Delete SongBloom

SongBloom/g2p/cn_zh_g2p/__init__.py

@@ -1,106 +0,0 @@
-from . import chinese,  english # , japanese 暂时干掉看看
-from .symbols import *
-import yaml
-language_module_map = {"zh": chinese, "en": english} #, "ja": japanese
-
-def is_chinese(uchar):
-    if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
-        return True
-    else:
-        return False
-
-import re
-
-# def split_text(text):
-#     chinese_pattern = r'[\u4e00-\u9fa5][\u4e00-\u9fa5\ \,\.\!\?\，\。]+'
-#     english_pattern = r'[a-zA-Z][a-zA-Z\'\ \,\.\!\?]+'
-    
-#     chinese_text = re.findall(chinese_pattern, text)
-#     print(chinese_text)
-#     english_text = re.findall(english_pattern, text)
-    
-#     return chinese_text, english_text
-
-def split_text(text):
-    pattern = re.compile("|".join(re.escape(p) for p in chinese.rep_map.keys()))
-    text = pattern.sub(lambda x: chinese.rep_map[x.group()], text)
-
-    result = []
-    lang = []
-    buffer = ""
-    chinese_pattern = r'[\u4e00-\u9fa5]'
-    special_pattern = r'[\,\.\!\?\…\-]'
-    # TODO check 一下
-    for char in text:
-        if re.match(special_pattern, char):
-            if buffer:
-                if not re.match(chinese_pattern, buffer[0]):
-                    result.append(buffer)
-                    lang.append('en')
-                else:
-                    result.append(buffer)
-                    lang.append("zh")
-            result.append(char)
-            lang.append('sp')
-            buffer = ""
-
-        
-        elif re.match(chinese_pattern, char):
-            if buffer and not re.match(chinese_pattern, buffer[-1]):
-                result.append(buffer)
-                buffer = ""
-                lang.append('en')
-            buffer += char
-        else:
-            if buffer and re.match(chinese_pattern, buffer[-1]):
-                result.append(buffer)
-                buffer = ""
-                lang.append("zh")
-            buffer += char
-
-    if buffer:
-        result.append(buffer)
-        lang.append("zh" if re.match(chinese_pattern, buffer[-1]) else 'en')
-
-    return result, lang
-
-def mixed_language_to_phoneme(text):
-    segments, lang = split_text(text)
-    # print(segments, lang)
-    result = [language_to_phoneme(s, l) for s, l in zip(segments, lang)]
-    phones, word2ph = [], []
-    for p, w, n in result:
-        phones += p
-        if w is None:
-            w = []
-        word2ph += w
-    return phones, word2ph
-
-
-def language_to_phoneme(text, language):
-    if language == 'sp':
-        return [text], None, text
-    language_module = language_module_map[language]
-    norm_text = language_module.text_normalize(text)
-    if language == "zh":
-        phones, word2ph = language_module.g2p(norm_text)
-        assert len(phones) == sum(word2ph)
-        assert len(norm_text) == len(word2ph)
-    else:
-        try:
-            phones = language_module.g2p(norm_text)
-        except:
-            phones = [norm_text]
-        word2ph = None
-
-    # for ph in phones:
-    #     assert ph in symbols, ph
-    return phones, word2ph, norm_text
-
-def gen_vocabs():
-    yaml.dump(symbols, open('./vocab.yaml', 'w'))
-
-class G2P_Mix():
-    def __call__(self, text):
-        phones, word2ph = mixed_language_to_phoneme(text)
-        return ' '.join(phones)

SongBloom/g2p/cn_zh_g2p/chinese.py

@@ -1,173 +0,0 @@
-import os
-import pdb
-import re
-
-import cn2an
-from pypinyin import lazy_pinyin, Style
-
-from .symbols import punctuation
-from .tone_sandhi import ToneSandhi
-from .zh_normalization.text_normlization import TextNormalizer
-
-normalizer = lambda x: cn2an.transform(x, "an2cn")
-
-current_file_path = os.path.dirname(__file__)
-pinyin_to_symbol_map = {
-    line.split("\t")[0]: line.strip().split("\t")[1]
-    for line in open(os.path.join(current_file_path, "opencpop-strict.txt")).readlines()
-}
-
-import jieba_fast.posseg as psg
-
-
-rep_map = {
-    "：": ",",
-    "；": ",",
-    "，": ",",
-    "。": ".",
-    "！": "!",
-    "？": "?",
-    "\n": ".",
-    "·": ",",
-    "、": ",",
-    "...": "…",
-    "$": ".",
-    "/": ",",
-    "—": "-",
-    "~": "…",
-    "～":"…",
-}
-
-tone_modifier = ToneSandhi()
-
-
-def replace_punctuation(text):
-    text = text.replace("嗯", "恩").replace("呣", "母")
-    pattern = re.compile("|".join(re.escape(p) for p in rep_map.keys()))
-
-    replaced_text = pattern.sub(lambda x: rep_map[x.group()], text)
-    replaced_text = re.sub(
-        r"[^\u4e00-\u9fa5" + "".join(punctuation) + r"]+", "", replaced_text
-    )
-
-    return replaced_text
-
-
-def g2p(text):
-    pattern = r"(?<=[{0}])\s*".format("".join(punctuation))
-    sentences = [i for i in re.split(pattern, text) if i.strip() != ""]
-    phones, word2ph = _g2p(sentences)
-    return phones, word2ph
-
-
-def _get_initials_finals(word):
-    initials = []
-    finals = []
-    orig_initials = lazy_pinyin(word, neutral_tone_with_five=True, style=Style.INITIALS)
-    orig_finals = lazy_pinyin(
-        word, neutral_tone_with_five=True, style=Style.FINALS_TONE3
-    )
-    for c, v in zip(orig_initials, orig_finals):
-        initials.append(c)
-        finals.append(v)
-    return initials, finals
-
-
-def _g2p(segments):
-    phones_list = []
-    word2ph = []
-    for seg in segments:
-        pinyins = []
-        # Replace all English words in the sentence
-        seg = re.sub("[a-zA-Z]+", "", seg)
-        seg_cut = psg.lcut(seg)
-        initials = []
-        finals = []
-        seg_cut = tone_modifier.pre_merge_for_modify(seg_cut)
-        for word, pos in seg_cut:
-            if pos == "eng":
-                continue
-            sub_initials, sub_finals = _get_initials_finals(word)
-            sub_finals = tone_modifier.modified_tone(word, pos, sub_finals)
-            initials.append(sub_initials)
-            finals.append(sub_finals)
-
-            # assert len(sub_initials) == len(sub_finals) == len(word)
-        initials = sum(initials, [])
-        finals = sum(finals, [])
-        #
-        for c, v in zip(initials, finals):
-            raw_pinyin = c + v
-            # NOTE: post process for pypinyin outputs
-            # we discriminate i, ii and iii
-            if c == v:
-                assert c in punctuation
-                phone = [c]
-                word2ph.append(1)
-            else:
-                v_without_tone = v[:-1]
-                tone = v[-1]
-
-                pinyin = c + v_without_tone
-                assert tone in "12345"
-
-                if c:
-                    # 多音节
-                    v_rep_map = {
-                        "uei": "ui",
-                        "iou": "iu",
-                        "uen": "un",
-                    }
-                    if v_without_tone in v_rep_map.keys():
-                        pinyin = c + v_rep_map[v_without_tone]
-                else:
-                    # 单音节
-                    pinyin_rep_map = {
-                        "ing": "ying",
-                        "i": "yi",
-                        "in": "yin",
-                        "u": "wu",
-                    }
-                    if pinyin in pinyin_rep_map.keys():
-                        pinyin = pinyin_rep_map[pinyin]
-                    else:
-                        single_rep_map = {
-                            "v": "yu",
-                            "e": "e",
-                            "i": "y",
-                            "u": "w",
-                        }
-                        if pinyin[0] in single_rep_map.keys():
-                            pinyin = single_rep_map[pinyin[0]] + pinyin[1:]
-
-                assert pinyin in pinyin_to_symbol_map.keys(), (pinyin, seg, raw_pinyin)
-                new_c, new_v = pinyin_to_symbol_map[pinyin].split(" ")
-                new_v = new_v + tone
-                phone = [new_c, new_v]
-                word2ph.append(len(phone))
-
-            phones_list += phone
-    return phones_list, word2ph
-
-
-def text_normalize(text):
-    # https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization
-    tx = TextNormalizer()
-    sentences = tx.normalize(text)
-    dest_text = ""
-    for sentence in sentences:
-        dest_text += replace_punctuation(sentence)
-    return dest_text
-
-
-if __name__ == "__main__":
-    text = "啊——但是《原神》是由,米哈\游自主，研发的一款全.新开放世界.冒险游戏"
-    text = "呣呣呣～就是…大人的鼹鼠党吧？"
-    text = "你好"
-    text = text_normalize(text)
-    print(g2p(text))
-
-
-# # 示例用法
-# text = "这是一个示例文本：,你好！这是一个测试..."
-# print(g2p_paddle(text))  # 输出: 这是一个示例文本你好这是一个测试

SongBloom/g2p/cn_zh_g2p/engdict-hot.rep

@@ -1,2 +0,0 @@
-CHATGPT CH AE1 T JH IY1 P IY1 T IY1
-JSON JH EY1 S AH0 N

SongBloom/g2p/cn_zh_g2p/engdict_cache.pickle

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9bff9393f4b192d873a11335efc8f124771087b6dc847d34fd240c2846889d2b
-size 5965909

SongBloom/g2p/cn_zh_g2p/english.py

@@ -1,369 +0,0 @@
-import pickle
-import os
-import re
-import wordsegment
-from g2p_en import G2p
-
-from string import punctuation
-
-from .symbols import symbols
-
-import unicodedata
-from builtins import str as unicode
-from g2p_en.expand import normalize_numbers
-
-# Set NLTK data path programmatically to avoid needing set_env.sh
-import nltk
-current_file_path = os.path.dirname(__file__)
-nltk_data_path = os.path.join(current_file_path, "nltk_data")
-if os.path.exists(nltk_data_path):
-    nltk.data.path.insert(0, nltk_data_path)
-
-from nltk.tokenize import TweetTokenizer
-word_tokenize = TweetTokenizer().tokenize
-from nltk import pos_tag
-
-CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
-CMU_DICT_FAST_PATH = os.path.join(current_file_path, "cmudict-fast.rep")
-CMU_DICT_HOT_PATH = os.path.join(current_file_path, "engdict-hot.rep")
-CACHE_PATH = os.path.join(current_file_path, "engdict_cache.pickle")
-NAMECACHE_PATH = os.path.join(current_file_path, "namedict_cache.pickle")
-
-arpa = {
-    "AH0",
-    "S",
-    "AH1",
-    "EY2",
-    "AE2",
-    "EH0",
-    "OW2",
-    "UH0",
-    "NG",
-    "B",
-    "G",
-    "AY0",
-    "M",
-    "AA0",
-    "F",
-    "AO0",
-    "ER2",
-    "UH1",
-    "IY1",
-    "AH2",
-    "DH",
-    "IY0",
-    "EY1",
-    "IH0",
-    "K",
-    "N",
-    "W",
-    "IY2",
-    "T",
-    "AA1",
-    "ER1",
-    "EH2",
-    "OY0",
-    "UH2",
-    "UW1",
-    "Z",
-    "AW2",
-    "AW1",
-    "V",
-    "UW2",
-    "AA2",
-    "ER",
-    "AW0",
-    "UW0",
-    "R",
-    "OW1",
-    "EH1",
-    "ZH",
-    "AE0",
-    "IH2",
-    "IH",
-    "Y",
-    "JH",
-    "P",
-    "AY1",
-    "EY0",
-    "OY2",
-    "TH",
-    "HH",
-    "D",
-    "ER0",
-    "CH",
-    "AO1",
-    "AE1",
-    "AO2",
-    "OY1",
-    "AY2",
-    "IH1",
-    "OW0",
-    "L",
-    "SH",
-}
-
-
-def replace_phs(phs):
-    rep_map = {"'": "-"}
-    phs_new = []
-    for ph in phs:
-        if ph in symbols:
-            phs_new.append(ph)
-        elif ph in rep_map.keys():
-            phs_new.append(rep_map[ph])
-        else:
-            print("ph not in symbols: ", ph)
-    return phs_new
-
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split("  ")
-                word = word_split[0].lower()
-
-                syllable_split = word_split[1].split(" - ")
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(" ")
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def read_dict_new():
-    g2p_dict = {}
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= 57:
-                line = line.strip()
-                word_split = line.split("  ")
-                word = word_split[0].lower()
-                g2p_dict[word] = [word_split[1].split(" ")]
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    with open(CMU_DICT_FAST_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= 0:
-                line = line.strip()
-                word_split = line.split(" ")
-                word = word_split[0].lower()
-                if word not in g2p_dict:
-                    g2p_dict[word] = [word_split[1:]]
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-def hot_reload_hot(g2p_dict):
-    with open(CMU_DICT_HOT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= 0:
-                line = line.strip()
-                word_split = line.split(" ")
-                word = word_split[0].lower()
-                # 自定义发音词直接覆盖字典
-                g2p_dict[word] = [word_split[1:]]
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, "wb") as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, "rb") as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict_new()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    g2p_dict = hot_reload_hot(g2p_dict)
-
-    return g2p_dict
-
-
-def get_namedict():
-    if os.path.exists(NAMECACHE_PATH):
-        with open(NAMECACHE_PATH, "rb") as pickle_file:
-            name_dict = pickle.load(pickle_file)
-    else:
-        name_dict = {}
-
-    return name_dict
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    # 适配中文及 g2p_en 标点
-    rep_map = {
-        "[;:：，；]": ",",
-        '["’]': "'",
-        "。": ".",
-        "！": "!",
-        "？": "?",
-    }
-    for p, r in rep_map.items():
-        text = re.sub(p, r, text)
-
-    # 来自 g2p_en 文本格式化处理
-    # 增加大写兼容
-    text = unicode(text)
-    text = normalize_numbers(text)
-    text = ''.join(char for char in unicodedata.normalize('NFD', text)
-                    if unicodedata.category(char) != 'Mn')  # Strip accents
-    text = re.sub("[^ A-Za-z'.,?!\-]", "", text)
-    text = re.sub(r"(?i)i\.e\.", "that is", text)
-    text = re.sub(r"(?i)e\.g\.", "for example", text)
-
-    return text
-
-
-class en_G2p(G2p):
-    def __init__(self):
-        super().__init__()
-        # 分词初始化
-        wordsegment.load()
-
-        # 扩展过时字典, 添加姓名字典
-        self.cmu = get_dict()
-        self.namedict = get_namedict()
-
-        # 剔除读音错误的几个缩写
-        for word in ["AE", "AI", "AR", "IOS", "HUD", "OS"]:
-            del self.cmu[word.lower()]
-
-        # 修正多音字
-        self.homograph2features["read"] = (['R', 'IY1', 'D'], ['R', 'EH1', 'D'], 'VBP')
-        self.homograph2features["complex"] = (['K', 'AH0', 'M', 'P', 'L', 'EH1', 'K', 'S'], ['K', 'AA1', 'M', 'P', 'L', 'EH0', 'K', 'S'], 'JJ')
-
-
-    def __call__(self, text):
-        # tokenization
-        words = word_tokenize(text)
-        tokens = pos_tag(words)  # tuples of (word, tag)
-
-        # steps
-        prons = []
-        for o_word, pos in tokens:
-            # 还原 g2p_en 小写操作逻辑
-            word = o_word.lower()
-
-            if re.search("[a-z]", word) is None:
-                pron = [word]
-            # 先把单字母推出去
-            elif len(word) == 1:
-                # 单读 A 发音修正, 这里需要原格式 o_word 判断大写
-                if o_word == "A":
-                    pron = ['EY1']
-                else:
-                    pron = self.cmu[word][0]
-            # g2p_en 原版多音字处理
-            elif word in self.homograph2features:  # Check homograph
-                pron1, pron2, pos1 = self.homograph2features[word]
-                if pos.startswith(pos1):
-                    pron = pron1
-                # pos1比pos长仅出现在read
-                elif len(pos) < len(pos1) and pos == pos1[:len(pos)]:
-                    pron = pron1
-                else:
-                    pron = pron2
-            else:
-                # 递归查找预测
-                pron = self.qryword(o_word)
-
-            prons.extend(pron)
-            prons.extend([" "])
-
-        return prons[:-1]
-
-
-    def qryword(self, o_word):
-        word = o_word.lower()
-
-        # 查字典, 单字母除外
-        if len(word) > 1 and word in self.cmu:  # lookup CMU dict
-            return self.cmu[word][0]
-
-        # 单词仅首字母大写时查找姓名字典
-        if o_word.istitle() and word in self.namedict:
-            return self.namedict[word][0]
-
-        # oov 长度小于等于 3 直接读字母
-        if len(word) <= 3:
-            phones = []
-            for w in word:
-                # 单读 A 发音修正, 此处不存在大写的情况
-                if w == "a":
-                    phones.extend(['EY1'])
-                else:
-                    phones.extend(self.cmu[w][0])
-            return phones
-
-        # 尝试分离所有格
-        if re.match(r"^([a-z]+)('s)$", word):
-            phones = self.qryword(word[:-2])[:]
-            # P T K F TH HH 无声辅音结尾 's 发 ['S']
-            if phones[-1] in ['P', 'T', 'K', 'F', 'TH', 'HH']:
-                phones.extend(['S'])
-            # S Z SH ZH CH JH 擦声结尾 's 发 ['IH1', 'Z'] 或 ['AH0', 'Z']
-            elif phones[-1] in ['S', 'Z', 'SH', 'ZH', 'CH', 'JH']:
-                phones.extend(['AH0', 'Z'])
-            # B D G DH V M N NG L R W Y 有声辅音结尾 's 发 ['Z']
-            # 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
-            # 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']
-            else:
-                phones.extend(['Z'])
-            return phones
-
-        # 尝试进行分词，应对复合词
-        comps = wordsegment.segment(word.lower())
-
-        # 无法分词的送回去预测
-        if len(comps)==1:
-            return self.predict(word)
-
-        # 可以分词的递归处理
-        return [phone for comp in comps for phone in self.qryword(comp)]
-
-
-_g2p = en_G2p()
-
-
-def g2p(text):
-    # g2p_en 整段推理，剔除不存在的arpa返回
-    phone_list = _g2p(text)
-    phones = [ph if ph != "<unk>" else "UNK" for ph in phone_list if ph not in [" ", "<pad>", "UW", "</s>", "<s>"]]
-
-    return replace_phs(phones)
-
-
-if __name__ == "__main__":
-    print(g2p("hello"))
-    print(g2p(text_normalize("e.g. I used openai's AI tool to draw a picture.")))
-    print(g2p(text_normalize("In this; paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")))

SongBloom/g2p/cn_zh_g2p/nltk_data/corpora/cmudict.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d07cca47fd72ad32ea9d8ad1219f85301eeaf4568f8b6b73747506a71fb5afd6
-size 896069

SongBloom/g2p/cn_zh_g2p/nltk_data/corpora/cmudict/README

@@ -1,76 +0,0 @@
-The Carnegie Mellon Pronouncing Dictionary [cmudict.0.7a]
-
-ftp://ftp.cs.cmu.edu/project/speech/dict/
-https://cmusphinx.svn.sourceforge.net/svnroot/cmusphinx/trunk/cmudict/cmudict.0.7a
-
-Copyright (C) 1993-2008 Carnegie Mellon University. All rights reserved.
-
-File Format: Each line consists of an uppercased word,
-a counter (for alternative pronunciations), and a transcription.
-Vowels are marked for stress (1=primary, 2=secondary, 0=no stress).
-E.g.: NATURAL 1 N AE1 CH ER0 AH0 L
-
-The dictionary contains 127069 entries.  Of these, 119400 words are assigned
-a unique pronunciation, 6830 words have two pronunciations, and 839 words have
-three or more pronunciations.  Many of these are fast-speech variants.
-
-Phonemes: There are 39 phonemes, as shown below:
-    
-    Phoneme Example Translation    Phoneme Example Translation
-    ------- ------- -----------    ------- ------- -----------
-    AA      odd     AA D           AE      at      AE T
-    AH      hut     HH AH T        AO      ought   AO T
-    AW      cow     K AW           AY      hide    HH AY D
-    B       be      B IY           CH      cheese  CH IY Z
-    D       dee     D IY           DH      thee    DH IY
-    EH      Ed      EH D           ER      hurt    HH ER T
-    EY      ate     EY T           F       fee     F IY
-    G       green   G R IY N       HH      he      HH IY
-    IH      it      IH T           IY      eat     IY T
-    JH      gee     JH IY          K       key     K IY
-    L       lee     L IY           M       me      M IY
-    N       knee    N IY           NG      ping    P IH NG
-    OW      oat     OW T           OY      toy     T OY
-    P       pee     P IY           R       read    R IY D
-    S       sea     S IY           SH      she     SH IY
-    T       tea     T IY           TH      theta   TH EY T AH
-    UH      hood    HH UH D        UW      two     T UW
-    V       vee     V IY           W       we      W IY
-    Y       yield   Y IY L D       Z       zee     Z IY
-    ZH      seizure S IY ZH ER
-
-(For NLTK, entries have been sorted so that, e.g. FIRE 1 and FIRE 2
-are contiguous, and not separated by FIRE'S 1.)
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions
-are met:
-
-1. Redistributions of source code must retain the above copyright
-   notice, this list of conditions and the following disclaimer.
-   The contents of this file are deemed to be source code.
-
-2. Redistributions in binary form must reproduce the above copyright
-   notice, this list of conditions and the following disclaimer in
-   the documentation and/or other materials provided with the
-   distribution.
-
-This work was supported in part by funding from the Defense Advanced
-Research Projects Agency, the Office of Naval Research and the National
-Science Foundation of the United States of America, and by member
-companies of the Carnegie Mellon Sphinx Speech Consortium. We acknowledge
-the contributions of many volunteers to the expansion and improvement of
-this dictionary.
-
-THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
-ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY
-NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-

SongBloom/g2p/cn_zh_g2p/nltk_data/taggers/averaged_perceptron_tagger.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e1f13cf2532daadfd6f3bc481a49859f0b8ea6432ccdcd83e6a49a5f19008de9
-size 2526731

SongBloom/g2p/cn_zh_g2p/nltk_data/taggers/averaged_perceptron_tagger/averaged_perceptron_tagger.pickle

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:25a5a19c7ced7b2bac3831da5bc0afcc2c34e5dd01cd4f361bb799949a696238
-size 6138625

SongBloom/g2p/cn_zh_g2p/opencpop-strict.txt

@@ -1,429 +0,0 @@
-a	AA a
-ai	AA ai
-an	AA an
-ang	AA ang
-ao	AA ao
-ba	b a
-bai	b ai
-ban	b an
-bang	b ang
-bao	b ao
-bei	b ei
-ben	b en
-beng	b eng
-bi	b i
-bian	b ian
-biao	b iao
-bie	b ie
-bin	b in
-bing	b ing
-bo	b o
-bu	b u
-ca	c a
-cai	c ai
-can	c an
-cang	c ang
-cao	c ao
-ce	c e
-cei	c ei
-cen	c en
-ceng	c eng
-cha	ch a
-chai	ch ai
-chan	ch an
-chang	ch ang
-chao	ch ao
-che	ch e
-chen	ch en
-cheng	ch eng
-chi	ch ir
-chong	ch ong
-chou	ch ou
-chu	ch u
-chua	ch ua
-chuai	ch uai
-chuan	ch uan
-chuang	ch uang
-chui	ch ui
-chun	ch un
-chuo	ch uo
-ci	c i0
-cong	c ong
-cou	c ou
-cu	c u
-cuan	c uan
-cui	c ui
-cun	c un
-cuo	c uo
-da	d a
-dai	d ai
-dan	d an
-dang	d ang
-dao	d ao
-de	d e
-dei	d ei
-den	d en
-deng	d eng
-di	d i
-dia	d ia
-dian	d ian
-diao	d iao
-die	d ie
-ding	d ing
-diu	d iu
-dong	d ong
-dou	d ou
-du	d u
-duan	d uan
-dui	d ui
-dun	d un
-duo	d uo
-e	EE e
-ei	EE ei
-en	EE en
-eng	EE eng
-er	EE er
-fa	f a
-fan	f an
-fang	f ang
-fei	f ei
-fen	f en
-feng	f eng
-fo	f o
-fou	f ou
-fu	f u
-ga	g a
-gai	g ai
-gan	g an
-gang	g ang
-gao	g ao
-ge	g e
-gei	g ei
-gen	g en
-geng	g eng
-gong	g ong
-gou	g ou
-gu	g u
-gua	g ua
-guai	g uai
-guan	g uan
-guang	g uang
-gui	g ui
-gun	g un
-guo	g uo
-ha	h a
-hai	h ai
-han	h an
-hang	h ang
-hao	h ao
-he	h e
-hei	h ei
-hen	h en
-heng	h eng
-hong	h ong
-hou	h ou
-hu	h u
-hua	h ua
-huai	h uai
-huan	h uan
-huang	h uang
-hui	h ui
-hun	h un
-huo	h uo
-ji	j i
-jia	j ia
-jian	j ian
-jiang	j iang
-jiao	j iao
-jie	j ie
-jin	j in
-jing	j ing
-jiong	j iong
-jiu	j iu
-ju	j v
-jv	j v
-juan	j van
-jvan	j van
-jue	j ve
-jve	j ve
-jun	j vn
-jvn	j vn
-ka	k a
-kai	k ai
-kan	k an
-kang	k ang
-kao	k ao
-ke	k e
-kei	k ei
-ken	k en
-keng	k eng
-kong	k ong
-kou	k ou
-ku	k u
-kua	k ua
-kuai	k uai
-kuan	k uan
-kuang	k uang
-kui	k ui
-kun	k un
-kuo	k uo
-la	l a
-lai	l ai
-lan	l an
-lang	l ang
-lao	l ao
-le	l e
-lei	l ei
-leng	l eng
-li	l i
-lia	l ia
-lian	l ian
-liang	l iang
-liao	l iao
-lie	l ie
-lin	l in
-ling	l ing
-liu	l iu
-lo	l o
-long	l ong
-lou	l ou
-lu	l u
-luan	l uan
-lun	l un
-luo	l uo
-lv	l v
-lve	l ve
-ma	m a
-mai	m ai
-man	m an
-mang	m ang
-mao	m ao
-me	m e
-mei	m ei
-men	m en
-meng	m eng
-mi	m i
-mian	m ian
-miao	m iao
-mie	m ie
-min	m in
-ming	m ing
-miu	m iu
-mo	m o
-mou	m ou
-mu	m u
-na	n a
-nai	n ai
-nan	n an
-nang	n ang
-nao	n ao
-ne	n e
-nei	n ei
-nen	n en
-neng	n eng
-ni	n i
-nian	n ian
-niang	n iang
-niao	n iao
-nie	n ie
-nin	n in
-ning	n ing
-niu	n iu
-nong	n ong
-nou	n ou
-nu	n u
-nuan	n uan
-nun	n un
-nuo	n uo
-nv	n v
-nve	n ve
-o	OO o
-ou	OO ou
-pa	p a
-pai	p ai
-pan	p an
-pang	p ang
-pao	p ao
-pei	p ei
-pen	p en
-peng	p eng
-pi	p i
-pian	p ian
-piao	p iao
-pie	p ie
-pin	p in
-ping	p ing
-po	p o
-pou	p ou
-pu	p u
-qi	q i
-qia	q ia
-qian	q ian
-qiang	q iang
-qiao	q iao
-qie	q ie
-qin	q in
-qing	q ing
-qiong	q iong
-qiu	q iu
-qu	q v
-qv	q v
-quan	q van
-qvan	q van
-que	q ve
-qve	q ve
-qun	q vn
-qvn	q vn
-ran	r an
-rang	r ang
-rao	r ao
-re	r e
-ren	r en
-reng	r eng
-ri	r ir
-rong	r ong
-rou	r ou
-ru	r u
-rua	r ua
-ruan	r uan
-rui	r ui
-run	r un
-ruo	r uo
-sa	s a
-sai	s ai
-san	s an
-sang	s ang
-sao	s ao
-se	s e
-sen	s en
-seng	s eng
-sha	sh a
-shai	sh ai
-shan	sh an
-shang	sh ang
-shao	sh ao
-she	sh e
-shei	sh ei
-shen	sh en
-sheng	sh eng
-shi	sh ir
-shou	sh ou
-shu	sh u
-shua	sh ua
-shuai	sh uai
-shuan	sh uan
-shuang	sh uang
-shui	sh ui
-shun	sh un
-shuo	sh uo
-si	s i0
-song	s ong
-sou	s ou
-su	s u
-suan	s uan
-sui	s ui
-sun	s un
-suo	s uo
-ta	t a
-tai	t ai
-tan	t an
-tang	t ang
-tao	t ao
-te	t e
-tei	t ei
-teng	t eng
-ti	t i
-tian	t ian
-tiao	t iao
-tie	t ie
-ting	t ing
-tong	t ong
-tou	t ou
-tu	t u
-tuan	t uan
-tui	t ui
-tun	t un
-tuo	t uo
-wa	w a
-wai	w ai
-wan	w an
-wang	w ang
-wei	w ei
-wen	w en
-weng	w eng
-wo	w o
-wu	w u
-xi	x i
-xia	x ia
-xian	x ian
-xiang	x iang
-xiao	x iao
-xie	x ie
-xin	x in
-xing	x ing
-xiong	x iong
-xiu	x iu
-xu	x v
-xv	x v
-xuan	x van
-xvan	x van
-xue	x ve
-xve	x ve
-xun	x vn
-xvn	x vn
-ya	y a
-yan	y En
-yang	y ang
-yao	y ao
-ye	y E
-yi	y i
-yin	y in
-ying	y ing
-yo	y o
-yong	y ong
-you	y ou
-yu	y v
-yv	y v
-yuan	y van
-yvan	y van
-yue	y ve
-yve	y ve
-yun	y vn
-yvn	y vn
-za	z a
-zai	z ai
-zan	z an
-zang	z ang
-zao	z ao
-ze	z e
-zei	z ei
-zen	z en
-zeng	z eng
-zha	zh a
-zhai	zh ai
-zhan	zh an
-zhang	zh ang
-zhao	zh ao
-zhe	zh e
-zhei	zh ei
-zhen	zh en
-zheng	zh eng
-zhi	zh ir
-zhong	zh ong
-zhou	zh ou
-zhu	zh u
-zhua	zh ua
-zhuai	zh uai
-zhuan	zh uan
-zhuang	zh uang
-zhui	zh ui
-zhun	zh un
-zhuo	zh uo
-zi	z i0
-zong	z ong
-zou	z ou
-zu	z u
-zuan	z uan
-zui	z ui
-zun	z un
-zuo	z uo

