mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 23:48:48 +08:00
603 lines
20 KiB
Python
603 lines
20 KiB
Python
'''
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按中英混合识别
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按日英混合识别
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多语种启动切分识别语种
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全部按中文识别
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全部按英文识别
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全部按日文识别
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'''
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import os, re, logging
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import LangSegment
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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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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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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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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:
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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"): "all_zh",#全部按中文识别
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i18n("英文"): "en",#全部按英文识别#######不变
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i18n("日文"): "all_ja",#全部按日文识别
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i18n("中英混合"): "zh",#按中英混合识别####不变
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i18n("日英混合"): "ja",#按日英混合识别####不变
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i18n("多语种混合"): "auto",#多语种启动切分识别语种
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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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# Merge punctuation into previous word
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for i in range(len(textlist)-1, 0, -1):
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if re.match(r'^[\W_]+$', textlist[i]):
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textlist[i-1] += textlist[i]
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del textlist[i]
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del langlist[i]
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# Merge consecutive words with the same language tag
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i = 0
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while i < len(langlist) - 1:
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if langlist[i] == langlist[i+1]:
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textlist[i] += textlist[i+1]
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del textlist[i+1]
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del langlist[i+1]
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else:
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i += 1
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return textlist, langlist
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def clean_text_inf(text, language):
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formattext = ""
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language = language.replace("all_","")
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for tmp in LangSegment.getTexts(text):
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if language == "ja":
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if tmp["lang"] == language or tmp["lang"] == "zh":
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formattext += tmp["text"] + " "
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continue
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if tmp["lang"] == language:
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formattext += tmp["text"] + " "
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while " " in formattext:
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formattext = formattext.replace(" ", " ")
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phones, word2ph, norm_text = clean_text(formattext, language)
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phones = cleaned_text_to_sequence(phones)
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return phones, word2ph, norm_text
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dtype=torch.float16 if is_half == True else torch.float32
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def get_bert_inf(phones, word2ph, norm_text, language):
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language=language.replace("all_","")
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
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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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if(language!="auto"):
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textlist, langlist = splite_en_inf(text, language)
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else:
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textlist=[]
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langlist=[]
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for tmp in LangSegment.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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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 == "zh":
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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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if(language!="auto"):
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textlist, langlist = splite_en_inf(text, language)
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else:
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textlist=[]
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langlist=[]
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for tmp in LangSegment.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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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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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(textlist[i], 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_cleaned_text_final(text,language):
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if language in {"en","all_zh","all_ja"}:
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phones, word2ph, norm_text = clean_text_inf(text, language)
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elif language in {"zh", "ja","auto"}:
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phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
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return phones, word2ph, norm_text
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def get_bert_final(phones, word2ph, text,language,device):
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if language == "en":
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bert = get_bert_inf(phones, word2ph, text, language)
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elif language in {"zh", "ja","auto"}:
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bert = nonen_get_bert_inf(text, language)
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elif language == "all_zh":
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bert = get_bert_feature(text, word2ph).to(device)
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else:
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bert = torch.zeros((1024, len(phones))).to(device)
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return bert
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def merge_short_text_in_array(texts, threshold):
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if (len(texts)) < 2:
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return texts
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result = []
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text = ""
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for ele in texts:
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text += ele
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if len(text) >= threshold:
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result.append(text)
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text = ""
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if (len(text) > 0):
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if len(result) == 0:
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result.append(text)
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else:
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result[len(result) - 1] += text
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return result
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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):
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if prompt_text is None or len(prompt_text) == 0:
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ref_free = True
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t0 = ttime()
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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if not ref_free:
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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_language != "en" else "."
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print(i18n("实际输入的参考文本:"), prompt_text)
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text = text.strip("\n")
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if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
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print(i18n("实际输入的目标文本:"), text)
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zero_wav = np.zeros(
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int(hps.data.sampling_rate * 0.3),
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dtype=np.float16 if is_half == True else np.float32,
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)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
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raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
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wav16k = torch.from_numpy(wav16k)
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zero_wav_torch = torch.from_numpy(zero_wav)
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if is_half == True:
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wav16k = wav16k.half().to(device)
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zero_wav_torch = zero_wav_torch.half().to(device)
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else:
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wav16k = wav16k.to(device)
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zero_wav_torch = zero_wav_torch.to(device)
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wav16k = torch.cat([wav16k, zero_wav_torch])
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ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
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"last_hidden_state"
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].transpose(
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1, 2
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) # .float()
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codes = vq_model.extract_latent(ssl_content)
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prompt_semantic = codes[0, 0]
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t1 = ttime()
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if (how_to_cut == i18n("凑四句一切")):
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text = cut1(text)
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elif (how_to_cut == i18n("凑50字一切")):
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text = cut2(text)
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elif (how_to_cut == i18n("按中文句号。切")):
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text = cut3(text)
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elif (how_to_cut == i18n("按英文句号.切")):
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text = cut4(text)
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elif (how_to_cut == i18n("按标点符号切")):
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text = cut5(text)
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while "\n\n" in text:
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text = text.replace("\n\n", "\n")
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print(i18n("实际输入的目标文本(切句后):"), text)
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texts = text.split("\n")
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texts = merge_short_text_in_array(texts, 5)
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audio_opt = []
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if not ref_free:
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phones1, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
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bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
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for text in texts:
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# 解决输入目标文本的空行导致报错的问题
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if (len(text.strip()) == 0):
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continue
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if (text[-1] not in splits): text += "。" if text_language != "en" else "."
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print(i18n("实际输入的目标文本(每句):"), text)
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phones2, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
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bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
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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)
|
||
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,
|
||
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]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut2(inp):
|
||
inp = inp.strip("\n")
|
||
inps = split(inp)
|
||
if len(inps) < 2:
|
||
return inp
|
||
opts = []
|
||
summ = 0
|
||
tmp_str = ""
|
||
for i in range(len(inps)):
|
||
summ += len(inps[i])
|
||
tmp_str += inps[i]
|
||
if summ > 50:
|
||
summ = 0
|
||
opts.append(tmp_str)
|
||
tmp_str = ""
|
||
if tmp_str != "":
|
||
opts.append(tmp_str)
|
||
# print(opts)
|
||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||
opts[-2] = opts[-2] + opts[-1]
|
||
opts = opts[:-1]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut3(inp):
|
||
inp = inp.strip("\n")
|
||
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
||
|
||
|
||
def cut4(inp):
|
||
inp = inp.strip("\n")
|
||
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
||
|
||
|
||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||
def cut5(inp):
|
||
# if not re.search(r'[^\w\s]', inp[-1]):
|
||
# inp += '。'
|
||
inp = inp.strip("\n")
|
||
punds = r'[,.;?!、，。？！;：]'
|
||
items = re.split(f'({punds})', inp)
|
||
items = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||
opt = "\n".join(items)
|
||
return opt
|
||
|
||
|
||
def custom_sort_key(s):
|
||
# 使用正则表达式提取字符串中的数字部分和非数字部分
|
||
parts = re.split('(\d+)', s)
|
||
# 将数字部分转换为整数，非数字部分保持不变
|
||
parts = [int(part) if part.isdigit() else part for part in parts]
|
||
return parts
|
||
|
||
|
||
def change_choices():
|
||
SoVITS_names, GPT_names = get_weights_names()
|
||
return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"}
|
||
|
||
|
||
pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
|
||
pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
|
||
SoVITS_weight_root = "SoVITS_weights"
|
||
GPT_weight_root = "GPT_weights"
|
||
os.makedirs(SoVITS_weight_root, exist_ok=True)
|
||
os.makedirs(GPT_weight_root, exist_ok=True)
|
||
|
||
|
||
def get_weights_names():
|
||
SoVITS_names = [pretrained_sovits_name]
|
||
for name in os.listdir(SoVITS_weight_root):
|
||
if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name))
|
||
GPT_names = [pretrained_gpt_name]
|
||
for name in os.listdir(GPT_weight_root):
|
||
if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
|
||
return SoVITS_names, GPT_names
|
||
|
||
|
||
SoVITS_names, GPT_names = get_weights_names()
|