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nodes.py
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import os
import torch
import shutil
import audiotsm
import logging
import audiotsm.io.wav
from time import time as ttime
import folder_paths
from pydub import AudioSegment
from tools.i18n.i18n import I18nAuto
from srt import parse as SrtPare
from .inference import dict_language,get_tts_wav
from .finetune import open1abc,default_batch_size,open1Ba,open1Bb
i18n = I18nAuto()
parent_directory = os.path.dirname(os.path.abspath(__file__))
input_path = folder_paths.get_input_directory()
out_path = folder_paths.get_output_directory()
language_list = [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")]
weights_path = os.path.join(parent_directory,"pretrained_models")
SoVITS_weight_root = os.path.join(out_path,"sovits_weights")
os.makedirs(SoVITS_weight_root,exist_ok=True)
GPT_weight_root = os.path.join(out_path,"gpt_weights")
os.makedirs(GPT_weight_root,exist_ok=True)
sovits_files = sorted(os.listdir(SoVITS_weight_root),reverse=True)
gpt_files = sorted(os.listdir(GPT_weight_root),reverse=True)
class GPT_SOVITS_TTS:
@classmethod
def INPUT_TYPES(s):
how_to_cuts = [i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ]
return {"required":
{
"renfer_audio":("AUDIO",),
"refer_srt":("SRT",),
"refer_language":(language_list,{
"default": i18n("中文")
}),
"text": ("STRING",{
"default": "你好啊！世界",
"multiline": True
}),
"text_language":(language_list,{
"default": i18n("中文")
}),
"gpt_weight":(gpt_files,),
"sovits_weight":(sovits_files,),
"how_to_cut":(how_to_cuts,{
"default": i18n("凑四句一切")
}),
"top_k":("INT",{
"default":20,
"min":1,
"max": 100,
"step": 1,
"display": "slider"
}),
"top_p":("FLOAT",{
"default":1,
"min":0,
"max": 1,
"step": 0.05,
"display": "slider"
}),
"temperature":("FLOAT",{
"default":1,
"min":0,
"max": 1,
"step": 0.05,
"display": "slider"
}),
}
}
CATEGORY = "AIFSH_GPT_SOVITS"
RETURN_TYPES = ('AUDIO',)
OUTPUT_NODE = False
FUNCTION = "get_tts_wav"
def get_tts_wav(self,renfer_audio,refer_srt,refer_language,
text,text_language,gpt_weight,sovits_weight,
how_to_cut,top_k,top_p,temperature):
with open(refer_srt, 'r', encoding="utf-8") as file:
file_content = file.read()
prompt_language = dict_language[refer_language]
dot_ = "。" if 'zh' in prompt_language else '.'
prompt_text = f'{dot_}'.join([sub.content for sub in list(SrtPare(file_content))])
print(f"prompt_text:{prompt_text}")
outfile = os.path.join(out_path, f"{ttime()}_gpt_sovits_tts.wav")
gpt_weight = os.path.join(GPT_weight_root, gpt_weight)
sovits_weight = os.path.join(SoVITS_weight_root, sovits_weight)
get_tts_wav(renfer_audio,prompt_text,prompt_language,
text,text_language,how_to_cut,top_k,top_p,temperature,
gpt_weight,sovits_weight,outfile)
return (outfile,)
class GPT_SOVITS_INFER:
@classmethod
def INPUT_TYPES(s):
how_to_cuts = [i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ]
return {"required":
{
"renfer_audio":("AUDIO",),
"refer_srt":("SRT",),
"if_aliginment":("BOOLEAN",{
"default": False
}),
"if_mutiple_speaker":("BOOLEAN",{
"default": False
}),
"refer_language":(language_list,{
"default": i18n("中文")
}),
"text_srt":("SRT",),
"text_language":(language_list,{
"default": i18n("中文")
}),
"gpt_weight":(gpt_files,),
"sovits_weight":(sovits_files,),
"how_to_cut":(how_to_cuts,{
"default": i18n("不切")
}),
"top_k":("INT",{
"default":20,
"min":1,
"max": 100,
"step": 1,
"display": "slider"
}),
"top_p":("FLOAT",{
"default":1,
"min":0,
"max": 1,
"step": 0.05,
"display": "slider"
}),
"temperature":("FLOAT",{
"default":1,
"min":0,
"max": 1,
"step": 0.05,
"display": "slider"
}),
}
}
