = value
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setattr(self, key, value)
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def __getattr__(self, item):
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try:
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return self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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def __setattr__(self, key, value):
|
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if isinstance(value, dict):
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value = DictToAttrRecursive(value)
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super(DictToAttrRecursive, self).__setitem__(key, value)
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super().__setattr__(key, value)
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def __delattr__(self, item):
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try:
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del self[item]
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except KeyError:
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raise AttributeError(f"Attribute {item} not found")
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ssl_model = cnhubert.get_model()
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if is_half == True:
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ssl_model = ssl_model.half().to(device)
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else:
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ssl_model = ssl_model.to(device)
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def change_sovits_weights(sovits_path):
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global vq_model,hps
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dict_s2=torch.load(sovits_path,map_location="cpu")
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hps=dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model
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)
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if("pretrained"not in sovits_path):
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del vq_model.enc_q
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if is_half == True:
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vq_model = vq_model.half().to(device)
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else:
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vq_model = vq_model.to(device)
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vq_model.eval()
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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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with open("./sweight.txt","w",encoding="utf-8")as f:f.write(sovits_path)
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change_sovits_weights(sovits_path)
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def change_gpt_weights(gpt_path):
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global hz,max_sec,t2s_model,config
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hz = 50
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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config = dict_s1["config"]
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max_sec = config["data"]["max_sec"]
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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if is_half == True:
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t2s_model = t2s_model.half()
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t2s_model = t2s_model.to(device)
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t2s_model.eval()
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total = sum([param.nelement() for param in t2s_model.parameters()])
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print("Number of parameter: %.2fM" % (total / 1e6))
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with open("./gweight.txt","w",encoding="utf-8")as f:f.write(gpt_path)
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change_gpt_weights(gpt_path)
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(
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audio_norm,
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hps.data.filter_length,
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hps.data.sampling_rate,
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hps.data.hop_length,
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hps.data.win_length,
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center=False,
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)
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return spec
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dict_language={
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i18n("中文"):"zh",
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i18n("英文"):"en",
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i18n("日文"):"ja"
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}
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def splite_en_inf(sentence, language):
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pattern = re.compile(r'[a-zA-Z. ]+')
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textlist = []
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langlist = []
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pos = 0
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for match in pattern.finditer(sentence):
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start, end = match.span()
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if start > pos:
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textlist.append(sentence[pos:start])
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langlist.append(language)
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textlist.append(sentence[start:end])
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langlist.append("en")
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pos = end
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if pos < len(sentence):
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textlist.append(sentence[pos:])
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langlist.append(language)
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return textlist, langlist
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def clean_text_inf(text, language):
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phones, word2ph, norm_text = clean_text(text, language)
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phones = cleaned_text_to_sequence(phones)
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return phones, word2ph, norm_text
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def get_bert_inf(phones, word2ph, norm_text, language):
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)
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else:
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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return bert
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def nonen_clean_text_inf(text, language):
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textlist, langlist = splite_en_inf(text, language)
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phones_list = []
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word2ph_list = []
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norm_text_list = []
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for i in range(len(textlist)):
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
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phones_list.append(phones)
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if lang == "en" or "ja":
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pass
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else:
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word2ph_list.append(word2ph)
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norm_text_list.append(norm_text)
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print(word2ph_list)
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phones = sum(phones_list, [])
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word2ph = sum(word2ph_list, [])
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norm_text = ' '.join(norm_text_list)
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return phones, word2ph, norm_text
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def nonen_get_bert_inf(text, language):
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textlist, langlist = splite_en_inf(text, language)
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print(textlist)
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print(langlist)
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bert_list = []
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for i in range(len(textlist)):
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text = textlist[i]
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(text, lang)
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||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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return bert
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|
||||
splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",}
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def get_first(text):
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pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
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||||
text = re.split(pattern, text)[0].strip()
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return text
|
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||||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")):
|
||||
t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_text!="en"else "."
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text = text.strip("\n")
|
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if(len(get_first(text))<4):text+="。"if text!="en"else "."
