mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 04:22:46 +08:00
Version Check (#1390)
* version check * fix webui and symbols * fix v1 language map
This commit is contained in:
KamioRinn
GitHub
parent
0c25e57959
commit
4e34814c70
@ -152,6 +152,11 @@ def change_sovits_weights(sovits_path):
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
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hps.model.version = "v1"
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else:
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hps.model.version = "v2"
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# print("sovits版本:",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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@ -231,9 +236,9 @@ dict_language = {
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}
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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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def clean_text_inf(text, language, version):
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phones, word2ph, norm_text = clean_text(text, language, version)
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phones = cleaned_text_to_sequence(phones, version)
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return phones, word2ph, norm_text
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dtype=torch.float16 if is_half == True else torch.float32
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@ -259,7 +264,7 @@ def get_first(text):
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return text
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from text import chinese
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def get_phones_and_bert(text,language):
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def get_phones_and_bert(text,language,version):
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if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
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language = language.replace("all_","")
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if language == "en":
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@ -274,16 +279,16 @@ def get_phones_and_bert(text,language):
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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.text_normalize(formattext)
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return get_phones_and_bert(formattext,"zh")
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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)
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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.text_normalize(formattext)
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return get_phones_and_bert(formattext,"yue")
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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)
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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.float16 if is_half == True else torch.float32,
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@ -317,7 +322,7 @@ def get_phones_and_bert(text,language):
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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, 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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@ -357,6 +362,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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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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version = vq_model.version
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if not ref_free:
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prompt_text = prompt_text.strip("\n")
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if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
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@ -413,7 +421,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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texts = merge_short_text_in_array(texts, 5)
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audio_opt = []
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if not ref_free:
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phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
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phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)
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for i_text,text in enumerate(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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@ -421,7 +429,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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continue
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if (text[-1] not in splits): text += "。" if text_language != "en" else "."
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print(i18n("实际输入的目标文本(每句):"), text)
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phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
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phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
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print(i18n("前端处理后的文本(每句):"), norm_text2)
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if not ref_free:
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bert = torch.cat([bert1, bert2], 1)
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@ -15,7 +15,9 @@ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from module.commons import init_weights, get_padding
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from module.mrte_model import MRTE
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from module.quantize import ResidualVectorQuantizer
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from text import symbols
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# from text import symbols
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from text import symbols as symbols_v1
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from text import symbols2 as symbols_v2
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from torch.cuda.amp import autocast
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import contextlib
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@ -185,6 +187,7 @@ class TextEncoder(nn.Module):
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kernel_size,
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p_dropout,
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latent_channels=192,
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version = "v2",
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):
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super().__init__()
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self.out_channels = out_channels
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@ -195,6 +198,7 @@ class TextEncoder(nn.Module):
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.latent_channels = latent_channels
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self.version = version
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self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
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@ -210,6 +214,11 @@ class TextEncoder(nn.Module):
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self.encoder_text = attentions.Encoder(
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hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
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)
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if self.version == "v1":
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symbols = symbols_v1.symbols
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else:
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symbols = symbols_v2.symbols
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self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
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self.mrte = MRTE()
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@ -827,6 +836,7 @@ class SynthesizerTrn(nn.Module):
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use_sdp=True,
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semantic_frame_rate=None,
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freeze_quantizer=None,
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version = "v2",
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**kwargs
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):
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super().__init__()
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@ -847,6 +857,7 @@ class SynthesizerTrn(nn.Module):
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self.segment_size = segment_size
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self.n_speakers = n_speakers
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self.gin_channels = gin_channels
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self.version = version
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self.use_sdp = use_sdp
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self.enc_p = TextEncoder(
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@ -857,6 +868,7 @@ class SynthesizerTrn(nn.Module):
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n_layers,
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kernel_size,
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p_dropout,
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version = version,
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)
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self.dec = Generator(
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inter_channels,
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@ -881,7 +893,7 @@ class SynthesizerTrn(nn.Module):
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inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
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)
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self.version=os.environ.get("version","v1")
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# self.version=os.environ.get("version","v1")
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if(self.version=="v1"):
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self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
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else:
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@ -1,18 +1,26 @@
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import os
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if os.environ.get("version","v1")=="v1":
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from text.symbols import symbols
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else:
