lop / VITS /GPT_SoVITS.py
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import LangSegment
import numpy as np
import librosa
import torch
import re, os
import librosa
from transformers import AutoModelForMaskedLM, AutoTokenizer
import sys
sys.path.append('GPT_SoVITS/')
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from feature_extractor import cnhubert
from my_utils import load_audio
from module.mel_processing import spectrogram_torch
from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from scipy.io.wavfile import write
from time import time as ttime
if torch.cuda.is_available():
device = "cuda"
elif torch.backends.mps.is_available():
device = "mps"
else:
device = "cpu"
is_half = True
splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", }
if device == "cuda":
gpu_name = torch.cuda.get_device_name(0)
if (
("16" in gpu_name and "V100" not in gpu_name.upper())
or "P40" in gpu_name.upper()
or "P10" in gpu_name.upper()
or "1060" in gpu_name
or "1070" in gpu_name
or "1080" in gpu_name
):
is_half=False
if device=="cpu":
is_half=False
dtype=torch.float16 if is_half == True else torch.float32
bert_path = os.environ.get(
"bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
)
cnhubert_base_path = os.environ.get(
"cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
)
cnhubert.cnhubert_base_path = cnhubert_base_path
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)
ssl_model = cnhubert.get_model()
if is_half == True:
ssl_model = ssl_model.half().to(device)
else:
ssl_model = ssl_model.to(device)
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
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)
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)
return phone_level_feature.T
class DictToAttrRecursive(dict):
def __init__(self, input_dict):
super().__init__(input_dict)
for key, value in input_dict.items():
if isinstance(value, dict):
value = DictToAttrRecursive(value)
self[key] = value
setattr(self, key, value)
def __getattr__(self, item):
try:
return self[item]
except KeyError:
raise AttributeError(f"Attribute {item} not found")
def __setattr__(self, key, value):
if isinstance(value, dict):
value = DictToAttrRecursive(value)
super(DictToAttrRecursive, self).__setitem__(key, value)
super().__setattr__(key, value)
def __delattr__(self, item):
try:
del self[item]
except KeyError:
raise AttributeError(f"Attribute {item} not found")
def clean_text_inf(text, language):
phones, word2ph, norm_text = clean_text(text, language.replace("all_",""))
phones = cleaned_text_to_sequence(phones)
return phones, word2ph, norm_text
def get_bert_inf(phones, word2ph, norm_text, language):
language=language.replace("all_","")
if language == "zh":
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
else:
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float16 if is_half == True else torch.float32,
).to(device)
return bert
def splite_en_inf(sentence, language):
pattern = re.compile(r'[a-zA-Z ]+')
textlist = []
langlist = []
pos = 0
for match in pattern.finditer(sentence):
start, end = match.span()
if start > pos:
textlist.append(sentence[pos:start])
langlist.append(language)
textlist.append(sentence[start:end])
langlist.append("en")
pos = end
if pos < len(sentence):
textlist.append(sentence[pos:])
langlist.append(language)
# Merge punctuation into previous word
for i in range(len(textlist)-1, 0, -1):
if re.match(r'^[\W_]+$', textlist[i]):
textlist[i-1] += textlist[i]
del textlist[i]
del langlist[i]
# Merge consecutive words with the same language tag
i = 0
while i < len(langlist) - 1:
if langlist[i] == langlist[i+1]:
textlist[i] += textlist[i+1]
del textlist[i+1]
del langlist[i+1]
else:
i += 1
return textlist, langlist
def nonen_clean_text_inf(text, language):
if(language!="auto"):
textlist, langlist = splite_en_inf(text, language)
else:
textlist=[]
langlist=[]
for tmp in LangSegment.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
print(textlist)
print(langlist)
phones_list = []
word2ph_list = []
norm_text_list = []
for i in range(len(textlist)):
lang = langlist[i]
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
phones_list.append(phones)
if lang == "zh":
word2ph_list.append(word2ph)
norm_text_list.append(norm_text)
print(word2ph_list)
phones = sum(phones_list, [])
word2ph = sum(word2ph_list, [])
norm_text = ' '.join(norm_text_list)
return phones, word2ph, norm_text
def nonen_get_bert_inf(text, language):
if(language!="auto"):
textlist, langlist = splite_en_inf(text, language)
else:
textlist=[]
langlist=[]
for tmp in LangSegment.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
print(textlist)
print(langlist)
bert_list = []
for i in range(len(textlist)):
text = textlist[i]
lang = langlist[i]
phones, word2ph, norm_text = clean_text_inf(text, lang)
bert = get_bert_inf(phones, word2ph, norm_text, lang)
bert_list.append(bert)
bert = torch.cat(bert_list, dim=1)
return bert
def get_first(text):
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
text = re.split(pattern, text)[0].strip()
return text
def get_cleaned_text_fianl(text,language):
if language in {"en","all_zh","all_ja"}:
phones, word2ph, norm_text = clean_text_inf(text, language)
elif language in {"zh", "ja","auto"}:
phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
return phones, word2ph, norm_text
def get_bert_final(phones, word2ph, norm_text, text_language, device, text):
if text_language == "en":