SongBloom/g2p/cn_zh_g2p/symbols.py

@@ -1,401 +0,0 @@
-import os
-
-# punctuation = ['!', '?', '…', ",", ".","@"]#@是SP停顿
-punctuation = ["!", "?", "…", ",", "."]  # @是SP停顿
-punctuation.append("-")
-pu_symbols = punctuation + ["SP", "SP2", "SP3", "UNK"]
-# pu_symbols = punctuation + ["SP", 'SP2', 'SP3','SP4', "UNK"]
-pad = "_"
-
-c = [
-    "AA",
-    "EE",
-    "OO",
-    "b",
-    "c",
-    "ch",
-    "d",
-    "f",
-    "g",
-    "h",
-    "j",
-    "k",
-    "l",
-    "m",
-    "n",
-    "p",
-    "q",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "w",
-    "x",
-    "y",
-    "z",
-    "zh",
-]
-v = [
-    "E1",
-    "En1",
-    "a1",
-    "ai1",
-    "an1",
-    "ang1",
-    "ao1",
-    "e1",
-    "ei1",
-    "en1",
-    "eng1",
-    "er1",
-    "i1",
-    "i01",
-    "ia1",
-    "ian1",
-    "iang1",
-    "iao1",
-    "ie1",
-    "in1",
-    "ing1",
-    "iong1",
-    "ir1",
-    "iu1",
-    "o1",
-    "ong1",
-    "ou1",
-    "u1",
-    "ua1",
-    "uai1",
-    "uan1",
-    "uang1",
-    "ui1",
-    "un1",
-    "uo1",
-    "v1",
-    "van1",
-    "ve1",
-    "vn1",
-    "E2",
-    "En2",
-    "a2",
-    "ai2",
-    "an2",
-    "ang2",
-    "ao2",
-    "e2",
-    "ei2",
-    "en2",
-    "eng2",
-    "er2",
-    "i2",
-    "i02",
-    "ia2",
-    "ian2",
-    "iang2",
-    "iao2",
-    "ie2",
-    "in2",
-    "ing2",
-    "iong2",
-    "ir2",
-    "iu2",
-    "o2",
-    "ong2",
-    "ou2",
-    "u2",
-    "ua2",
-    "uai2",
-    "uan2",
-    "uang2",
-    "ui2",
-    "un2",
-    "uo2",
-    "v2",
-    "van2",
-    "ve2",
-    "vn2",
-    "E3",
-    "En3",
-    "a3",
-    "ai3",
-    "an3",
-    "ang3",
-    "ao3",
-    "e3",
-    "ei3",
-    "en3",
-    "eng3",
-    "er3",
-    "i3",
-    "i03",
-    "ia3",
-    "ian3",
-    "iang3",
-    "iao3",
-    "ie3",
-    "in3",
-    "ing3",
-    "iong3",
-    "ir3",
-    "iu3",
-    "o3",
-    "ong3",
-    "ou3",
-    "u3",
-    "ua3",
-    "uai3",
-    "uan3",
-    "uang3",
-    "ui3",
-    "un3",
-    "uo3",
-    "v3",
-    "van3",
-    "ve3",
-    "vn3",
-    "E4",
-    "En4",
-    "a4",
-    "ai4",
-    "an4",
-    "ang4",
-    "ao4",
-    "e4",
-    "ei4",
-    "en4",
-    "eng4",
-    "er4",
-    "i4",
-    "i04",
-    "ia4",
-    "ian4",
-    "iang4",
-    "iao4",
-    "ie4",
-    "in4",
-    "ing4",
-    "iong4",
-    "ir4",
-    "iu4",
-    "o4",
-    "ong4",
-    "ou4",
-    "u4",
-    "ua4",
-    "uai4",
-    "uan4",
-    "uang4",
-    "ui4",
-    "un4",
-    "uo4",
-    "v4",
-    "van4",
-    "ve4",
-    "vn4",
-    "E5",
-    "En5",
-    "a5",
-    "ai5",
-    "an5",
-    "ang5",
-    "ao5",
-    "e5",
-    "ei5",
-    "en5",
-    "eng5",
-    "er5",
-    "i5",
-    "i05",
-    "ia5",
-    "ian5",
-    "iang5",
-    "iao5",
-    "ie5",
-    "in5",
-    "ing5",
-    "iong5",
-    "ir5",
-    "iu5",
-    "o5",
-    "ong5",
-    "ou5",
-    "u5",
-    "ua5",
-    "uai5",
-    "uan5",
-    "uang5",
-    "ui5",
-    "un5",
-    "uo5",
-    "v5",
-    "van5",
-    "ve5",
-    "vn5",
-]
-
-v_without_tone = [
-    "E",
-    "En",
-    "a",
-    "ai",
-    "an",
-    "ang",
-    "ao",
-    "e",
-    "ei",
-    "en",
-    "eng",
-    "er",
-    "i",
-    "i0",
-    "ia",
-    "ian",
-    "iang",
-    "iao",
-    "ie",
-    "in",
-    "ing",
-    "iong",
-    "ir",
-    "iu",
-    "o",
-    "ong",
-    "ou",
-    "u",
-    "ua",
-    "uai",
-    "uan",
-    "uang",
-    "ui",
-    "un",
-    "uo",
-    "v",
-    "van",
-    "ve",
-    "vn",
-]
-
-# japanese
-ja_symbols = [
-    "I",
-    "N",
-    "U",
-    "a",
-    "b",
-    "by",
-    "ch",
-    "cl",
-    "d",
-    "dy",
-    "e",
-    "f",
-    "g",
-    "gy",
-    "h",
-    "hy",
-    "i",
-    "j",
-    "k",
-    "ky",
-    "m",
-    "my",
-    "n",
-    "ny",
-    "o",
-    "p",
-    "py",
-    "r",
-    "ry",
-    "s",
-    "sh",
-    "t",
-    "ts",
-    "u",
-    "v",
-    "w",
-    "y",
-    "z",
-    # "[", #上升调型
-    # "]", #下降调型
-    # "$", #结束符
-    # "^", #开始符
-]
-
-arpa = {
-    "AH0",
-    "S",
-    "AH1",
-    "EY2",
-    "AE2",
-    "EH0",
-    "OW2",
-    "UH0",
-    "NG",
-    "B",
-    "G",
-    "AY0",
-    "M",
-    "AA0",
-    "F",
-    "AO0",
-    "ER2",
-    "UH1",
-    "IY1",
-    "AH2",
-    "DH",
-    "IY0",
-    "EY1",
-    "IH0",
-    "K",
-    "N",
-    "W",
-    "IY2",
-    "T",
-    "AA1",
-    "ER1",
-    "EH2",
-    "OY0",
-    "UH2",
-    "UW1",
-    "Z",
-    "AW2",
-    "AW1",
-    "V",
-    "UW2",
-    "AA2",
-    "ER",
-    "AW0",
-    "UW0",
-    "R",
-    "OW1",
-    "EH1",
-    "ZH",
-    "AE0",
-    "IH2",
-    "IH",
-    "Y",
-    "JH",
-    "P",
-    "AY1",
-    "EY0",
-    "OY2",
-    "TH",
-    "HH",
-    "D",
-    "ER0",
-    "CH",
-    "AO1",
-    "AE1",
-    "AO2",
-    "OY1",
-    "AY2",
-    "IH1",
-    "OW0",
-    "L",
-    "SH",
-}
-
-symbols = [pad] + c + v + ja_symbols + pu_symbols + list(arpa)
-symbols = sorted(set(symbols))
-if __name__ == "__main__":
-    print(len(symbols))

SongBloom/g2p/cn_zh_g2p/tone_sandhi.py

@@ -1,806 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import List
-from typing import Tuple
-
-import jieba_fast as jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
-
-
-class ToneSandhi:
-    def __init__(self):
-        self.must_neural_tone_words = {
-            "麻烦",
-            "麻利",
-            "鸳鸯",
-            "高粱",
-            "骨头",
-            "骆驼",
-            "马虎",
-            "首饰",
-            "馒头",
-            "馄饨",
-            "风筝",
-            "难为",
-            "队伍",
-            "阔气",
-            "闺女",
-            "门道",
-            "锄头",
-            "铺盖",
-            "铃铛",
-            "铁匠",
-            "钥匙",
-            "里脊",
-            "里头",
-            "部分",
-            "那么",
-            "道士",
-            "造化",
-            "迷糊",
-            "连累",
-            "这么",
-            "这个",
-            "运气",
-            "过去",
-            "软和",
-            "转悠",
-            "踏实",
-            "跳蚤",
-            "跟头",
-            "趔趄",
-            "财主",
-            "豆腐",
-            "讲究",
-            "记性",
-            "记号",
-            "认识",
-            "规矩",
-            "见识",
-            "裁缝",
-            "补丁",
-            "衣裳",
-            "衣服",
-            "衙门",
-            "街坊",
-            "行李",
-            "行当",
-            "蛤蟆",
-            "蘑菇",
-            "薄荷",
-            "葫芦",
-            "葡萄",
-            "萝卜",
-            "荸荠",
-            "苗条",
-            "苗头",
-            "苍蝇",
-            "芝麻",
-            "舒服",
-            "舒坦",
-            "舌头",
-            "自在",
-            "膏药",
-            "脾气",
-            "脑袋",
-            "脊梁",
-            "能耐",
-            "胳膊",
-            "胭脂",
-            "胡萝",
-            "胡琴",
-            "胡同",
-            "聪明",
-            "耽误",
-            "耽搁",
-            "耷拉",
-            "耳朵",
-            "老爷",
-            "老实",
-            "老婆",
-            "老头",
-            "老太",
-            "翻腾",
-            "罗嗦",
-            "罐头",
-            "编辑",
-            "结实",
-            "红火",
-            "累赘",
-            "糨糊",
-            "糊涂",
-            "精神",
-            "粮食",
-            "簸箕",
-            "篱笆",
-            "算计",
-            "算盘",
-            "答应",
-            "笤帚",
-            "笑语",
-            "笑话",
-            "窟窿",
-            "窝囊",
-            "窗户",
-            "稳当",
-            "稀罕",
-            "称呼",
-            "秧歌",
-            "秀气",
-            "秀才",
-            "福气",
-            "祖宗",
-            "砚台",
-            "码头",
-            "石榴",
-            "石头",
-            "石匠",
-            "知识",
-            "眼睛",
-            "眯缝",
-            "眨巴",
-            "眉毛",
-            "相声",
-            "盘算",
-            "白净",
-            "痢疾",
-            "痛快",
-            "疟疾",
-            "疙瘩",
-            "疏忽",
-            "畜生",
-            "生意",
-            "甘蔗",
-            "琵琶",
-            "琢磨",
-            "琉璃",
-            "玻璃",
-            "玫瑰",
-            "玄乎",
-            "狐狸",
-            "状元",
-            "特务",
-            "牲口",
-            "牙碜",
-            "牌楼",
-            "爽快",
-            "爱人",
-            "热闹",
-            "烧饼",
-            "烟筒",
-            "烂糊",
-            "点心",
-            "炊帚",
-            "灯笼",
-            "火候",
-            "漂亮",
-            "滑溜",
-            "溜达",
-            "温和",
-            "清楚",
-            "消息",
-            "浪头",
-            "活泼",
-            "比方",
-            "正经",
-            "欺负",
-            "模糊",
-            "槟榔",
-            "棺材",
-            "棒槌",
-            "棉花",
-            "核桃",
-            "栅栏",
-            "柴火",
-            "架势",
-            "枕头",
-            "���杷",
-            "机灵",
-            "本事",
-            "木头",
-            "木匠",
-            "朋友",
-            "月饼",
-            "月亮",
-            "暖和",
-            "明白",
-            "时候",
-            "新鲜",
-            "故事",
-            "收拾",
-            "收成",
-            "提防",
-            "挖苦",
-            "挑剔",
-            "指甲",
-            "指头",
-            "拾掇",
-            "拳头",
-            "拨弄",
-            "招牌",
-            "招呼",
-            "抬举",
-            "护士",
-            "折腾",
-            "扫帚",
-            "打量",
-            "打算",
-            "打点",
-            "打扮",
-            "打听",
-            "打发",
-            "扎实",
-            "扁担",
-            "戒指",
-            "懒得",
-            "意识",
-            "意思",
-            "情形",
-            "悟性",
-            "怪物",
-            "思量",
-            "怎么",
-            "念头",
-            "念叨",
-            "快活",
-            "忙活",
-            "志气",
-            "心思",
-            "得罪",
-            "张罗",
-            "弟兄",
-            "开通",
-            "应酬",
-            "庄稼",
-            "干事",
-            "帮手",
-            "帐篷",
-            "希罕",
-            "师父",
-            "师傅",
-            "巴结",
-            "巴掌",
-            "差事",
-            "工夫",
-            "岁数",
-            "屁股",
-            "尾巴",
-            "少爷",
-            "小气",
-            "小伙",
-            "将就",
-            "对头",
-            "对付",
-            "寡妇",
-            "家伙",
-            "客气",
-            "实在",
-            "官司",
-            "学问",
-            "学生",
-            "字号",
-            "嫁妆",
-            "媳妇",
-            "媒人",
-            "婆家",
-            "娘家",
-            "委屈",
-            "姑娘",
-            "姐夫",
-            "妯娌",
-            "妥当",
-            "妖精",
-            "奴才",
-            "女婿",
-            "头发",
-            "太阳",
-            "大爷",
-            "大方",
-            "大意",
-            "大夫",
-            "多少",
-            "多么",
-            "外甥",
-            "壮实",
-            "地道",
-            "地方",
-            "在乎",
-            "困难",
-            "嘴巴",
-            "嘱咐",
-            "嘟囔",
-            "嘀咕",
-            "喜欢",
-            "喇嘛",
-            "喇叭",
-            "商量",
-            "唾沫",
-            "哑巴",
-            "哈欠",
-            "哆嗦",
-            "咳嗽",
-            "和尚",
-            "告诉",
-            "告示",
-            "含糊",
-            "吓唬",
-            "后头",
-            "名字",
-            "名堂",
-            "合同",
-            "吆喝",
-            "叫唤",
-            "口袋",
-            "厚道",
-            "厉害",
-            "千斤",
-            "包袱",
-            "包涵",
-            "匀称",
-            "勤快",
-            "动静",
-            "动弹",
-            "功夫",
-            "力气",
-            "前头",
-            "刺猬",
-            "刺激",
-            "别扭",
-            "利落",
-            "利索",
-            "利害",
-            "分析",
-            "出息",
-            "凑合",
-            "凉快",
-            "冷战",
-            "冤枉",
-            "冒失",
-            "养活",
-            "关系",
-            "先生",
-            "兄弟",
-            "便宜",
-            "使唤",
-            "佩服",
-            "作坊",
-            "体面",
-            "位置",
-            "似的",
-            "伙计",
-            "休息",
-            "什么",
-            "人家",
-            "亲戚",
-            "亲家",
-            "交情",
-            "云彩",
-            "事情",
-            "买卖",
-            "主意",
-            "丫头",
-            "丧气",
-            "两口",
-            "东西",
-            "东家",
-            "世故",
-            "不由",
-            "不在",
-            "下水",
-            "下巴",
-            "上头",
-            "上司",
-            "丈夫",
-            "丈人",
-            "一辈",
-            "那个",
-            "菩萨",
-            "父亲",
-            "母亲",
-            "咕噜",
-            "邋遢",
-            "费用",
-            "冤家",
-            "甜头",
-            "介绍",
-            "荒唐",
-            "大人",
-            "泥鳅",
-            "幸福",
-            "熟悉",
-            "计划",
-            "扑腾",
-            "蜡烛",
-            "姥爷",
-            "照顾",
-            "喉咙",
-            "吉他",
-            "弄堂",
-            "蚂蚱",
-            "凤凰",
-            "拖沓",
-            "寒碜",
-            "糟蹋",
-            "倒腾",
-            "报复",
-            "逻辑",
-            "盘缠",
-            "喽啰",
-            "牢骚",
-            "咖喱",
-            "扫把",
-            "惦记",
-        }
-        self.must_not_neural_tone_words = {
-            "男子",
-            "女子",
-            "分子",
-            "原子",
-            "量子",
-            "莲子",
-            "石子",
-            "瓜子",
-            "电子",
-            "人人",
-            "虎虎",
-            "幺幺",
-            "干嘛",
-            "学子",
-            "哈哈",
-            "数数",
-            "袅袅",
-            "局地",
-            "以下",
-            "娃哈哈",
-            "花花草草",
-            "留得",
-            "耕地",
-            "想想",
-            "熙熙",
-            "攘攘",
-            "卵子",
-            "死死",
-            "冉冉",
-            "恳恳",
-            "佼佼",
-            "吵吵",
-            "打打",
-            "考考",
-            "整整",
-            "莘莘",
-            "落地",
-            "算子",
-            "家家户户",
-            "青青",
-        }
-        self.punc = "：，；。？！“”‘’':,;.?!"
-
-    # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
-    # e.g.
-    # word: "家里"
-    # pos: "s"
-    # finals: ['ia1', 'i3']
-    def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
-        # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
-        for j, item in enumerate(word):
-            if (
-                j - 1 >= 0
-                and item == word[j - 1]
-                and pos[0] in {"n", "v", "a"}
-                and word not in self.must_not_neural_tone_words
-            ):
-                finals[j] = finals[j][:-1] + "5"
-        ge_idx = word.find("个")
-        if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
-            finals[-1] = finals[-1][:-1] + "5"
-        elif len(word) >= 1 and word[-1] in "的地得":
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 走了, 看着, 去过
-        elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
-            finals[-1] = finals[-1][:-1] + "5"
-        elif (
-            len(word) > 1
-            and word[-1] in "们子"
-            and pos in {"r", "n"}
-            and word not in self.must_not_neural_tone_words
-        ):
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 桌上, 地下, 家里
-        elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
-            finals[-1] = finals[-1][:-1] + "5"
-        # e.g. 上来, 下去
-        elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
-            finals[-1] = finals[-1][:-1] + "5"
-        # 个做量词
-        elif (
-            ge_idx >= 1
-            and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
-        ) or word == "个":
-            finals[ge_idx] = finals[ge_idx][:-1] + "5"
-        else:
-            if (
-                word in self.must_neural_tone_words
-                or word[-2:] in self.must_neural_tone_words
-            ):
-                finals[-1] = finals[-1][:-1] + "5"
-
-        word_list = self._split_word(word)
-        finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
-        for i, word in enumerate(word_list):
-            # conventional neural in Chinese
-            if (
-                word in self.must_neural_tone_words
-                or word[-2:] in self.must_neural_tone_words
-            ):
-                finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
-        finals = sum(finals_list, [])
-        return finals
-
-    def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # e.g. 看不懂
-        if len(word) == 3 and word[1] == "不":
-            finals[1] = finals[1][:-1] + "5"
-        else:
-            for i, char in enumerate(word):
-                # "不" before tone4 should be bu2, e.g. 不怕
-                if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
-                    finals[i] = finals[i][:-1] + "2"
-        return finals
-
-    def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        # "一" in number sequences, e.g. 一零零, 二一零
-        if word.find("一") != -1 and all(
-            [item.isnumeric() for item in word if item != "一"]
-        ):
-            return finals
-        # "一" between reduplication words shold be yi5, e.g. 看一看
-        elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
-            finals[1] = finals[1][:-1] + "5"
-        # when "一" is ordinal word, it should be yi1
-        elif word.startswith("第一"):
-            finals[1] = finals[1][:-1] + "1"
-        else:
-            for i, char in enumerate(word):
-                if char == "一" and i + 1 < len(word):
-                    # "一" before tone4 should be yi2, e.g. 一段
-                    if finals[i + 1][-1] == "4":
-                        finals[i] = finals[i][:-1] + "2"
-                    # "一" before non-tone4 should be yi4, e.g. 一天
-                    else:
-                        # "一" 后面如果是标点，还读一声
-                        if word[i + 1] not in self.punc:
-                            finals[i] = finals[i][:-1] + "4"
-        return finals
-
-    def _split_word(self, word: str) -> List[str]:
-        word_list = jieba.cut_for_search(word)
-        word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
-        first_subword = word_list[0]
-        first_begin_idx = word.find(first_subword)
-        if first_begin_idx == 0:
-            second_subword = word[len(first_subword) :]
-            new_word_list = [first_subword, second_subword]
-        else:
-            second_subword = word[: -len(first_subword)]
-            new_word_list = [second_subword, first_subword]
-        return new_word_list
-
-    def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
-        if len(word) == 2 and self._all_tone_three(finals):
-            finals[0] = finals[0][:-1] + "2"
-        elif len(word) == 3:
-            word_list = self._split_word(word)
-            if self._all_tone_three(finals):
-                #  disyllabic + monosyllabic, e.g. 蒙古/包
-                if len(word_list[0]) == 2:
-                    finals[0] = finals[0][:-1] + "2"
-                    finals[1] = finals[1][:-1] + "2"
-                #  monosyllabic + disyllabic, e.g. 纸/老虎
-                elif len(word_list[0]) == 1:
-                    finals[1] = finals[1][:-1] + "2"
-            else:
-                finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
-                if len(finals_list) == 2:
-                    for i, sub in enumerate(finals_list):
-                        # e.g. 所有/人
-                        if self._all_tone_three(sub) and len(sub) == 2:
-                            finals_list[i][0] = finals_list[i][0][:-1] + "2"
-                        # e.g. 好/喜欢
-                        elif (
-                            i == 1
-                            and not self._all_tone_three(sub)
-                            and finals_list[i][0][-1] == "3"
-                            and finals_list[0][-1][-1] == "3"
-                        ):
-                            finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
-                        finals = sum(finals_list, [])
-        # split idiom into two words who's length is 2
-        elif len(word) == 4:
-            finals_list = [finals[:2], finals[2:]]
-            finals = []
-            for sub in finals_list:
-                if self._all_tone_three(sub):
-                    sub[0] = sub[0][:-1] + "2"
-                finals += sub
-
-        return finals
-
-    def _all_tone_three(self, finals: List[str]) -> bool:
-        return all(x[-1] == "3" for x in finals)
-
-    # merge "不" and the word behind it
-    # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
-    def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        last_word = ""
-        for word, pos in seg:
-            if last_word == "不":
-                word = last_word + word
-            if word != "不":
-                new_seg.append((word, pos))
-            last_word = word[:]
-        if last_word == "不":
-            new_seg.append((last_word, "d"))
-            last_word = ""
-        return new_seg
-
-    # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
-    # function 2: merge single  "一" and the word behind it
-    # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
-    # e.g.
-    # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
-    # output seg: [['听一听', 'v']]
-    def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        # function 1
-        for i, (word, pos) in enumerate(seg):
-            if (
-                i - 1 >= 0
-                and word == "一"
-                and i + 1 < len(seg)
-                and seg[i - 1][0] == seg[i + 1][0]
-                and seg[i - 1][1] == "v"
-                and seg[i + 1][1] == "v"
-            ):
-                new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
-            else:
-                if (
-                    i - 2 >= 0
-                    and seg[i - 1][0] == "一"
-                    and seg[i - 2][0] == word
-                    and pos == "v"
-                ):
-                    continue
-                else:
-                    new_seg.append([word, pos])
-        seg = new_seg
-        new_seg = []
-        # function 2
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and new_seg[-1][0] == "一":
-                new_seg[-1][0] = new_seg[-1][0] + word
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    # the first and the second words are all_tone_three
-    def _merge_continuous_three_tones(
-        self, seg: List[Tuple[str, str]]
-    ) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if (
-                i - 1 >= 0
-                and self._all_tone_three(sub_finals_list[i - 1])
-                and self._all_tone_three(sub_finals_list[i])
-                and not merge_last[i - 1]
-            ):
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if (
-                    not self._is_reduplication(seg[i - 1][0])
-                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
-                ):
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-
-        return new_seg
-
-    def _is_reduplication(self, word: str) -> bool:
-        return len(word) == 2 and word[0] == word[1]
-
-    # the last char of first word and the first char of second word is tone_three
-    def _merge_continuous_three_tones_2(
-        self, seg: List[Tuple[str, str]]
-    ) -> List[Tuple[str, str]]:
-        new_seg = []
-        sub_finals_list = [
-            lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
-            for (word, pos) in seg
-        ]
-        assert len(sub_finals_list) == len(seg)
-        merge_last = [False] * len(seg)
-        for i, (word, pos) in enumerate(seg):
-            if (
-                i - 1 >= 0
-                and sub_finals_list[i - 1][-1][-1] == "3"
-                and sub_finals_list[i][0][-1] == "3"
-                and not merge_last[i - 1]
-            ):
-                # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
-                if (
-                    not self._is_reduplication(seg[i - 1][0])
-                    and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
-                ):
-                    new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-                    merge_last[i] = True
-                else:
-                    new_seg.append([word, pos])
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        new_seg = []
-        for i, (word, pos) in enumerate(seg):
-            if new_seg and word == new_seg[-1][0]:
-                new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
-            else:
-                new_seg.append([word, pos])
-        return new_seg
-
-    def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
-        seg = self._merge_bu(seg)
-        try:
-            seg = self._merge_yi(seg)
-        except:
-            print("_merge_yi failed")
-        seg = self._merge_reduplication(seg)
-        try:
-            seg = self._merge_continuous_three_tones(seg)
-        except:
-            print("_merge_continuous_three_tones failed")
-        try:
-            seg = self._merge_continuous_three_tones_2(seg)
-        except:
-            print("_merge_continuous_three_tones_2 failed")
-
-        seg = self._merge_er(seg)
-        return seg
-
-    def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
-        finals = self._bu_sandhi(word, finals)
-        finals = self._yi_sandhi(word, finals)
-        finals = self._neural_sandhi(word, pos, finals)
-        finals = self._three_sandhi(word, finals)
-        return finals