CATEGORY = "AIFSH_GPT_SOVITS"
RETURN_TYPES = ('AUDIO',)
OUTPUT_NODE = False
FUNCTION = "get_tts_wav"
def get_tts_wav(self,renfer_audio,refer_srt,if_aliginment,
if_mutiple_speaker,refer_language,text_srt,text_language,
gpt_weight,sovits_weight,how_to_cut,top_k,top_p,temperature):
prompt_language = dict_language[refer_language]
refer_srt_path = folder_paths.get_annotated_filepath(refer_srt)
text_srt_path = folder_paths.get_annotated_filepath(text_srt)
with open(refer_srt_path, 'r', encoding="utf-8") as file:
refer_file_content = file.read()
with open(text_srt_path, 'r', encoding="utf-8") as file:
text_file_content = file.read()
refer_wav_root = os.path.join(input_path, "gpt_sovits_infer")
os.makedirs(refer_wav_root,exist_ok=True)
audio_path = folder_paths.get_annotated_filepath(renfer_audio)
audio_seg = AudioSegment.from_file(audio_path)
new_audio_seg = AudioSegment.silent(0)
refer_subtitles = list(SrtPare(refer_file_content))
for i, (refer_sub, text_sub) in enumerate(zip(refer_subtitles, list(SrtPare(text_file_content)))):
start_time = refer_sub.start.total_seconds() * 1000
end_time = refer_sub.end.total_seconds() * 1000
if i == 0:
new_audio_seg += audio_seg[:start_time]
refer_wav_seg = audio_seg[start_time:end_time]
refer_wav = os.path.join(refer_wav_root, f"{i}_gpt_sovits_refer.wav")
refer_wav_seg.export(refer_wav, format='wav')
outfile = os.path.join(refer_wav_root, f"{i}_gpt_sovits_infer.wav")
text = text_sub.content
refer_text = refer_sub.content
if if_mutiple_speaker:
speaker_name = f"speaker_{text[0]}"
text = text[1:]
refer_text = refer_text[1:]
gpt_weight = sorted([f for f in os.listdir(GPT_weight_root) if speaker_name in f], key=lambda x:x[-8:-5])[-1]
gpt_weight = os.path.join(GPT_weight_root, gpt_weight)
sovits_weight = sorted([f for f in os.listdir(SoVITS_weight_root) if speaker_name in f])[-1]
sovits_weight = os.path.join(SoVITS_weight_root, sovits_weight)
print(f"gpt_weight:\t{gpt_weight}\nsovits_weight:\t{sovits_weight}")
else:
gpt_weight = os.path.join(GPT_weight_root, gpt_weight)
sovits_weight = os.path.join(SoVITS_weight_root, sovits_weight)
get_tts_wav(refer_wav,refer_text,prompt_language,
text,text_language,how_to_cut,top_k,top_p,temperature,
gpt_weight,sovits_weight,outfile)
text_audio = AudioSegment.from_file(outfile)
text_audio_dur_time = text_audio.duration_seconds * 1000
if i < len(refer_subtitles) - 1:
nxt_start = refer_subtitles[i+1].start.total_seconds() * 1000
dur_time = nxt_start - start_time
else:
org_dur_time = audio_seg.duration_seconds * 1000
dur_time = org_dur_time - start_time
ratio = text_audio_dur_time / dur_time
if text_audio_dur_time > dur_time:
if if_aliginment:
tmp_audio = self.map_vocal(audio=text_audio,ratio=ratio,dur_time=dur_time,
wav_name=f"map_{i}_refer.wav",temp_folder=refer_wav_root)
tmp_audio += AudioSegment.silent(dur_time - tmp_audio.duration_seconds*1000)
else:
tmp_audio = text_audio
else:
tmp_audio = text_audio + AudioSegment.silent(dur_time - text_audio_dur_time)
new_audio_seg += tmp_audio
infer_audio = os.path.join(out_path, f"{ttime()}_gpt_sovits_refer.wav")
new_audio_seg.export(infer_audio, format="wav")
return (infer_audio,)
def map_vocal(self,audio:AudioSegment,ratio:float,dur_time:float,wav_name:str,temp_folder:str):
tmp_path = f"{temp_folder}/map_{wav_name}"
audio.export(tmp_path, format="wav")
clone_path = f"{temp_folder}/cloned_{wav_name}"
reader = audiotsm.io.wav.WavReader(tmp_path)
writer = audiotsm.io.wav.WavWriter(clone_path,channels=reader.channels,
samplerate=reader.samplerate)
wsloa = audiotsm.wsola(channels=reader.channels,speed=ratio)
wsloa.run(reader=reader,writer=writer)
audio_extended = AudioSegment.from_file(clone_path)