bert = get_bert_inf(phones, word2ph, norm_text, text_language)
elif text_language in {"zh", "ja","auto"}:
bert = nonen_get_bert_inf(text, text_language)
elif text_language == "all_zh":
bert = get_bert_feature(norm_text, word2ph).to(device)
else:
bert = torch.zeros((1024, len(phones))).to(device)
return bert
def 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
class GPT_SoVITS:
def __init__(self):
self.model = None
# is_half = True
# device = "cuda" if torch.cuda.is_available() else "cpu"
def load_model(self, gpt_path, sovits_path):
self.hz = 50
dict_s1 = torch.load(gpt_path, map_location="cpu")
self.config = dict_s1["config"]
self.max_sec = self.config["data"]["max_sec"]
t2s_model = Text2SemanticLightningModule(self.config, "****", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if is_half == True:
t2s_model = t2s_model.half()
self.t2s_model = t2s_model.to(device)
self.t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
dict_s2 = torch.load(sovits_path, map_location="cpu")
self.hps = dict_s2["config"]
self.hps = DictToAttrRecursive(self.hps)
self.hps.model.semantic_frame_rate = "25hz"
vq_model = SynthesizerTrn(
self.hps.data.filter_length // 2 + 1,
self.hps.train.segment_size // self.hps.data.hop_length,
n_speakers=self.hps.data.n_speakers,
**self.hps.model
)
if ("pretrained" not in sovits_path):
del vq_model.enc_q
if is_half == True:
self.vq_model = vq_model.half().to(device)
else:
self.vq_model = vq_model.to(device)
self.vq_model.eval()
print(self.vq_model.load_state_dict(dict_s2["weight"], strict=False))
def predict(self, ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut="不切", save_path = 'vits_res.wav'):
print(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut)
return self.get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut, save_path)
def get_tts_wav(self, ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut="不切", save_path = 'vits_res.wav'):
t0 = ttime()
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
text = text.strip("\n")
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
print("实际输入的参考文本:", prompt_text)
print("实际输入的目标文本:", text)
zero_wav = np.zeros(
int(self.hps.data.sampling_rate * 0.3),
dtype=np.float16 if is_half == True else np.float32,
)
with torch.no_grad():
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
raise OSError("参考音频在3~10秒范围外,请更换!")
wav16k = torch.from_numpy(wav16k)
zero_wav_torch = torch.from_numpy(zero_wav)
if is_half == True:
wav16k = wav16k.half().to(device)
zero_wav_torch = zero_wav_torch.half().to(device)
else:
wav16k = wav16k.to(device)
zero_wav_torch = zero_wav_torch.to(device)
wav16k = torch.cat([wav16k, zero_wav_torch])
ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
"last_hidden_state"
].transpose(
1, 2
) # .float()
codes = self.vq_model.extract_latent(ssl_content)
prompt_semantic = codes[0, 0]
t1 = ttime()
dict_language = {
"中文": "all_zh",#全部按中文识别
"英文": "en",#全部按英文识别#######不变
"日文": "all_ja",#全部按日文识别
"中英混合": "zh",#按中英混合识别####不变
"日英混合": "ja",#按日英混合识别####不变
"多语种混合": "auto",#多语种启动切分识别语种
}
prompt_language = dict_language[prompt_language]
text_language = dict_language[text_language]
phones1, word2ph1, norm_text1=get_cleaned_text_fianl(prompt_text, prompt_language)
if (how_to_cut == "凑四句一切"):
text = cut1(text)
elif (how_to_cut == "凑50字一切"):
text = cut2(text)
elif (how_to_cut == "按中文句号。切"):
text = cut3(text)
elif (how_to_cut == "按英文句号.切"):
text = cut4(text)
elif (how_to_cut == "按标点符号切"):
text = cut5(text)
text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n")
print("实际输入的目标文本(切句后):", text)
texts = text.split("\n")
audio_opt = []
bert1=get_bert_final(phones1, word2ph1, norm_text1, prompt_language, device, text).to(dtype)
for text in texts:
# 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0):
continue
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
print("实际输入的目标文本(每句):", text)
phones2, word2ph2, norm_text2 = get_cleaned_text_fianl(text, text_language)
bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device, text).to(dtype)
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 = self.t2s_model.model.infer_panel(
all_phoneme_ids,
all_phoneme_len,
prompt,
bert,
# prompt_phone_len=ph_offset,
top_k=self.config["inference"]["top_k"],
early_stop_num=self.hz * self.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(self.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 = (
self.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))
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
# yield self.hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
# np.int16
# )
write(save_path, self.hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16))
return save_path
if __name__ == "__main__":
GPT_SoVITS_inference = GPT_SoVITS()
gpt_path = "../../GPT-SoVITS/GPT_weights/yansang-e15.ckpt"
sovits_path = "../../GPT-SoVITS/SoVITS_weights/yansang_e16_s144.pth"
GPT_SoVITS_inference.load_model(gpt_path, sovits_path)
ref_wav_path = "../../GPT-SoVITS/output/slicer_opt/vocal_output.wav_10.wav_0000846400_0000957760.wav"
prompt_text = "你为什么要一次一次的伤我的心啊?"
prompt_language = "中文"
text = "大家好,这是我语音克隆的声音,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.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LICENSE."
text_language = "中英混合"
how_to_cut = "不切" # ["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"]
GPT_SoVITS_inference.predict(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut)