mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-08 16:00:01 +08:00
Merge branch 'main' of https://github.com/KakaruHayate/GPT-SoVITS-musa
This commit is contained in:
KakaruHayate
commit
adad163625
@ -1 +1,2 @@
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CHATGPT CH AE1 T JH IY1 P IY1 T IY1
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CHATGPT CH AE1 T JH IY1 P IY1 T IY1
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JSON JH EY1 S AH0 N
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@ -8,11 +8,19 @@ from string import punctuation
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from text import symbols
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import unicodedata
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from builtins import str as unicode
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from g2p_en.expand import normalize_numbers
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from nltk.tokenize import TweetTokenizer
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word_tokenize = TweetTokenizer().tokenize
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from nltk import pos_tag
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current_file_path = os.path.dirname(__file__)
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CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
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CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
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CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
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CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
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NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
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arpa = {
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"AH0",
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@ -155,6 +163,9 @@ def read_dict_new():
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line_index = line_index + 1
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line = f.readline()
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return g2p_dict
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def hot_reload_hot(g2p_dict):
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with open(CMU_DICT_HOT_PATH) as f:
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line = f.readline()
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line_index = 1
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@ -168,7 +179,7 @@ def read_dict_new():
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line_index = line_index + 1
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line = f.readline()
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return g2p_dict
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@ -185,10 +196,19 @@ def get_dict():
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g2p_dict = read_dict_new()
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cache_dict(g2p_dict, CACHE_PATH)
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g2p_dict = hot_reload_hot(g2p_dict)
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return g2p_dict
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eng_dict = get_dict()
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def get_namedict():
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if os.path.exists(NAMECACHE_PATH):
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with open(NAMECACHE_PATH, "rb") as pickle_file:
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name_dict = pickle.load(pickle_file)
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else:
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name_dict = {}
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return name_dict
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def text_normalize(text):
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@ -204,6 +224,16 @@ def text_normalize(text):
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for p, r in rep_map.items():
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text = re.sub(p, r, text)
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# 来自 g2p_en 文本格式化处理
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# 增加大写兼容
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text = unicode(text)
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text = normalize_numbers(text)
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text = ''.join(char for char in unicodedata.normalize('NFD', text)
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if unicodedata.category(char) != 'Mn') # Strip accents
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text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
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text = re.sub(r"(?i)i\.e\.", "that is", text)
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text = re.sub(r"(?i)e\.g\.", "for example", text)
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return text
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@ -213,37 +243,106 @@ class en_G2p(G2p):
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# 分词初始化
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wordsegment.load()
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# 扩展过时字典
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# 扩展过时字典, 添加姓名字典
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self.cmu = get_dict()
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self.namedict = get_namedict()
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# 剔除读音错误的几个缩写
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for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
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del self.cmu[word.lower()]
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# "A" 落单不读 "AH0" 读 "EY1"
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self.cmu['a'] = [['EY1']]
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# 修正多音字
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self.homograph2features["read"] = (['R', 'IY1', 'D'], ['R', 'EH1', 'D'], 'VBP')
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self.homograph2features["complex"] = (['K', 'AH0', 'M', 'P', 'L', 'EH1', 'K', 'S'], ['K', 'AA1', 'M', 'P', 'L', 'EH0', 'K', 'S'], 'JJ')
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def predict(self, word):
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# 小写 oov 长度小于等于 3 直接读字母
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if (len(word) <= 3):
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return [phone for w in word for phone in self(w)]
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def __call__(self, text):
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# tokenization
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words = word_tokenize(text)
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tokens = pos_tag(words) # tuples of (word, tag)
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# steps
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prons = []
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for o_word, pos in tokens:
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# 还原 g2p_en 小写操作逻辑
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word = o_word.lower()
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if re.search("[a-z]", word) is None:
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pron = [word]
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# 先把单字母推出去
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elif len(word) == 1:
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# 单读 A 发音修正, 这里需要原格式 o_word 判断大写
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if o_word == "A":
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pron = ['EY1']
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else:
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pron = self.cmu[word][0]
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# g2p_en 原版多音字处理
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elif word in self.homograph2features: # Check homograph
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pron1, pron2, pos1 = self.homograph2features[word]
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if pos.startswith(pos1):
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pron = pron1
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# pos1比pos长仅出现在read
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elif len(pos) < len(pos1) and pos == pos1[:len(pos)]:
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pron = pron1
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else:
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pron = pron2
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else:
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# 递归查找预测
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pron = self.qryword(o_word)
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prons.extend(pron)
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prons.extend([" "])
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return prons[:-1]
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def qryword(self, o_word):
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word = o_word.lower()
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# 查字典, 单字母除外
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if len(word) > 1 and word in self.cmu: # lookup CMU dict
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return self.cmu[word][0]
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# 单词仅首字母大写时查找姓名字典
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if o_word.istitle() and word in self.namedict:
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return self.namedict[word][0]
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# oov 长度小于等于 3 直接读字母
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if len(word) <= 3:
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phones = []
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for w in word:
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# 单读 A 发音修正, 此处不存在大写的情况
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if w == "a":
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phones.extend(['EY1'])
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else:
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phones.extend(self.cmu[w][0])
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return phones
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# 尝试分离所有格
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if re.match(r"^([a-z]+)('s)$", word):
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phone = self(word[:-2])
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phone.extend(['Z'])
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return phone
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phones = self.qryword(word[:-2])
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# P T K F TH HH 无声辅音结尾 's 发 ['S']
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if phones[-1] in ['P', 'T', 'K', 'F', 'TH', 'HH']:
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phones.extend(['S'])
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# S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
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elif phones[-1] in ['S', 'Z', 'SH', 'ZH', 'CH', 'JH']:
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phones.extend(['AH0', 'Z'])
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# B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
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# AH0 AH1 AH2 EY0 EY1 EY2 AE0 AE1 AE2 EH0 EH1 EH2 OW0 OW1 OW2 UH0 UH1 UH2 IY0 IY1 IY2 AA0 AA1 AA2 AO0 AO1 AO2
