mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 07:02:57 +08:00
This commit is contained in:
林北1941783147
commit
4130c15d4d
@ -4,12 +4,30 @@ 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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os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
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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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)
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sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
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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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@ -62,7 +80,7 @@ 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) #####输入是long不用管精度问题，精度随bert_model
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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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@ -119,13 +137,15 @@ def change_sovits_weights(sovits_path):
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n_speakers=hps.data.n_speakers,
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**hps.model
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)
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del vq_model.enc_q
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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: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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@ -142,6 +162,7 @@ def change_gpt_weights(gpt_path):
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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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@ -167,7 +188,84 @@ dict_language={
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}
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
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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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return textlist, langlist
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def clean_text_inf(text, language):
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phones, word2ph, norm_text = clean_text(text, language)
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phones = cleaned_text_to_sequence(phones)
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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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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.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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textlist, langlist = splite_en_inf(text, language)
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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 == "en" or "ja":
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pass
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else:
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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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textlist, langlist = splite_en_inf(text, language)
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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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text = textlist[i]
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(text, 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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#i18n("不切"),i18n("凑五句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切")
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")):
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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prompt_language, text = prompt_language, text.strip("\n")
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@ -196,27 +294,38 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
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t1 = 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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phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
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phones1 = cleaned_text_to_sequence(phones1)
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texts = text.split("\n")
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if prompt_language == "en":
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phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
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else:
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phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
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if(how_to_cut==i18n("凑五句一切")):text=cut1(text)
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elif(how_to_cut==i18n("凑50字一切")):text=cut2(text)
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elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text)
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elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text)
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text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
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if(text[-1]not in splits):text+="。"if text_language!="en"else "."
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texts=text.split("\n")
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audio_opt = []
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if prompt_language == "en":
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bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
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else:
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bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
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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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phones2, word2ph2, norm_text2 = clean_text(text, text_language)
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phones2 = cleaned_text_to_sequence(phones2)
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if prompt_language == "zh":
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bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
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if text_language == "en":
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phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
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else:
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bert1 = torch.zeros(
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(1024, len(phones1)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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if text_language == "zh":
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bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
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phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
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if text_language == "en":
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bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
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else:
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bert2 = torch.zeros((1024, len(phones2))).to(bert1)
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bert2 = nonen_get_bert_inf(text, text_language)
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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@ -339,6 +448,9 @@ def cut2(inp):
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def cut3(inp):
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inp = inp.strip("\n")
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return "\n".join(["%s。" % item for item in inp.strip("。").split("。")])
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def cut4(inp):
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inp = inp.strip("\n")
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return "\n".join(["%s." % item for item in inp.strip(".").split(".")])
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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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@ -445,17 +557,24 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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prompt_language = gr.Dropdown(
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label=i18n("参考音频的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文")
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)
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gr.Markdown(value=i18n("*请填写需要合成的目标文本"))
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gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。"))
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with gr.Row():
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text = gr.Textbox(label=i18n("需要合成的文本"), value="")
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文")
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)
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how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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choices=[i18n("不切"),i18n("凑五句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切"),],
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value=i18n("凑50字一切"),
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interactive=True,
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)
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inference_button = gr.Button(i18n("合成语音"), variant="primary")
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output = gr.Audio(label=i18n("输出的语音"))
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inference_button.click(
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get_tts_wav,
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[inp_ref, prompt_text, prompt_language, text, text_language],
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[inp_ref, prompt_text, prompt_language, text, text_language,how_to_cut],
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[output],
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)
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history_audio = []
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@ -485,10 +604,12 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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button1 = gr.Button(i18n("凑五句一切"), variant="primary")
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button2 = gr.Button(i18n("凑50字一切"), variant="primary")
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button3 = gr.Button(i18n("按中文句号。切"), variant="primary")
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button4 = gr.Button(i18n("按英文句号.切"), variant="primary")
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text_opt = gr.Textbox(label=i18n("切分后文本"), value="")
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button1.click(cut1, [text_inp], [text_opt])
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button2.click(cut2, [text_inp], [text_opt])
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button3.click(cut3, [text_inp], [text_opt])
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button4.click(cut4, [text_inp], [text_opt])
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gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。"))
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app.queue(concurrency_count=511, max_size=1022).launch(
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@ -47,12 +47,12 @@ if os.path.exists(txt_path) == False:
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bert_dir = "%s/3-bert" % (opt_dir)
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os.makedirs(opt_dir, exist_ok=True)
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os.makedirs(bert_dir, exist_ok=True)
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if torch.cuda.is_available():
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device = "cuda:0"
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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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if torch.cuda.is_available():
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device = "cuda:0"
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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_pretrained_dir)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_pretrained_dir)
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if is_half == True:
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@ -38,12 +38,12 @@ semantic_path = "%s/6-name2semantic-%s.tsv" % (opt_dir, i_part)
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if os.path.exists(semantic_path) == False:
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os.makedirs(opt_dir, exist_ok=True)
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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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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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hps = utils.get_hparams_from_file(s2config_path)
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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@ -455,6 +455,35 @@ class ToneSandhi:
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"电子",
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"人人",
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"虎虎",
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"幺幺",
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"干嘛",
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"学子",
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"哈哈",
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"数数",
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"袅袅",
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"局地",
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"以下",
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"娃哈哈",
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"花花草草",
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"留得",
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"耕地",
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"想想",
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"熙熙",
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"攘攘",
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"卵子",
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"死死",
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"冉冉",
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"恳恳",
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"佼佼",
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"吵吵",
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"打打",
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"考考",
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"整整",
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"莘莘",
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"落地",
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"算子",
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"家家户户",
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"青青",
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}
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self.punc = "：，；。？！“”‘’':,;.?!"