SongBloom/g2p/cn_zh_g2p/zh_normalization/README.md

@@ -1,16 +0,0 @@
-## Supported NSW (Non-Standard-Word) Normalization
-
-|NSW type|raw|normalized|
-|:--|:-|:-|
-|serial number|电影中梁朝伟扮演的陈永仁的编号27149|电影中梁朝伟扮演的陈永仁的编号二七一四九|
-|cardinal|这块黄金重达324.75克<br>我们班的最高总分为583分|这块黄金重达三百二十四点七五克<br>我们班的最高总分为五百八十三分|
-|numeric range |12\~23<br>-1.5\~2|十二到二十三<br>负一点五到二|
-|date|她出生于86年8月18日，她弟弟出生于1995年3月1日|她出生于八六年八月十八日， 她弟弟出生于一九九五年三月一日|
-|time|等会请在12:05请通知我|等会请在十二点零五分请通知我
-|temperature|今天的最低气温达到-10°C|今天的最低气温达到零下十度
-|fraction|现场有7/12的观众投出了赞成票|现场有十二分之七的观众投出了赞成票|
-|percentage|明天有62％的概率降雨|明天有百分之六十二的概率降雨|
-|money|随便来几个价格12块5，34.5元，20.1万|随便来几个价格十二块五，三十四点五元，二十点一万|
-|telephone|这是固话0421-33441122<br>这是手机+86 18544139121|这是固话零四二一三三四四一一二二<br>这是手机八六一八五四四一三九一二一|
-## References
-[Pull requests #658 of DeepSpeech](https://github.com/PaddlePaddle/DeepSpeech/pull/658/files)

SongBloom/g2p/cn_zh_g2p/zh_normalization/__init__.py

@@ -1,14 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from .text_normlization import *

SongBloom/g2p/cn_zh_g2p/zh_normalization/char_convert.py

@@ -1,46 +0,0 @@
-# coding=utf-8
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters.
-"""
-simplified_charcters = 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-
-traditional_characters = 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鐃鐄鐇鐏鐒鐓鐔鐗馗鐙鐝鐠鐡鐦鐨鐩鐫鐬鐱鐳鐶鐻鐽鐿鑀鑅鑌鑐鑕鑚鑛鑢鑤鑥鑪鑭鑯鑱鑴鑵鑷钁钃镻閆閈閌閎閒閔閗閟閡関閤閤閧閬閲閹閺閻閼閽閿闇闉闋闐闑闒闓闘闚闞闟闠闤闥阞阢阤阨阬阯阹阼阽陁陑陔陛陜陡陥陬騭陴険陼陾隂隃隈隒隗隞隠隣隤隩隮隰顴隳隷隹雂雈雉雊雎雑雒雗雘雚雝雟雩雰雱驛霂霅霈霊霑霒霓霙霝霢霣霤霨霩霪霫霮靁靆靉靑靚靣靦靪靮靰靳靷靸靺靼靿鞀鞃鞄鞌鞗鞙鞚鞝鞞鞡鞣鞨鞫鞬鞮鞶鞹鞾韃韅韉馱韍韎韔韖韘韝韞韡韣韭韮韱韹韺頀颳頄頇頊頍頎頏頒頖頞頠頫頬顱頯頲頴頼顇顋顑顒顓顔顕顚顜顢顣顬顳颭颮颱颶颸颺颻颽颾颿飀飂飈飌飜飡飣飤飥飩飫飮飱飶餀餂餄餎餇餈餑餔餕餖餗餚餛餜餟餠餤餧餩餪餫餬餮餱餲餳餺餻餼餽餿饁饅饇饉饊饍饎饐饘饟饢馘馥馝馡馣騮騾馵馹駃駄駅駆駉駋駑駓駔駗駘駙駜駡駢駪駬駰駴駸駹駽駾騂騄騅騆騉騋騍騏驎騑騒験騕騖騠騢騣騤騧驤騵騶騸騺驀驂驃驄驆驈驊驌驍驎驏驒驔驖驙驦驩驫骺鯁骫骭骯骱骴骶骷髏骾髁髂髄髆髈髐髑髕髖髙髝髞髟髡髣髧髪髫髭髯髲髳髹髺髽髾鬁鬃鬅鬈鬋鬎鬏鬐鬑鬒鬖鬗鬘鬙鬠鬣鬪鬫鬬鬮鬯鬰鬲鬵鬷魆魈魊魋魍魎魑魖鰾魛魟魣魦魨魬魴魵魸鮀鮁鮆鮌鮎鮑鮒鮓鮚鮞鮟鱇鮠鮦鮨鮪鮭鮶鮸鮿鯀鯄鯆鯇鯈鯔鯕鯖鯗鯙鯠鯤鯥鯫鯰鯷鯸鯿鰂鰆鶼鰉鰋鰐鰒鰕鰛鰜鰣鰤鰥鰦鰨鰩鰮鰳鰶鰷鱺鰼鰽鱀鱄鱅鱆鱈鱎鱐鱓鱔鱖鱘鱟鱠鱣鱨鱭鱮鱲鱵鱻鲅鳦鳧鳯鳲鳷鳻鴂鴃鴄鴆鴈鴎鴒鴔鴗鴛鴦鴝鵒鴟鴠鴢鴣鴥鴯鶓鴳鴴鴷鴽鵀鵁鵂鵓鵖鵙鵜鶘鵞鵟鵩鵪鵫鵵鵷鵻鵾鶂鶊鶏鶒鶖鶗鶡鶤鶦鶬鶱鶲鶵鶸鶹鶺鶿鷀鷁鷃鷄鷇鷈鷉鷊鷏鷓鷕鷖鷙鷞鷟鷥鷦鷯鷩鷫鷭鷳鷴鷽鷾鷿鸂鸇鸊鸏鸑鸒鸓鸕鸛鸜鸝鹸鹹鹺麀麂麃麄麇麋麌麐麑麒麚麛麝麤麩麪麫麮麯麰麺麾黁黈黌黢黒黓黕黙黝黟黥黦黧黮黰黱黲黶黹黻黼黽黿鼂鼃鼅鼈鼉鼏鼐鼒鼕鼖鼙鼚鼛鼡鼩鼱鼪鼫鼯鼷鼽齁齆齇齈齉齌齎齏齔齕齗齙齚齜齞齟齬齠齢齣齧齩齮齯齰齱齵齾龎龑龒龔龖龘龝龡龢龤'
-
-assert len(simplified_charcters) == len(simplified_charcters)
-
-s2t_dict = {}
-t2s_dict = {}
-for i, item in enumerate(simplified_charcters):
-    s2t_dict[item] = traditional_characters[i]
-    t2s_dict[traditional_characters[i]] = item
-
-
-def tranditional_to_simplified(text: str) -> str:
-    return "".join(
-        [t2s_dict[item] if item in t2s_dict else item for item in text])
-
-
-def simplified_to_traditional(text: str) -> str:
-    return "".join(
-        [s2t_dict[item] if item in s2t_dict else item for item in text])
-
-
-if __name__ == "__main__":
-    text = "一般是指存取一個應用程式啟動時始終顯示在網站或網頁瀏覽器中的一個或多個初始網頁等畫面存在的站點"
-    print(text)
-    text_simple = tranditional_to_simplified(text)
-    print(text_simple)
-    text_traditional = simplified_to_traditional(text_simple)
-    print(text_traditional)

SongBloom/g2p/cn_zh_g2p/zh_normalization/chronology.py

@@ -1,134 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-
-from .num import DIGITS
-from .num import num2str
-from .num import verbalize_cardinal
-from .num import verbalize_digit
-
-
-def _time_num2str(num_string: str) -> str:
-    """A special case for verbalizing number in time."""
-    result = num2str(num_string.lstrip('0'))
-    if num_string.startswith('0'):
-        result = DIGITS['0'] + result
-    return result
-
-
-# 时刻表达式
-RE_TIME = re.compile(r'([0-1]?[0-9]|2[0-3])'
-                     r':([0-5][0-9])'
-                     r'(:([0-5][0-9]))?')
-
-# 时间范围，如8:30-12:30
-RE_TIME_RANGE = re.compile(r'([0-1]?[0-9]|2[0-3])'
-                           r':([0-5][0-9])'
-                           r'(:([0-5][0-9]))?'
-                           r'(~|-)'
-                           r'([0-1]?[0-9]|2[0-3])'
-                           r':([0-5][0-9])'
-                           r'(:([0-5][0-9]))?')
-
-
-def replace_time(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-
-    is_range = len(match.groups()) > 5
-
-    hour = match.group(1)
-    minute = match.group(2)
-    second = match.group(4)
-
-    if is_range:
-        hour_2 = match.group(6)
-        minute_2 = match.group(7)
-        second_2 = match.group(9)
-
-    result = f"{num2str(hour)}点"
-    if minute.lstrip('0'):
-        if int(minute) == 30:
-            result += "半"
-        else:
-            result += f"{_time_num2str(minute)}分"
-    if second and second.lstrip('0'):
-        result += f"{_time_num2str(second)}秒"
-
-    if is_range:
-        result += "至"
-        result += f"{num2str(hour_2)}点"
-        if minute_2.lstrip('0'):
-            if int(minute) == 30:
-                result += "半"
-            else:
-                result += f"{_time_num2str(minute_2)}分"
-        if second_2 and second_2.lstrip('0'):
-            result += f"{_time_num2str(second_2)}秒"
-
-    return result
-
-
-RE_DATE = re.compile(r'(\d{4}|\d{2})年'
-                     r'((0?[1-9]|1[0-2])月)?'
-                     r'(((0?[1-9])|((1|2)[0-9])|30|31)([日号]))?')
-
-
-def replace_date(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    year = match.group(1)
-    month = match.group(3)
-    day = match.group(5)
-    result = ""
-    if year:
-        result += f"{verbalize_digit(year)}年"
-    if month:
-        result += f"{verbalize_cardinal(month)}月"
-    if day:
-        result += f"{verbalize_cardinal(day)}{match.group(9)}"
-    return result
-
-
-# 用 / 或者 - 分隔的 YY/MM/DD 或者 YY-MM-DD 日期
-RE_DATE2 = re.compile(
-    r'(\d{4})([- /.])(0[1-9]|1[012])\2(0[1-9]|[12][0-9]|3[01])')
-
-
-def replace_date2(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    year = match.group(1)
-    month = match.group(3)
-    day = match.group(4)
-    result = ""
-    if year:
-        result += f"{verbalize_digit(year)}年"
-    if month:
-        result += f"{verbalize_cardinal(month)}月"
-    if day:
-        result += f"{verbalize_cardinal(day)}日"
-    return result

SongBloom/g2p/cn_zh_g2p/zh_normalization/constants.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-import string
-
-from pypinyin.constants import SUPPORT_UCS4
-
-# 全角半角转换
-# 英文字符全角 -> 半角映射表 (num: 52)
-F2H_ASCII_LETTERS = {
-    ord(char) + 65248: ord(char)
-    for char in string.ascii_letters
-}
-
-# 英文字符半角 -> 全角映射表
-H2F_ASCII_LETTERS = {value: key for key, value in F2H_ASCII_LETTERS.items()}
-
-# 数字字符全角 -> 半角映射表 (num: 10)
-F2H_DIGITS = {ord(char) + 65248: ord(char) for char in string.digits}
-# 数字字符半角 -> 全角映射表
-H2F_DIGITS = {value: key for key, value in F2H_DIGITS.items()}
-
-# 标点符号全角 -> 半角映射表 (num: 32)
-F2H_PUNCTUATIONS = {ord(char) + 65248: ord(char) for char in string.punctuation}
-# 标点符号半角 -> 全角映射表
-H2F_PUNCTUATIONS = {value: key for key, value in F2H_PUNCTUATIONS.items()}
-
-# 空格 (num: 1)
-F2H_SPACE = {'\u3000': ' '}
-H2F_SPACE = {' ': '\u3000'}
-
-# 非"有拼音的汉字"的字符串，可用于NSW提取
-if SUPPORT_UCS4:
-    RE_NSW = re.compile(r'(?:[^'
-                        r'\u3007'  # 〇
-                        r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
-                        r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
-                        r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
-                        r'\U00020000-\U0002A6DF'  # CJK扩展B:[20000-2A6DF]
-                        r'\U0002A703-\U0002B73F'  # CJK扩展C:[2A700-2B73F]
-                        r'\U0002B740-\U0002B81D'  # CJK扩展D:[2B740-2B81D]
-                        r'\U0002F80A-\U0002FA1F'  # CJK兼容扩展:[2F800-2FA1F]
-                        r'])+')
-else:
-    RE_NSW = re.compile(  # pragma: no cover
-        r'(?:[^'
-        r'\u3007'  # 〇
-        r'\u3400-\u4dbf'  # CJK扩展A:[3400-4DBF]
-        r'\u4e00-\u9fff'  # CJK基本:[4E00-9FFF]
-        r'\uf900-\ufaff'  # CJK兼容:[F900-FAFF]
-        r'])+')

SongBloom/g2p/cn_zh_g2p/zh_normalization/num.py

@@ -1,282 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Rules to verbalize numbers into Chinese characters.
-https://zh.wikipedia.org/wiki/中文数字#現代中文
-"""
-import re
-from collections import OrderedDict
-from typing import List
-
-DIGITS = {str(i): tran for i, tran in enumerate('零一二三四五六七八九')}
-UNITS = OrderedDict({
-    1: '十',
-    2: '百',
-    3: '千',
-    4: '万',
-    8: '亿',
-})
-
-COM_QUANTIFIERS = '(封|艘|把|目|套|段|人|所|朵|匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|毫|厘|(公)分|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|小时|旬|纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|元|(亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|美|)元|(亿|千万|百万|万|千|百|十|)吨|(亿|千万|百万|万|千|百|)块|角|毛|分)'
-
-# 分数表达式
-RE_FRAC = re.compile(r'(-?)(\d+)/(\d+)')
-
-
-def replace_frac(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    sign = match.group(1)
-    nominator = match.group(2)
-    denominator = match.group(3)
-    sign: str = "负" if sign else ""
-    nominator: str = num2str(nominator)
-    denominator: str = num2str(denominator)
-    result = f"{sign}{denominator}分之{nominator}"
-    return result
-
-
-# 百分数表达式
-RE_PERCENTAGE = re.compile(r'(-?)(\d+(\.\d+)?)%')
-
-
-def replace_percentage(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    sign = match.group(1)
-    percent = match.group(2)
-    sign: str = "负" if sign else ""
-    percent: str = num2str(percent)
-    result = f"{sign}百分之{percent}"
-    return result
-
-
-# 整数表达式
-# 带负号的整数 -10
-RE_INTEGER = re.compile(r'(-)' r'(\d+)')
-
-
-def replace_negative_num(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    sign = match.group(1)
-    number = match.group(2)
-    sign: str = "负" if sign else ""
-    number: str = num2str(number)
-    result = f"{sign}{number}"
-    return result
-
-
-# 编号-无符号整形
-# 00078
-RE_DEFAULT_NUM = re.compile(r'\d{3}\d*')
-
-
-def replace_default_num(match):
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    number = match.group(0)
-    return verbalize_digit(number, alt_one=True)
-
-
-# 加减乘除
-RE_ASMD = re.compile(
-    r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))([\+\-\×÷=])((-?)((\d+)(\.\d+)?)|(\.(\d+)))')
-asmd_map = {
-    '+': '加',
-    '-': '减',
-    '×': '乘',
-    '÷': '除',
-    '=': '等于'
-}
-
-
-def replace_asmd(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    result = match.group(1) + asmd_map[match.group(8)] + match.group(9)
-    return result
-
-
-# 数字表达式
-# 纯小数
-RE_DECIMAL_NUM = re.compile(r'(-?)((\d+)(\.\d+))' r'|(\.(\d+))')
-# 正整数 + 量词
-RE_POSITIVE_QUANTIFIERS = re.compile(r"(\d+)([多余几\+])?" + COM_QUANTIFIERS)
-RE_NUMBER = re.compile(r'(-?)((\d+)(\.\d+)?)' r'|(\.(\d+))')
-
-
-def replace_positive_quantifier(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    number = match.group(1)
-    match_2 = match.group(2)
-    if match_2 == "+":
-        match_2 = "多"
-    match_2: str = match_2 if match_2 else ""
-    quantifiers: str = match.group(3)
-    number: str = num2str(number)
-    result = f"{number}{match_2}{quantifiers}"
-    return result
-
-
-def replace_number(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    sign = match.group(1)
-    number = match.group(2)
-    pure_decimal = match.group(5)
-    if pure_decimal:
-        result = num2str(pure_decimal)
-    else:
-        sign: str = "负" if sign else ""
-        number: str = num2str(number)
-        result = f"{sign}{number}"
-    return result
-
-
-# 范围表达式
-# match.group(1) and match.group(8) are copy from RE_NUMBER
-
-RE_RANGE = re.compile(
-    r"""
-    (?<![\d\+\-\×÷=])      # 使用反向前瞻以确保数字范围之前没有其他数字和操作符
-    ((-?)((\d+)(\.\d+)?))  # 匹配范围起始的负数或正数（整数或小数）
-    [-~]                   # 匹配范围分隔符
-    ((-?)((\d+)(\.\d+)?))  # 匹配范围结束的负数或正数（整数或小数）
-    (?![\d\+\-\×÷=])       # 使用正向前瞻以确保数字范围之后没有其他数字和操作符
-    """, re.VERBOSE)
-
-
-def replace_range(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    first, second = match.group(1), match.group(6)
-    first = RE_NUMBER.sub(replace_number, first)
-    second = RE_NUMBER.sub(replace_number, second)
-    result = f"{first}到{second}"
-    return result
-
-
-# ~至表达式
-RE_TO_RANGE = re.compile(
-    r'((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)[~]((-?)((\d+)(\.\d+)?)|(\.(\d+)))(%|°C|℃|度|摄氏度|cm2|cm²|cm3|cm³|cm|db|ds|kg|km|m2|m²|m³|m3|ml|m|mm|s)')
-
-def replace_to_range(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    result = match.group(0).replace('~', '至')
-    return result
-
-
-def _get_value(value_string: str, use_zero: bool=True) -> List[str]:
-    stripped = value_string.lstrip('0')
-    if len(stripped) == 0:
-        return []
-    elif len(stripped) == 1:
-        if use_zero and len(stripped) < len(value_string):
-            return [DIGITS['0'], DIGITS[stripped]]
-        else:
-            return [DIGITS[stripped]]
-    else:
-        largest_unit = next(
-            power for power in reversed(UNITS.keys()) if power < len(stripped))
-        first_part = value_string[:-largest_unit]
-        second_part = value_string[-largest_unit:]
-        return _get_value(first_part) + [UNITS[largest_unit]] + _get_value(
-            second_part)
-
-
-def verbalize_cardinal(value_string: str) -> str:
-    if not value_string:
-        return ''
-
-    # 000 -> '零' , 0 -> '零'
-    value_string = value_string.lstrip('0')
-    if len(value_string) == 0:
-        return DIGITS['0']
-
-    result_symbols = _get_value(value_string)
-    # verbalized number starting with '一十*' is abbreviated as `十*`
-    if len(result_symbols) >= 2 and result_symbols[0] == DIGITS[
-            '1'] and result_symbols[1] == UNITS[1]:
-        result_symbols = result_symbols[1:]
-    return ''.join(result_symbols)
-
-
-def verbalize_digit(value_string: str, alt_one=False) -> str:
-    result_symbols = [DIGITS[digit] for digit in value_string]
-    result = ''.join(result_symbols)
-    if alt_one:
-        result = result.replace("一", "幺")
-    return result
-
-
-def num2str(value_string: str) -> str:
-    integer_decimal = value_string.split('.')
-    if len(integer_decimal) == 1:
-        integer = integer_decimal[0]
-        decimal = ''
-    elif len(integer_decimal) == 2:
-        integer, decimal = integer_decimal
-    else:
-        raise ValueError(
-            f"The value string: '${value_string}' has more than one point in it."
-        )
-
-    result = verbalize_cardinal(integer)
-
-    decimal = decimal.rstrip('0')
-    if decimal:
-        # '.22' is verbalized as '零点二二'
-        # '3.20' is verbalized as '三点二
-        result = result if result else "零"
-        result += '点' + verbalize_digit(decimal)
-    return result

SongBloom/g2p/cn_zh_g2p/zh_normalization/phonecode.py

@@ -1,63 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-
-from .num import verbalize_digit
-
-# 规范化固话/手机号码
-# 手机
-# http://www.jihaoba.com/news/show/13680
-# 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
-# 联通：130、131、132、156、155、186、185、176
-# 电信：133、153、189、180、181、177
-RE_MOBILE_PHONE = re.compile(
-    r"(?<!\d)((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})(?!\d)")
-RE_TELEPHONE = re.compile(
-    r"(?<!\d)((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})(?!\d)")
-
-# 全国统一的号码400开头
-RE_NATIONAL_UNIFORM_NUMBER = re.compile(r"(400)(-)?\d{3}(-)?\d{4}")
-
-
-def phone2str(phone_string: str, mobile=True) -> str:
-    if mobile:
-        sp_parts = phone_string.strip('+').split()
-        result = '，'.join(
-            [verbalize_digit(part, alt_one=True) for part in sp_parts])
-        return result
-    else:
-        sil_parts = phone_string.split('-')
-        result = '，'.join(
-            [verbalize_digit(part, alt_one=True) for part in sil_parts])
-        return result
-
-
-def replace_phone(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    return phone2str(match.group(0), mobile=False)
-
-
-def replace_mobile(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    return phone2str(match.group(0))

SongBloom/g2p/cn_zh_g2p/zh_normalization/quantifier.py

@@ -1,63 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-
-from .num import num2str
-
-# 温度表达式，温度会影响负号的读法
-# -3°C 零下三度
-RE_TEMPERATURE = re.compile(r'(-?)(\d+(\.\d+)?)(°C|℃|度|摄氏度)')
-measure_dict = {
-    "cm2": "平方厘米",
-    "cm²": "平方厘米",
-    "cm3": "立方厘米",
-    "cm³": "立方厘米",
-    "cm": "厘米",
-    "db": "分贝",
-    "ds": "毫秒",
-    "kg": "千克",
-    "km": "千米",
-    "m2": "平方米",
-    "m²": "平方米",
-    "m³": "立方米",
-    "m3": "立方米",
-    "ml": "毫升",
-    "m": "米",
-    "mm": "毫米",
-    "s": "秒"
-}
-
-
-def replace_temperature(match) -> str:
-    """
-    Args:
-        match (re.Match)
-    Returns:
-        str
-    """
-    sign = match.group(1)
-    temperature = match.group(2)
-    unit = match.group(3)
-    sign: str = "零下" if sign else ""
-    temperature: str = num2str(temperature)
-    unit: str = "摄氏度" if unit == "摄氏度" else "度"
-    result = f"{sign}{temperature}{unit}"
-    return result
-
-
-def replace_measure(sentence) -> str:
-    for q_notation in measure_dict:
-        if q_notation in sentence:
-            sentence = sentence.replace(q_notation, measure_dict[q_notation])
-    return sentence