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# ER ER0 ER1 ER2 UW0 UW1 UW2 AY0 AY1 AY2 AW0 AW1 AW2 OY0 OY1 OY2 IH IH0 IH1 IH2 元音结尾 's 发 ['Z']
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else:
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phones.extend(['Z'])
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return phones
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# 尝试进行分词，应对复合词
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comps = wordsegment.segment(word.lower())
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# 无法分词的送回去预测
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if len(comps)==1:
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return super().predict(word)
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return self.predict(word)
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# 可以分词的递归处理
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return [phone for comp in comps for phone in self(comp)]
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return [phone for comp in comps for phone in self.qryword(comp)]
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_g2p = en_G2p()
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@ -258,12 +357,6 @@ def g2p(text):
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if __name__ == "__main__":
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# print(get_dict())
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print(g2p("hello"))
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print(g2p("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
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# all_phones = set()
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# for k, syllables in eng_dict.items():
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# for group in syllables:
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# for ph in group:
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# all_phones.add(ph)
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# print(all_phones)
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print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
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print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))
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BIN
GPT_SoVITS/text/namedict_cache.pickle
Normal file
BIN
GPT_SoVITS/text/namedict_cache.pickle
Normal file
Binary file not shown.
14
README.md
14
README.md
@ -141,7 +141,15 @@ Users in China region can download these two models by entering the links below
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- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
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For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/damo_asr/models`.
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For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/asr/models`.
|
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For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint.
|
||||
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Users in China region can download this model by entering the links below
|
||||
|
||||
- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/0c4pQxFs7oWyVU1iMTq2DbmLA#faster-whisper-large-v3) (clicking "Download a copy")
|
||||
|
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- [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3) (HuggingFace mirror site)
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## Dataset Format
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@ -204,13 +212,13 @@ python audio_slicer.py \
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```
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This is how dataset ASR processing is done using the command line(Only Chinese)
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```
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python tools/damo_asr/cmd-asr.py "<Path to the directory containing input audio files>"
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python tools/asr/funasr_asr.py -i <input> -o <output>
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```
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ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
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(No progress bars, GPU performance may cause time delays)
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```
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python ./tools/damo_asr/WhisperASR.py -i <input> -o <output> -f <file_name.list> -l <language>
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python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
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```
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A custom list save path is enabled
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515
api.py
515
api.py
@ -18,6 +18,9 @@
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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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`-hp` - `覆盖 config.py 使用半精度`
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`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
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·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
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·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`
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`-hb` - `cnhubert路径`
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`-b` - `bert路径`
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@ -39,6 +42,18 @@ POST:
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}
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```
|
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|
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使用执行参数指定的参考音频并设定分割符号:
|
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GET:
|
