mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
631 lines
22 KiB
Python
631 lines
22 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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import gradio as gr
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import numpy as np
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import librosa
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from feature_extractor import cnhubert
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from GPT_SoVITS.module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from text import chinese, cleaned_text_to_sequence
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from text.cleaner import clean_text
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from text.LangSegmenter import LangSegmenter
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from time import time as ttime
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from module.mel_processing import spectrogram_torch, spec_to_mel_torch
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from tools.my_utils import load_audio
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import torch, torchaudio
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import traceback
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import os, re
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# 模型路径
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gpt_path = 'GPT_weights_v2/amiya-e50.ckpt'
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sovits_path = 'SoVITS_weights_v2/amiya_e25_s950.pth'
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cnhubert_base_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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cnhubert.cnhubert_base_path = cnhubert_base_path
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# 参考音频相关配置
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REFERENCE_AUDIO = "/Users/baysonfox/Desktop/amiya-chatbot/reference.mp3"
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REFERENCE_TEXT = "博士，休息好了吗？还觉得累的话，不用勉强的。有我在呢。"
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INF_REFS = [os.path.join("/Users/baysonfox/Desktop/amiya-chatbot/references", f) for f in os.listdir("/Users/baysonfox/Desktop/amiya-chatbot/references")]
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# 模型相关设置
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DEVICE = 'cpu'
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dict_language = {
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"中文": "all_zh",#全部按中文识别
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"英文": "en",#全部按英文识别#######不变
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"日文": "all_ja",#全部按日文识别
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"中英混合": "zh",#按中英混合识别####不变
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"日英混合": "ja",#按日英混合识别####不变
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"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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os.environ["TOKENIZERS_PARALLELISM"] = "False"
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DTYPE = torch.float32
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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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ssl_model = cnhubert.get_model()
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# Accelerated Inference
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tokenizer = torch.compile(tokenizer)
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bert_model = torch.compile(bert_model)
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ssl_model = torch.compile(ssl_model)
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# 标点符号
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PUNCTUATION = {'!', '?', '…', ',', '.', '-', " "}
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# 中文标点符号
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SPLITS = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
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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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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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resample_transform_dict={}
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def resample(audio_tensor, sr0):
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global resample_transform_dict
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if sr0 not in resample_transform_dict:
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resample_transform_dict[sr0] = torchaudio.transforms.Resample(
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sr0, 24000
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).to(DEVICE)
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return resample_transform_dict[sr0](audio_tensor)
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def change_sovits_weights(prompt_language=None,text_language=None):
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global vq_model, hps, version, model_version, dict_language
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model_version = version = "v2"
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if prompt_language is not None and text_language is not None:
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prompt_text_update, prompt_language_update = {'__type__':'update'}, {'__type__':'update', 'value': "all_zh"}
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if text_language in list(dict_language.keys()):
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text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
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else:
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text_update = {'__type__':'update', 'value':''}
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text_language_update = {'__type__':'update', 'value':"中文"}
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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": False},{"__type__": "update", "visible": False},{"__type__": "update", "value": False,"interactive": False}
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dict_s2 = torch.load(sovits_path, map_location="cpu")
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hps = DictToAttrRecursive(dict_s2["config"])
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hps.model.semantic_frame_rate = "25hz"
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hps.model.version = "v2"
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version = hps.model.version
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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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model_version = version
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vq_model = vq_model.to(DEVICE)
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print("loading sovits_%s" % model_version,vq_model.load_state_dict(
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dict_s2["weight"],
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strict=False))
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vq_model.eval()
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vq_model = torch.compile(vq_model)
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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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t2s_model = t2s_model.to(DEVICE)
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t2s_model.eval()
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t2s_model = torch.compile(t2s_model)
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def get_spepc(hps, filename):
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print("hps samplingrate", hps.data.sampling_rate)
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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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maxx = audio.abs().max()
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if(maxx > 1):
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audio /= min(2, maxx.item())
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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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def clean_text_inf(text, language, version):
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phones, word2ph, norm_text = clean_text(text, language, version)
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print("phones: ", phones)
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print("word2ph: ", word2ph)
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print("norm_text: ", norm_text)
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phones = cleaned_text_to_sequence(phones, version)
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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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language=language.replace("all_","")
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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.float32
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).to(DEVICE)
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return bert
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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_phones_and_bert(text,language,version,final=False):
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if language in {"en", "all_zh", "all_ja"}:
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language = language.replace("all_","")
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formattext = text
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while " " in formattext:
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formattext = formattext.replace(" ", " ")
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if language == "zh":
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if re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return get_phones_and_bert(formattext,"zh",version)
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else:
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phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
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bert = get_bert_feature(norm_text, word2ph).to(DEVICE)
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elif language == "yue" and re.search(r'[A-Za-z]', formattext):
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formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
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formattext = chinese.mix_text_normalize(formattext)
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return get_phones_and_bert(formattext,"yue",version)
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else:
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phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float32
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).to(DEVICE)
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elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
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textlist=[]
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langlist=[]
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if language == "auto":
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for tmp in LangSegmenter.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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else:
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for tmp in LangSegmenter.getTexts(text):
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if tmp["lang"] == "en":
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langlist.append(tmp["lang"])
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else:
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# 因无法区别中日韩文汉字,以用户输入为准
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langlist.append(language)