return audio_extended[:dur_time]
def splitall(path):
allparts = []
while 1:
parts = os.path.split(path)
if parts[0] == path: # sentinel for absolute paths
allparts.insert(0, parts[0])
break
elif parts[1] == path: # sentinel for relative paths
allparts.insert(0, parts[1])
break
else:
path = parts[0]
allparts.insert(0, parts[1])
return allparts
def get_files(end_with="pth",model_type="D"):
file_list = []
for filepath,dirnames,filenames in os.walk(os.path.join(parent_directory, "logs")):
for filename in filenames:
if filename.endswith(end_with) and model_type in filename:
tmp_path = os.path.join(filepath, filename)
name_list = splitall(tmp_path)
if model_type == "ckpt":
file_n = name_list[-4] + '&' + name_list[-1]
else:
file_n = name_list[-3] + '&' + name_list[-1]
file_list.append(file_n)
return file_list
class GPT_SOVITS_FT:
@classmethod
def INPUT_TYPES(s):
ft_language_list = ["zh", "en", "ja"]
return {"required":
{"audio": ("AUDIO",),
"srt": ("SRT",),
"exp_name": ("STRING",{
"default": "auto"
}),
"language":(ft_language_list,{
"default": "zh"
}),
"pretrained_s2G":(get_files('pth','G')+["s2G488k.pth"],{
"default": "s2G488k.pth"
}),
"pretrained_s2D":(get_files('pth','D')+["s2D488k.pth"],{
"default": "s2D488k.pth"
}),
"sovits_batch_size": ("INT",{
"min": 1,
"max": 40,
"step": 1,
"default":default_batch_size,
"display": "slider"
}),
"sovits_total_epoch": ("INT",{
"min": 1,
"max": 25,
"step": 1,
"default":8,
"display": "slider"
}),
"text_low_lr_rate": ("FLOAT",{
"min": 0.2,
"max": 0.6,
"step": 0.05,
"default":0.4,
"display": "slider"
}),
"sovits_save_every_epoch": ("INT",{
"min": 1,
"max": 25,
"step": 1,
"default":4,
"display": "slider"
}),
"if_save_latest_sovits":("BOOLEAN",{
"default": True
}),
"if_save_every_sovits_weights":("BOOLEAN",{
"default": True
}),
"pretrained_s1":(get_files("ckpt","ckpt")+["s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"],{
"default": "s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
}),
"gpt_batch_size": ("INT",{
"min": 1,
"max": 40,
"step": 1,
"default":default_batch_size,
"display": "slider"
}),
"gpt_total_epoch": ("INT",{
"min": 2,
"max": 50,
"step": 1,
"default":15,
"display": "slider"
}),
"if_dpo":("BOOLEAN",{
"default": False
}),
"if_save_latest_gpt":("BOOLEAN",{
"default": True
}),
"if_save_every_gpt_weights":("BOOLEAN",{
"default": True
}),
"gpt_save_every_epoch": ("INT",{
"min": 1,
"max": 50,
"step": 1,
"default":5,
"display": "slider"
}),
}
}
CATEGORY = "AIFSH_GPT_SOVITS"
RETURN_TYPES = ()
OUTPUT_NODE = True
FUNCTION = "finetune"
def finetune(self,audio,srt,exp_name,language,pretrained_s2G,
pretrained_s2D,sovits_batch_size,sovits_total_epoch,
text_low_lr_rate,sovits_save_every_epoch,if_save_latest_sovits,
if_save_every_sovits_weights,pretrained_s1,gpt_batch_size,
gpt_total_epoch,if_dpo,if_save_latest_gpt,if_save_every_gpt_weights,
gpt_save_every_epoch):
logging.disable(logging.WARNING)
logs_path = os.path.join(parent_directory,"logs")
shutil.rmtree(logs_path,ignore_errors=True)
srt_path = folder_paths.get_annotated_filepath(srt)
audio_path = folder_paths.get_annotated_filepath(audio)
audio_seg = AudioSegment.from_file(audio_path)
if pretrained_s2D == "s2D488k.pth":
pretrained_s2D = os.path.join(weights_path,"s2D488k.pth")
else:
pretrained_s2D = pretrained_s2D.split("&")
pretrained_s2D = os.path.join(logs_path,pretrained_s2D[0],"logs_s2",pretrained_s2D[1])
if pretrained_s2G == "s2G488k.pth":
pretrained_s2G = os.path.join(weights_path,"s2G488k.pth")
else:
pretrained_s2G = pretrained_s2G.split("&")
pretrained_s2G = os.path.join(logs_path,pretrained_s2G[0],"logs_s2",pretrained_s2G[1])
if pretrained_s1 == "s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt":
pretrained_s1 = os.path.join(weights_path,"s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
else:
pretrained_s1 = pretrained_s1.split("&")
pretrained_s1 = os.path.join(logs_path,pretrained_s1[0],"logs_s1/ckpt",pretrained_s1[1])
with open(srt_path, 'r', encoding="utf-8") as file:
file_content = file.read()
work_path_list = []
for i, sub in enumerate(list(SrtPare(file_content))):
start_time = sub.start.total_seconds() * 1000
end_time = sub.end.total_seconds() * 1000
if exp_name == "auto":
try:
text = sub.content[1:]
new_exp_name = f"speaker_{int(sub.content[0])}"
except:
text = sub.content
new_exp_name = "speaker_0"
else:
text = sub.content
new_exp_name = exp_name
work_path = os.path.join(parent_directory,"logs",new_exp_name)
if work_path not in work_path_list: work_path_list.append(work_path)
os.makedirs(work_path, exist_ok=True)
inp_text = os.path.join(work_path, "annotation.list")
inp_wav_dir = os.path.join(work_path,"wav")
os.makedirs(inp_wav_dir, exist_ok=True)
vocal_path = os.path.join(inp_wav_dir, f"{new_exp_name}-%04d.wav" % (i+1))
vocal_seg = audio_seg[start_time:end_time]
vocal_seg.export(vocal_path, format="wav")
with open(inp_text, 'a', encoding="utf-8") as w:
line = f'{vocal_path}|{new_exp_name}|{language}|{text}\n'
w.write(line)
for work_path in work_path_list:
inp_text = os.path.join(work_path, "annotation.list")
inp_wav_dir = os.path.join(work_path,"wav")
exp_name = os.path.basename(work_path)
open1abc(inp_text,inp_wav_dir,exp_name,pretrained_s2G,work_path)
import gc;gc.collect();torch.cuda.empty_cache()
open1Ba(batch_size=sovits_batch_size,total_epoch=sovits_total_epoch,
exp_name=exp_name,text_low_lr_rate=text_low_lr_rate,
if_save_latest=if_save_latest_sovits,if_save_every_weights=if_save_every_sovits_weights,
save_every_epoch=sovits_save_every_epoch,pretrained_s2G=pretrained_s2G,
pretrained_s2D=pretrained_s2D,work_path=work_path)
import gc;gc.collect();torch.cuda.empty_cache()
open1Bb(batch_size=gpt_batch_size,total_epoch=gpt_total_epoch,exp_name=exp_name,
if_dpo=if_dpo,if_save_latest=if_save_latest_gpt,if_save_every_weights=if_save_every_gpt_weights,
save_every_epoch=gpt_save_every_epoch,pretrained_s1=pretrained_s1,work_path=work_path)
import gc;gc.collect();torch.cuda.empty_cache()
return {"ui":{"finetune":[SoVITS_weight_root,GPT_weight_root]}}
class PreViewAudio:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"audio": ("AUDIO",),}
}
CATEGORY = "AIFSH_GPT_SOVITS"
DESCRIPTION = "hello world!"
RETURN_TYPES = ()
OUTPUT_NODE = True
FUNCTION = "load_audio"
def load_audio(self, audio):
audio_name = os.path.basename(audio)
tmp_path = os.path.dirname(audio)
audio_root = os.path.basename(tmp_path)
return {"ui": {"audio":[audio_name,audio_root]}}
class LoadAudio:
@classmethod
def INPUT_TYPES(s):
files = [f for f in os.listdir(input_path) if os.path.isfile(os.path.join(input_path, f)) and f.split('.')[-1] in ["wav", "mp3","WAV","flac","m4a"]]
return {"required":
{"audio": (sorted(files),)},
}
CATEGORY = "AIFSH_GPT_SOVITS"
RETURN_TYPES = ("AUDIO",)
FUNCTION = "load_audio"
def load_audio(self, audio):
audio_path = folder_paths.get_annotated_filepath(audio)
return (audio_path,)
class LoadSRT:
@classmethod
def INPUT_TYPES(s):
files = [f for f in os.listdir(input_path) if os.path.isfile(os.path.join(input_path, f)) and f.split('.')[-1] in ["srt", "txt"]]
return {"required":
{"srt": (sorted(files),)},
}
CATEGORY = "AIFSH_GPT_SOVITS"
RETURN_TYPES = ("SRT",)
FUNCTION = "load_srt"
def load_srt(self, srt):
srt_path = folder_paths.get_annotated_filepath(srt)
return (srt_path,)