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`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&cut_punc=，。`
|
||||
POST:
|
||||
```json
|
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{
|
||||
"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
|
||||
"text_language": "zh",
|
||||
"cut_punc": "，。",
|
||||
}
|
||||
```
|
||||
|
||||
手动指定当次推理所使用的参考音频:
|
||||
GET:
|
||||
`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
|
||||
@ -103,14 +118,10 @@ RESP: 无
|
||||
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import os,re
|
||||
import sys
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
sys.path.append("%s/GPT_SoVITS" % (now_dir))
|
||||
|
||||
import signal
|
||||
import LangSegment
|
||||
from time import time as ttime
|
||||
import torch
|
||||
import librosa
|
||||
@ -129,35 +140,8 @@ from text.cleaner import clean_text
|
||||
from module.mel_processing import spectrogram_torch
|
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from my_utils import load_audio
|
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import config as global_config
|
||||
|
||||
g_config = global_config.Config()
|
||||
|
||||
# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
|
||||
parser = argparse.ArgumentParser(description="GPT-SoVITS api")
|
||||
|
||||
parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
|
||||
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
|
||||
|
||||
parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
|
||||
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
|
||||
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
|
||||
|
||||
parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
|
||||
parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
|
||||
parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
|
||||
parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
|
||||
parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
|
||||
# bool值的用法为 `python ./api.py -fp ...`
|
||||
# 此时 full_precision==True, half_precision==False
|
||||
|
||||
parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
|
||||
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
sovits_path = args.sovits_path
|
||||
gpt_path = args.gpt_path
|
||||
import logging
|
||||
import subprocess
|
||||
|
||||
|
||||
class DefaultRefer:
|
||||
@ -170,50 +154,6 @@ class DefaultRefer:
|
||||
return is_full(self.path, self.text, self.language)
|
||||
|
||||
|
||||
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
|
||||
|
||||
device = args.device
|
||||
port = args.port
|
||||
host = args.bind_addr
|
||||
|
||||
if sovits_path == "":
|
||||
sovits_path = g_config.pretrained_sovits_path
|
||||
print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
|
||||
if gpt_path == "":
|
||||
gpt_path = g_config.pretrained_gpt_path
|
||||
print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")
|
||||
|
||||
# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
|
||||
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
|
||||
default_refer.path, default_refer.text, default_refer.language = "", "", ""
|
||||
print("[INFO] 未指定默认参考音频")
|
||||
else:
|
||||
print(f"[INFO] 默认参考音频路径: {default_refer.path}")
|
||||
print(f"[INFO] 默认参考音频文本: {default_refer.text}")
|
||||
print(f"[INFO] 默认参考音频语种: {default_refer.language}")
|
||||
|
||||
is_half = g_config.is_half
|
||||
if args.full_precision:
|
||||
is_half = False
|
||||
if args.half_precision:
|
||||
is_half = True
|
||||
if args.full_precision and args.half_precision:
|
||||
is_half = g_config.is_half # 炒饭fallback
|
||||
|
||||
print(f"[INFO] 半精: {is_half}")
|
||||
|
||||
cnhubert_base_path = args.hubert_path
|
||||
bert_path = args.bert_path
|
||||
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
if is_half:
|
||||
bert_model = bert_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
|
||||
|
||||
def is_empty(*items): # 任意一项不为空返回False
|
||||
for item in items:
|
||||
if item is not None and item != "":
|
||||
@ -227,6 +167,7 @@ def is_full(*items): # 任意一项为空返回False
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def change_sovits_weights(sovits_path):
|
||||
global vq_model, hps
|
||||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||||
@ -246,9 +187,9 @@ def change_sovits_weights(sovits_path):
|
||||
else:
|
||||
vq_model = vq_model.to(device)
|
||||
vq_model.eval()
|
||||
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
||||
with open("./sweight.txt", "w", encoding="utf-8") as f:
|
||||
f.write(sovits_path)
|
||||
vq_model.load_state_dict(dict_s2["weight"], strict=False)
|
||||
|
||||
|
||||
def change_gpt_weights(gpt_path):
|
||||
global hz, max_sec, t2s_model, config
|
||||
hz = 50
|
||||
@ -262,8 +203,7 @@ def change_gpt_weights(gpt_path):
|
||||
t2s_model = t2s_model.to(device)
|
||||
t2s_model.eval()
|
||||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||||
print("Number of parameter: %.2fM" % (total / 1e6))
|
||||
with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
|
||||
logger.info("Number of parameter: %.2fM" % (total / 1e6))
|
||||
|
||||
|
||||
def get_bert_feature(text, word2ph):
|
||||
@ -283,9 +223,81 @@ def get_bert_feature(text, word2ph):
|
||||
return phone_level_feature.T
|
||||
|
||||
|
||||
n_semantic = 1024
|
||||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||||
hps = dict_s2["config"]
|
||||
def clean_text_inf(text, language):
|
||||
phones, word2ph, norm_text = clean_text(text, language)
|
||||
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 get_phones_and_bert(text,language):
|
||||
if language in {"en","all_zh","all_ja"}:
|
||||
language = language.replace("all_","")
|
||||
if language == "en":
|
||||
LangSegment.setfilters(["en"])
|
||||
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
|
||||
else:
|
||||
# 因无法区别中日文汉字,以用户输入为准
|
||||
formattext = text
|
||||
while " " in formattext:
|
||||
formattext = formattext.replace(" ", " ")
|
||||
phones, word2ph, norm_text = clean_text_inf(formattext, language)
|
||||
if language == "zh":
|
||||
