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RVC-Boss
2024-01-16 17:38:48 +08:00
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import os
gpt_path=os.environ.get("gpt_path","pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
sovits_path=os.environ.get("sovits_path","pretrained_models/s2G488k.pth")
cnhubert_base_path=os.environ.get("cnhubert_base_path","pretrained_models/chinese-hubert-base")
bert_path=os.environ.get("bert_path","pretrained_models/chinese-roberta-wwm-ext-large")
infer_ttswebui=os.environ.get("infer_ttswebui",9872)
infer_ttswebui=int(infer_ttswebui)
if("_CUDA_VISIBLE_DEVICES"in os.environ):
os.environ["CUDA_VISIBLE_DEVICES"]=os.environ["_CUDA_VISIBLE_DEVICES"]
is_half=eval(os.environ.get("is_half","True"))
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer
import sys,torch,numpy as np
from pathlib import Path
import os,pdb,utils,librosa,math,traceback,requests,argparse,torch,multiprocessing,pandas as pd,torch.multiprocessing as mp,soundfile
# torch.backends.cuda.sdp_kernel("flash")
# torch.backends.cuda.enable_flash_sdp(True)
# torch.backends.cuda.enable_mem_efficient_sdp(True) # Not avaliable if torch version is lower than 2.0
# torch.backends.cuda.enable_math_sdp(True)
from random import shuffle
from AR.utils import get_newest_ckpt
from glob import glob
from tqdm import tqdm
from feature_extractor import cnhubert
cnhubert.cnhubert_base_path=cnhubert_base_path
from io import BytesIO
from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from AR.utils.io import load_yaml_config
from text import cleaned_text_to_sequence
from text.cleaner import text_to_sequence, clean_text
from time import time as ttime
from module.mel_processing import spectrogram_torch
from my_utils import load_audio
device="cuda"
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model=AutoModelForMaskedLM.from_pretrained(bert_path)
if(is_half==True):bert_model=bert_model.half().to(device)
else:bert_model=bert_model.to(device)
# bert_model=bert_model.to(device)
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")
for i in inputs:
inputs[i] = inputs[i].to(device)#####输入是long不用管精度问题，精度随bert_model
res = bert_model(**inputs, output_hidden_states=True)
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
assert len(word2ph) == len(text)
phone_level_feature = []
for i in range(len(word2ph)):
repeat_feature = res[i].repeat(word2ph[i], 1)
phone_level_feature.append(repeat_feature)
phone_level_feature = torch.cat(phone_level_feature, dim=0)
# if(is_half==True):phone_level_feature=phone_level_feature.half()
return phone_level_feature.T
n_semantic = 1024
dict_s2=torch.load(sovits_path,map_location="cpu")
hps=dict_s2["config"]
class DictToAttrRecursive:
def __init__(self, input_dict):
for key, value in input_dict.items():
if isinstance(value, dict):
# 如果值是字典，递归调用构造函数
setattr(self, key, DictToAttrRecursive(value))
else:
setattr(self, key, value)
hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate="25hz"
dict_s1=torch.load(gpt_path,map_location="cpu")
config=dict_s1["config"]
ssl_model=cnhubert.get_model()
if(is_half==True):ssl_model=ssl_model.half().to(device)
else:ssl_model=ssl_model.to(device)
vq_model = SynthesizerTrn(
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model)
if(is_half==True):vq_model=vq_model.half().to(device)
else:vq_model=vq_model.to(device)
vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"],strict=False))
hz = 50
max_sec = config['data']['max_sec']
# t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo
t2s_model = Text2SemanticLightningModule(config,"ojbk",is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if(is_half==True):t2s_model=t2s_model.half()
t2s_model=t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
def get_spepc(hps, filename):
audio=load_audio(filename,int(hps.data.sampling_rate))
audio=torch.FloatTensor(audio)
audio_norm = audio
audio_norm = audio_norm.unsqueeze(0)
spec = spectrogram_torch(audio_norm, hps.data.filter_length,hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,center=False)
return spec
dict_language={
"中文":"zh",
"英文":"en",
"日文":"ja"
}
def get_tts_wav(ref_wav_path,prompt_text,prompt_language,text,text_language):
t0 = ttime()
prompt_text=prompt_text.strip("\n")
prompt_language,text=prompt_language,text.strip("\n")
with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙
wav16k = torch.from_numpy(wav16k)
if(is_half==True):wav16k=wav16k.half().to(device)
else:wav16k=wav16k.to(device)
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]
phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
phones1=cleaned_text_to_sequence(phones1)
texts=text.split("\n")
audio_opt = []
zero_wav=np.zeros(int(hps.data.sampling_rate*0.3),dtype=np.float16 if is_half==True else np.float32)
for text in texts:
phones2, word2ph2, norm_text2 = clean_text(text, text_language)
phones2 = cleaned_text_to_sequence(phones2)
if(prompt_language=="zh"):bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
else:bert1 = torch.zeros((1024, len(phones1)),dtype=torch.float16 if is_half==True else torch.float32).to(device)
if(text_language=="zh"):bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
else:bert2 = torch.zeros((1024, len(phones2))).to(bert1)
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)
splits={"，","。","？","！",",",".","?","!","~",":","：","—","…",}#不考虑省略号
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),5))
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)
if(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("。")])
with gr.Blocks(title="GPT-SoVITS WebUI") as app:
gr.Markdown(
value=
"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=
"*请上传并填写参考信息"
)
with gr.Row():
inp_ref = gr.Audio(label="请上传参考音频", type="filepath")
prompt_text= gr.Textbox(label="参考音频的文本",value="")
prompt_language= gr.Dropdown(label="参考音频的语种",choices=["中文","英文","日文"],value="中文")
gr.Markdown(
value=
"*请填写需要合成的目标文本"
)
with gr.Row():
text=gr.Textbox(label="需要合成的文本",value="")
text_language = gr.Dropdown(label="需要合成的语种", choices=["中文", "英文", "日文"],value="中文")
inference_button=gr.Button("合成语音", variant="primary")
output = gr.Audio(label="输出的语音")
inference_button.click(get_tts_wav, [inp_ref, prompt_text,prompt_language, text,text_language], [output])
gr.Markdown(
value=
"文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。"
)
with gr.Row():
text_inp=gr.Textbox(label="需要合成的切分前文本",value="")
button1 = gr.Button("凑五句一切", variant="primary")
button2 = gr.Button("凑50字一切", variant="primary")
button3 = gr.Button("按中文句号。切", variant="primary")
text_opt = gr.Textbox(label="切分后文本", value="")
button1.click(cut1,[text_inp],[text_opt])
button2.click(cut2,[text_inp],[text_opt])
button3.click(cut3,[text_inp],[text_opt])
gr.Markdown(
value=
"后续将支持混合语种编码文本输入。"
)
app.queue(concurrency_count=511, max_size=1022).launch(
server_name="0.0.0.0",
inbrowser=True,
server_port=infer_ttswebui,
quiet=True,
)