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from text.symbols2 import symbols
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# if os.environ.get("version","v1")=="v1":
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# from text.symbols import symbols
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# else:
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# from text.symbols2 import symbols
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_symbol_to_id = {s: i for i, s in enumerate(symbols)}
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from text import symbols as symbols_v1
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from text import symbols2 as symbols_v2
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def cleaned_text_to_sequence(cleaned_text):
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_symbol_to_id_v1 = {s: i for i, s in enumerate(symbols_v1.symbols)}
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_symbol_to_id_v2 = {s: i for i, s in enumerate(symbols_v2.symbols)}
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def cleaned_text_to_sequence(cleaned_text, version):
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'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
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Args:
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text: string to convert to a sequence
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Returns:
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List of integers corresponding to the symbols in the text
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'''
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phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
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if version == "v1":
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phones = [_symbol_to_id_v1[symbol] for symbol in cleaned_text]
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else:
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phones = [_symbol_to_id_v2[symbol] for symbol in cleaned_text]
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return phones
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@ -1,13 +1,17 @@
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from text import japanese, cleaned_text_to_sequence, english,korean,cantonese
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import os
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if os.environ.get("version","v1")=="v1":
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from text import chinese
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from text.symbols import symbols
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else:
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from text import chinese2 as chinese
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from text.symbols2 import symbols
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# if os.environ.get("version","v1")=="v1":
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# from text import chinese
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# from text.symbols import symbols
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# else:
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# from text import chinese2 as chinese
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# from text.symbols2 import symbols
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from text import symbols as symbols_v1
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from text import symbols2 as symbols_v2
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from text import chinese as chinese_v1
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from text import chinese2 as chinese_v2
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language_module_map = {"zh": chinese, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
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special = [
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# ("%", "zh", "SP"),
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("￥", "zh", "SP2"),
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@ -16,13 +20,20 @@ special = [
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]
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def clean_text(text, language):
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def clean_text(text, language, version):
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if version == "v1":
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symbols = symbols_v1.symbols
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language_module_map = {"zh": chinese_v1, "ja": japanese, "en": english}
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else:
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symbols = symbols_v2.symbols
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language_module_map = {"zh": chinese_v2, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
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if(language not in language_module_map):
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language="en"
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text=" "
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for special_s, special_l, target_symbol in special:
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if special_s in text and language == special_l:
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return clean_special(text, language, special_s, target_symbol)
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return clean_special(text, language, special_s, target_symbol, version)
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language_module = language_module_map[language]
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if hasattr(language_module,"text_normalize"):
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norm_text = language_module.text_normalize(text)
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@ -42,11 +53,18 @@ def clean_text(text, language):
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word2ph = None
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for ph in phones:
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assert ph in symbols
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phones = ['UNK' if ph not in symbols else ph for ph in phones]
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return phones, word2ph, norm_text
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def clean_special(text, language, special_s, target_symbol):
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def clean_special(text, language, special_s, target_symbol, version):
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if version == "v1":
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symbols = symbols_v1.symbols
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language_module_map = {"zh": chinese_v1, "ja": japanese, "en": english}
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else:
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symbols = symbols_v2.symbols
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language_module_map = {"zh": chinese_v2, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
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"""
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特殊静音段sp符号处理
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"""
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@ -6,10 +6,7 @@ from g2p_en import G2p
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from text.symbols import punctuation
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if os.environ.get("version","v1")=="v1":
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from text.symbols import symbols
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else:
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from text.symbols2 import symbols
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from text.symbols2 import symbols
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import unicodedata
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from builtins import str as unicode
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@ -4,12 +4,6 @@ import sys
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import pyopenjtalk
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import os
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if os.environ.get("version","v1")=="v1":
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from text.symbols import symbols
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else:
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from text.symbols2 import symbols
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from text.symbols import punctuation
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# Regular expression matching Japanese without punctuation marks:
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_japanese_characters = re.compile(
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@ -61,12 +55,13 @@ def post_replace_ph(ph):
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"、": ",",
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"...": "…",
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}
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if ph in rep_map.keys():
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ph = rep_map[ph]
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if ph in symbols:
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return ph
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if ph not in symbols:
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ph = "UNK"
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# if ph in symbols:
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# return ph
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# if ph not in symbols:
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# ph = "UNK"
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return ph
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@ -2,11 +2,8 @@ import re
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from jamo import h2j, j2hcj
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import ko_pron
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from g2pk2 import G2p
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import os
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if os.environ.get("version","v1")=="v1":
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from text.symbols import symbols
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else:
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from text.symbols2 import symbols
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from text.symbols2 import symbols
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# This is a list of Korean classifiers preceded by pure Korean numerals.