bert = get_bert_feature(norm_text, word2ph).to(device)
|
||||
else:
|
||||
bert = torch.zeros(
|
||||
(1024, len(phones)),
|
||||
dtype=torch.float16 if is_half == True else torch.float32,
|
||||
).to(device)
|
||||
elif language in {"zh", "ja","auto"}:
|
||||
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"])
|
||||
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
|
||||
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)
|
||||
bert = get_bert_inf(phones, word2ph, norm_text, lang)
|
||||
phones_list.append(phones)
|
||||
norm_text_list.append(norm_text)
|
||||
bert_list.append(bert)
|
||||
bert = torch.cat(bert_list, dim=1)
|
||||
phones = sum(phones_list, [])
|
||||
norm_text = ''.join(norm_text_list)
|
||||
|
||||
return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
|
||||
|
||||
|
||||
class DictToAttrRecursive:
|
||||
@ -298,39 +310,6 @@ class DictToAttrRecursive:
|
||||
setattr(self, key, value)
|
||||
|
||||
|
||||
hps = DictToAttrRecursive(hps)
|
||||
hps.model.semantic_frame_rate = "25hz"
|
||||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||||
config = dict_s1["config"]
|
||||
ssl_model = cnhubert.get_model()
|
||||
if is_half:
|
||||
ssl_model = ssl_model.half().to(device)
|
||||
else:
|
||||
ssl_model = ssl_model.to(device)
|
||||
|
||||
vq_model = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
n_speakers=hps.data.n_speakers,
|
||||
**hps.model)
|
||||
if is_half:
|
||||
vq_model = vq_model.half().to(device)
|
||||
else:
|
||||
vq_model = vq_model.to(device)
|
||||
vq_model.eval()
|
||||
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
||||
hz = 50
|
||||
max_sec = config['data']['max_sec']
|
||||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||||
t2s_model.load_state_dict(dict_s1["weight"])
|
||||
if is_half:
|
||||
t2s_model = t2s_model.half()
|
||||
t2s_model = t2s_model.to(device)
|
||||
t2s_model.eval()
|
||||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||||
print("Number of parameter: %.2fM" % (total / 1e6))
|
||||
|
||||
|
||||
def get_spepc(hps, filename):
|
||||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||||
audio = torch.FloatTensor(audio)
|
||||
@ -341,17 +320,86 @@ def get_spepc(hps, filename):
|
||||
return spec
|
||||
|
||||
|
||||
dict_language = {
|
||||
"中文": "zh",
|
||||
"英文": "en",
|
||||
"日文": "ja",
|
||||
"ZH": "zh",
|
||||
"EN": "en",
|
||||
"JA": "ja",
|
||||
"zh": "zh",
|
||||
"en": "en",
|
||||
"ja": "ja"
|
||||
}
|
||||
def pack_audio(audio_bytes, data, rate):
|
||||
if media_type == "ogg":
|
||||
audio_bytes = pack_ogg(audio_bytes, data, rate)
|
||||
elif media_type == "aac":
|
||||
audio_bytes = pack_aac(audio_bytes, data, rate)
|
||||
else:
|
||||
# wav无法流式, 先暂存raw
|
||||
audio_bytes = pack_raw(audio_bytes, data, rate)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_ogg(audio_bytes, data, rate):
|
||||
with sf.SoundFile(audio_bytes, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
|
||||
audio_file.write(data)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_raw(audio_bytes, data, rate):
|
||||
audio_bytes.write(data.tobytes())
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def pack_wav(audio_bytes, rate):
|
||||
data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
|
||||
wav_bytes = BytesIO()
|
||||
sf.write(wav_bytes, data, rate, format='wav')
|
||||
|
||||
return wav_bytes
|
||||
|
||||
|
||||
def pack_aac(audio_bytes, data, rate):
|
||||
process = subprocess.Popen([
|
||||
'ffmpeg',
|
||||
'-f', 's16le', # 输入16位有符号小端整数PCM
|
||||
'-ar', str(rate), # 设置采样率
|
||||
'-ac', '1', # 单声道
|
||||
'-i', 'pipe:0', # 从管道读取输入
|
||||
'-c:a', 'aac', # 音频编码器为AAC
|
||||
'-b:a', '192k', # 比特率
|
||||
'-vn', # 不包含视频
|
||||
'-f', 'adts', # 输出AAC数据流格式
|
||||
'pipe:1' # 将输出写入管道
|
||||
], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
out, _ = process.communicate(input=data.tobytes())
|
||||
audio_bytes.write(out)
|
||||
|
||||
return audio_bytes
|
||||
|
||||
|
||||
def read_clean_buffer(audio_bytes):
|
||||
audio_chunk = audio_bytes.getvalue()
|
||||
audio_bytes.truncate(0)
|
||||
audio_bytes.seek(0)
|
||||
|
||||
return audio_bytes, audio_chunk
|
||||
|
||||
|
||||
def cut_text(text, punc):
|
||||
punc_list = [p for p in punc if p in {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", ";", "：", "…"}]
|
||||
if len(punc_list) > 0:
|
||||
punds = r"[" + "".join(punc_list) + r"]"
|
||||
text = text.strip("\n")
|
||||
items = re.split(f"({punds})", text)
|
||||
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
||||
# 在句子不存在符号或句尾无符号的时候保证文本完整
|
||||
if len(items)%2 == 1:
|
||||
mergeitems.append(items[-1])
|
||||
text = "\n".join(mergeitems)
|
||||
|
||||
while "\n\n" in text:
|
||||
text = text.replace("\n\n", "\n")
|
||||
|
||||
return text
|
||||
|
||||
|
||||
def only_punc(text):
|
||||
return not any(t.isalnum() or t.isalpha() for t in text)
|
||||
|
||||
|
||||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
|
||||
@ -374,25 +422,19 @@ 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()
|
||||
prompt_language = dict_language[prompt_language]
|
||||
text_language = dict_language[text_language]
|
||||
phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
|
||||
phones1 = cleaned_text_to_sequence(phones1)
|
||||
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)
|
||||
texts = text.split("\n")
|
||||
audio_opt = []
|
||||
audio_bytes = BytesIO()
|
||||
|
||||
for text in texts:
|
||||
phones2, word2ph2, norm_text2 = clean_text(text, text_language)
|
||||
phones2 = cleaned_text_to_sequence(phones2)
|
||||
if (prompt_language == "zh"):
|
||||
bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
|
||||
else:
|
||||
bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
|
||||
device)
|
||||
if (text_language == "zh"):
|
||||
bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
|
||||
else:
|
||||
bert2 = torch.zeros((1024, len(phones2))).to(bert1)
|
||||
# 简单防止纯符号引发参考音频泄露
|
||||
if only_punc(text):
|
||||
continue
|
||||
|
||||
audio_opt = []
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
|
||||