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3
api.py
3
api.py
@ -13,7 +13,7 @@
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`-dt` - `默认参考音频文本`
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`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
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`-d` - `推理设备, "cuda","cpu"`
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`-d` - `推理设备, "cuda","cpu","mps"`
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`-a` - `绑定地址, 默认"127.0.0.1"`
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`-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
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`-fp` - `覆盖 config.py 使用全精度`
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@ -139,7 +139,6 @@ parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="
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parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
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|
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parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu / mps")
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parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
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parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
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parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
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parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
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|
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12
config.py
12
config.py
@ -29,6 +29,18 @@ webui_port_subfix = 9871
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|
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api_port = 9880
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gpu_name = torch.cuda.get_device_name(0)
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if (
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("16" in gpu_name and "V100" not in gpu_name.upper())
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or "P40" in gpu_name.upper()
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or "P10" in gpu_name.upper()
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or "1060" in gpu_name
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or "1070" in gpu_name
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or "1080" in gpu_name
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||||
):
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is_half=False
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|
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if(infer_device=="cpu"):is_half=False
|
||||
|
||||
class Config:
|
||||
def __init__(self):
|
||||
|
||||
@ -29,3 +29,22 @@
|
||||
3-优化模型文件排序逻辑
|
||||
|
||||
4-中文分词使用jieba_fast代替jieba
|
||||
|
||||
### 20240126更新
|
||||
|
||||
1-支持输出文本中英混合、日英混合
|
||||
|
||||
2-输出可选切分模式
|
||||
|
||||
3-修复uvr5读取到目录自动跳出的问题
|
||||
|
||||
4-修复多个换行导致推理报错
|
||||
|
||||
5-去除推理界面大量冗余log
|
||||
|
||||
6-支持mac训练推理
|
||||
|
||||
7-自动识别不支持半精度的卡强制单精度。cpu推理下强制单精度。
|
||||
|
||||
|
||||
|
||||
|
||||
@ -52,39 +52,32 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
|
||||
paths = [path.name for path in paths]
|
||||
for path in paths:
|
||||
inp_path = os.path.join(inp_root, path)
|
||||
need_reformat = 1
|
||||
done = 0
|
||||
if(os.path.isfile(inp_path)==False):continue
|
||||
try:
|
||||
y, sr = librosa.load(inp_path, sr=None)
|
||||
info = sf.info(inp_path)
|
||||
channels = info.channels
|
||||
if channels == 2 and sr == 44100:
|
||||
need_reformat = 0
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3
|
||||
)
|
||||
done = 1
|
||||
else:
|
||||
done = 0
|
||||
try:
|
||||
y, sr = librosa.load(inp_path, sr=None)
|
||||
info = sf.info(inp_path)
|
||||
channels = info.channels
|
||||
if channels == 2 and sr == 44100:
|
||||
need_reformat = 0
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3
|
||||
)
|
||||
done = 1
|
||||
else:
|
||||
need_reformat = 1
|
||||
except:
|
||||
need_reformat = 1
|
||||
except:
|
||||
need_reformat = 1
|
||||
traceback.print_exc()
|
||||
if need_reformat == 1:
|
||||
tmp_path = "%s/%s.reformatted.wav" % (
|
||||
os.path.join(os.environ["TEMP"]),
|
||||
os.path.basename(inp_path),
|
||||
)
|
||||
y_resampled = librosa.resample(y, sr, 44100)
|
||||
sf.write(tmp_path, y_resampled, 44100, "PCM_16")
|
||||
inp_path = tmp_path
|
||||
try:
|
||||
if done == 0:
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0
|
||||
traceback.print_exc()
|
||||
if need_reformat == 1:
|
||||
tmp_path = "%s/%s.reformatted.wav" % (
|
||||
os.path.join(os.environ["TEMP"]),
|
||||
os.path.basename(inp_path),
|
||||
)
|
||||
infos.append("%s->Success" % (os.path.basename(inp_path)))
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
y_resampled = librosa.resample(y, sr, 44100)
|
||||
sf.write(tmp_path, y_resampled, 44100, "PCM_16")
|
||||
inp_path = tmp_path
|
||||
try:
|
||||
if done == 0:
|
||||
pre_fun._path_audio_(
|
||||
@ -93,10 +86,21 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
|
||||
infos.append("%s->Success" % (os.path.basename(inp_path)))
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
infos.append(
|
||||
"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
|
||||
)
|
||||
yield "\n".join(infos)
|
||||
try:
|
||||
if done == 0:
|
||||
pre_fun._path_audio_(
|
||||
inp_path, save_root_ins, save_root_vocal, format0
|
||||
)
|
||||
infos.append("%s->Success" % (os.path.basename(inp_path)))
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
infos.append(
|
||||
"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
|
||||
)
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
infos.append("Oh my god. %s->%s"%(os.path.basename(inp_path), traceback.format_exc()))
|
||||
yield "\n".join(infos)
|
||||
except:
|
||||
infos.append(traceback.format_exc())
|
||||
yield "\n".join(infos)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user