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textlist.append(tmp["text"])
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print(textlist)
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print(langlist)
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phones_list = []
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bert_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, version)
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bert = get_bert_inf(phones, word2ph, norm_text, lang)
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phones_list.append(phones)
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norm_text_list.append(norm_text)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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phones = sum(phones_list, [])
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norm_text = ''.join(norm_text_list)
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if not final and len(phones) < 6:
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return get_phones_and_bert("." + text,language,version,final=True)
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return phones,bert.to(DTYPE),norm_text
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def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
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spec=spectrogram_torch(y,n_fft,sampling_rate,hop_size,win_size,center)
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mel=spec_to_mel_torch(spec,n_fft,num_mels,sampling_rate,fmin,fmax)
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return mel
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mel_fn_args = {
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"n_fft": 1024,
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"win_size": 1024,
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"hop_size": 256,
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"num_mels": 100,
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"sampling_rate": 24000,
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"fmin": 0,
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"fmax": None,
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"center": False
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}
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spec_min = -12
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spec_max = 2
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def norm_spec(x):
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return (x - spec_min) / (spec_max - spec_min) * 2 - 1
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def denorm_spec(x):
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return (x + 1) / 2 * (spec_max - spec_min) + spec_min
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mel_fn=lambda x: mel_spectrogram(x, **mel_fn_args)
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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 split(todo_text):
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todo_text = todo_text.replace("……", "。").replace("——", "，")
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if todo_text[-1] not in SPLITS:
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todo_text += "。"
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i_split_head = i_split_tail = 0
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len_text = len(todo_text)
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todo_texts = []
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while 1:
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if i_split_head >= len_text:
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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
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if todo_text[i_split_head] in SPLITS:
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i_split_head += 1
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todo_texts.append(todo_text[i_split_tail:i_split_head])
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i_split_tail = i_split_head
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else:
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i_split_head += 1
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return todo_texts
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def cut1(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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split_idx = list(range(0, len(inps), 4))
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split_idx[-1] = None
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if len(split_idx) > 1:
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opts = []
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for idx in range(len(split_idx) - 1):
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opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
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else:
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opts = [inp]
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opts = [item for item in opts if not set(item).issubset(PUNCTUATION)]
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return "\n".join(opts)
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def cut2(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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if len(inps) < 2:
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return inp
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opts = []
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summ = 0
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tmp_str = ""
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for i in range(len(inps)):
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summ += len(inps[i])
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tmp_str += inps[i]
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if summ > 50:
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summ = 0
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opts.append(tmp_str)
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tmp_str = ""
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if tmp_str != "":
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opts.append(tmp_str)
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# print(opts)
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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
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opts[-2] = opts[-2] + opts[-1]
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opts = opts[:-1]
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opts = [item for item in opts if not set(item).issubset(PUNCTUATION)]
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return "\n".join(opts)
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def cut3(inp):
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inp = inp.strip("\n")
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opts = ["%s" % item for item in inp.strip("。").split("。")]
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opts = [item for item in opts if not set(item).issubset(PUNCTUATION)]
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return "\n".join(opts)
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def cut4(inp):
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inp = inp.strip("\n")
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opts = re.split(r'(?<!\d)\.(?!\d)', inp.strip("."))
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opts = [item for item in opts if not set(item).issubset(PUNCTUATION)]
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return "\n".join(opts)
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def cut5(inp):
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inp = inp.strip("\n")
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punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
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mergeitems = []
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items = []
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for i, char in enumerate(inp):
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if char in punds:
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if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
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items.append(char)
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else:
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items.append(char)
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mergeitems.append("".join(items))
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items = []
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else:
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items.append(char)
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if items:
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mergeitems.append("".join(items))
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opt = [item for item in mergeitems if not set(item).issubset(punds)]
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return "\n".join(opt)
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def custom_sort_key(s):
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# 使用正则表达式提取字符串中的数字部分和非数字部分
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parts = re.split('(\d+)', s)
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# 将数字部分转换为整数，非数字部分保持不变
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parts = [int(part) if part.isdigit() else part for part in parts]
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return parts
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def process_text(texts):
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_text=[]
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if all(text in [None, " ", "\n",""] for text in texts):
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raise ValueError("请输入有效文本")
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for text in texts:
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if text in [None, " ", ""]:
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pass
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else:
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_text.append(text)
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return _text
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cache = {}
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def get_tts_wav(text,
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text_language,
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how_to_cut="不切",
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top_k=20,
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top_p=0.6,
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temperature=0.6,
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speed=1,
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if_freeze=False,
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sample_steps=8):
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global cache
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prompt_text = REFERENCE_TEXT
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prompt_language = "中文"
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ref_wav_path = REFERENCE_AUDIO
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inp_refs = INF_REFS
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t = []
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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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# 去除prompt_text的换行
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prompt_text = prompt_text.strip("\n")
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# 手动添加标点符号
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if (prompt_text[-1] not in SPLITS): prompt_text += "。" if prompt_language != "en" else "."
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print("实际输入的参考文本:", prompt_text)
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text = text.strip("\n")
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print("实际输入的目标文本:", text)
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zero_wav = np.zeros(
|
||
int(hps.data.sampling_rate * 0.3),
|
||
dtype=np.float32
|
||
)
|
||
|
||
with torch.no_grad():
|
||
# 参考音频和sampling rate，numpy格式
|
||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||
# numpy -> torch
|
||
wav16k = torch.from_numpy(wav16k)
|
||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||
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
|
||
)
|
||
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 != "不切":
|
||
cut_map = {
|
||
"凑四句一切": cut1,
|
||
"凑50字一切": cut2,
|
||
"按中文句号。切": cut3,
|
||
"按英文句号.切": cut4,
|
||
"按标点符号切": cut5
|
||
}
|
||
cut_map[how_to_cut](text)
|
||
|
||
while "\n\n" in text:
|
||
text = text.replace("\n\n", "\n")
|
||
|
||
print("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 = []
|
||
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("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("前端处理后的文本(每句):", norm_text2)
|
||
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)
|
||
|
||
t2 = ttime()
|
||
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,
|
||
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()
|
||
refers=[]
|
||
if(inp_refs):
|
||
for path in inp_refs:
|
||
try:
|
||
refer = get_spepc(hps, path).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).detach().cpu().numpy()[0, 0])
|
||
|
||
audio_opt.append(audio)
|
||
audio_opt.append(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]))
|
||
)
|
||
sr=hps.data.sampling_rate if model_version!="v3"else 24000
|
||
yield sr, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
||
|
||
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||
|
||
try:next(change_sovits_weights(sovits_path))
|
||
except:pass
|
||
change_gpt_weights(gpt_path) # 初始化GPT模型
|
||
|
||
gr.Markdown(value="<h1 style='color: #2b5278; font-size: 24px; text-align: center;'>大概可能也许是阿米娅的声音（</h1>")
|
||
with gr.Row() as main_row:
|
||
with gr.Column(scale=7) as text_column:
|
||
text = gr.Textbox(
|
||
label="需要合成的文本",
|
||
value="",
|
||
lines=13,
|
||
max_lines=13
|
||
)
|
||
|
||
with gr.Row():
|
||
inference_button = gr.Button("合成语音", variant="primary", size='lg')
|
||
output = gr.Audio(label="输出的语音")
|
||
|
||
with gr.Column(scale=5) as control_column:
|
||
text_language = gr.Dropdown(
|
||
label="需要合成的语种。限制范围越小判别效果越好。",
|
||
choices=list(dict_language.keys()),
|
||
value="中文"
|
||
)
|
||
|
||
how_to_cut = gr.Dropdown(
|
||
label="怎么切",
|
||
choices=["不切", "凑四句一切", "凑50字一切", "按中文句号。切", "按英文句号.切", "按标点符号切"],
|
||
value="凑四句一切"
|
||
)
|
||
|
||
gr.Markdown(value="语速调整，高为更快")
|
||
|
||
if_freeze = gr.Checkbox(
|
||
label="是否直接对上次合成结果调整语速和音色。防止随机性。",
|
||
value=False
|
||
)
|
||
|
||
speed = gr.Slider(
|
||
minimum=0.6,
|
||
maximum=1.65,
|
||
step=0.05,
|
||
label="语速",
|
||
value=1
|
||
)
|
||
|
||
gr.Markdown("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")
|
||
|
||
top_k = gr.Slider(
|
||
minimum=1,
|
||
maximum=100,
|
||
step=1,
|
||
label="top_k",
|
||
value=15
|
||
)
|
||
|
||
top_p = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
step=0.05,
|
||
label="top_p",
|
||
value=1
|
||
)
|
||
|
||
temperature = gr.Slider(
|
||
minimum=0,
|
||
maximum=1,
|
||
step=0.05,
|
||
label="temperature",
|
||
value=1
|
||
)
|
||
|
||
inference_button.click(
|
||
get_tts_wav,
|
||
[text, text_language, how_to_cut, top_k, top_p, temperature, speed, if_freeze],
|
||
[output],
|
||
)
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
app.queue().launch(#concurrency_count=511, max_size=1022
|
||
server_name="0.0.0.0",
|
||
inbrowser=False,
|
||
share=False,
|
||
server_port=9872,
|
||
quiet=True,
|
||
)
|