bert = torch.cat([bert1, bert2], 1)
|
||||
|
||||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||||
@ -426,8 +468,17 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
|
||||
audio_opt.append(audio)
|
||||
audio_opt.append(zero_wav)
|
||||
t4 = ttime()
|
||||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
||||
audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),hps.data.sampling_rate)
|
||||
# logger.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||||
if stream_mode == "normal":
|
||||
audio_bytes, audio_chunk = read_clean_buffer(audio_bytes)
|
||||
yield audio_chunk
|
||||
|
||||
if not stream_mode == "normal":
|
||||
if media_type == "wav":
|
||||
audio_bytes = pack_wav(audio_bytes,hps.data.sampling_rate)
|
||||
yield audio_bytes.getvalue()
|
||||
|
||||
|
||||
|
||||
def handle_control(command):
|
||||
@ -449,15 +500,16 @@ def handle_change(path, text, language):
|
||||
if language != "" or language is not None:
|
||||
default_refer.language = language
|
||||
|
||||
print(f"[INFO] 当前默认参考音频路径: {default_refer.path}")
|
||||
print(f"[INFO] 当前默认参考音频文本: {default_refer.text}")
|
||||
print(f"[INFO] 当前默认参考音频语种: {default_refer.language}")
|
||||
print(f"[INFO] is_ready: {default_refer.is_ready()}")
|
||||
logger.info(f"当前默认参考音频路径: {default_refer.path}")
|
||||
logger.info(f"当前默认参考音频文本: {default_refer.text}")
|
||||
logger.info(f"当前默认参考音频语种: {default_refer.language}")
|
||||
logger.info(f"is_ready: {default_refer.is_ready()}")
|
||||
|
||||
|
||||
return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
|
||||
|
||||
|
||||
def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
|
||||
def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc):
|
||||
if (
|
||||
refer_wav_path == "" or refer_wav_path is None
|
||||
or prompt_text == "" or prompt_text is None
|
||||
@ -471,24 +523,145 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
|
||||
if not default_refer.is_ready():
|
||||
return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
|
||||
|
||||
with torch.no_grad():
|
||||
gen = get_tts_wav(
|
||||
refer_wav_path, prompt_text, prompt_language, text, text_language
|
||||
)
|
||||
sampling_rate, audio_data = next(gen)
|
||||
if cut_punc == None:
|
||||
text = cut_text(text,default_cut_punc)
|
||||
else:
|
||||
text = cut_text(text,cut_punc)
|
||||
|
||||
wav = BytesIO()
|
||||
sf.write(wav, audio_data, sampling_rate, format="wav")
|
||||
wav.seek(0)
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
return StreamingResponse(wav, media_type="audio/wav")
|
||||
return StreamingResponse(get_tts_wav(refer_wav_path, prompt_text, prompt_language, text, text_language), media_type="audio/"+media_type)
|
||||
|
||||
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 初始化部分
|
||||
# --------------------------------
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(now_dir)
|
||||
sys.path.append("%s/GPT_SoVITS" % (now_dir))
|
||||
|
||||
dict_language = {
|
||||
"中文": "all_zh",
|
||||
"英文": "en",
|
||||
"日文": "all_ja",
|
||||
"中英混合": "zh",
|
||||
"日英混合": "ja",
|
||||
"多语种混合": "auto", #多语种启动切分识别语种
|
||||
"all_zh": "all_zh",
|
||||
"en": "en",
|
||||
"all_ja": "all_ja",
|
||||
"zh": "zh",
|
||||
"ja": "ja",
|
||||
"auto": "auto",
|
||||
}
|
||||
|
||||
# logger
|
||||
logging.config.dictConfig(uvicorn.config.LOGGING_CONFIG)
|
||||
logger = logging.getLogger('uvicorn')
|
||||
|
||||
# 获取配置
|
||||
g_config = global_config.Config()
|
||||
|
||||
# 获取参数
|
||||
parser = argparse.ArgumentParser(description="GPT-SoVITS api")
|
||||
|
||||
parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
|
||||
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
|
||||
parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
|
||||
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
|
||||
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
|
||||
parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
|
||||
parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
|
||||
parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
|
||||
parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
|
||||
parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
|
||||
# bool值的用法为 `python ./api.py -fp ...`
|
||||
# 此时 full_precision==True, half_precision==False
|
||||
parser.add_argument("-sm", "--stream_mode", type=str, default="close", help="流式返回模式, close / normal / keepalive")
|
||||
parser.add_argument("-mt", "--media_type", type=str, default="wav", help="音频编码格式, wav / ogg / aac")
|
||||
parser.add_argument("-cp", "--cut_punc", type=str, default="", help="文本切分符号设定, 符号范围,.;?!、，。？！;：…")
|
||||
# 切割常用分句符为 `python ./api.py -cp ".?!。？！"`
|
||||
parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
|
||||
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
|
||||
|
||||
args = parser.parse_args()
|
||||
sovits_path = args.sovits_path
|
||||
gpt_path = args.gpt_path
|
||||
device = args.device
|
||||
port = args.port
|
||||
host = args.bind_addr
|
||||
cnhubert_base_path = args.hubert_path
|
||||
bert_path = args.bert_path
|
||||
default_cut_punc = args.cut_punc
|
||||
|
||||
# 应用参数配置
|
||||
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
|
||||
|
||||
# 模型路径检查
|
||||
if sovits_path == "":
|
||||
sovits_path = g_config.pretrained_sovits_path
|
||||
logger.warn(f"未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
|
||||
if gpt_path == "":
|
||||
gpt_path = g_config.pretrained_gpt_path
|
||||
logger.warn(f"未指定GPT模型路径, fallback后当前值: {gpt_path}")
|
||||
|
||||
# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
|
||||
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
|
||||
default_refer.path, default_refer.text, default_refer.language = "", "", ""
|
||||
logger.info("未指定默认参考音频")
|
||||
else:
|
||||
logger.info(f"默认参考音频路径: {default_refer.path}")
|
||||
logger.info(f"默认参考音频文本: {default_refer.text}")
|
||||
logger.info(f"默认参考音频语种: {default_refer.language}")
|
||||
|
||||
# 获取半精度
|
||||
is_half = g_config.is_half
|
||||
if args.full_precision:
|
||||
is_half = False
|
||||
if args.half_precision:
|
||||
is_half = True
|
||||
if args.full_precision and args.half_precision:
|
||||
is_half = g_config.is_half # 炒饭fallback
|
||||
logger.info(f"半精: {is_half}")
|
||||
|
||||
# 流式返回模式
|
||||
if args.stream_mode.lower() in ["normal","n"]:
|
||||
stream_mode = "normal"
|
||||
logger.info("流式返回已开启")
|
||||
else:
|
||||
stream_mode = "close"
|
||||
|
||||
# 音频编码格式
|
||||
if args.media_type.lower() in ["aac","ogg"]:
|
||||
media_type = args.media_type.lower()
|
||||
elif stream_mode == "close":
|
||||
media_type = "wav"
|
||||
else:
|
||||
media_type = "ogg"
|
||||
logger.info(f"编码格式: {media_type}")
|
||||
|
||||
# 初始化模型
|
||||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||||
ssl_model = cnhubert.get_model()
|
||||
if is_half:
|
||||
bert_model = bert_model.half().to(device)
|
||||
ssl_model = ssl_model.half().to(device)
|
||||
else:
|
||||
bert_model = bert_model.to(device)
|
||||
ssl_model = ssl_model.to(device)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
|
||||
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 接口部分
|
||||
# --------------------------------
|
||||
app = FastAPI()
|
||||
|
||||
#clark新增-----2024-02-21
|
||||
#可在启动后动态修改模型，以此满足同一个api不同的朗读者请求
|
||||
@app.post("/set_model")
|
||||
async def set_model(request: Request):
|
||||
json_post_raw = await request.json()
|
||||
@ -496,11 +669,11 @@ async def set_model(request: Request):
|
||||
gpt_path=json_post_raw.get("gpt_model_path")
|
||||
global sovits_path
|
||||
sovits_path=json_post_raw.get("sovits_model_path")
|
||||
print("gptpath"+gpt_path+";vitspath"+sovits_path)
|
||||
logger.info("gptpath"+gpt_path+";vitspath"+sovits_path)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
return "ok"
|
||||
# 新增-----end------
|
||||
|
||||
|
||||
@app.post("/control")
|
||||
async def control(request: Request):
|
||||
@ -541,6 +714,7 @@ async def tts_endpoint(request: Request):
|
||||
json_post_raw.get("prompt_language"),
|
||||
json_post_raw.get("text"),
|
||||
json_post_raw.get("text_language"),
|
||||
json_post_raw.get("cut_punc"),
|
||||
)
|
||||
|
||||
|
||||
@ -551,8 +725,9 @@ async def tts_endpoint(
|
||||
prompt_language: str = None,
|
||||
text: str = None,
|
||||
text_language: str = None,
|
||||
cut_punc: str = None,
|
||||
):
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language)
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@ -141,7 +141,15 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
|
||||
|
||||
- [UVR5 Weights](https://www.icloud.com.cn/iclouddrive/0bekRKDiJXboFhbfm3lM2fVbA#UVR5_Weights)
|
||||
|
||||
对于中文自动语音识别（附加），从 [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), 和 [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) 下载模型，并将它们放置在 `tools/damo_asr/models` 中。
|
||||
对于中文自动语音识别（附加），从 [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), 和 [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) 下载模型，并将它们放置在 `tools/asr/models` 中。
|
||||
|
||||
对于英语与日语自动语音识别（附加),从 [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) 下载模型，并将它们放置在 `tools/asr/models` 中。 此外，[其他模型](https://huggingface.co/Systran)可能具有类似效果，但占用更小的磁盘空间。
|
||||
|
||||
中国地区用户可以通过以下链接下载：
|
||||
- [Faster Whisper Large V3](https://www.icloud.com/iclouddrive/0c4pQxFs7oWyVU1iMTq2DbmLA#faster-whisper-large-v3)(点击“下载副本”)
|
||||
|
||||
- [Faster Whisper Large V3](https://hf-mirror.com/Systran/faster-whisper-large-v3)(Hugging Face镜像站)
|
||||
|
||||
|
||||
## 数据集格式
|
||||
|
||||
@ -204,13 +212,13 @@ python audio_slicer.py \
|
||||
````
|
||||
这是使用命令行完成数据集ASR处理的方式（仅限中文）
|
||||
````
|
||||
python tools/damo_asr/cmd-asr.py "<Path to the directory containing input audio files>"
|
||||
python tools/asr/funasr_asr.py -i <input> -o <output>
|
||||
````
|
||||
通过Faster_Whisper进行ASR处理（除中文之外的ASR标记）
|
||||
|
||||
（没有进度条，GPU性能可能会导致时间延迟）
|
||||
````
|
||||
python ./tools/damo_asr/WhisperASR.py -i <input> -o <output> -f <file_name.list> -l <language>
|
||||
python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
|
||||
````
|
||||
启用自定义列表保存路径
|
||||
## 致谢
|
||||
|
||||
@ -127,7 +127,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
|
||||
|
||||
[GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) から事前訓練済みモデルをダウンロードし、`GPT_SoVITSpretrained_models` に置きます。
|
||||
|
||||
中国語 ASR（追加）については、[Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files)、[Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files)、[Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) からモデルをダウンロードし、`tools/damo_asr/models` に置いてください。
|
||||
中国語 ASR（追加）については、[Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files)、[Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files)、[Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) からモデルをダウンロードし、`tools/asr/models` に置いてください。
|
||||
|
||||
UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally) の場合は、[UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) からモデルをダウンロードして `tools/uvr5/uvr5_weights` に置きます。
|
||||
|
||||
@ -156,7 +156,7 @@ D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
|
||||
- [ ] **優先度 高:**
|
||||
|
||||
- [x] 日本語と英語でのローカライズ。
|
||||
- [ ] ユーザーガイド。
|
||||
- [] ユーザーガイド。
|
||||
- [x] 日本語データセットと英語データセットのファインチューニングトレーニング。
|
||||
|
||||
- [ ] **機能:**
|
||||
@ -192,13 +192,13 @@ python audio_slicer.py \
|
||||
```
|
||||
コマンドラインを使用してデータセット ASR 処理を行う方法です (中国語のみ)
|
||||
```
|
||||
python tools/damo_asr/cmd-asr.py "<Path to the directory containing input audio files>"
|
||||
python tools/asr/funasr_asr.py -i <input> -o <output>
|
||||
```
|
||||
ASR処理はFaster_Whisperを通じて実行されます(中国語を除くASRマーキング)
|
||||
|
||||
(進行状況バーは表示されません。GPU のパフォーマンスにより時間遅延が発生する可能性があります)
|
||||
```
|
||||
python ./tools/damo_asr/WhisperASR.py -i <input> -o <output> -f <file_name.list> -l <language>
|
||||
python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
|
||||
```
|
||||
カスタムリストの保存パスが有効になっています
|
||||
## クレジット
|
||||
|
||||
@ -130,7 +130,7 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
|
||||
|
||||
[GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS)에서 사전 훈련된 모델을 다운로드하고 `GPT_SoVITS\pretrained_models`에 넣습니다.
|
||||
|
||||
중국어 자동 음성 인식(ASR), 음성 반주 분리 및 음성 제거를 위해 [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files) 및 [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files)을 다운로드하고 `tools/damo_asr/models`에 넣습니다.
|
||||
중국어 자동 음성 인식(ASR), 음성 반주 분리 및 음성 제거를 위해 [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files) 및 [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files)을 다운로드하고 `tools/asr/models`에 넣습니다.
|
||||
|
||||
UVR5(음성/반주 분리 및 잔향 제거)를 위해 [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights)에서 모델을 다운로드하고 `tools/uvr5/uvr5_weights`에 넣습니다.
|
||||
|
||||
@ -196,13 +196,13 @@ python audio_slicer.py \
|
||||
```
|
||||
명령줄을 사용하여 데이터 세트 ASR 처리를 수행하는 방법입니다(중국어만 해당).
|
||||
```
|
||||
python tools/damo_asr/cmd-asr.py "<Path to the directory containing input audio files>"
|
||||
python tools/asr/funasr_asr.py -i <input> -o <output>
|
||||
```
|
||||
ASR 처리는 Faster_Whisper(중국어를 제외한 ASR 마킹)를 통해 수행됩니다.
|
||||
|
||||
(진행률 표시줄 없음, GPU 성능으로 인해 시간 지연이 발생할 수 있음)
|
||||
```
|
||||
python ./tools/damo_asr/WhisperASR.py -i <input> -o <output> -f <file_name.list> -l <language>
|
||||
python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language>
|
||||
```
|
||||
사용자 정의 목록 저장 경로가 활성화되었습니다.
|
||||
## 감사의 말
|
||||
|
||||
@ -2,6 +2,18 @@
|
||||
"很遗憾您这没有能用的显卡来支持您训练": "很遗憾您这没有能用的显卡来支持您训练",
|
||||
"UVR5已开启": "UVR5已开启",
|
||||
"UVR5已关闭": "UVR5已关闭",
|
||||
"输入文件夹路径": "输入文件夹路径",
|
||||
"输出文件夹路径": "输出文件夹路径",
|
||||
"ASR 模型": "ASR 模型",
|
||||
"ASR 模型尺寸": "ASR 模型尺寸",
|
||||
"ASR 语言设置": "ASR 语言设置",
|
||||
"模型切换": "模型切换",
|
||||
"是否开启dpo训练选项(实验性)": "是否开启dpo训练选项(实验性)",
|
||||
"开启无参考文本模式。不填参考文本亦相当于开启。": "开启无参考文本模式。不填参考文本亦相当于开启。",
|
||||
"使用无参考文本模式时建议使用微调的GPT": "使用无参考文本模式时建议使用微调的GPT",
|
||||
"后续将支持转音素、手工修改音素、语音合成分步执行。": "后续将支持转音素、手工修改音素、语音合成分步执行。",
|
||||
"gpt采样参数(无参考文本时不要太低)：": "gpt采样参数(无参考文本时不要太低)：",
|
||||
"按标点符号切": "按标点符号切",
|
||||
"本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.": "本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.",
|
||||
"0-前置数据集获取工具": "0-前置数据集获取工具",
|
||||
"0a-UVR5人声伴奏分离&去混响去延迟工具": "0a-UVR5人声伴奏分离&去混响去延迟工具",
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user