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zero_wav = np.zeros(
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||||
int(hps.data.sampling_rate * 0.3),
|
||||
dtype=np.float16 if is_half == True else np.float32,
|
||||
)
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with torch.no_grad():
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||||
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()
|
||||
prompt_language = dict_language[prompt_language]
|
||||
text_language = dict_language[text_language]
|
||||
|
||||
if prompt_language == "en":
|
||||
phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
|
||||
else:
|
||||
phones1, word2ph1, norm_text1 = nonen_clean_text_inf(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)
|
||||
text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
|
||||
if(text[-1]not in splits):text+="。"if text_language!="en"else "."
|
||||
texts=text.split("\n")
|
||||
audio_opt = []
|
||||
if prompt_language == "en":
|
||||
bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
|
||||
else:
|
||||
bert1 = nonen_get_bert_inf(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_language == "en":
|
||||
phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
|
||||
else:
|
||||
phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
|
||||
|
||||
if text_language == "en":
|
||||
bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
|
||||
else:
|
||||
bert2 = nonen_get_bert_inf(text, text_language)
|
||||
|
||||
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
|
||||
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(".")])
|
||||
|
||||
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()
|
||||
from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav
|
||||
|
||||
|
||||
class GPTSoVITSGUI(QMainWindow):
|
||||
|
||||
@ -1,11 +1,18 @@
|
||||
'''
|
||||
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
|
||||
@ -193,9 +200,12 @@ def get_spepc(hps, filename):
|
||||
|
||||
|
||||
dict_language = {
|
||||
i18n("中文"): "zh",
|
||||
i18n("英文"): "en",
|
||||
i18n("日文"): "ja"
|
||||
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
|
||||
}
|
||||
|
||||
|
||||
@ -235,15 +245,15 @@ def splite_en_inf(sentence, language):
|
||||
|
||||
|
||||
def clean_text_inf(text, language):
|
||||
phones, word2ph, norm_text = clean_text(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)
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
@ -254,7 +264,16 @@ def get_bert_inf(phones, word2ph, norm_text, language):
|
||||
|
||||
|
||||
def nonen_clean_text_inf(text, language):
|
||||
textlist, langlist = splite_en_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 = []
|
||||
@ -262,9 +281,7 @@ def nonen_clean_text_inf(text, language):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
||||
phones_list.append(phones)
|
||||
if lang == "en" or "ja":
|
||||
pass
|
||||
else:
|
||||
if lang == "zh":
|
||||
word2ph_list.append(word2ph)
|
||||
norm_text_list.append(norm_text)
|
||||
print(word2ph_list)
|
||||
@ -276,7 +293,14 @@ def nonen_clean_text_inf(text, language):
|
||||
|
||||
|
||||
def nonen_get_bert_inf(text, language):
|
||||
textlist, langlist = splite_en_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 = []
|
||||
@ -300,6 +324,24 @@ def get_first(text):
|
||||
return text
|
||||
|
||||
|
||||
def get_cleaned_text_fianl(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, norm_text,language,device):
|
||||
if text_language == "en":
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, text_language)
|
||||
elif text_language in {"zh", "ja","auto"}:
|
||||
bert = nonen_get_bert_inf(text, text_language)
|
||||
elif text_language == "all_zh":
|
||||
bert = get_bert_feature(norm_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_text = prompt_text.strip("\n")
|
||||
@ -335,10 +377,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
|
||||
t1 = ttime()
|
||||
prompt_language = dict_language[prompt_language]
|
||||
text_language = dict_language[text_language]
|
||||
if prompt_language == "en":
|
||||
phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
|
||||
else:
|
||||
phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
|
||||
|
||||
phones1, word2ph1, norm_text1=get_cleaned_text_fianl(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")):
|
||||
@ -353,25 +394,16 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
|
||||
print(i18n("实际输入的目标文本(切句后):"), text)
|
||||
texts = text.split("\n")
|
||||
audio_opt = []
|
||||
if prompt_language == "en":
|
||||
bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
|
||||
else:
|
||||
bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
|
||||
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)
|
||||
if text_language == "en":
|
||||
phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
|
||||
else:
|
||||
phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
|
||||
|
||||
if text_language == "en":
|
||||
bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
|
||||
else:
|
||||
bert2 = nonen_get_bert_inf(text, text_language)
|
||||
phones2, word2ph2, norm_text2 = get_cleaned_text_fianl(text, text_language)
|
||||
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
|
||||
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
|
||||
@ -557,7 +589,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
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")], value=i18n("中文")
|
||||
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"),
|
||||
|
||||
@ -10,6 +10,9 @@ special = [
|
||||
|
||||
|
||||
def clean_text(text, language):
|
||||
if(language not in language_module_map):
|
||||
language="en"
|
||||
text=" "
|
||||
for special_s, special_l, target_symbol in special:
|
||||
if special_s in text and language == special_l:
|
||||
return clean_special(text, language, special_s, target_symbol)
|
||||
|
||||
@ -97,7 +97,7 @@ def text_normalize(text):
|
||||
return text
|
||||
|
||||
# Copied from espnet https://github.com/espnet/espnet/blob/master/espnet2/text/phoneme_tokenizer.py
|
||||
def pyopenjtalk_g2p_prosody(text: str, drop_unvoiced_vowels: bool = True) -> list[str]:
|
||||
def pyopenjtalk_g2p_prosody(text, drop_unvoiced_vowels=True):
|
||||
"""Extract phoneme + prosoody symbol sequence from input full-context labels.
|
||||
|
||||
The algorithm is based on `Prosodic features control by symbols as input of
|
||||
|
||||
@ -75,6 +75,12 @@
|
||||
|
||||
3-增加按标点符号切分
|
||||
|
||||
### 20240201更新
|
||||
|
||||
1-修复uvr5读取格式错误导致分离失败的问题
|
||||
|
||||
2-支持中日英混合多种文本自动切分识别语种
|
||||
|
||||
|
||||
todolist：
|
||||
|
||||
@ -84,4 +90,3 @@ todolist：
|
||||
|
||||
3-%百分号在文本里会导致error不能推理 还有 元/吨 会读成 元吨 而不是元每吨，像这类问题，是什么库来弄文本解析到语音的，应该怎么改善这个问题呀
|
||||
|
||||
4-中日英、中英、日英 五种目标语言
|
||||
|
||||
@ -5,6 +5,7 @@ import sys,os,traceback
|
||||
from funasr import AutoModel
|
||||
|
||||
dir=sys.argv[1]
|
||||
if(dir[-1]=="/"):dir=dir[:-1]
|
||||
# opt_name=dir.split("\\")[-1].split("/")[-1]
|
||||
opt_name=os.path.basename(dir)
|
||||
|
||||
|
||||
178
tools/webui.py
Normal file
178
tools/webui.py
Normal file
@ -0,0 +1,178 @@
|
||||
import os
|
||||
import traceback,gradio as gr
|
||||
import logging
|
||||
from tools.i18n.i18n import I18nAuto
|
||||
i18n = I18nAuto()
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
import librosa,ffmpeg
|
||||
import soundfile as sf
|
||||
import torch
|
||||
import sys
|
||||
from mdxnet import MDXNetDereverb
|
||||
from vr import AudioPre, AudioPreDeEcho
|
||||
|
||||
weight_uvr5_root = "tools/uvr5/uvr5_weights"
|
||||
uvr5_names = []
|
||||
for name in os.listdir(weight_uvr5_root):
|
||||
if name.endswith(".pth") or "onnx" in name:
|
||||
uvr5_names.append(name.replace(".pth", ""))
|
||||
|
||||
device=sys.argv[1]
|
||||
is_half=sys.argv[2]
|
||||
webui_port_uvr5=int(sys.argv[3])
|
||||
is_share=eval(sys.argv[4])
|
||||
|
||||
def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
|
||||
infos = []
|
||||
try:
|
||||
inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
||||
save_root_vocal = (
|
||||
save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
||||
)
|
||||
save_root_ins = (
|
||||
save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
|
||||
)
|
||||
if model_name == "onnx_dereverb_By_FoxJoy":
|
||||
from MDXNet import MDXNetDereverb
|
||||
|
||||
pre_fun = MDXNetDereverb(15)
|
||||
else:
|
||||
func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
|
||||
pre_fun = func(
|
||||
agg=int(agg),
|
||||
model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
|
||||
device=device,
|
||||
is_half=is_half,
|
||||
)
|
||||
if inp_root != "":
|
||||
paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
|
||||
else:
|
||||
paths = [path.name for path in paths]
|
||||
for path in paths:
|
||||
inp_path = os.path.join(inp_root, path)
|
||||
if(os.path.isfile(inp_path)==False):continue
|
||||
need_reformat = 1
|
||||
done = 0
|
||||
try:
|
||||
info = ffmpeg.probe(inp_path, cmd="ffprobe")
|
||||
if (
|
||||
info["streams"][0]["channels"] == 2
|
||||
and info["streams"][0]["sample_rate"] == "44100"
|
||||
):
|
||||
need_reformat = 0
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0
|
||||
)
|
||||
done = 1
|
||||
except:
|
||||
need_reformat = 1
|
||||
traceback.print_exc()
|
||||
if need_reformat == 1:
|
||||
tmp_path = "%s/%s.reformatted.wav" % (
|
||||
os.path.join(os.environ["TEMP"]),
|
||||
os.path.basename(inp_path),
|
||||
)
|
||||
os.system(
|
||||
"ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y"