SongBloom/g2p/cn_zh_g2p/zh_normalization/text_normlization.py

@@ -1,165 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import re
-from typing import List
-
-from .char_convert import tranditional_to_simplified
-from .chronology import RE_DATE
-from .chronology import RE_DATE2
-from .chronology import RE_TIME
-from .chronology import RE_TIME_RANGE
-from .chronology import replace_date
-from .chronology import replace_date2
-from .chronology import replace_time
-from .constants import F2H_ASCII_LETTERS
-from .constants import F2H_DIGITS
-from .constants import F2H_SPACE
-from .num import RE_DECIMAL_NUM
-from .num import RE_DEFAULT_NUM
-from .num import RE_FRAC
-from .num import RE_INTEGER
-from .num import RE_NUMBER
-from .num import RE_PERCENTAGE
-from .num import RE_POSITIVE_QUANTIFIERS
-from .num import RE_RANGE
-from .num import RE_TO_RANGE
-from .num import RE_ASMD
-from .num import replace_default_num
-from .num import replace_frac
-from .num import replace_negative_num
-from .num import replace_number
-from .num import replace_percentage
-from .num import replace_positive_quantifier
-from .num import replace_range
-from .num import replace_to_range
-from .num import replace_asmd
-from .phonecode import RE_MOBILE_PHONE
-from .phonecode import RE_NATIONAL_UNIFORM_NUMBER
-from .phonecode import RE_TELEPHONE
-from .phonecode import replace_mobile
-from .phonecode import replace_phone
-from .quantifier import RE_TEMPERATURE
-from .quantifier import replace_measure
-from .quantifier import replace_temperature
-
-
-class TextNormalizer():
-    def __init__(self):
-        self.SENTENCE_SPLITOR = re.compile(r'([：、，；。？！,;?!][”’]?)')
-
-    def _split(self, text: str, lang="zh") -> List[str]:
-        """Split long text into sentences with sentence-splitting punctuations.
-        Args:
-            text (str): The input text.
-        Returns:
-            List[str]: Sentences.
-        """
-        # Only for pure Chinese here
-        if lang == "zh":
-            text = text.replace(" ", "")
-            # 过滤掉特殊字符
-            text = re.sub(r'[——《》【】<>{}()（）#&@“”^_|\\]', '', text)
-        text = self.SENTENCE_SPLITOR.sub(r'\1\n', text)
-        text = text.strip()
-        sentences = [sentence.strip() for sentence in re.split(r'\n+', text)]
-        return sentences
-
-    def _post_replace(self, sentence: str) -> str:
-        sentence = sentence.replace('/', '每')
-        # sentence = sentence.replace('~', '至')
-        # sentence = sentence.replace('～', '至')
-        sentence = sentence.replace('①', '一')
-        sentence = sentence.replace('②', '二')
-        sentence = sentence.replace('③', '三')
-        sentence = sentence.replace('④', '四')
-        sentence = sentence.replace('⑤', '五')
-        sentence = sentence.replace('⑥', '六')
-        sentence = sentence.replace('⑦', '七')
-        sentence = sentence.replace('⑧', '八')
-        sentence = sentence.replace('⑨', '九')
-        sentence = sentence.replace('⑩', '十')
-        sentence = sentence.replace('α', '阿尔法')
-        sentence = sentence.replace('β', '贝塔')
-        sentence = sentence.replace('γ', '伽玛').replace('Γ', '伽玛')
-        sentence = sentence.replace('δ', '德尔塔').replace('Δ', '德尔塔')
-        sentence = sentence.replace('ε', '艾普西龙')
-        sentence = sentence.replace('ζ', '捷塔')
-        sentence = sentence.replace('η', '依塔')
-        sentence = sentence.replace('θ', '西塔').replace('Θ', '西塔')
-        sentence = sentence.replace('ι', '艾欧塔')
-        sentence = sentence.replace('κ', '喀帕')
-        sentence = sentence.replace('λ', '拉姆达').replace('Λ', '拉姆达')
-        sentence = sentence.replace('μ', '缪')
-        sentence = sentence.replace('ν', '拗')
-        sentence = sentence.replace('ξ', '克西').replace('Ξ', '克西')
-        sentence = sentence.replace('ο', '欧米克伦')
-        sentence = sentence.replace('π', '派').replace('Π', '派')
-        sentence = sentence.replace('ρ', '肉')
-        sentence = sentence.replace('ς', '西格玛').replace('Σ', '西格玛').replace(
-            'σ', '西格玛')
-        sentence = sentence.replace('τ', '套')
-        sentence = sentence.replace('υ', '宇普西龙')
-        sentence = sentence.replace('φ', '服艾').replace('Φ', '服艾')
-        sentence = sentence.replace('χ', '器')
-        sentence = sentence.replace('ψ', '普赛').replace('Ψ', '普赛')
-        sentence = sentence.replace('ω', '欧米伽').replace('Ω', '欧米伽')
-        # re filter special characters, have one more character "-" than line 68
-        sentence = re.sub(r'[-——《》【】<=>{}()（）#&@“”^_|\\]', '', sentence)
-        return sentence
-
-    def normalize_sentence(self, sentence: str) -> str:
-        # basic character conversions
-        sentence = tranditional_to_simplified(sentence)
-        sentence = sentence.translate(F2H_ASCII_LETTERS).translate(
-            F2H_DIGITS).translate(F2H_SPACE)
-
-        # number related NSW verbalization
-        sentence = RE_DATE.sub(replace_date, sentence)
-        sentence = RE_DATE2.sub(replace_date2, sentence)
-
-        # range first
-        sentence = RE_TIME_RANGE.sub(replace_time, sentence)
-        sentence = RE_TIME.sub(replace_time, sentence)
-
-        # 处理~波浪号作为至的替换
-        sentence = RE_TO_RANGE.sub(replace_to_range, sentence)
-        sentence = RE_TEMPERATURE.sub(replace_temperature, sentence)
-        sentence = replace_measure(sentence)
-        sentence = RE_FRAC.sub(replace_frac, sentence)
-        sentence = RE_PERCENTAGE.sub(replace_percentage, sentence)
-        sentence = RE_MOBILE_PHONE.sub(replace_mobile, sentence)
-
-        sentence = RE_TELEPHONE.sub(replace_phone, sentence)
-        sentence = RE_NATIONAL_UNIFORM_NUMBER.sub(replace_phone, sentence)
-
-        sentence = RE_RANGE.sub(replace_range, sentence)
-
-        # 处理加减乘除
-        while RE_ASMD.search(sentence):
-            sentence = RE_ASMD.sub(replace_asmd, sentence)
-
-        sentence = RE_INTEGER.sub(replace_negative_num, sentence)
-        sentence = RE_DECIMAL_NUM.sub(replace_number, sentence)
-        sentence = RE_POSITIVE_QUANTIFIERS.sub(replace_positive_quantifier,
-                                               sentence)
-        sentence = RE_DEFAULT_NUM.sub(replace_default_num, sentence)
-        sentence = RE_NUMBER.sub(replace_number, sentence)
-        sentence = self._post_replace(sentence)
-
-        return sentence
-
-    def normalize(self, text: str) -> List[str]:
-        sentences = self._split(text)
-        sentences = [self.normalize_sentence(sent) for sent in sentences]
-        return sentences

SongBloom/g2p/lyric_common.py

@@ -1,81 +0,0 @@
-import os, sys
-
-sys.path.insert(0, os.path.dirname(__file__))
-from pinyin.pinyin import G2P_PinYin
-from cn_zh_g2p import G2P_Mix, symbols
-
-key2processor = {
-    'pinyin': G2P_PinYin(), 
-    'phoneme': G2P_Mix(),
-}
-
-valid_struct_type = ['[chorus]', '[verse]', '[bridge]']
-start_struct_type = ['[intro]', '[start]']
-end_struct_type = ['[outro]', '[end]']
-conn_struct_type = ['[inst]', '[solo]', '[break]']
-
-LABELS = {
-    '[intro]': 0,
-    '[outro]': 1,
-    '[bridge]': 2,
-    '[inst]': 3,
-    '[verse]': 4,
-    '[chorus]': 5,
-    '[silence]': 6,
-}
-
-NUMBERS = {
-    '0': ['零', 'zero'],
-    '1': ['一', 'one'],
-    '2': ['二', 'two'],
-    '3': ['三', 'three'],
-    '4': ['四', 'four'],
-    '5': ['五', 'five'],
-    '6': ['六', 'six'],
-    '7': ['七', 'seven'],
-    '8': ['八', 'eight'],
-    '9': ['九', 'nine']
-}
-
-def detect_structure(structure):
-    valid_start = ['start', 'intro']
-    valid_end = ['outro', 'end']
-    valid_instru = ['solo', 'inst', 'break']
-    valid_bridge = ['bridge']
-
-    if structure in ['verse', 'chorus', 'silence']:
-        return structure
-    
-    if structure in valid_start:
-        return 'intro'
-    if structure in valid_end:
-        return 'outro'
-    if structure in valid_instru:
-        return 'inst'
-    if structure in valid_bridge:
-        return 'bridge'
-
-def merge_structure(start_time, end_time, structure, lyric):
-    cnt = 1
-    while cnt < len(start_time):
-        if structure[cnt] == structure[cnt-1]:
-            end_time[cnt-1] = end_time[cnt]
-            if structure[cnt] not in ["verse", "chorus", "bridge"]:
-                del start_time[cnt]
-                del end_time[cnt]
-                del structure[cnt]
-                del lyric[cnt]
-            else:
-                cnt += 1
-        else:
-            cnt += 1
-    
-    return start_time, end_time, structure, lyric
-
-
-def is_struct_legal(struct, text):
-    if struct in valid_struct_type and text != "":
-        return True
-    elif struct not in valid_struct_type and text == "":
-        return True
-    return False

SongBloom/g2p/pinyin/__init__.py

@@ -1,430 +0,0 @@
-from .symbols import symbols
-
-
-
-
-pinyin_dict = {
-    "a": ("^", "a"),
-    "ai": ("^", "ai"),
-    "an": ("^", "an"),
-    "ang": ("^", "ang"),
-    "ao": ("^", "ao"),
-    "ba": ("b", "a"),
-    "bai": ("b", "ai"),
-    "ban": ("b", "an"),
-    "bang": ("b", "ang"),
-    "bao": ("b", "ao"),
-    "be": ("b", "e"),
-    "bei": ("b", "ei"),
-    "ben": ("b", "en"),
-    "beng": ("b", "eng"),
-    "bi": ("b", "i"),
-    "bian": ("b", "ian"),
-    "biao": ("b", "iao"),
-    "bie": ("b", "ie"),
-    "bin": ("b", "in"),
-    "bing": ("b", "ing"),
-    "bo": ("b", "o"),
-    "bu": ("b", "u"),
-    "ca": ("c", "a"),
-    "cai": ("c", "ai"),
-    "can": ("c", "an"),
-    "cang": ("c", "ang"),
-    "cao": ("c", "ao"),
-    "ce": ("c", "e"),
-    "cen": ("c", "en"),
-    "ceng": ("c", "eng"),
-    "cha": ("ch", "a"),
-    "chai": ("ch", "ai"),
-    "chan": ("ch", "an"),
-    "chang": ("ch", "ang"),
-    "chao": ("ch", "ao"),
-    "che": ("ch", "e"),
-    "chen": ("ch", "en"),
-    "cheng": ("ch", "eng"),
-    "chi": ("ch", "iii"),
-    "chong": ("ch", "ong"),
-    "chou": ("ch", "ou"),
-    "chu": ("ch", "u"),
-    "chua": ("ch", "ua"),
-    "chuai": ("ch", "uai"),
-    "chuan": ("ch", "uan"),
-    "chuang": ("ch", "uang"),
-    "chui": ("ch", "uei"),
-    "chun": ("ch", "uen"),
-    "chuo": ("ch", "uo"),
-    "ci": ("c", "ii"),
-    "cong": ("c", "ong"),
-    "cou": ("c", "ou"),
-    "cu": ("c", "u"),
-    "cuan": ("c", "uan"),
-    "cui": ("c", "uei"),
-    "cun": ("c", "uen"),
-    "cuo": ("c", "uo"),
-    "da": ("d", "a"),
-    "dai": ("d", "ai"),
-    "dan": ("d", "an"),
-    "dang": ("d", "ang"),
-    "dao": ("d", "ao"),
-    "de": ("d", "e"),
-    "dei": ("d", "ei"),
-    "den": ("d", "en"),
-    "deng": ("d", "eng"),
-    "di": ("d", "i"),
-    "dia": ("d", "ia"),
-    "dian": ("d", "ian"),
-    "diao": ("d", "iao"),
-    "die": ("d", "ie"),
-    "ding": ("d", "ing"),
-    "diu": ("d", "iou"),
-    "dong": ("d", "ong"),
-    "dou": ("d", "ou"),
-    "du": ("d", "u"),
-    "duan": ("d", "uan"),
-    "dui": ("d", "uei"),
-    "dun": ("d", "uen"),
-    "duo": ("d", "uo"),
-    "e": ("^", "e"),
-    "ei": ("^", "ei"),
-    "en": ("^", "en"),
-    "ng": ("^", "en"),
-    "eng": ("^", "eng"),
-    "er": ("^", "er"),
-    "fa": ("f", "a"),
-    "fan": ("f", "an"),
-    "fang": ("f", "ang"),
-    "fei": ("f", "ei"),
-    "fen": ("f", "en"),
-    "feng": ("f", "eng"),
-    "fo": ("f", "o"),
-    "fou": ("f", "ou"),
-    "fu": ("f", "u"),
-    "ga": ("g", "a"),
-    "gai": ("g", "ai"),
-    "gan": ("g", "an"),
-    "gang": ("g", "ang"),
-    "gao": ("g", "ao"),
-    "ge": ("g", "e"),
-    "gei": ("g", "ei"),
-    "gen": ("g", "en"),
-    "geng": ("g", "eng"),
-    "gong": ("g", "ong"),
-    "gou": ("g", "ou"),
-    "gu": ("g", "u"),
-    "gua": ("g", "ua"),
-    "guai": ("g", "uai"),
-    "guan": ("g", "uan"),
-    "guang": ("g", "uang"),
-    "gui": ("g", "uei"),
-    "gun": ("g", "uen"),
-    "guo": ("g", "uo"),
-    "ha": ("h", "a"),
-    "hai": ("h", "ai"),
-    "han": ("h", "an"),
-    "hang": ("h", "ang"),
-    "hao": ("h", "ao"),
-    "he": ("h", "e"),
-    "hei": ("h", "ei"),
-    "hen": ("h", "en"),
-    "heng": ("h", "eng"),
-    "hong": ("h", "ong"),
-    "hou": ("h", "ou"),
-    "hu": ("h", "u"),
-    "hua": ("h", "ua"),
-    "huai": ("h", "uai"),
-    "huan": ("h", "uan"),
-    "huang": ("h", "uang"),
-    "hui": ("h", "uei"),
-    "hun": ("h", "uen"),
-    "huo": ("h", "uo"),
-    "ji": ("j", "i"),
-    "jia": ("j", "ia"),
-    "jian": ("j", "ian"),
-    "jiang": ("j", "iang"),
-    "jiao": ("j", "iao"),
-    "jie": ("j", "ie"),
-    "jin": ("j", "in"),
-    "jing": ("j", "ing"),
-    "jiong": ("j", "iong"),
-    "jiu": ("j", "iou"),
-    "ju": ("j", "v"),
-    "juan": ("j", "van"),
-    "jue": ("j", "ve"),
-    "jun": ("j", "vn"),
-    "ka": ("k", "a"),
-    "kai": ("k", "ai"),
-    "kan": ("k", "an"),
-    "kang": ("k", "ang"),
-    "kao": ("k", "ao"),
-    "ke": ("k", "e"),
-    "kei": ("k", "ei"),
-    "ken": ("k", "en"),
-    "keng": ("k", "eng"),
-    "kong": ("k", "ong"),
-    "kou": ("k", "ou"),
-    "ku": ("k", "u"),
-    "kua": ("k", "ua"),
-    "kuai": ("k", "uai"),
-    "kuan": ("k", "uan"),
-    "kuang": ("k", "uang"),
-    "kui": ("k", "uei"),
-    "kun": ("k", "uen"),
-    "kuo": ("k", "uo"),
-    "la": ("l", "a"),
-    "lai": ("l", "ai"),
-    "lan": ("l", "an"),
-    "lang": ("l", "ang"),
-    "lao": ("l", "ao"),
-    "le": ("l", "e"),
-    "lei": ("l", "ei"),
-    "leng": ("l", "eng"),
-    "li": ("l", "i"),
-    "lia": ("l", "ia"),
-    "lian": ("l", "ian"),
-    "liang": ("l", "iang"),
-    "liao": ("l", "iao"),
-    "lie": ("l", "ie"),
-    "lin": ("l", "in"),
-    "ling": ("l", "ing"),
-    "liu": ("l", "iou"),
-    "lo": ("l", "o"),
-    "long": ("l", "ong"),
-    "lou": ("l", "ou"),
-    "lu": ("l", "u"),
-    "lv": ("l", "v"),
-    "luan": ("l", "uan"),
-    "lve": ("l", "ve"),
-    "lue": ("l", "ve"),
-    "lun": ("l", "uen"),
-    "luo": ("l", "uo"),
-    "ma": ("m", "a"),
-    "mai": ("m", "ai"),
-    "man": ("m", "an"),
-    "mang": ("m", "ang"),
-    "mao": ("m", "ao"),
-    "me": ("m", "e"),
-    "mei": ("m", "ei"),
-    "men": ("m", "en"),
-    "meng": ("m", "eng"),
-    "mi": ("m", "i"),
-    "mian": ("m", "ian"),
-    "miao": ("m", "iao"),
-    "mie": ("m", "ie"),
-    "min": ("m", "in"),
-    "ming": ("m", "ing"),
-    "miu": ("m", "iou"),
-    "mo": ("m", "o"),
-    "mou": ("m", "ou"),
-    "mu": ("m", "u"),
-    "na": ("n", "a"),
-    "nai": ("n", "ai"),
-    "nan": ("n", "an"),
-    "nang": ("n", "ang"),
-    "nao": ("n", "ao"),
-    "ne": ("n", "e"),
-    "nei": ("n", "ei"),
-    "nen": ("n", "en"),
-    "neng": ("n", "eng"),
-    "ni": ("n", "i"),
-    "nia": ("n", "ia"),
-    "nian": ("n", "ian"),
-    "niang": ("n", "iang"),
-    "niao": ("n", "iao"),
-    "nie": ("n", "ie"),
-    "nin": ("n", "in"),
-    "ning": ("n", "ing"),
-    "niu": ("n", "iou"),
-    "nong": ("n", "ong"),
-    "nou": ("n", "ou"),
-    "nu": ("n", "u"),
-    "nv": ("n", "v"),
-    "nuan": ("n", "uan"),
-    "nve": ("n", "ve"),
-    "nue": ("n", "ve"),
-    "nuo": ("n", "uo"),
-    "o": ("^", "o"),
-    "ou": ("^", "ou"),
-    "pa": ("p", "a"),
-    "pai": ("p", "ai"),
-    "pan": ("p", "an"),
-    "pang": ("p", "ang"),
-    "pao": ("p", "ao"),
-    "pe": ("p", "e"),
-    "pei": ("p", "ei"),
-    "pen": ("p", "en"),
-    "peng": ("p", "eng"),
-    "pi": ("p", "i"),
-    "pian": ("p", "ian"),
-    "piao": ("p", "iao"),
-    "pie": ("p", "ie"),
-    "pin": ("p", "in"),
-    "ping": ("p", "ing"),
-    "po": ("p", "o"),
-    "pou": ("p", "ou"),
-    "pu": ("p", "u"),
-    "qi": ("q", "i"),
-    "qia": ("q", "ia"),
-    "qian": ("q", "ian"),
-    "qiang": ("q", "iang"),
-    "qiao": ("q", "iao"),
-    "qie": ("q", "ie"),
-    "qin": ("q", "in"),
-    "qing": ("q", "ing"),
-    "qiong": ("q", "iong"),
-    "qiu": ("q", "iou"),
-    "qu": ("q", "v"),
-    "quan": ("q", "van"),
-    "que": ("q", "ve"),
-    "qun": ("q", "vn"),
-    "ran": ("r", "an"),
-    "rang": ("r", "ang"),
-    "rao": ("r", "ao"),
-    "re": ("r", "e"),
-    "ren": ("r", "en"),
-    "reng": ("r", "eng"),
-    "ri": ("r", "iii"),
-    "rong": ("r", "ong"),
-    "rou": ("r", "ou"),
-    "ru": ("r", "u"),
-    "rua": ("r", "ua"),
-    "ruan": ("r", "uan"),
-    "rui": ("r", "uei"),
-    "run": ("r", "uen"),
-    "ruo": ("r", "uo"),
-    "sa": ("s", "a"),
-    "sai": ("s", "ai"),
-    "san": ("s", "an"),
-    "sang": ("s", "ang"),
-    "sao": ("s", "ao"),
-    "se": ("s", "e"),
-    "sen": ("s", "en"),
-    "seng": ("s", "eng"),
-    "sha": ("sh", "a"),
-    "shai": ("sh", "ai"),
-    "shan": ("sh", "an"),
-    "shang": ("sh", "ang"),
-    "shao": ("sh", "ao"),
-    "she": ("sh", "e"),
-    "shei": ("sh", "ei"),
-    "shen": ("sh", "en"),
-    "sheng": ("sh", "eng"),
-    "shi": ("sh", "iii"),
-    "shou": ("sh", "ou"),
-    "shu": ("sh", "u"),
-    "shua": ("sh", "ua"),
-    "shuai": ("sh", "uai"),
-    "shuan": ("sh", "uan"),
-    "shuang": ("sh", "uang"),
-    "shui": ("sh", "uei"),
-    "shun": ("sh", "uen"),
-    "shuo": ("sh", "uo"),
-    "si": ("s", "ii"),
-    "song": ("s", "ong"),
-    "sou": ("s", "ou"),
-    "su": ("s", "u"),
-    "suan": ("s", "uan"),
-    "sui": ("s", "uei"),
-    "sun": ("s", "uen"),
-    "suo": ("s", "uo"),
-    "ta": ("t", "a"),
-    "tai": ("t", "ai"),
-    "tan": ("t", "an"),
-    "tang": ("t", "ang"),
-    "tao": ("t", "ao"),
-    "te": ("t", "e"),
-    "tei": ("t", "ei"),
-    "teng": ("t", "eng"),
-    "ti": ("t", "i"),
-    "tian": ("t", "ian"),
-    "tiao": ("t", "iao"),
-    "tie": ("t", "ie"),
-    "ting": ("t", "ing"),
-    "tong": ("t", "ong"),
-    "tou": ("t", "ou"),
-    "tu": ("t", "u"),
-    "tuan": ("t", "uan"),
-    "tui": ("t", "uei"),
-    "tun": ("t", "uen"),
-    "tuo": ("t", "uo"),
-    "wa": ("^", "ua"),
-    "wai": ("^", "uai"),
-    "wan": ("^", "uan"),
-    "wang": ("^", "uang"),
-    "wei": ("^", "uei"),
-    "wen": ("^", "uen"),
-    "weng": ("^", "ueng"),
-    "wo": ("^", "uo"),
-    "wu": ("^", "u"),
-    "xi": ("x", "i"),
-    "xia": ("x", "ia"),
-    "xian": ("x", "ian"),
-    "xiang": ("x", "iang"),
-    "xiao": ("x", "iao"),
-    "xie": ("x", "ie"),
-    "xin": ("x", "in"),
-    "xing": ("x", "ing"),
-    "xiong": ("x", "iong"),
-    "xiu": ("x", "iou"),
-    "xu": ("x", "v"),
-    "xuan": ("x", "van"),
-    "xue": ("x", "ve"),
-    "xun": ("x", "vn"),
-    "ya": ("^", "ia"),
-    "yan": ("^", "ian"),
-    "yang": ("^", "iang"),
-    "yao": ("^", "iao"),
-    "ye": ("^", "ie"),
-    "yi": ("^", "i"),
-    "yin": ("^", "in"),
-    "ying": ("^", "ing"),
-    "yo": ("^", "iou"),
-    "yong": ("^", "iong"),
-    "you": ("^", "iou"),
-    "yu": ("^", "v"),
-    "yuan": ("^", "van"),
-    "yue": ("^", "ve"),
-    "yun": ("^", "vn"),
-    "za": ("z", "a"),
-    "zai": ("z", "ai"),
-    "zan": ("z", "an"),
-    "zang": ("z", "ang"),
-    "zao": ("z", "ao"),
-    "ze": ("z", "e"),
-    "zei": ("z", "ei"),
-    "zen": ("z", "en"),
-    "zeng": ("z", "eng"),
-    "zha": ("zh", "a"),
-    "zhai": ("zh", "ai"),
-    "zhan": ("zh", "an"),
-    "zhang": ("zh", "ang"),
-    "zhao": ("zh", "ao"),
-    "zhe": ("zh", "e"),
-    "zhei": ("zh", "ei"),
-    "zhen": ("zh", "en"),
-    "zheng": ("zh", "eng"),
-    "zhi": ("zh", "iii"),
-    "zhong": ("zh", "ong"),
-    "zhou": ("zh", "ou"),
-    "zhu": ("zh", "u"),
-    "zhua": ("zh", "ua"),
-    "zhuai": ("zh", "uai"),
-    "zhuan": ("zh", "uan"),
-    "zhuang": ("zh", "uang"),
-    "zhui": ("zh", "uei"),
-    "zhun": ("zh", "uen"),
-    "zhuo": ("zh", "uo"),
-    "zi": ("z", "ii"),
-    "zong": ("z", "ong"),
-    "zou": ("z", "ou"),
-    "zu": ("z", "u"),
-    "zuan": ("z", "uan"),
-    "zui": ("z", "uei"),
-    "zun": ("z", "uen"),
-    "zuo": ("z", "uo"),
-}
-
-
-def gen_vocabs():
-  import yaml
-  vocab = [f"<{c}{i}>" for c in list(pinyin_dict.keys()) for i in range(1,6)]
-  yaml.dump(vocab, open('./vocab.yaml', 'w'))

SongBloom/g2p/pinyin/pinyin.py

@@ -1,137 +0,0 @@
-import re
-
-from pypinyin import Style
-from pypinyin.contrib.neutral_tone import NeutralToneWith5Mixin
-from pypinyin.converter import DefaultConverter
-from pypinyin.core import Pinyin
-
-from . import pinyin_dict
-import torch
-
-
-class MyConverter(NeutralToneWith5Mixin, DefaultConverter):
-    pass
-
-
-def is_chinese(uchar):
-    if uchar >= u'\u4e00' and uchar <= u'\u9fa5':
-        return True
-    else:
-        return False
-
-
-def clean_chinese(text: str):
-    text = text.strip()
-    text_clean = []
-    for char in text:
-        if (is_chinese(char)):
-            text_clean.append(char)
-        else:
-            if len(text_clean) > 1 and is_chinese(text_clean[-1]):
-                text_clean.append(',')
-    text_clean = ''.join(text_clean).strip(',')
-    return text_clean
-
-
-class G2P_PinYin():
-
-    def __init__(self):
-        super(G2P_PinYin, self).__init__()
-        self.pinyin_parser = Pinyin(MyConverter())
-
-    def get_phoneme4pinyin(self, pinyins):
-        result = []
-        count_phone = []
-        for pinyin in pinyins:
-            if pinyin[:-1] in pinyin_dict:
-                tone = pinyin[-1]
-                a = pinyin[:-1]
-                a1, a2 = pinyin_dict[a]
-                result += [a1, a2 + tone]
-                count_phone.append(2)
-        return result, count_phone
-
-    # def chinese_to_phonemes(self, text):
-    #     text = clean_chinese(text)
-    #     phonemes = ["sil"]
-    #     chars = ['[PAD]']
-    #     all_pinyins = []
-    #     count_phone = []
-    #     count_phone.append(1)
-    #     for subtext in text.split(","):
-    #         if (len(subtext) == 0):
-    #             continue
-    #         pinyins = self.correct_pinyin_tone3(subtext)
-    #         all_pinyins.append(' '.join(pinyins))
-    #         sub_p, sub_c = self.get_phoneme4pinyin(pinyins)
-    #         phonemes.extend(sub_p)
-    #         phonemes.append(",")
-    #         count_phone.extend(sub_c)
-    #         count_phone.append(1)
-    #         chars.append(subtext)
-    #         chars.append(',')
-    #     phonemes.append("sil")
-    #     count_phone.append(1)
-    #     chars.append('[PAD]')
-    #     # char_embeds = self.prosody.expand_for_phone(char_embeds, count_phone)
-    #     return " ".join(phonemes), " ".join(chars), ' , '.join(all_pinyins)
-
-    def chinese_to_phonemes(self, text):
-        all_pinyins = []
-        subtext = []
-        for chr in text:
-            if is_chinese(chr):
-                subtext.append(chr)
-            else:
-                if subtext != []:
-                    subtext = ''.join(subtext)
-                    pinyins = self.correct_pinyin_tone3(subtext) 
-                    pinyins = [f"<{i}>" for i in pinyins]   
-                    all_pinyins.append(' '+ ' '.join(pinyins)+ ' ')
-                all_pinyins.append(chr)
-                subtext = []
-        if subtext != []:
-            subtext = ''.join(subtext)
-            pinyins = self.correct_pinyin_tone3(subtext)
-            pinyins = [f"<{i}>" for i in pinyins]     
-            all_pinyins.append(' '+ ' '.join(pinyins)+ ' ')
-        # char_embeds = self.prosody.expand_for_phone(char_embeds, count_phone)
-        return  ''.join(all_pinyins)
-    
-    def correct_pinyin_tone3(self, text):
-        pinyin_list = [
-            p[0]
-            for p in self.pinyin_parser.pinyin(text,
-                                               style=Style.TONE3,
-                                               strict=False,
-                                               neutral_tone_with_five=True)
-        ]
-        if len(pinyin_list) >= 2:
-            for i in range(1, len(pinyin_list)):
-                try:
-                    if re.findall(r'\d',
-                                  pinyin_list[i - 1])[0] == '3' and re.findall(
-                                      r'\d', pinyin_list[i])[0] == '3':
-                        pinyin_list[i - 1] = pinyin_list[i - 1].replace(
-                            '3', '2')
-                except IndexError:
-                    pass
-        return pinyin_list
-
-    # def expand_for_phone(self, char_embeds, length):  # length of phones for char
-    #     if(char_embeds.size(0) > len(length)):
-    #         print(char_embeds.shape, len(length))
-    #         char_embeds = char_embeds[0:len(length),:]
-    #     elif(char_embeds.size(0) < len(length)):
-    #         print(char_embeds.shape, len(length))
-    #         length = length[0:char_embeds.size(0)]
-    #     expand_vecs = list()
-    #     for vec, leng in zip(char_embeds, length):
-    #         vec = vec.expand(leng, -1)
-    #         expand_vecs.append(vec)
-    #     expand_embeds = torch.cat(expand_vecs, 0)
-    #     assert expand_embeds.size(0) == sum(length)
-    #     return expand_embeds.numpy()
-
-    def __call__(self, text):
-        return self.chinese_to_phonemes(text)

SongBloom/g2p/pinyin/symbols.py

@@ -1,71 +0,0 @@
-_pause = ["sil", "eos", "sp", "#0", "#1", "#2", "#3"]
-
-_initials = [
-    "^",
-    "b",
-    "c",
-    "ch",
-    "d",
-    "f",
-    "g",
-    "h",
-    "j",
-    "k",
-    "l",
-    "m",
-    "n",
-    "p",
-    "q",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "x",
-    "z",
-    "zh",
-]
-
-_tones = ["1", "2", "3", "4", "5"]
-
-_finals = [
-    "a",
-    "ai",
-    "an",
-    "ang",
-    "ao",
-    "e",
-    "ei",
-    "en",
-    "eng",
-    "er",
-    "i",
-    "ia",
-    "ian",
-    "iang",
-    "iao",
-    "ie",
-    "ii",
-    "iii",
-    "in",
-    "ing",
-    "iong",
-    "iou",
-    "o",
-    "ong",
-    "ou",
-    "u",
-    "ua",
-    "uai",
-    "uan",
-    "uang",
-    "uei",
-    "uen",
-    "ueng",
-    "uo",
-    "v",
-    "van",
-    "ve",
-    "vn",
-]
-
-symbols = _pause + _initials + [i + j for i in _finals for j in _tones]

SongBloom/models/base/sample.py

@@ -1,57 +0,0 @@
-import torch
-
-
-def multinomial(input: torch.Tensor, num_samples: int, replacement=False, *, generator=None):
-    """torch.multinomial with arbitrary number of dimensions, and number of candidates on the last dimension.
-
-    Args:
-        input (torch.Tensor): The input tensor containing probabilities.
-        num_samples (int): Number of samples to draw.
-        replacement (bool): Whether to draw with replacement or not.
-    Keywords args:
-        generator (torch.Generator): A pseudorandom number generator for sampling.
-    Returns:
-        torch.Tensor: Last dimension contains num_samples indices
-            sampled from the multinomial probability distribution
-            located in the last dimension of tensor input.
-    """
-    input_ = input.reshape(-1, input.shape[-1])
-    output_ = torch.multinomial(input_, num_samples=num_samples, replacement=replacement, generator=generator)
-    output = output_.reshape(*list(input.shape[:-1]), -1)
-    return output
-
-
-def sample_top_k(probs: torch.Tensor, k: int) -> torch.Tensor:
-    """Sample next token from top K values along the last dimension of the input probs tensor.
-
-    Args:
-        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
-        k (int): The k in “top-k”.
-    Returns:
-        torch.Tensor: Sampled tokens.
-    """
-    top_k_value, _ = torch.topk(probs, k, dim=-1)
-    min_value_top_k = top_k_value[..., [-1]]
-    probs *= (probs >= min_value_top_k).float()
-    probs.div_(probs.sum(dim=-1, keepdim=True))
-    next_token = multinomial(probs, num_samples=1)
-    return next_token
-
-
-def sample_top_p(probs: torch.Tensor, p: float) -> torch.Tensor:
-    """Sample next token from top P probabilities along the last dimension of the input probs tensor.
-
-    Args:
-        probs (torch.Tensor): Input probabilities with token candidates on the last dimension.
-        p (int): The p in “top-p”.
-    Returns:
-        torch.Tensor: Sampled tokens.
-    """
-    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
-    probs_sum = torch.cumsum(probs_sort, dim=-1)
-    mask = probs_sum - probs_sort > p
-    probs_sort *= (~mask).float()
-    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
-    next_token = multinomial(probs_sort, num_samples=1)
-    next_token = torch.gather(probs_idx, -1, next_token)
-    return next_token

SongBloom/models/base/utils.py

@@ -1,57 +0,0 @@
-import torch
-import torch.nn as nn
-import typing as tp
-
-def length_to_mask(lengths: torch.Tensor, max_len: tp.Optional[int] = None) -> torch.Tensor:
-    """Utility function to convert a tensor of sequence lengths to a mask (useful when working on padded sequences).
-    For example: [3, 5] => [[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]]
-
-    Args:
-        lengths (torch.Tensor): tensor with lengths
-        max_len (int): can set the max length manually. Defaults to None.
-    Returns:
-        torch.Tensor: mask with 0s where there is pad tokens else 1s
-    """
-    assert len(lengths.shape) == 1, "Length shape should be 1 dimensional."
-    final_length = lengths.max().item() if not max_len else max_len
-    final_length = max(final_length, 1)  # if all seqs are of len zero we don't want a zero-size tensor
-    return torch.arange(final_length)[None, :].to(lengths.device) < lengths[:, None]
-
-
-def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000,
-                         dtype: torch.dtype = torch.float32) -> torch.Tensor:
-    """Create sinusoidal positional embedding, with shape `[B, T, C]`.
-
-    Args:
-        positions (torch.Tensor): LongTensor of positions.
-        dim (int): Dimension of the embedding.
-        max_period (float): Maximum period of the cosine/sine functions.
-        dtype (torch.dtype or str): dtype to use to generate the embedding.
-    Returns:
-        torch.Tensor: Sinusoidal positional embedding.
-    """
-    # We aim for BTC format
-    assert dim % 2 == 0
-    half_dim = dim // 2
-    positions = positions.to(dtype)
-    adim = torch.arange(half_dim, device=positions.device, dtype=dtype).view(1, 1, -1)
-    max_period_tensor = torch.full([], max_period, device=positions.device, dtype=dtype)  # avoid sync point
-    phase = positions / (max_period_tensor ** (adim / (half_dim - 1)))
-    # phase = phase.to(torch.bfloat16)
-    return torch.cat([torch.cos(phase), torch.sin(phase)], dim=-1)
-
-
-def create_norm_fn(norm_type: str, dim: int, **kwargs) -> nn.Module:
-    """Create normalization module for transformer encoder layer.
-
-    Args:
-        norm_type (str): Normalization method.
-        dim (int): Dimension of the normalized layer.
-        **kwargs (dict): Additional parameters for normalization layer.
-    Returns:
-        nn.Module: Normalization module.
-    """
-    if norm_type == 'layer_norm':
-        return nn.LayerNorm(dim, eps=1e-5, **kwargs)
-    else:
-        raise ValueError(f"Unknown norm type: {norm_type}")

SongBloom/models/musicgen/conditioners/__init__.py

@@ -1,37 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import omegaconf
-from .base import *
-from .text import *
-from .wav import *
-
-KLASS = {
-    'phoneme_tokenizer': PhonemeTokenizerConditioner,
-    'audio_tokenizer_wrapper': AudioTokenizerConditioner,
-}
-
-def get_condition_fuser(fuser_cfgs) -> ConditionFuser:
-    """Instantiate a condition fuser object."""
-    fuser_methods = ['sum', 'cross', 'prepend', 'input_interpolate']
-    fuse2cond = {k: fuser_cfgs[k] for k in fuser_methods}
-    kwargs = {k: v for k, v in fuser_cfgs.items() if k not in fuser_methods}
-    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
-    return fuser
-
-def get_conditioner_provider(cfg) -> ConditioningProvider:
-    """Instantiate a conditioning model."""
-
-    dict_cfg = {} if cfg is None else dict(cfg)
-    conditioners: tp.Dict[str, BaseConditioner] = {}
-
-    # import pdb; pdb.set_trace()
-    for cond, cond_cfg in dict_cfg.items():
-        model_args = cond_cfg.copy()
-        model_type = model_args.pop('type')
-        conditioners[str(cond)] = KLASS[model_type](**model_args)
-    conditioner = ConditioningProvider(conditioners)
-    return conditioner

SongBloom/models/musicgen/conditioners/base.py

@@ -1,872 +0,0 @@
-from collections import defaultdict
-from copy import deepcopy
-from dataclasses import dataclass, field
-from itertools import chain
-import logging
-import typing as tp
-import einops
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from torch.nn.utils.rnn import pad_sequence
-
-from dataclasses import dataclass, field, fields, replace
-
-from ..modules.streaming import StreamingModule
-from ...base.utils import length_to_mask, create_sin_embedding
-
-
-def collate(tensors: tp.List[torch.Tensor], dim: int = 0) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-    """Get a list of tensors and collate them to a single tensor. according to the following logic:
-    - `dim` specifies the time dimension which will be stacked and padded.
-    - The output will contain 1 new dimension (dimension index 0) which will be the size of
-    of the original list.
-
-    Args:
-        tensors (tp.List[torch.Tensor]): List of tensors to collate.
-        dim (int): Dimension which will be stacked and padded.
-    Returns:
-        tp.Tuple[torch.Tensor, torch.Tensor]:
-            torch.Tensor: Stacked and padded tensor. The output will contain 1 new dimension
-                (dimension index 0) which will be the size of the original list.
-            torch.Tensor: Tensor containing length of original tensor sizes (without padding).
-    """
-    tensors = [x.transpose(0, dim) for x in tensors]
-    lens = torch.LongTensor([len(x) for x in tensors])
-    padded_tensors = pad_sequence(tensors)
-    padded_tensors = padded_tensors.transpose(0, 1)
-    padded_tensors = padded_tensors.transpose(1, dim + 1)
-    return padded_tensors, lens
-
-
-
-@dataclass(order=True)
-class PathInZip:
-    """Hold a path of file within a zip file.
-
-    Args:
-        path (str): The convention is <path_to_zip>:<relative_path_inside_zip>.
-            Let's assume there is a zip file /some/location/foo.zip
-            and inside of it is a json file located at /data/file1.json,
-            Then we expect path = "/some/location/foo.zip:/data/file1.json".
-    """
-
-    INFO_PATH_SEP = ':'
-    zip_path: str
-    file_path: str
-
-    def __init__(self, path: str) -> None:
-        split_path = path.split(self.INFO_PATH_SEP)
-        assert len(split_path) == 2
-        self.zip_path, self.file_path = split_path
-
-    @classmethod
-    def from_paths(cls, zip_path: str, file_path: str):
-        return cls(zip_path + cls.INFO_PATH_SEP + file_path)
-
-    def __str__(self) -> str:
-        return self.zip_path + self.INFO_PATH_SEP + self.file_path
-
-
-@dataclass(order=True)
-class BaseInfo:
-
-    @classmethod
-    def _dict2fields(cls, dictionary: dict):
-        return {
-                field.name: dictionary[field.name]
-                for field in fields(cls) if field.name in dictionary
-            }
-        # try:
-        #     return {
-        #         field.name: dictionary[field.name]
-        #         for field in fields(cls) if field.name in dictionary
-        #     }
-        # except:
-        #     print(dictionary)
-
-    @classmethod
-    def from_dict(cls, dictionary: dict):
-        _dictionary = cls._dict2fields(dictionary)
-        return cls(**_dictionary)
-
-    def to_dict(self):
-        return {
-            field.name: self.__getattribute__(field.name)
-            for field in fields(self)
-            }
-
-
-@dataclass(order=True)
-class AudioMeta(BaseInfo):
-    path: str
-    duration: float
-    sample_rate: int
-    amplitude: tp.Optional[float] = None
-    weight: tp.Optional[float] = None
-    # info_path is used to load additional information about the audio file that is stored in zip files.
-    info_path: tp.Optional[PathInZip] = None
-
-    @classmethod
-    def from_dict(cls, dictionary: dict):
-        base = cls._dict2fields(dictionary)
-        if 'info_path' in base and base['info_path'] is not None:
-            base['info_path'] = PathInZip(base['info_path'])
-        return cls(**base)
-
-    def to_dict(self):
-        d = super().to_dict()
-        if d['info_path'] is not None:
-            d['info_path'] = str(d['info_path'])
-        return d
-
-
-@dataclass(order=True)
-class SegmentInfo(BaseInfo):
-    meta: AudioMeta
-    seek_time: float
-    # The following values are given once the audio is processed, e.g.
-    # at the target sample rate and target number of channels.
-    n_frames: int      # actual number of frames without padding
-    total_frames: int  # total number of frames, padding included
-    sample_rate: int   # actual sample rate
-    channels: int      # number of audio channels.
-
-
-logger = logging.getLogger(__name__)
-TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
-ConditionType = tp.Tuple[torch.Tensor, torch.Tensor]  # condition, mask
-
-
-class WavCondition(tp.NamedTuple):
-    wav: torch.Tensor
-    length: torch.Tensor
-    sample_rate: tp.List[int]
-    path: tp.List[tp.Optional[str]] = []
-    seek_time: tp.List[tp.Optional[float]] = []
-
-
-class JointEmbedCondition(tp.NamedTuple):
-    wav: torch.Tensor
-    text: tp.List[tp.Optional[str]]
-    length: torch.Tensor
-    sample_rate: tp.List[int]
-    path: tp.List[tp.Optional[str]] = []
-    seek_time: tp.List[tp.Optional[float]] = []
-
-
-@dataclass
-class ConditioningAttributes:
-    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
-    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
-    joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict)
-
-    def __getitem__(self, item):
-        return getattr(self, item)
-
-    @property
-    def text_attributes(self):
-        return self.text.keys()
-
-    @property
-    def wav_attributes(self):
-        return self.wav.keys()
-
-    @property
-    def joint_embed_attributes(self):
-        return self.joint_embed.keys()
-
-    @property
-    def attributes(self):
-        return {
-            "text": self.text_attributes,
-            "wav": self.wav_attributes,
-            "joint_embed": self.joint_embed_attributes,
-        }
-
-    def to_flat_dict(self):
-        return {
-            **{f"text.{k}": v for k, v in self.text.items()},
-            **{f"wav.{k}": v for k, v in self.wav.items()},
-            **{f"joint_embed.{k}": v for k, v in self.joint_embed.items()}
-        }
-
-    @classmethod
-    def from_flat_dict(cls, x):
-        out = cls()
-        for k, v in x.items():
-            kind, att = k.split(".")
-            out[kind][att] = v
-        return out
-
-
-
-# class SegmentWithAttributes(SegmentInfo):
-#     """Base class for all dataclasses that are used for conditioning.
-#     All child classes should implement `to_condition_attributes` that converts
-#     the existing attributes to a dataclass of type ConditioningAttributes.
-#     """
-#     def to_condition_attributes(self) -> ConditioningAttributes:
-#         raise NotImplementedError()
-
-
-
-def nullify_condition(condition: ConditionType, dim: int = 1):
-    """Transform an input condition to a null condition.
-    The way it is done by converting it to a single zero vector similarly
-    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
-
-    Args:
-        condition (ConditionType): A tuple of condition and mask (tuple[torch.Tensor, torch.Tensor])
-        dim (int): The dimension that will be truncated (should be the time dimension)
-        WARNING!: dim should not be the batch dimension!
-    Returns:
-        ConditionType: A tuple of null condition and mask
-    """
-    assert dim != 0, "dim cannot be the batch dimension!"
-    assert isinstance(condition, tuple) and \
-        isinstance(condition[0], torch.Tensor) and \
-        isinstance(condition[1], torch.Tensor), "'nullify_condition' got an unexpected input type!"
-    cond, mask = condition
-    B = cond.shape[0]
-    last_dim = cond.dim() - 1
-    out = cond.transpose(dim, last_dim)
-    out = 0. * out[..., :1]
-    out = out.transpose(dim, last_dim)
-    mask = torch.zeros((B, 1), device=out.device).int()
-    assert cond.dim() == out.dim()
-    return out, mask
-
-
-def nullify_wav(cond: WavCondition) -> WavCondition:
-    """Transform a WavCondition to a nullified WavCondition.
-    It replaces the wav by a null tensor, forces its length to 0, and replaces metadata by dummy attributes.
-
-    Args:
-        cond (WavCondition): Wav condition with wav, tensor of shape [B, T].
-    Returns:
-        WavCondition: Nullified wav condition.
-    """
-    #TODO by YCY, fix this to support zero-length input (as None)
-    null_wav, _ = nullify_condition((cond.wav, torch.zeros_like(cond.wav)), dim=cond.wav.dim() - 1) # B,1 all-zero
-    return WavCondition(
-        wav=null_wav,
-        length=torch.tensor([0] * cond.wav.shape[0], device=cond.wav.device),
-        sample_rate=cond.sample_rate,
-        path=[None] * cond.wav.shape[0],
-        seek_time=[None] * cond.wav.shape[0],
-    )
-
-
-def nullify_joint_embed(embed: JointEmbedCondition) -> JointEmbedCondition:
-    """Nullify the joint embedding condition by replacing it by a null tensor, forcing its length to 0,
-    and replacing metadata by dummy attributes.
-
-    Args:
-        cond (JointEmbedCondition): Joint embedding condition with wav and text, wav tensor of shape [B, C, T].
-    """
-    null_wav, _ = nullify_condition((embed.wav, torch.zeros_like(embed.wav)), dim=embed.wav.dim() - 1)
-    return JointEmbedCondition(
-        wav=null_wav, text=[None] * len(embed.text),
-        length=torch.LongTensor([0]).to(embed.wav.device),
-        sample_rate=embed.sample_rate,
-        path=[None] * embed.wav.shape[0],
-        seek_time=[0] * embed.wav.shape[0],
-    )
-
-
-
-class BaseConditioner(nn.Module):
-    """Base model for all conditioner modules.
-    We allow the output dim to be different than the hidden dim for two reasons:
-    1) keep our LUTs small when the vocab is large;
-    2) make all condition dims consistent.
-
-    Args:
-        dim (int): Hidden dim of the model.
-        output_dim (int): Output dim of the conditioner.
-    """
-    def __init__(self, dim: int, output_dim: int, input_token = False, padding_idx=None):
-        super().__init__()
-        self.dim = dim
-        self.output_dim = output_dim
-        if input_token:
-            self.output_proj = nn.Embedding(dim, output_dim, padding_idx)
-        else:
-            self.output_proj = nn.Linear(dim, output_dim)
-
-    def tokenize(self, *args, **kwargs) -> tp.Any:
-        """Should be any part of the processing that will lead to a synchronization
-        point, e.g. BPE tokenization with transfer to the GPU.
-
-        The returned value will be saved and return later when calling forward().
-        """
-        raise NotImplementedError()
-
-    def forward(self, inputs: tp.Any) -> ConditionType:
-        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
-        Outputs a ConditionType, after the input data was embedded as a dense vector.
-
-        Returns:
-            ConditionType:
-                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
-                  output embedding and D is the dimension of the embedding.
-                - And a mask indicating where the padding tokens.
-        """
-        raise NotImplementedError()
-
-
-
-def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str) -> ConditioningAttributes:
-    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
-    If the condition is of type "wav", then nullify it using `nullify_condition` function.
-    If the condition is of any other type, set its value to None.
-    Works in-place.
-    """
-    if condition_type not in ['text', 'wav', 'joint_embed']:
-        raise ValueError(
-            "dropout_condition got an unexpected condition type!"
-            f" expected 'text', 'wav' or 'joint_embed' but got '{condition_type}'"
-        )
-
-    if condition not in getattr(sample, condition_type):
-        raise ValueError(
-            "dropout_condition received an unexpected condition!"
-            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
-            f" but got '{condition}' of type '{condition_type}'!"
-        )
-
-    if condition_type == 'wav':
-        wav_cond = sample.wav[condition]
-        sample.wav[condition] = nullify_wav(wav_cond)
-    elif condition_type == 'joint_embed':
-        embed = sample.joint_embed[condition]
-        sample.joint_embed[condition] = nullify_joint_embed(embed)
-    else:
-        sample.text[condition] = None
-
-    return sample
-
-
-class DropoutModule(nn.Module):
-    """Base module for all dropout modules."""
-    def __init__(self, seed: int = 1234):
-        super().__init__()
-        self.rng = torch.Generator()
-        self.rng.manual_seed(seed)
-
-
-class AttributeDropout(DropoutModule):
-    """Dropout with a given probability per attribute.
-    This is different from the behavior of ClassifierFreeGuidanceDropout as this allows for attributes
-    to be dropped out separately. For example, "artist" can be dropped while "genre" remains.
-    This is in contrast to ClassifierFreeGuidanceDropout where if "artist" is dropped "genre"
-    must also be dropped.
-
-    Args:
-        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
-            ...
-            "genre": 0.1,
-            "artist": 0.5,
-            "wav": 0.25,
-            ...
-        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
-        seed (int, optional): Random seed.
-    """
-    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.active_on_eval = active_on_eval
-        # construct dict that return the values from p otherwise 0
-        self.p = {}
-        for condition_type, probs in p.items():
-            self.p[condition_type] = defaultdict(lambda: 0, probs)
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (list[ConditioningAttributes]): List of conditions.
-        Returns:
-            list[ConditioningAttributes]: List of conditions after certain attributes were set to None.
-        """
-        if not self.training and not self.active_on_eval:
-            return samples
-
-        samples = deepcopy(samples)
-        for condition_type, ps in self.p.items():  # for condition types [text, wav]
-            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
-                # import pdb; pdb.set_trace()
-                # print(condition, p)
-                if torch.rand(1, generator=self.rng).item() < p:
-                    for sample in samples:
-                        dropout_condition(sample, condition_type, condition)
-        return samples
-
-    def __repr__(self):
-        return f"AttributeDropout({dict(self.p)})"
-
-
-class ClassifierFreeGuidanceDropout(DropoutModule):
-    """Classifier Free Guidance dropout.
-    All attributes are dropped with the same probability.
-
-    Args:
-        p (float): Probability to apply condition dropout during training.
-        seed (int): Random seed.
-    """
-    def __init__(self, p: float, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.p = p
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (list[ConditioningAttributes]): List of conditions.
-        Returns:
-            list[ConditioningAttributes]: List of conditions after all attributes were set to None.
-        """
-
-        if not self.training:
-            return samples
-        # import pdb; pdb.set_trace()
-        # decide on which attributes to drop in a batched fashion
-        drop = torch.rand(1, generator=self.rng).item() < self.p
-        if not drop:
-            return samples
-
-        # nullify conditions of all attributes
-        samples = deepcopy(samples)
-        for condition_type in ["text", "wav","joint_embed"]:
-            for sample in samples:
-                for condition in sample.attributes[condition_type]:
-                    dropout_condition(sample, condition_type, condition)
-        return samples
-
-    def __repr__(self):
-        return f"ClassifierFreeGuidanceDropout(p={self.p})"
-
-
-class TextConditioner(BaseConditioner):
-    ...
-
-
-class WaveformConditioner(BaseConditioner):
-    """Base class for all conditioners that take a waveform as input.
-    Classes that inherit must implement `_get_wav_embedding` that outputs
-    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
-    factor of the embedding model.
-
-    Args:
-        dim (int): The internal representation dimension.
-        output_dim (int): Output dimension.
-    """
-    def __init__(self, dim: int, output_dim: int, input_token = False, padding_idx=None):
-        super().__init__(dim, output_dim, input_token, padding_idx)
-
-    def tokenize(self, x: WavCondition) -> WavCondition:
-        wav, length, sample_rate, path, seek_time = x
-        assert length is not None
-        return WavCondition(wav, length, sample_rate, path, seek_time)
-
-    def _get_wav_embedding(self, x: WavCondition) -> torch.Tensor:
-        """Gets as input a WavCondition and returns a dense embedding."""
-        raise NotImplementedError()
-
-    def _downsampling_factor(self):
-        """Returns the downsampling factor of the embedding model."""
-        raise NotImplementedError()
-
-    def forward(self, x: WavCondition) -> ConditionType:
-        """Extract condition embedding and mask from a waveform and its metadata.
-        Args:
-            x (WavCondition): Waveform condition containing raw waveform and metadata.
-        Returns:
-            ConditionType: a dense vector representing the conditioning along with its mask
-        """
-
-        wav, lengths, *_ = x
-        # import pdb; pdb.set_trace()
-        with torch.no_grad():
-            embeds = self._get_wav_embedding(x)
-        embeds = embeds.to(self.output_proj.weight)
-        embeds = self.output_proj(embeds)
-        # import pdb; pdb.set_trace()
-        if lengths is not None:
-            lengths = lengths / self._downsampling_factor()
-            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
-        else:
-            mask = torch.ones_like(embeds)
-        embeds = (embeds * mask.unsqueeze(2))
-
-        return embeds, mask
-
-
-class JointEmbeddingConditioner(BaseConditioner):
-    """Joint embedding conditioning supporting both audio or text conditioning.
-
-    Args:
-        dim (int): Dimension.
-        output_dim (int): Output dimension.
-        autocast_dtype (str): Autocast for the conditioner.
-        quantize (bool): Whether to quantize the CLAP embedding.
-        n_q (int): Number of residual quantizers (used if quantize is true).
-        bins (int): Quantizers' codebooks size (used if quantize is true).
-        kwargs: Additional parameters for residual vector quantizer.
-    """
-    def __init__(self, dim: int, output_dim: int, 
-                 autocast_dtype: tp.Optional[str] = 'float32', #quantize: bool = False,
-                 **kwargs):
-        super().__init__(dim=dim, output_dim=output_dim)
-        self.autocast_dtype = getattr(torch, autocast_dtype) if autocast_dtype is not None \
-                                else None
-        if self.autocast_dtype is None:
-            logger.warning("JointEmbeddingConditioner has no autocast, this might lead to NaN.")
-
-        # # residual vector quantizer to discretize the conditioned embedding
-        # self.quantizer  = None
-        # if quantize:
-        #     from ..modules.quantization import ResidualVectorQuantizer
-        #     self.quantizer = ResidualVectorQuantizer(dim, n_q=n_q, bins=bins, **kwargs)
-
-    def _get_embed(self, x: JointEmbedCondition) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-        """Get joint embedding in latent space from the inputs.
-
-        Returns:
-            tuple[torch.Tensor, torch.Tensor]: Tensor for the latent embedding
-                and corresponding empty indexes.
-        """
-        raise NotImplementedError()
-
-    def forward(self, x: JointEmbedCondition) -> ConditionType:
-        with torch.cuda.amp.autocast(dtype=self.autocast_dtype):
-            embed, empty_idx = self._get_embed(x)
-            if self.quantizer is not None:
-                embed = embed.view(-1, self.dim, 1)
-                q_res = self.quantizer(embed, frame_rate=1)
-                out_embed = q_res.x.view(-1, self.dim)
-            else:
-                out_embed = embed
-            out_embed = self.output_proj(out_embed).view(-1, 1, self.output_dim)
-            mask = torch.ones(*out_embed.shape[:2], device=out_embed.device)
-            mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
-            out_embed = (out_embed * mask.unsqueeze(-1))
-            return out_embed, mask
-
-    def tokenize(self, x: JointEmbedCondition) -> JointEmbedCondition:
-        return x
-
-
-class ConditioningProvider(nn.Module):
-    """Prepare and provide conditions given all the supported conditioners.
-
-    Args:
-        conditioners (dict): Dictionary of conditioners.
-    """
-    def __init__(self, conditioners: tp.Dict[str, BaseConditioner]):
-        super().__init__()
-        self.conditioners = nn.ModuleDict(conditioners)
-        def _check_conditioner_type(c):
-            if isinstance(c, WaveformConditioner):
-                return "wav"
-            elif isinstance(c, TextConditioner):
-                return "text"
-            elif isinstance(c, JointEmbeddingConditioner):
-                return "joint_embed"
-            else:
-                raise NotImplementedError(f"{type(c)} are not Implemented!")
-        self.conditioner_type = {k: _check_conditioner_type(self.conditioners[k]) for k in self.conditioners}
-
-
-    @property
-    def joint_embed_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, JointEmbeddingConditioner)]
-
-    @property
-    def has_joint_embed_conditions(self):
-        return len(self.joint_embed_conditions) > 0
-
-    @property
-    def text_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
-
-    @property
-    def wav_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
-
-    @property
-    def has_wav_condition(self):
-        return len(self.wav_conditions) > 0
-
-    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
-        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
-        This should be called before starting any real GPU work to avoid synchronization points.
-        This will return a dict matching conditioner names to their arbitrary tokenized representations.
-
-        Args:
-            inputs (list[ConditioningAttributes]): List of ConditioningAttributes objects containing
-                text and wav conditions.
-        """
-        assert all([isinstance(x, ConditioningAttributes) for x in inputs]), (
-            "Got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]",
-            f" but types were {set([type(x) for x in inputs])}"
-        )
-
-        # import pdb; pdb.set_trace()
-        output = {}
-        text = self._collate_text(inputs)
-        wavs = self._collate_wavs(inputs)
-        joint_embeds = self._collate_joint_embeds(inputs)
-
-        assert set(text.keys() | wavs.keys() | joint_embeds.keys()).issubset(set(self.conditioners.keys())), (
-            f"Got an unexpected attribute! Expected {self.conditioners.keys()}, ",
-            f"got {text.keys(), wavs.keys(), joint_embeds.keys()}"
-        )
-
-        for attribute, batch in chain(text.items(), wavs.items(), joint_embeds.items()):
-            output[attribute] = self.conditioners[attribute].tokenize(batch)
-        return output
-
-    def forward(self, tokenized: tp.Dict[str, tp.Any], texts = None) -> tp.Dict[str, ConditionType]:
-        """Compute pairs of `(embedding, mask)` using the configured conditioners and the tokenized representations.
-        The output is for example:
-        {
-            "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
-            "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
-            ...
-        }
-
-        Args:
-            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
-        """
-        # import pdb; pdb.set_trace()
-        output = {}
-        for attribute, inputs in tokenized.items():
-            if attribute == 'self_wav' and texts is not None:
-                condition, mask = self.conditioners[attribute](inputs, texts = texts)
-            else:
-                condition, mask = self.conditioners[attribute](inputs)
-            output[attribute] = (condition, mask)
-        return output
-
-    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
-        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
-        are the attributes and the values are the aggregated input per attribute.
-        For example:
-        Input:
-        [
-            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
-            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
-        ]
-        Output:
-        {
-            "genre": ["Rock", "Hip-hop"],
-            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
-        }
-
-        Args:
-            samples (list of ConditioningAttributes): List of ConditioningAttributes samples.
-        Returns:
-            dict[str, list[str, optional]]: A dictionary mapping an attribute name to text batch.
-        """
-        out: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
-        texts = [x.text for x in samples]
-        for text in texts:
-            for condition in self.text_conditions:
-                out[condition].append(text[condition])
-        return out
-
-    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, WavCondition]:
-        """Generate a dict where the keys are attributes by which we fetch similar wavs,
-        and the values are Tensors of wavs according to said attributes.
-
-        *Note*: by the time the samples reach this function, each sample should have some waveform
-        inside the "wav" attribute. It should be either:
-        1. A real waveform
-        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
-        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
-
-        Args:
-            samples (list of ConditioningAttributes): List of ConditioningAttributes samples.
-        Returns:
-            dict[str, WavCondition]: A dictionary mapping an attribute name to wavs.
-        """
-        # import pdb; pdb.set_trace()
-        wavs = defaultdict(list)
-        lengths = defaultdict(list)
-        sample_rates = defaultdict(list)
-        paths = defaultdict(list)
-        seek_times = defaultdict(list)
-        out: tp.Dict[str, WavCondition] = {}
-
-        for sample in samples:
-            for attribute in self.wav_conditions:
-                wav, length, sample_rate, path, seek_time = sample.wav[attribute]
-                assert wav.dim() == 3, f"Got wav with dim={wav.dim()}, but expected 3 [1, C, T]"
-                assert wav.size(0) == 1, f"Got wav [B, C, T] with shape={wav.shape}, but expected B == 1"
-                # mono-channel conditioning
-                # wav = wav.mean(1, keepdim=True)  # [1, 1, T] # by cyy, 为了实现后续功能注释掉了，请手动确保channel=1，or 输入channel 符合预期
-                wavs[attribute].append(wav.flatten())  # [C*T]
-                lengths[attribute].append(length)
-                sample_rates[attribute].extend(sample_rate)
-                paths[attribute].extend(path)
-                seek_times[attribute].extend(seek_time)
-
-        # stack all wavs to a single tensor
-        for attribute in self.wav_conditions:
-            stacked_wav, _ = collate(wavs[attribute], dim=0)
-            out[attribute] = WavCondition(
-                stacked_wav.unsqueeze(1), torch.cat(lengths[attribute]), sample_rates[attribute],
-                paths[attribute], seek_times[attribute])
-
-        return out
-
-    def _collate_joint_embeds(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, JointEmbedCondition]:
-        """Generate a dict where the keys are attributes by which we compute joint embeddings,
-        and the values are Tensors of pre-computed embeddings and the corresponding text attributes.
-
-        Args:
-            samples (list[ConditioningAttributes]): List of ConditioningAttributes samples.
-        Returns:
-            A dictionary mapping an attribute name to joint embeddings.
-        """
-        texts = defaultdict(list)
-        wavs = defaultdict(list)
-        lengths = defaultdict(list)
-        sample_rates = defaultdict(list)
-        paths = defaultdict(list)
-        seek_times = defaultdict(list)
-        channels: int = 0
-        
-        out = {}
-        for sample in samples:
-            for attribute in self.joint_embed_conditions:
-                wav, text, length, sample_rate, path, seek_time = sample.joint_embed[attribute]
-                assert wav.dim() == 3
-                if channels == 0:
-                    channels = wav.size(1)
-                else:
-                    assert channels == wav.size(1), "not all audio has same number of channels in batch"
-                assert wav.size(0) == 1, "Expecting single-wav batch in the collate method"
-                wav = einops.rearrange(wav, "b c t -> (b c t)")  # [1, C, T] => [C * T] 
-                wavs[attribute].append(wav)
-                texts[attribute].extend(text)
-                lengths[attribute].append(length)
-                sample_rates[attribute].extend(sample_rate)
-                paths[attribute].extend(path)
-                seek_times[attribute].extend(seek_time)
-
-        for attribute in self.joint_embed_conditions:
-            stacked_texts = texts[attribute]
-            stacked_paths = paths[attribute]
-            stacked_seek_times = seek_times[attribute]
-            stacked_wavs = pad_sequence(wavs[attribute])
-            stacked_wavs = einops.rearrange(stacked_wavs, "(c t) b -> b c t", c=channels)
-            stacked_sample_rates = sample_rates[attribute]
-            stacked_lengths = torch.cat(lengths[attribute])
-
-            assert stacked_lengths.size(0) == stacked_wavs.size(0)
-            assert len(stacked_sample_rates) == stacked_wavs.size(0)
-            assert len(stacked_texts) == stacked_wavs.size(0)
-            out[attribute] = JointEmbedCondition(
-                text=stacked_texts, wav=stacked_wavs,
-                length=stacked_lengths, sample_rate=stacked_sample_rates,
-                path=stacked_paths, seek_time=stacked_seek_times)
-
-        return out
-
-
-class ConditionFuser(StreamingModule):
-    """Condition fuser handles the logic to combine the different conditions
-    to the actual model input.
-
-    Args:
-        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
-            each condition. For example:
-            {
-                "prepend": ["description"],
-                "sum": ["genre", "bpm"],
-                "cross": ["description"],
-            }
-        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
-        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
-    """
-    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
-
-    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
-                 cross_attention_pos_emb_scale: float = 1.0):
-        super().__init__()
-        assert all(
-            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
-        ), f"Got invalid fuse method, allowed methods: {self.FUSING_METHODS}"
-        self.cross_attention_pos_emb = cross_attention_pos_emb
-        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
-        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
-        self.cond2fuse: tp.Dict[str, str] = {}
-        for fuse_method, conditions in fuse2cond.items():
-            for condition in conditions:
-                self.cond2fuse[condition] = fuse_method
-
-    def forward(
-        self,
-        input: torch.Tensor,
-        conditions: tp.Dict[str, ConditionType]
-    ) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        """Fuse the conditions to the provided model input.
-
-        Args:
-            input (torch.Tensor): Transformer input.
-            conditions (dict[str, ConditionType]): Dict of conditions.
-        Returns:
-            tuple[torch.Tensor, torch.Tensor]: The first tensor is the transformer input
-                after the conditions have been fused. The second output tensor is the tensor
-                used for cross-attention or None if no cross attention inputs exist.
-        """
-        # import pdb; pdb.set_trace()
-        B, T, _ = input.shape
-
-        if 'offsets' in self._streaming_state:
-            first_step = False
-            offsets = self._streaming_state['offsets']
-        else:
-            first_step = True
-            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
-
-        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
-            f"given conditions contain unknown attributes for fuser, " \
-            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
-        cross_attention_output = None
-        prepend_input = input[:, :0]
-        for cond_type, (cond, cond_mask) in conditions.items():
-            op = self.cond2fuse[cond_type]
-            if op == 'sum': 
-                input += cond
-            elif op == 'input_interpolate':
-                cond = einops.rearrange(cond, "b t d -> b d t")
-                cond = F.interpolate(cond, size=input.shape[1])
-                input += einops.rearrange(cond, "b d t -> b t d")
-            elif op == 'prepend':
-                prepend_input = torch.cat([cond.to(input.dtype), prepend_input], dim=1) 
-                # NOTE 这里cond应该在后,这样顺序才符合配置文件,否则为逆序
-                # 但是之前实验是这样的为了保持一致就没改
-            elif op == 'cross':
-                if cross_attention_output is not None:
-                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
-                else:
-                    cross_attention_output = cond
-            else:
-                raise ValueError(f"unknown op ({op})")
-
-        if self.cross_attention_pos_emb and cross_attention_output is not None:
-            positions = torch.arange(
-                cross_attention_output.shape[1],
-                device=cross_attention_output.device
-            ).view(1, -1, 1)
-            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
-            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
-
-        if first_step:
-            input = torch.cat([prepend_input, input], dim=1)
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return input, cross_attention_output

SongBloom/models/musicgen/conditioners/text.py

@@ -1,254 +0,0 @@
-from .base import *
-
-import spacy
-import warnings
-import random
-import hashlib
-from transformers import RobertaTokenizer, T5EncoderModel, T5Tokenizer, AutoTokenizer, XLMRobertaModel, XLMRobertaTokenizer  # type: ignore
-from num2words import num2words
-
-def hash_trick(word: str, vocab_size: int) -> int:
-    """Hash trick to pair each word with an index
-
-    Args:
-        word (str): word we wish to convert to an index
-        vocab_size (int): size of the vocabulary
-    Returns:
-        int: index of the word in the embedding LUT
-    """
-
-    hash = int(hashlib.sha256(word.encode("utf-8")).hexdigest(), 16)
-    return hash % vocab_size
-
-
-
-class PhonemeTokenizerConditioner(TextConditioner):
-    def __init__(self, 
-                 output_dim: int, 
-                 vocab_list,
-                 max_len = 600, 
-                 max_sentence_per_structure = 50,
-                 structure_tokens=None,
-                 structure_split_tokens=[','],
-                 sentence_split_tokens=['.'],
-                 mode='sum',
-                 structure_output_dim = 64,
-                 sentence_output_dim = 64,
-                 max_duration = 120,
-                 interpolate = False,
-                 ): 
-        
-        self.vocab_list = vocab_list
-        self.max_len = max_len
-        self.mode = mode
-        self.max_sentence_per_structure = max_sentence_per_structure
-        voc_size = len(self.vocab_list)
-        self.interpolate = interpolate
-        
-        if structure_tokens is None:
-            structure_tokens = [i for i in vocab_list if len(i) > 1 and i[0] == '[' and i[-1] == ']']
-        self.structure_token_ids = [vocab_list.index(i) for i in structure_tokens if i in vocab_list]
-        self.structure_split_token_ids = [vocab_list.index(i) for i in structure_split_tokens]
-        self.sentence_split_token_ids = [vocab_list.index(i) for i in sentence_split_tokens]
-
-        # here initialize a output_proj (nn.Embedding) layer
-        # By default the first vocab is "" (null)
-        if mode == 'sum':
-            content_output_dim = output_dim
-            sentence_output_dim = output_dim
-            structure_output_dim = output_dim
-        else:   # concat
-            content_output_dim = output_dim - sentence_output_dim - structure_output_dim   # by default
-            
-        super().__init__(voc_size, content_output_dim, input_token=True, padding_idx=0)
-        if self.mode != 'sum':
-            self.special_emb = nn.Embedding(len(self.structure_token_ids)+len(self.structure_split_token_ids)+len(self.sentence_split_token_ids)+1, 
-                                             structure_output_dim, padding_idx=0)
-            
-        self.blank_emb = nn.Parameter(torch.zeros(1, output_dim), requires_grad=False)
-
-        # the first index is "empty structure" token
-        self.sentence_idx_in_structure_emb = nn.Embedding(max_sentence_per_structure, sentence_output_dim, padding_idx=0) 
-
-        # print("max_len", self.max_len)
-        print(self.structure_token_ids)
-        
-        self.resolution = max_duration / max_len    # e.g., 120 / 600 = 0.2s 
-        print(self.__class__, f"resolution = {self.resolution}")
-    
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
-        inputs = []
-        for xx in x:
-            xx = '' if xx is None else xx
-            vocab_id = [self.vocab_list.index(item) for item in xx.split(" ") if item in self.vocab_list]
-            inputs.append(torch.tensor(vocab_id).long()) # [T]        
-        return inputs
-    
-    
-    def interpolate_with_structure_duration(self, special_tokens, embeds, structure_dur):
-        # embeds: [T, N]
-        def sec2idx(sec):   # convert duration sec to token index
-            return int(sec / self.resolution)
-        
-        def target_token_types2list(tokens, target_token_types):
-
-            is_target_list = torch.any(torch.stack([tokens == i for i in target_token_types], dim=-1), dim=-1)
-            is_target_list = torch.where(is_target_list)[0].tolist()
-            return is_target_list
-        
-        structure_ids = []
-        for (structure, st, et) in structure_dur:
-            structure_ids.append([structure, sec2idx(st), sec2idx(et)])
-            
-        """
-        interpolate embeddings of each structure according to its duration 
-        """
-        is_structure_list = target_token_types2list(special_tokens, self.structure_token_ids)
-        is_structure_list.append(special_tokens.shape[-1])
-        
-        split_tokens = deepcopy(self.structure_split_token_ids)
-        split_tokens.extend(self.sentence_split_token_ids)
-        # is_split_list = target_token_types2list(special_tokens, split_tokens)
-                
-        
-        interpolated_embeds = embeds[:is_structure_list[0]]
-        for i, st in enumerate(is_structure_list[:-1]):
-            # (lorry) Explain "-tmp": 
-            # All structures are connected with " , " token,
-            # " ," is also the final token of each structure except the final one,
-            # but here we dont want to interpolate " , " token
-            tmp = 1
-            if i == len(is_structure_list[:-1]) - 1:  # the final structure, no need for "-1"
-                tmp = 0
-            
-       #     print(st, is_structure_list[i+1]-tmp)
-            to_interpolate = embeds[st: is_structure_list[i+1] - tmp]
-            interpolate_size = structure_ids[i][2] - structure_ids[i][1] - tmp
-       #     print(interpolate_size)
-            
-            #import pdb; pdb.set_trace()
-            # print(interpolated_embeds.shape, to_interpolate.shape, interpolate_size, )
-            if to_interpolate.shape[0] == 0:
-                import pdb; pdb.set_trace()
-            this_interpolated_embeds = F.interpolate(to_interpolate.unsqueeze(0).transpose(2, 1), 
-                                        size=interpolate_size, 
-                                        mode='nearest-exact').squeeze(0).transpose(1, 0)
-            
-            if tmp == 1:
-                interpolated_embeds = torch.cat((interpolated_embeds, this_interpolated_embeds, 
-                                                embeds[is_structure_list[i+1]].unsqueeze(0)), 0)
-            else:
-                interpolated_embeds = torch.cat((interpolated_embeds, this_interpolated_embeds), 0)
-        return interpolated_embeds
-            
-            
-    def forward(self, batch_tokens: tp.List, structure_dur = None) -> ConditionType:
-        """
-        Encode token_id into three types of embeddings:
-        1) content embedding: phoneme only (or meaningful contents to be sung out) 
-        2) structure embedding: structure / separation embeddings, including structures (verse/chorus/...), separators (. / ,)
-        The two above share the same embedding layer, can be changed to separate embedding layers.
-        3) sentence_idx embedding (per structure): 
-        """
-        embeds_batch = []
-        # print(batch_tokens)
-        for b in range(len(batch_tokens)):
-            tokens = batch_tokens[b]  
-
-            content_tokens = torch.zeros_like(tokens)
-            special_tokens = torch.zeros_like(tokens)
-            sentence_idx_in_structure_tokens = torch.zeros_like(tokens) 
-
-            current_structure_idx = 1
-            current_sentence_in_structure_idx = 1
-            current_structure = 0
-
-            for i in range(tokens.shape[-1]):
-                token = tokens[i]
-                if token in self.structure_token_ids:       # structure token
-                    # only update structure token, leave content and sentence index token null (default 0)
-                    if self.mode == 'sum':
-                        special_tokens[i] = token
-                    else:
-                        special_tokens[i] = self.structure_token_ids.index(token) + 1
-                    current_structure = token
-                    current_structure_idx += 1
-                    current_sentence_in_structure_idx = 1
-
-                elif token in self.sentence_split_token_ids:    # utterance split token
-                    # only update structure token, leave content and sentence index token null (default 0)
-                    # add up sentence index
-                    if self.mode == 'sum':
-                        special_tokens[i] = token
-                    else:
-                        special_tokens[i] = self.sentence_split_token_ids.index(token) + 1 + len(self.structure_token_ids)
-                    current_sentence_in_structure_idx += 1
-
-                elif token in self.structure_split_token_ids:    # structure split token
-                    # update structure token (current structure), content token (current token), 
-                    # blank index token 
-                    if self.mode == 'sum':
-                        special_tokens[i] = token
-                    else:
-                        special_tokens[i] = self.structure_split_token_ids.index(token) + 1 + len(self.structure_token_ids) + len(self.sentence_split_token_ids)
-
-                else:       # content tokens
-                    content_tokens[i] = token
-                    special_tokens[i] = current_structure
-                    sentence_idx_in_structure_tokens[i] = min(current_sentence_in_structure_idx, self.max_sentence_per_structure - 1)
-
-            # print("tokens", tokens.max(), tokens.min())
-            # print("special tokens", special_tokens.max(), special_tokens.min())
-            # print("sentence idx in structure", sentence_idx_in_structure_tokens.max(), sentence_idx_in_structure_tokens.min())
-            device = self.output_proj.weight.device
-            
-            # import pdb; pdb.set_trace()
-            content_embeds = self.output_proj(tokens.to(device))    # [T, N]
-            if self.mode == 'sum':
-                structure_embeds = self.output_proj(special_tokens.to(device))
-            else:
-                structure_embeds = self.special_emb(special_tokens.to(device))
-            sentence_idx_embeds = self.sentence_idx_in_structure_emb(sentence_idx_in_structure_tokens.to(device))
-
-            if self.mode == 'sum':
-                embeds = content_embeds + structure_embeds + sentence_idx_embeds
-            else:
-                embeds = torch.cat((content_embeds, structure_embeds, sentence_idx_embeds), -1) # [T, N]
-                
-            if self.interpolate:
-                embeds = self.interpolate_with_structure_duration(tokens, embeds, structure_dur[b])
-            embeds_batch.append(embeds)
-
-        # set batch_size = 1, [B, T, N]
-        if self.max_len is not None:
-            max_len = self.max_len
-        else:
-            max_len = max([e.shape[0] for e in embeds_batch])
-        embeds, mask = self.pad_2d_tensor(embeds_batch, max_len)
-        
-        return embeds, mask
-    
-    
-    def pad_2d_tensor(self, xs, max_len):
-        new_tensor = []
-        new_mask = []
-        for x in xs:
-            seq_len, dim = x.size()
-            pad_len = max_len - seq_len
-
-            if pad_len > 0:
-                pad_tensor = self.blank_emb.repeat(pad_len, 1).to(x.device)  # T, D
-                padded_tensor = torch.cat([x, pad_tensor], dim=0)
-                mask = torch.cat((torch.ones_like(x[:, 0]), 
-                                  torch.zeros_like(pad_tensor[:, 0])), 0)   # T
-            elif pad_len < 0:
-                padded_tensor = x[:max_len]
-                mask = torch.ones_like(padded_tensor[:, 0])
-            else:
-                padded_tensor = x
-                mask = torch.ones_like(x[:, 0])
-
-            new_tensor.append(padded_tensor)
-            new_mask.append(mask)
-        # [B, T, D] & [B, T]
-        return torch.stack(new_tensor, 0), torch.stack(new_mask, 0)   

SongBloom/models/musicgen/conditioners/wav.py

@@ -1,74 +0,0 @@
-
-from .base import *
-import omegaconf
-from ...vae_frontend import AbstractVAE
-
-def pad_to_fix_length(x, max_len, pad_value=0.):
-    bsz, seq_len = x.shape[:2]
-    if seq_len >= max_len:
-        return x[:, :max_len]
-    else:
-        pad_len = max_len - seq_len
-        pad_tensor = torch.full((bsz, pad_len, *x.shape[2:]), pad_value, dtype=x.dtype, device=x.device)
-        padded_tensor = torch.cat([x, pad_tensor], dim=1)
-        return padded_tensor
-
-class AudioTokenizerConditioner(WaveformConditioner):
-    def __init__(self, output_dim, audio_tokenizer, cache=False, max_len=None):
-        super().__init__(output_dim, output_dim)
-        self.max_len = max_len
-        self.use_cache = cache
-        
-        self.tokenizer = audio_tokenizer
-        # breakpoint()
-        
-        # TODO if cached and not load vae, receive a dict instead
-        if isinstance(self.tokenizer, dict):
-            self.tokenizer = omegaconf.DictConfig(self.tokenizer)
-            self.code_depth = self.tokenizer.channel_dim
-            
-            
-        elif isinstance(self.tokenizer, AbstractVAE):
-            self.tokenizer_tp = "vae"
-            if self.use_cache:
-                self.code_depth = self.tokenizer.channel_dim
-            else:
-                self.code_depth = 1 # TODO 强制把输入channel设成1了 self.tokenizer.input_channel
-            self.output_proj = nn.Identity() if self.output_dim == self.tokenizer.channel_dim \
-                            else nn.Linear(self.tokenizer.channel_dim, self.output_dim, bias=False)
-                            
-        else:
-            raise NotImplementedError
-        
-        
-    def forward(self, x: WavCondition):
-        wav, lengths, *_ = x
-        B = wav.shape[0]
-        wav = wav.reshape(B, self.code_depth, -1)
-        # print(wav.shape)
-        # import torchaudio
-        # torchaudio.save("/apdcephfs_cq7/share_1297902/common/erichtchen/shixisheng/cyy/project/music_generation_repo/core/models/musicgen/conditioners/111.wav", wav[0].cpu(), 48000)
-        if self.tokenizer_tp == "vae":
-            if self.use_cache:
-                audio_latents = wav.transpose(-1,-2)
-            else:
-                with torch.no_grad():
-                    audio_latents = self.tokenizer.encode(wav).transpose(-1,-2)
-                    # print('transform wav to vae')
-            audio_latents = self.output_proj(audio_latents)
-
-        # print(audio_latents.shape)
-        if self.max_len is not None:
-            audio_latents = pad_to_fix_length(audio_latents, self.max_len, 0.)
-                    
-        if lengths is not None:
-            lengths = torch.round(lengths.float() * audio_latents.shape[1] / wav.shape[-1])
-            mask = length_to_mask(lengths, max_len=audio_latents.shape[1]).int()  # type: ignore
-        else:
-            mask = torch.ones((B, audio_latents.shape[1]), device=audio_latents.device,dtype=torch.int)
-
-        audio_latents = audio_latents * mask[..., None]
-        
-        return audio_latents, mask
-     
-

SongBloom/models/musicgen/get_backend.py

@@ -1,76 +0,0 @@
-import torch
-import os,sys
-from transformers.utils import is_flash_attn_2_available
-from transformers.models.llama import LlamaModel, LlamaConfig
-from transformers.models.bart.modeling_bart import BartEncoder, BartDecoder, BartConfig
-import warnings
-# from transformers.models.musicgen.modeling_musicgen import MusicgenModel, MusicgenDecoder, MusicgenDecoderConfig # 用的就是BartDecoder，但是没有cross-attn
-
-try:
-    assert is_flash_attn_2_available()
-    assert torch.cuda.get_device_capability(torch.device("cuda")) >= (8, 0)
-    assert os.environ.get("DISABLE_FLASH_ATTN",'0') != "1"
-    _enable_flash_attention = True
-except:
-    _enable_flash_attention = False
-
-if not _enable_flash_attention:
-    warnings.warn("Not support flash-attn!")
-
-def get_backend(name, dim, num_heads, num_layers, hidden_scale, init_std=0.02, rope_theta=10000,):
-    # SA (causal) - FF
-    if name == 'llama':
-        model_cfg = LlamaConfig(
-            hidden_size=dim,
-            intermediate_size=dim * hidden_scale,
-            num_attention_heads=num_heads,
-            num_hidden_layers=num_layers,
-            num_key_value_heads=num_heads,
-            vocab_size=dim,
-            use_cache=False,
-            max_position_embeddings=4096,
-            hidden_act="silu",
-            initializer_range=init_std,
-            rope_theta=rope_theta,
-            _attn_implementation="flash_attention_2" if _enable_flash_attention else "eager",
-        )
-        model = LlamaModel(model_cfg)
-        
-    # SA -FF
-    elif name == 'bart_enc':
-        model_cfg = BartConfig(
-            d_model=dim,
-            max_position_embeddings=4096,
-            dropout=0., 
-            use_cache=False,
-            _attn_implementation="flash_attention_2" if _enable_flash_attention else "eager",
-            activation_function='gelu',
-            # for BartEncoder
-            encoder_layers=num_layers, 
-            encoder_attention_heads=num_heads,
-            init_std=init_std,
-            encoder_ffn_dim=dim * hidden_scale,
-        )
-        model = BartEncoder(model_cfg)
-        
-    # SA - CA - FF
-    elif name == 'bart_dec':
-        model_cfg = BartConfig(
-            d_model=dim,
-            max_position_embeddings=4096,
-            dropout=0., 
-            use_cache=False,
-            _attn_implementation="flash_attention_2" if _enable_flash_attention else "eager",
-            activation_function='gelu',
-            # for BartDecoder
-            decoder_layers=num_layers,
-            decoder_attention_heads=num_heads,
-            decoder_ffn_dim=dim * hidden_scale,
-        )
-        model = BartDecoder(model_cfg)
-
-    else:
-        raise NotImplementedError
-
-    delattr(model, "embed_tokens")
-    return model

SongBloom/models/musicgen/modules/streaming.py

@@ -1,125 +0,0 @@
-"""
-Streaming module API that should be implemented by all Streaming components,
-"""
-
-from contextlib import contextmanager
-import typing as tp
-from torch import nn
-import torch
-
-
-State = tp.Dict[str, torch.Tensor]
-
-
-class StreamingModule(nn.Module):
-    """Common API for streaming components.
-
-    Each streaming component has a streaming state, which is just a dict[str, Tensor].
-    By convention, the first dim of each tensor must be the batch size.
-    Don't use dots in the key names, as this would clash with submodules
-    (like in state_dict).
-
-    If `self._is_streaming` is True, the component should use and remember
-    the proper state inside `self._streaming_state`.
-
-    To set a streaming component in streaming state, use
-
-        with module.streaming():
-            ...
-
-    This will automatically reset the streaming state when exiting the context manager.
-    This also automatically propagates to all streaming children module.
-
-    Some module might also implement the `StreamingModule.flush` method, although
-    this one is trickier, as all parents module must be StreamingModule and implement
-    it as well for it to work properly. See `StreamingSequential` after.
-    """
-    def __init__(self) -> None:
-        super().__init__()
-        self._streaming_state: State = {}
-        self._is_streaming = False
-
-    def _apply_named_streaming(self, fn: tp.Any):
-        for name, module in self.named_modules():
-            if isinstance(module, StreamingModule):
-                fn(name, module)
-
-    def _set_streaming(self, streaming: bool):
-        def _set_streaming(name, module):
-            module._is_streaming = streaming
-        self._apply_named_streaming(_set_streaming)
-
-    @contextmanager
-    def streaming(self):
-        """Context manager to enter streaming mode. Reset streaming state on exit."""
-        self._set_streaming(True)
-        try:
-            yield
-        finally:
-            self._set_streaming(False)
-            self.reset_streaming()
-
-    def reset_streaming(self):
-        """Reset the streaming state."""
-        def _reset(name: str, module: StreamingModule):
-            module._streaming_state.clear()
-
-        self._apply_named_streaming(_reset)
-
-    def get_streaming_state(self) -> State:
-        """Return the streaming state, including that of sub-modules."""
-        state: State = {}
-
-        def _add(name: str, module: StreamingModule):
-            if name:
-                name += "."
-            for key, value in module._streaming_state.items():
-                state[name + key] = value
-
-        self._apply_named_streaming(_add)
-        return state
-
-    def set_streaming_state(self, state: State):
-        """Set the streaming state, including that of sub-modules."""
-        state = dict(state)
-
-        def _set(name: str, module: StreamingModule):
-            if name:
-                name += "."
-            module._streaming_state.clear()
-            for key, value in list(state.items()):
-                # complexity is not ideal here, but probably fine.
-                if key.startswith(name):
-                    local_key = key[len(name):]
-                    if '.' not in local_key:
-                        module._streaming_state[local_key] = value
-                        del state[key]
-
-        self._apply_named_streaming(_set)
-        assert len(state) == 0, list(state.keys())
-
-    def flush(self, x: tp.Optional[torch.Tensor] = None):
-        """Flush any remaining outputs that were waiting for completion.
-        Typically, for convolutions, this will add the final padding
-        and process the last buffer.
-
-        This should take an optional argument `x`, which will be provided
-        if a module before this one in the streaming pipeline has already
-        spitted out a flushed out buffer.
-        """
-        if x is None:
-            return None
-        else:
-            return self(x)
-
-
-class StreamingSequential(StreamingModule, nn.Sequential):
-    """A streaming compatible alternative of `nn.Sequential`.
-    """
-    def flush(self, x: tp.Optional[torch.Tensor] = None):
-        for module in self:
-            if isinstance(module, StreamingModule):
-                x = module.flush(x)
-            elif x is not None:
-                x = module(x)
-        return x

SongBloom/models/musicldm/inference/sampling.py

@@ -1,271 +0,0 @@
-import torch
-import math
-from tqdm import trange, tqdm
-
-# import k_diffusion as K
-
-# Define the noise schedule and sampling loop
-def get_alphas_sigmas(t):
-    """Returns the scaling factors for the clean image (alpha) and for the
-    noise (sigma), given a timestep."""
-    return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)
-
-def alpha_sigma_to_t(alpha, sigma):
-    """Returns a timestep, given the scaling factors for the clean image and for
-    the noise."""
-    return torch.atan2(sigma, alpha) / math.pi * 2
-
-def t_to_alpha_sigma(t):
-    """Returns the scaling factors for the clean image and for the noise, given
-    a timestep."""
-    return torch.cos(t * math.pi / 2), torch.sin(t * math.pi / 2)
-
-
-@torch.no_grad()
-def sample_discrete_euler(model, x, steps, sigma_max=1.0, prog_bar=False, **extra_args):
-    """Draws samples from a model given starting noise. Euler method"""
-
-    # Make tensor of ones to broadcast the single t values
-    ts = x.new_ones([x.shape[0]])
-
-    # Create the noise schedule
-    t = torch.linspace(sigma_max, 0, steps + 1)
-    # all = {}
-
-    #alphas, sigmas = 1-t, t
-    iterator = tqdm(zip(t[:-1], t[1:]), total=steps) if prog_bar else zip(t[:-1], t[1:])
-    for t_curr, t_prev in iterator:
-            # Broadcast the current timestep to the correct shape
-            t_curr_tensor = t_curr * torch.ones(
-                (x.shape[0],), dtype=x.dtype, device=x.device
-            )
-            dt = t_prev - t_curr  # we solve backwards in our formulation
-            v = model(x, t_curr_tensor, **extra_args)
-            # all[t_curr.item()] = x-t_curr*v
-            x = x + dt * v #.denoise(x, denoiser, t_curr_tensor, cond, uc)
-            
-    # If we are on the last timestep, output the denoised image
-    return x #, all
-
-@torch.no_grad()
-def sample_discrete_euler_with_temperature(model, x, steps, temperature=1.0, sigma_max=1.0, prog_bar=False, **extra_args):
-    """Draws samples from a model given starting noise. Euler method"""
-
-    # Make tensor of ones to broadcast the single t values
-    ts = x.new_ones([x.shape[0]])
-    noise = x
-
-    # Create the noise schedule
-    t = torch.linspace(sigma_max, 0, steps + 1)
-    # all = {}
-    x = torch.zeros_like(noise)
-    if temperature >= sigma_max:
-        x = noise
-        
-    #alphas, sigmas = 1-t, t
-    iterator = tqdm(zip(t[:-1], t[1:]), total=steps) if prog_bar else zip(t[:-1], t[1:])
-    for t_curr, t_prev in iterator:
-            # Broadcast the current timestep to the correct shape
-                
-            t_curr_tensor = t_curr * torch.ones(
-                (x.shape[0],), dtype=x.dtype, device=x.device
-            )
-            dt = t_prev - t_curr  # we solve backwards in our formulation
-            v = model(x, t_curr_tensor, **extra_args)
-            # all[t_curr.item()] = x-t_curr*v
-            if  t_curr > temperature and t_prev <= temperature:
-                x_0 = x - v
-                x = (1-t_prev) * x_0 + t_prev * noise
-            else:
-                x = x + dt * v #.denoise(x, denoiser, t_curr_tensor, cond, uc)
-            
-    # If we are on the last timestep, output the denoised image
-    return x #, all
-
-
-@torch.no_grad()
-def sample(model, x, steps, eta, prog_bar=False, **extra_args):
-    """Draws samples from a model given starting noise. v-diffusion"""
-    ts = x.new_ones([x.shape[0]])
-    origin_dtype = x.dtype
-    # Create the noise schedule
-    t = torch.linspace(1, 0, steps + 1)[:-1]
-
-    alphas, sigmas = get_alphas_sigmas(t)
-
-    # The sampling loop
-    bar = trange if prog_bar else range
-    for i in bar(steps):
-
-        # Get the model output (v, the predicted velocity)
-        with torch.cuda.amp.autocast():
-            v = model(x, ts * t[i], **extra_args).float()
-
-        # Predict the noise and the denoised image
-        pred = x * alphas[i] - v * sigmas[i]
-        eps = x * sigmas[i] + v * alphas[i]
-
-        # If we are not on the last timestep, compute the noisy image for the
-        # next timestep.
-        if i < steps - 1:
-            # If eta > 0, adjust the scaling factor for the predicted noise
-            # downward according to the amount of additional noise to add
-            ddim_sigma = eta * (sigmas[i + 1]**2 / sigmas[i]**2).sqrt() * \
-                (1 - alphas[i]**2 / alphas[i + 1]**2).sqrt()
-            adjusted_sigma = (sigmas[i + 1]**2 - ddim_sigma**2).sqrt()
-
-            # Recombine the predicted noise and predicted denoised image in the
-            # correct proportions for the next step
-            x = pred * alphas[i + 1] + eps * adjusted_sigma
-            # Add the correct amount of fresh noise
-            if eta:
-                x += torch.randn_like(x) * ddim_sigma
-
-    # If we are on the last timestep, output the denoised image
-    return pred.to(origin_dtype)
-
-# Soft mask inpainting is just shrinking hard (binary) mask inpainting
-# Given a float-valued soft mask (values between 0 and 1), get the binary mask for this particular step
-def get_bmask(i, steps, mask):
-    strength = (i+1)/(steps)
-    # convert to binary mask
-    bmask = torch.where(mask<=strength,1,0)
-    return bmask
-
-def make_cond_model_fn(model, cond_fn):
-    def cond_model_fn(x, sigma, **kwargs):
-        with torch.enable_grad():
-            x = x.detach().requires_grad_()
-            denoised = model(x, sigma, **kwargs)
-            cond_grad = cond_fn(x, sigma, denoised=denoised, **kwargs).detach()
-            cond_denoised = denoised.detach() + cond_grad * K.utils.append_dims(sigma**2, x.ndim)
-        return cond_denoised
-    return cond_model_fn
-
-# Uses k-diffusion from https://github.com/crowsonkb/k-diffusion
-# init_data is init_audio as latents (if this is latent diffusion)
-# For sampling, set both init_data and mask to None
-# For variations, set init_data 
-# For inpainting, set both init_data & mask 
-def sample_k(
-        model_fn, 
-        noise, 
-        init_data=None,
-        mask=None,
-        steps=100, 
-        sampler_type="dpmpp-2m-sde", 
-        sigma_min=0.5, 
-        sigma_max=50, 
-        rho=1.0, device="cuda", 
-        callback=None, 
-        cond_fn=None,
-        **extra_args
-    ):
-
-    denoiser = K.external.VDenoiser(model_fn)
-
-    if cond_fn is not None:
-        denoiser = make_cond_model_fn(denoiser, cond_fn)
-
-    # Make the list of sigmas. Sigma values are scalars related to the amount of noise each denoising step has
-    sigmas = K.sampling.get_sigmas_polyexponential(steps, sigma_min, sigma_max, rho, device=device)
-    # Scale the initial noise by sigma 
-    noise = noise * sigmas[0]
-
-    wrapped_callback = callback
-
-    if mask is None and init_data is not None:
-        # VARIATION (no inpainting)
-        # set the initial latent to the init_data, and noise it with initial sigma
-        x = init_data + noise 
-    elif mask is not None and init_data is not None:
-        # INPAINTING
-        bmask = get_bmask(0, steps, mask)
-        # initial noising
-        input_noised = init_data + noise
-        # set the initial latent to a mix of init_data and noise, based on step 0's binary mask
-        x = input_noised * bmask + noise * (1-bmask)
-        # define the inpainting callback function (Note: side effects, it mutates x)
-        # See https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py#L596C13-L596C105
-        # callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        # This is called immediately after `denoised = model(x, sigmas[i] * s_in, **extra_args)`
-        def inpainting_callback(args):
-            i = args["i"]
-            x = args["x"]
-            sigma = args["sigma"]
-            #denoised = args["denoised"]
-            # noise the init_data input with this step's appropriate amount of noise
-            input_noised = init_data + torch.randn_like(init_data) * sigma
-            # shrinking hard mask
-            bmask = get_bmask(i, steps, mask)
-            # mix input_noise with x, using binary mask
-            new_x = input_noised * bmask + x * (1-bmask)
-            # mutate x
-            x[:,:,:] = new_x[:,:,:]
-        # wrap together the inpainting callback and the user-submitted callback. 
-        if callback is None: 
-            wrapped_callback = inpainting_callback
-        else:
-            wrapped_callback = lambda args: (inpainting_callback(args), callback(args))
-    else:
-        # SAMPLING
-        # set the initial latent to noise
-        x = noise
-
-
-    with torch.cuda.amp.autocast():
-        if sampler_type == "k-heun":
-            return K.sampling.sample_heun(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "k-lms":
-            return K.sampling.sample_lms(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "k-dpmpp-2s-ancestral":
-            return K.sampling.sample_dpmpp_2s_ancestral(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "k-dpm-2":
-            return K.sampling.sample_dpm_2(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "k-dpm-fast":
-            return K.sampling.sample_dpm_fast(denoiser, x, sigma_min, sigma_max, steps, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "k-dpm-adaptive":
-            return K.sampling.sample_dpm_adaptive(denoiser, x, sigma_min, sigma_max, rtol=0.01, atol=0.01, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "dpmpp-2m-sde":
-            return K.sampling.sample_dpmpp_2m_sde(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
-        elif sampler_type == "dpmpp-3m-sde":
-            return K.sampling.sample_dpmpp_3m_sde(denoiser, x, sigmas, disable=False, callback=wrapped_callback, extra_args=extra_args)
-
-# Uses discrete Euler sampling for rectified flow models
-# init_data is init_audio as latents (if this is latent diffusion)
-# For sampling, set both init_data and mask to None
-# For variations, set init_data 
-# For inpainting, set both init_data & mask 
-def sample_rf(
-        model_fn, 
-        noise, 
-        init_data=None,
-        steps=100, 
-        sigma_max=1,
-        device="cuda", 
-        callback=None, 
-        cond_fn=None,
-        **extra_args
-    ):
-
-    if sigma_max > 1:
-        sigma_max = 1
-
-    if cond_fn is not None:
-        denoiser = make_cond_model_fn(denoiser, cond_fn)
-
-    wrapped_callback = callback
-
-    if init_data is not None:
-        # VARIATION (no inpainting)
-        # Interpolate the init data and the noise for init audio
-        x = init_data * (1 - sigma_max) + noise * sigma_max
-    else:
-        # SAMPLING
-        # set the initial latent to noise
-        x = noise
-
-    with torch.cuda.amp.autocast():
-        # TODO: Add callback support
-        #return sample_discrete_euler(model_fn, x, steps, sigma_max, callback=wrapped_callback, **extra_args)
-        return sample_discrete_euler(model_fn, x, steps, sigma_max, **extra_args)

SongBloom/models/musicldm/musicldm_dit.py

@@ -1,24 +0,0 @@
-from contextlib import contextmanager
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-
-import torch
-from torch import nn
-
-
-
-class FourierFeatures(nn.Module):
-    def __init__(self, in_features, out_features, std=1.):
-        super().__init__()
-        assert out_features % 2 == 0
-        self.weight = nn.Parameter(torch.randn(
-            [out_features // 2, in_features]) * std)
-
-    def forward(self, input):
-        f = 2 * math.pi * input @ self.weight.T
-        return torch.cat([f.cos(), f.sin()], dim=-1)
-
-
-