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_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
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100
api.py
100
api.py
@ -11,7 +11,7 @@
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调用请求缺少参考音频时使用
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`-dr` - `默认参考音频路径`
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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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`-dl` - `默认参考音频语种, "中文","英文","日文","韩文","粤语,"zh","en","ja","ko","yue"`
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`-d` - `推理设备, "cuda","cpu"`
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`-a` - `绑定地址, 默认"127.0.0.1"`
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@ -201,6 +201,11 @@ def change_sovits_weights(sovits_path):
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
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hps.model.version = "v1"
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else:
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hps.model.version = "v2"
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print("sovits版本:",hps.model.version)
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model_params_dict = vars(hps.model)
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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@ -251,9 +256,9 @@ def get_bert_feature(text, word2ph):
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return phone_level_feature.T
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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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def clean_text_inf(text, language, version):
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phones, word2ph, norm_text = clean_text(text, language, version)
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phones = cleaned_text_to_sequence(phones, version)
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return phones, word2ph, norm_text
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@ -269,54 +274,64 @@ def get_bert_inf(phones, word2ph, norm_text, language):
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return bert
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def get_phones_and_bert(text,language):
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if language in {"en","all_zh","all_ja"}:
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from text import chinese
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def get_phones_and_bert(text,language,version):
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if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
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language = language.replace("all_","")
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if language == "en":
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LangSegment.setfilters(["en"])
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formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
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else:
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# 因无法区别中日文汉字,以用户输入为准
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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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formattext = text
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while " " in formattext:
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formattext = formattext.replace(" ", " ")
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phones, word2ph, norm_text = clean_text_inf(formattext, 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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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.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.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(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
elif language in {"zh", "ja","auto"}:
|
||||
elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
|
||||
textlist=[]
|
||||
langlist=[]
|
||||
LangSegment.setfilters(["zh","ja","en","ko"])
|
||||
if language == "auto":
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "ko":
|
||||
langlist.append("zh")
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
elif language == "auto_yue":
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "zh":
|
||||
tmp["lang"] = "yue"
|
||||
langlist.append(tmp["lang"])
|
||||
textlist.append(tmp["text"])
|
||||
else:
|
||||
for tmp in LangSegment.getTexts(text):
|
||||
if tmp["lang"] == "en":
|
||||
langlist.append(tmp["lang"])
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
# 因无法区别中日韩文汉字,以用户输入为准
|
||||
langlist.append(language)
|
||||
textlist.append(tmp["text"])
|
||||
# logger.info(textlist)
|
||||
# logger.info(langlist)
|
||||
phones_list = []
|
||||
bert_list = []
|
||||
norm_text_list = []
|
||||
for i in range(len(textlist)):
|
||||
lang = langlist[i]
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
||||
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
@ -328,14 +343,32 @@ def get_phones_and_bert(text,language):
|
||||
return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
|
||||
|
||||
|
||||
class DictToAttrRecursive:
|
||||
class DictToAttrRecursive(dict):
|
||||
def __init__(self, input_dict):
|
||||
super().__init__(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)
|
||||
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 get_spepc(hps, filename):
|
||||
@ -488,9 +521,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
|
||||
codes = vq_model.extract_latent(ssl_content)
|
||||
prompt_semantic = codes[0, 0]
|
||||
t1 = ttime()
|
||||
version = vq_model.version
|
||||
prompt_language = dict_language[prompt_language.lower()]
|
||||
text_language = dict_language[text_language.lower()]
|
||||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
|
||||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
|
||||
texts = text.split("\n")
|
||||
audio_bytes = BytesIO()
|
||||
|
||||
@ -500,7 +534,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
|
||||
continue
|
||||
|
||||
audio_opt = []
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
|
||||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||||
@ -606,17 +640,27 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cu
|
||||
# --------------------------------
|
||||
dict_language = {
|
||||
"中文": "all_zh",
|
||||
"粤语": "all_yue",
|
||||
"英文": "en",
|
||||
"日文": "all_ja",
|
||||
"韩文": "all_ko",
|
||||
"中英混合": "zh",
|
||||
"粤英混合": "yue",
|
||||
"日英混合": "ja",
|
||||
"韩英混合": "ko",
|
||||
"多语种混合": "auto", #多语种启动切分识别语种
|
||||
"多语种混合(粤语)": "auto_yue",
|
||||
"all_zh": "all_zh",
|
||||
"all_yue": "all_yue",
|
||||
"en": "en",
|
||||
"all_ja": "all_ja",
|
||||
"all_ko": "all_ko",
|
||||
"zh": "zh",
|
||||
"yue": "yue",
|
||||
"ja": "ja",
|
||||
"ko": "ko",
|
||||
"auto": "auto",
|
||||
"auto_yue": "auto_yue",
|
||||
}
|
||||
|
||||
# logger
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user