|
||||
% (inp_path, tmp_path)
|
||||
)
|
||||
inp_path = tmp_path
|
||||
try:
|
||||
if done == 0:
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0
|
||||
)
|
||||
infos.append("%s->Success" % (os.path.basename(inp_path)))
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
infos.append(
|
||||
"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
|
||||
)
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
infos.append(traceback.format_exc())
|
||||
yield "\n".join(infos)
|
||||
finally:
|
||||
try:
|
||||
if model_name == "onnx_dereverb_By_FoxJoy":
|
||||
del pre_fun.pred.model
|
||||
del pre_fun.pred.model_
|
||||
else:
|
||||
del pre_fun.model
|
||||
del pre_fun
|
||||
except:
|
||||
traceback.print_exc()
|
||||
print("clean_empty_cache")
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
yield "\n".join(infos)
|
||||
|
||||
with gr.Blocks(title="UVR5 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. <br>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<b>LICENSE</b>.")
|
||||
)
|
||||
with gr.Tabs():
|
||||
with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
|
||||
with gr.Group():
|
||||
gr.Markdown(
|
||||
value=i18n(
|
||||
"人声伴奏分离批量处理， 使用UVR5模型。 <br>合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。 <br>模型分为三类： <br>1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点； <br>2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型； <br> 3、去混响、去延迟模型（by FoxJoy）：<br> (1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；<br> (234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。<br>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"
|
||||
)
|
||||
)
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
dir_wav_input = gr.Textbox(
|
||||
label=i18n("输入待处理音频文件夹路径"),
|
||||
placeholder="C:\\Users\\Desktop\\todo-songs",
|
||||
)
|
||||
wav_inputs = gr.File(
|
||||
file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
|
||||
)
|
||||
with gr.Column():
|
||||
model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
|
||||
agg = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=20,
|
||||
step=1,
|
||||
label=i18n("人声提取激进程度"),
|
||||
value=10,
|
||||
interactive=True,
|
||||
visible=False, # 先不开放调整
|
||||
)
|
||||
opt_vocal_root = gr.Textbox(
|
||||
label=i18n("指定输出主人声文件夹"), value="output/uvr5_opt"
|
||||
)
|
||||
opt_ins_root = gr.Textbox(
|
||||
label=i18n("指定输出非主人声文件夹"), value="output/uvr5_opt"
|
||||
)
|
||||
format0 = gr.Radio(
|
||||
label=i18n("导出文件格式"),
|
||||
choices=["wav", "flac", "mp3", "m4a"],
|
||||
value="flac",
|
||||
interactive=True,
|
||||
)
|
||||
but2 = gr.Button(i18n("转换"), variant="primary")
|
||||
vc_output4 = gr.Textbox(label=i18n("输出信息"))
|
||||
but2.click(
|
||||
uvr,
|
||||
[
|
||||
model_choose,
|
||||
dir_wav_input,
|
||||
opt_vocal_root,
|
||||
wav_inputs,
|
||||
opt_ins_root,
|
||||
agg,
|
||||
format0,
|
||||
],
|
||||
[vc_output4],
|
||||
api_name="uvr_convert",
|
||||
)
|
||||
app.queue(concurrency_count=511, max_size=1022).launch(
|
||||
server_name="0.0.0.0",
|
||||
inbrowser=True,
|
||||
share=is_share,
|
||||
server_port=webui_port_uvr5,
|
||||
quiet=True,
|
||||
)
|
||||
Loading…
x
Reference in New Issue
Block a user