SongBloom/models/songbloom/songbloom_mvsa.py

@@ -1,572 +0,0 @@
-from contextlib import contextmanager
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-from einops.layers.torch import Rearrange
-from einops import rearrange
-import tqdm
-
-from ..base.utils import create_norm_fn
-from ..base.sample import sample_top_k, sample_top_p, multinomial
-from ..musicgen.modules.streaming import StreamingModule
-from ..musicgen.conditioners import (
-    get_condition_fuser,
-    get_conditioner_provider,
-     ConditionType,
-     ConditioningProvider,
-     ConditionFuser,
-     AttributeDropout,
-     ClassifierFreeGuidanceDropout,
-     ConditioningAttributes,
-     WavCondition,
-    JointEmbedCondition
-)
-
-from ..musicgen.get_backend import get_backend
-
-from ..transformer import ContinuousTransformer as DiT_block
-from ..musicldm.musicldm_dit import FourierFeatures
-from ..musicldm.inference.sampling import get_alphas_sigmas, sample, sample_discrete_euler, sample_discrete_euler_with_temperature
-
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-@dataclass
-class DiTAROutput:
-    ar_logit: torch.Tensor  
-    ar_target: torch.Tensor
-    nar_pred: torch.Tensor
-    nar_target: torch.Tensor
-    nar_t: torch.Tensor
-
-
-
-class MVSA_DiTAR(StreamingModule):
-    """
-    Multiple skeleton embedding, single compressed vae latent 
-    eg. V1 V2 V3 A1-3 V4 V5 V6 A4-6
-        V -> cross entropy (skeleton)
-        A -> local-DiT uncompress -> (A1-3 -> E1 E2 E3)
-
-    Args:
-        StreamingModule (_type_): _description_
-    """
-
-    def __init__(self, condition_provider_cfg, fuser_cfg, 
-                 block_size: int = 32, dim: int = 1024, num_heads: int = 8,
-                 num_pitch: int = 128, hidden_scale: int = 4, lm_layers: int = 16,
-                 norm: str = 'layer_norm', pre_norm: bool = False,
-                 backend='llama',init_std: float=0.02, 
-                 # ======================
-                 latent_dim: int = 64, diff_layers: int = 8,
-                 time_cond_type: tp.Literal['adaLM', "prepend"] = "prepend", 
-                 timestep_features_dim: int = 256,
-                 diffusion_objective: tp.Literal["v", "rectified_flow"] = "v",
-                 timestep_sampler: tp.Literal["uniform", "logit_normal"] = "uniform",
-                 rotary_base_val=10000, h_dropout: float = None, 
-                 # ======================
-                 cfg_dropout: float = 0, cfg_coef: float = 1.0,
-                 attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}
-                 ):
-        super().__init__()
-
-        self.condition_provider = get_conditioner_provider(condition_provider_cfg)
-        self.fuser = get_condition_fuser(fuser_cfg)
-        
-        self.dim = dim  
-        self.latent_dim = latent_dim
-        self.block_size = block_size
-            
-        self.cfg_coef = cfg_coef
-        self.h_dropout = h_dropout if h_dropout is not None else 0.
-        self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
-        self.att_dropout = AttributeDropout(p=attribute_dropout)
-
-
-        # Build AR lm
-        self.num_pitch = num_pitch + 1 # self.num_pitch = <EOS>, self.num_pitch+1 = special
-        self.skeleton_emb = nn.Embedding(self.num_pitch + 1, dim)
-        self.bos_token = nn.Parameter(torch.empty(dim).normal_(mean=0.0, std=init_std), requires_grad=True)
-
-        
-        # self.lm_type = lm_type
-        self.backend = backend
-        
-        if self.backend == 'llama':
-            self.ar_transformer = get_backend('llama', 
-                                        dim, num_heads, lm_layers, hidden_scale,init_std=init_std, rope_theta=rotary_base_val)
-            self.ar_transformer.gradient_checkpointing_enable()
-        elif self.backend == 'bart':
-            self.cross_encoder = get_backend('bart_enc',
-                                        dim, num_heads, lm_layers // 4, hidden_scale,init_std=init_std)
-            self.ar_transformer = get_backend('bart_dec',
-                                        dim, num_heads, lm_layers, hidden_scale,init_std=init_std)
-        else:
-            raise NotImplementedError(f"Illegal backend: {self.backend}!")
-        
-
-        self.skeleton_classifier =  nn.Sequential(nn.Linear(dim, dim, bias=False), 
-                                                    nn.SiLU(),
-                                                    nn.Linear(dim, self.num_pitch),)
-        
-        self.pre_norm: tp.Optional[nn.Module] = None
-        if pre_norm:
-            self.pre_norm = create_norm_fn(norm, dim)
-        self.reset_streaming()
-        
-        # Build NAR DiT
-        self.block_conv = nn.Sequential(
-            Rearrange("b d (n s) -> b n (s d)", s=self.block_size),
-            nn.Linear(self.block_size * latent_dim, dim),
-            nn.SiLU(),
-            nn.Linear(dim, dim)
-        )
-        self.project_in = nn.Linear(latent_dim, dim) if latent_dim != dim else nn.Identity()
-        self.project_out = nn.Linear(dim, latent_dim) if latent_dim != dim else nn.Identity()
-        
-        self.timestep_features_dim = timestep_features_dim
-        self.time_cond_type = time_cond_type 
-        assert self.time_cond_type in ['adaLN', "prepend"]
-        self.timestep_features = FourierFeatures(1, timestep_features_dim)
-        self.to_timestep_embed = nn.Sequential(
-            nn.Linear(timestep_features_dim, dim, bias=False),
-            nn.SiLU(),
-            nn.Linear(dim, dim),
-        )
-        
-        self.time_cond_type = time_cond_type
-        self.nar_dit = DiT_block(
-            dim=dim,
-            depth=diff_layers,
-            dim_heads= dim // num_heads,
-            rotary_pos_emb=True,
-            cross_attend=False,
-            causal=False,
-            ff_kwargs={"dim_ff": dim * hidden_scale, "no_bias": True},
-            global_cond_dim=self.dim if self.time_cond_type=="adaLN" else None,
-            rotary_base_val = rotary_base_val,
-            # init_std=init_std
-        )
-        self.nar_dit.gradient_checkpointing_enable()
-        
-        self.diffusion_objective = diffusion_objective
-        self.timestep_sampler = timestep_sampler
-        self.rng = torch.quasirandom.SobolEngine(1, scramble=True)
-        
-        self.init_weights(init_std=init_std)
-            
-        
-
-
-    @property
-    def special_token_id(self) -> int:
-        return self.num_pitch
-
-
-    @property
-    def eos_token_id(self) -> int:
-        return self.num_pitch-1
-
-    def forward(self, x_sketch, x_latent, x_len, condition_tensors) -> DiTAROutput:
-        '''
-        only for train: lm_forward + diffusion_forward (random_t)
-        x_sketch: (B,T) # T % block_sz == 0 (no <eos> token) padded with <eos>
-        x_latent: (B, D_{in}, T)
-        '''
-        # AR 
-        assert torch.all(x_len % self.block_size == 0), f"{x_len}"
-        block_num = x_len // self.block_size
-        
-        sketch_emb = self.skeleton_emb(x_sketch)
-        latent_emb = self.block_conv(x_latent)
-
-        B, T, D = sketch_emb.shape
-        
-        lm_input = rearrange(torch.cat([rearrange(sketch_emb, "b (n s) d -> b n s d", s=self.block_size), 
-                            latent_emb.unsqueeze(dim=2)], dim=2), "b n s d -> b (n s) d")
-        lm_input = torch.cat([self.bos_token.reshape(1,1,-1).expand(B,-1,-1),
-                            lm_input], dim=1) #add <sos>
-        
-        new_seq_len = x_len + block_num + 1
-        
-        ar_target = F.pad(x_sketch, (0,1), value=self.eos_token_id)
-        for b,l in enumerate(x_len):
-            ar_target[b, l+1:] = self.special_token_id # 用来mask掉多余的eos
-        
-
-    
-        lm_out = self.lm_forward(lm_input, condition_tensors)
-        
-        
-        indices = torch.arange(lm_out.shape[1])
-        h_ind = indices[(indices+1) % (self.block_size+1) == 0]
-        not_h_ind = indices[(indices+1) % (self.block_size+1) != 0]
-        
-        x_sketch_logit = self.skeleton_classifier(lm_out[:, not_h_ind])
-        
-        # NAR (h + prev_block)
-        h_pad = lm_out[:, h_ind] # B, N, D        
-        h = torch.cat([hh[:hl] for hh, hl in zip(h_pad, block_num)], dim=0) 
-        block_semantic = rearrange(sketch_emb, "b (n s) d -> b n s d", s=self.block_size) # B, N, 32, D 
-        current_block_semantic = torch.cat([bb[:bl] for bb, bl in zip(block_semantic, block_num)], dim=0) 
-        
-        if self.training: # for CFG
-            drop_h_idx = torch.rand((h.shape[0], 1), device=h.device) < self.h_dropout
-            h = torch.masked_fill(h, drop_h_idx, 0)
-            # current_block_semantic = torch.masked_fill(current_block_semantic, drop_h_idx.unsqueeze(-1), 0)
-            
-            drop_s_idx = torch.rand((current_block_semantic.shape[0], 1), device=current_block_semantic.device) < self.h_dropout
-            current_block_semantic = torch.masked_fill(current_block_semantic, drop_s_idx.unsqueeze(-1), 0)
-        
-        with torch.no_grad():
-            block_latent = rearrange(x_latent, "b d (n s) -> b n s d", s=self.block_size) # B, N, 32, D
-            current_block = torch.cat([bb[:bl] for bb, bl in zip(block_latent, block_num)], dim=0) 
-            prev_block = torch.cat([bb[:bl] for bb, bl in zip(F.pad(block_latent, (0,0,0,0,1,0)), block_num)], dim=0)
-       
-            # b_indices = torch.randperm(block_latent.shape[0])[:B*16]
-            # h, current_block, prev_block = h[b_indices], current_block[b_indices], prev_block[b_indices]
-            
-            orig_type = x_latent.dtype
-            with torch.cuda.amp.autocast(enabled=False):
-                if self.timestep_sampler == "uniform":
-                    # Draw uniformly distributed continuous timesteps
-                    t = self.rng.draw(h.shape[0])[:, 0].to(device=h.device, dtype=h.dtype)
-                elif self.timestep_sampler == "logit_normal":
-                    t = torch.sigmoid(torch.randn(h.shape[0], device=h.device, dtype=h.dtype))
-                elif self.timestep_sampler == "trunc_logit_normal":
-                    # Draw from logistic truncated normal distribution
-                    from ..musicldm.musicldm_pl import truncated_logistic_normal_rescaled
-                    t = truncated_logistic_normal_rescaled(h.shape[0]).to(h.device)
-                    # Flip the distribution
-                    t = 1 - t
-                    
-                # Calculate the noise schedule parameters for those timesteps
-                if self.diffusion_objective == "v":
-                    alphas, sigmas = get_alphas_sigmas(t)
-                elif self.diffusion_objective == "rectified_flow":
-                    alphas, sigmas = 1-t, t
-                # Combine the ground truth data and the noise
-                alphas = alphas[:, None, None]
-                sigmas = sigmas[:, None, None]
-                noise = torch.randn_like(current_block)
-                noised_inputs = current_block * alphas + noise * sigmas
-                if self.diffusion_objective == "v": # (a_t - a_{t-1})x_0 + (b_t-b_{t-1}) e = -b x_0 + a e
-                    targets = noise * alphas - current_block * sigmas
-                elif self.diffusion_objective == "rectified_flow": #||(XT-X0) - p(x_t, t)||      
-                    targets = noise - current_block
-    
-        nar_output = self.diffusion_forward(noised_inputs.to(orig_type), t.to(orig_type), h, current_block_semantic, prev_block)
-
-        return DiTAROutput(
-            ar_logit=x_sketch_logit,
-            ar_target=ar_target, 
-            nar_pred=nar_output,
-            nar_target=targets.to(orig_type),
-            nar_t=t
-        )
-
-
-
-    def lm_forward(self, sequence, condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
-        # import pdb; pdb.set_trace()
-        B, T, D = sequence.shape
-        if self.pre_norm:
-            sequence = self.pre_norm(sequence.to(self.pre_norm.weight.data.dtype))
-            
-        input_, cross_attention_input = self.fuser(sequence, condition_tensors)
-
-        transformer_input = {
-            "inputs_embeds":input_,
-            "use_cache": self._is_streaming, 
-            "past_key_values": self._streaming_state.get('past_key_values', None),
-        }
-        if self.backend == 'bart': # TODO infer 的时候这个玩意不用重复算
-            # TODO attention_mask
-            cross_attention_input = self.cross_encoder(inputs_embeds=cross_attention_input) 
-            transformer_input["encoder_hidden_states"] = cross_attention_input.last_hidden_state
-
-        output = self.ar_transformer(**transformer_input)
-        if self._is_streaming:
-            self._streaming_state['past_key_values'] = output.past_key_values
-        out = output.last_hidden_state
-             
-
-            
-        if len(self.fuser.fuse2cond['prepend']) > 0:
-            out = out[:, -T:, :]
-
-        return out
-
-
-
-
-    def diffusion_forward(self, 
-                x: torch.Tensor,
-                t: torch.Tensor, # B,
-                h: torch.Tensor,
-                s: torch.Tensor, # B, self.block_size, D
-                history_x: torch.Tensor,
-                cfg_coef: float = None) -> torch.Tensor:
-
-        if cfg_coef is not None:
-            # only for infer
-            assert not self.training # only for inference
-            x = torch.cat([x,x], dim=0)
-            t = torch.cat([t,t], dim=0)
-            h = torch.cat([h,torch.zeros_like(h)], dim=0)
-            s = torch.cat([s,torch.zeros_like(s)], dim=0)
-            history_x = torch.cat([history_x,history_x], dim=0)
-            
-        B, T, _ = x.shape
-        
-        input_ = self.project_in(torch.cat([history_x, x], dim=1))
-        # print(h.shape, s.shape, input_.shape)
-        input_ = torch.cat([h.unsqueeze(1), s, input_], dim=1)
-        # Get the batch of timestep embeddings
-        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]))# (b, embed_dim)
-        # breakpoint()
-        if self.time_cond_type == "prepend":
-            input_ = torch.cat([timestep_embed.unsqueeze(1), input_], dim=1)
-
-        transformer_input = {
-            "x": input_,
-            "global_cond": timestep_embed if self.time_cond_type == "adaLN" else None}
-        
-        output = self.nar_dit(**transformer_input)
-
-        # remove the prefix from the model outputs
-        output = output[:, -T:, :]
-        output = self.project_out(output)
-
-        if cfg_coef is not None:
-            cond_output, uncond_output = torch.chunk(output, 2, dim=0)
-            output = uncond_output + (cond_output - uncond_output) * cfg_coef
-        
-        return output  # [B, T, D]
-
-
-
-    def _sample_next_block(self,
-                           sequence: torch.Tensor,
-                           prev_latents: torch.Tensor, 
-                           condition_tensors: tp.Optional[ConditionTensors] = None,
-                           cfg_coef: tp.Optional[tp.Union[float, tp.List[float]]] = None,
-                           steps: int = 50,
-                           dit_cfg_type: str = 'h',
-                           use_sampling: bool = False,
-                           temp: float = 1.0,
-                           diff_temp: float = 1.0, 
-                           top_k: int = 0,
-                           top_p: float = 0.0,
-                           penalty_token_pool: tp.Optional[list] = None) -> torch.Tensor:
-        # infer: lm next_token -> (if % block_sz == 0) infer diff
-        # 1. sample sketch (lm) -> 2. sample latent (lm+diff)
-        sequence = sequence.clone()
-        
-        if isinstance(cfg_coef, tp.Iterable):
-            assert len(cfg_coef) == 2
-            cfg_coef_lm, cfg_coef_diff = cfg_coef
-        else:
-            cfg_coef_lm, cfg_coef_diff = cfg_coef, cfg_coef
-        
-        B = sequence.shape[0]
-        # import pdb; pdb.set_trace()
-
-        if condition_tensors:
-            # Preparing for CFG, predicting both conditional and unconditional logits.
-            sequence = torch.cat([sequence, sequence], dim=0)
-        
-        
-        # ############### decode sketch #########################
-        next_tokens = []
-        next_token_embs = []
-            
-        for k in range(self.block_size):
-            if self._is_streaming and k > 0:
-                lm_inp = sequence[:,-1:]
-            else:
-                lm_inp = sequence
-                
-            lm_out = self.lm_forward(
-                lm_inp,
-                condition_tensors=condition_tensors)
-            next_pitch_logit = self.skeleton_classifier(lm_out[:, -1:]) # B, 1, card
-
-            if condition_tensors:
-                cond_logit, uncond_logit = next_pitch_logit.split(B, dim=0)  
-                next_pitch_logit = uncond_logit + (cond_logit - uncond_logit) * cfg_coef_lm
-
-            # add penalty to pre-sampled tokens
-            if penalty_token_pool is not None and len(penalty_token_pool) > 0: # B, T
-                for b in range(B):
-                    # q_count = torch.bincount(penalty_token_pool)
-                    q_count = torch.bincount(torch.unique(penalty_token_pool[b]))
-                    tmp = min(q_count.shape[-1], self.num_pitch - 1) 
-                    next_pitch_logit[b, -1, :tmp] /= (1.1 ** q_count[:tmp])
-                    
-            # sample k
-            if use_sampling and temp > 0.0:
-                probs = torch.softmax(next_pitch_logit  / temp, dim=-1)
-                if top_p > 0.0:
-                    next_token = sample_top_p(probs, p=top_p)
-                elif top_k > 0:
-                    next_token = sample_top_k(probs, k=top_k)
-                else:
-                    next_token = multinomial(probs, num_samples=1)
-                next_token = next_token.squeeze(-1)
-            else:
-                next_token = torch.argmax(next_pitch_logit, dim=-1) # B, 1
-            if penalty_token_pool is not None and len(penalty_token_pool) > 0: # B, T
-                penalty_token_pool = torch.cat([penalty_token_pool, next_token], dim=-1)[:,1:]
-            next_token_emb = self.skeleton_emb(next_token) #B, 1, d
-            next_tokens.append(next_token)
-            next_token_embs.append(next_token_emb)
-            
-            if condition_tensors:
-                doubled_next_emb = torch.cat([next_token_emb, next_token_emb], dim=0)
-                sequence = torch.cat([sequence, doubled_next_emb], dim=1)
-            else:
-                sequence = torch.cat([sequence, next_token_emb], dim=1)
-            
-        next_tokens = torch.cat(next_tokens, dim=1)
-        next_token_embs = torch.cat(next_token_embs, dim=1)
-        
-        # ############### decode latent ###########################
-        # 这里求h虽然double了 但是没用classifier-free guidance
-        if self._is_streaming:
-            lm_inp = sequence[:,-1:]
-        else:
-            lm_inp = sequence
-                
-        lm_out = self.lm_forward(
-            lm_inp,
-            condition_tensors=condition_tensors)
-        
-        h = lm_out[:,-1] 
-
-        noise = torch.randn((B, self.block_size, self.latent_dim), device=h.device, dtype=h.dtype)
-        
-        assert dit_cfg_type in ['h', 'global', 'none']
-        """
-        global: same cfg setting as next-token-prediction
-        none: no cfg
-        h: no cfg during ar-stage and apply cfg via ar output
-        """
-        if condition_tensors:
-            if dit_cfg_type == 'global':
-                noise = torch.cat([noise, noise], dim=0)
-                prev_latents = torch.cat([prev_latents, prev_latents], dim=0)
-                semantic_embs = torch.cat([next_token_embs, next_token_embs], dim=0)
-            else:
-                h, _ = h.chunk(2, dim=0)      
-                semantic_embs = next_token_embs
-
-        
-        if self.diffusion_objective == "v":
-            next_latent = sample(self.diffusion_forward, noise, steps=steps, eta=0, h=h, s=semantic_embs, history_x=prev_latents, 
-                                 cfg_coef=(cfg_coef_diff if dit_cfg_type=='h' else None))
-        elif self.diffusion_objective == "rectified_flow":
-            # next_latent = sample_discrete_euler(self.diffusion_forward, noise, steps=steps, h=h, s=semantic_embs, history_x=prev_latents, 
-            #                                     cfg_coef=(cfg_coef_diff if dit_cfg_type=='h' else None))
-            next_latent = sample_discrete_euler_with_temperature(self.diffusion_forward, noise, steps=steps, temperature=diff_temp, h=h, s=semantic_embs, history_x=prev_latents, 
-                                                cfg_coef=(cfg_coef_diff if dit_cfg_type=='h' else None))
-        if condition_tensors and dit_cfg_type == 'global':
-            cond_next_latent, uncond_next_latent = torch.chunk(next_latent, 2, dim=0)
-            next_latent = uncond_next_latent + (cond_next_latent - uncond_next_latent) * cfg_coef_diff
-            
-        latent_emb = self.block_conv(next_latent.transpose(1,2))
-
-        next_block_seq = torch.cat([next_token_embs, latent_emb], dim=1) # B, self.block_size+1, d
-        
-        return next_tokens, next_latent, next_block_seq
-        
-            
-        
-    @torch.no_grad()
-    def generate(self,
-                 prompt: tp.Optional[torch.Tensor] = None,
-                 conditions: tp.List[ConditioningAttributes] = [],
-                 cfg_coef: tp.Optional[tp.Union[float, tp.List[float]]] = None,
-                 steps=50,
-                 dit_cfg_type: str = 'h',
-                 max_frames: int = 1500, # 60 * 25
-                 use_sampling: bool = True,
-                 temp: float = 1.0,
-                 diff_temp: float = 1.0,
-                 top_k: int = 0,
-                 top_p: float = 0.0,
-                 penalty_repeat: bool = False,
-                 penalty_window: int = 50) -> torch.Tensor: 
-        assert not self.training, "generation shouldn't be used in training mode."
-
-        B = len(conditions)
-        assert B==1, "currently  do not support batch decoding"
-        null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
-        conditions = conditions + null_conditions
-        tokenized = self.condition_provider.tokenize(conditions)
-        condition_tensors = self.condition_provider(tokenized)          
-        
-        
-        sequence = self.bos_token.reshape(1,1,-1).expand(B, 1, -1)
-        if prompt is not None:
-            # TODO 
-            raise NotImplementedError
-            # sequence = torch.cat([sequence, prompt])
-
-            
-        prev_blocks = torch.zeros((B, self.block_size, self.latent_dim), device=sequence.device, dtype=sequence.dtype)
-        latent_seq, token_seq = None, None
-            
-        with self.streaming():
-            prog_bar = tqdm.tqdm()
-            while True:
-                if token_seq is None or not penalty_repeat:
-                    penalty_token_pool = None
-                else:
-                    penalty_token_pool = token_seq[: ,-penalty_window:]
-                    if penalty_token_pool.shape[-1] < penalty_window:
-                        penalty_token_pool = F.pad(penalty_token_pool, (penalty_window - penalty_token_pool.shape[-1], 0), value=self.eos_token_id)
-                next_tokens, next_latent, next_block_seq = self._sample_next_block(sequence[:, -1: ], prev_blocks, condition_tensors, 
-                                                                                   cfg_coef=cfg_coef, steps=steps, dit_cfg_type=dit_cfg_type,
-                                                                                    use_sampling=use_sampling, temp=temp, diff_temp=diff_temp,
-                                                                                    top_k=top_k, top_p=top_p,
-                                                                                    penalty_token_pool=penalty_token_pool)
-                
-                if (next_tokens == self.eos_token_id).any() or sequence.shape[1] > max_frames  / self.block_size * (self.block_size+1):
-                    break
-                
-                latent_seq = next_latent if latent_seq is None else torch.cat([latent_seq, next_latent], dim=1) # B,T, D
-                token_seq = next_tokens if token_seq is None else torch.cat([token_seq, next_tokens], dim=1) # B,T
-                sequence = torch.cat([sequence, next_block_seq], dim=1)
-                prev_blocks = next_latent
-                
-                prog_bar.update(self.block_size)
-                
-                
-        if latent_seq is None:
-            latent_seq = prev_blocks
-        return latent_seq.transpose(1,2), token_seq    
-        
-        
-    
-    
-    def init_weights(self, init_std=0.02):
-        
-        def _init_weights(module, init_std=0.02):
-            if isinstance(module, nn.Linear):
-                module.weight.data.normal_(mean=0.0, std=init_std)
-                # torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-            elif isinstance(module, nn.Embedding):
-                module.weight.data.normal_(mean=0.0, std=init_std)
-                if module.padding_idx is not None:
-                    module.weight.data[module.padding_idx].zero_()
-                    
-        self.apply(partial(_init_weights, init_std=init_std))

SongBloom/models/songbloom/songbloom_pl.py

@@ -1,224 +0,0 @@
-
-from functools import partial
-import typing as tp
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-import torchaudio
-import numpy as np
-import random
-from omegaconf import OmegaConf
-import copy
-import lightning as pl
-
-import os, sys
-
-from ..musicgen.conditioners import WavCondition, JointEmbedCondition, ConditioningAttributes
-from ..vae_frontend import StableVAE
-from .songbloom_mvsa import MVSA_DiTAR
-from ...g2p.lyric_common import key2processor, symbols, LABELS
-
-
-os.environ['TOKENIZERS_PARALLELISM'] = "false"
-
-
-class SongBloom_PL(pl.LightningModule):
-    def __init__(self, cfg):
-        super().__init__()
-        # 关闭自动优化
-        # self.automatic_optimization = False
-
-        self.cfg = cfg
-
-        # Build VAE
-        self.vae = StableVAE(**cfg.vae).eval()
-        assert self.cfg.model['latent_dim'] == self.vae.channel_dim
-
-            
-        self.save_hyperparameters(cfg)
-        if self.vae is not None:
-            for param in self.vae.parameters():
-                param.requires_grad = False
-                
-        # Build DiT
-        model_cfg = OmegaConf.to_container(copy.deepcopy(cfg.model), resolve=True)
-        for cond_name in model_cfg["condition_provider_cfg"]:
-            if model_cfg["condition_provider_cfg"][cond_name]['type'] == 'audio_tokenizer_wrapper':
-                model_cfg["condition_provider_cfg"][cond_name]["audio_tokenizer"] = self.vae
-                model_cfg["condition_provider_cfg"][cond_name]["cache"] = False
-        
-        
-        self.model = MVSA_DiTAR(**model_cfg)
-        # print(self.model)
-        
-
-
-
-
-
-####################################
-
-class SongBloom_Sampler:    
-    
-    def __init__(self, compression_model: StableVAE, diffusion: MVSA_DiTAR, lyric_processor_key,
-                 max_duration: float, prompt_duration: tp.Optional[float] = None):
-        self.compression_model = compression_model
-        self.diffusion = diffusion
-        self.lyric_processor_key = lyric_processor_key
-        self.lyric_processor = key2processor.get(lyric_processor_key) if lyric_processor_key is not None else lambda x: x
-        # import pdb; pdb.set_trace()
-
-        assert max_duration is not None
-        self.max_duration: float = max_duration
-        self.prompt_duration = prompt_duration
-        
-        
-        self.device = next(iter(diffusion.parameters())).device
-        self.generation_params: dict = {}
-        # self.set_generation_params(duration=15)  # 15 seconds by default
-        self.set_generation_params(cfg_coef=1.5, steps=50, dit_cfg_type='h',
-                                   use_sampling=True, top_k=200, max_frames=self.max_duration * 25)
-        self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
-
-    @classmethod
-    def build_from_trainer(cls, cfg, strict=True, dtype=torch.float32):
-        model_light = SongBloom_PL(cfg)
-        incompatible = model_light.load_state_dict(torch.load(cfg.pretrained_path, map_location='cpu'), strict=strict)
-        
-        lyric_processor_key = cfg.train_dataset.lyric_processor
-    
-        print(incompatible)
-        
-        model_light = model_light.eval().cuda().to(dtype=dtype)  
-        model = cls(
-            compression_model = model_light.vae,
-            diffusion = model_light.model,
-            lyric_processor_key = lyric_processor_key,
-            max_duration = cfg.max_dur,
-            prompt_duration = cfg.sr * cfg.train_dataset.prompt_len
-            
-        )
-        model.set_generation_params(**cfg.inference)
-        return model
-        
-    @property
-    def frame_rate(self) -> float:
-        """Roughly the number of AR steps per seconds."""
-        return self.compression_model.frame_rate
-
-    @property
-    def sample_rate(self) -> int:
-        """Sample rate of the generated audio."""
-        return self.compression_model.sample_rate
-
-
-    def set_generation_params(self, **kwargs):
-        """Set the generation parameters."""
-        self.generation_params.update(kwargs)
-
-    # Mulan Inference
-    @torch.no_grad()
-    def generate(self, lyrics, prompt_wav) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, torch.Tensor]]:
-        """ Generate samples conditioned on text and melody.
-        """
-        # breakpoint()
-        assert prompt_wav.ndim == 2
-        if self.prompt_duration is not None:
-            prompt_wav = prompt_wav[..., :self.prompt_duration]
-            
-        attributes, _ = self._prepare_tokens_and_attributes(conditions={"lyrics": [self._process_lyric(lyrics)], "prompt_wav": [prompt_wav]}, 
-                                                                        prompt=None, prompt_tokens=None)
-
-        # breakpoint()
-        print(self.generation_params)
-        latent_seq, token_seq = self.diffusion.generate(None, attributes, **self.generation_params)
-        # print(token_seq)
-        audio_recon = self.compression_model.decode(latent_seq).float()
-        
-        return audio_recon
-    
-
-    def _process_lyric(self, input_lyric):
-        if self.lyric_processor_key == 'pinyin':
-            processed_lyric = self.lyric_processor(input_lyric)
-        else:
-            processed_lyric = []
-            check_lyric = input_lyric.split(" ")
-            for ii in range(len(check_lyric)):
-                if check_lyric[ii] not in symbols and check_lyric[ii] not in LABELS.keys() and len(check_lyric[ii]) > 0:
-                    new = self.lyric_processor(check_lyric[ii])
-                    check_lyric[ii] = new
-            processed_lyric = " ".join(check_lyric)
-        
-        return processed_lyric
-    
-    @torch.no_grad()
-    def _prepare_tokens_and_attributes(
-            self,
-            conditions: tp.Dict[str, tp.List[tp.Union[str, torch.Tensor]]],
-            prompt: tp.Optional[torch.Tensor],
-            prompt_tokens: tp.Optional[torch.Tensor] = None,
-    ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
-        """Prepare model inputs.
-
-        Args:
-            descriptions (list of str): A list of strings used as text conditioning.
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-            melody_wavs (torch.Tensor, optional): A batch of waveforms
-                used as melody conditioning. Defaults to None.
-        """
-        batch_size = len(list(conditions.values())[0])
-        assert batch_size == 1
-        # breakpoint()
-        attributes = [ConditioningAttributes() for _ in range(batch_size)]
-        for k in self.diffusion.condition_provider.conditioners:
-            conds = conditions.pop(k, [None for _ in attributes])
-            for attr, cond in zip(attributes, conds):
-                if self.diffusion.condition_provider.conditioner_type[k] == 'wav':
-                    if cond is None:
-                        attr.wav[k] = WavCondition(
-                            torch.zeros((1, 1, 1), device=self.device),
-                            torch.tensor([0], device=self.device).long(),
-                            sample_rate=[self.sample_rate],
-                            path=[None])  
-                    else:
-                        attr.wav[k] = WavCondition(
-                            cond.to(device=self.device).unsqueeze(0), # 1,C,T .mean(dim=0, keepdim=True)
-                            torch.tensor([cond.shape[-1]], device=self.device).long(),
-                            sample_rate=[self.sample_rate],
-                            path=[None])  
-                elif self.diffusion.condition_provider.conditioner_type[k] == 'text':
-                    attr.text[k] = cond
-                elif self.diffusion.condition_provider.conditioner_type[k] == 'joint_embed':
-                    if cond is None or isinstance(cond, str):
-                        attr.joint_embed[k] = JointEmbedCondition(
-                            torch.zeros((1, 1, 1), device=self.device),
-                            [cond],
-                            torch.tensor([0], device=self.device).long(),
-                            sample_rate=[self.sample_rate],
-                            path=[None])  
-                    elif isinstance(cond, torch.Tensor):
-                        attr.joint_embed[k] = JointEmbedCondition(
-                            cond.to(device=self.device).mean(dim=0, keepdim=True).unsqueeze(0),
-                            [None], 
-                            torch.tensor([cond.shape[-1]], device=self.device).long(),
-                            sample_rate=[self.sample_rate],
-                            path=[None])  
-                    else:
-                        raise NotImplementedError
-        assert conditions == {}, f"Find illegal conditions: {conditions}, support keys: {self.lm.condition_provider.conditioners}"
-        # breakpoint()
-        print(attributes)
-        
-        if prompt_tokens is not None:
-            prompt_tokens = prompt_tokens.to(self.device)
-            assert prompt is None
-        elif prompt is not None:
-            assert len(attributes) == len(prompt), "Prompt and nb. attributes doesn't match"
-            prompt = prompt.to(self.device)
-            prompt_tokens = self.compression_model.encode(prompt)
-        else:
-            prompt_tokens = None
-
-        return attributes, prompt_tokens
-

SongBloom/models/transformer.py

@@ -1,937 +0,0 @@
-from functools import reduce, partial
-from packaging import version
-
-from einops import rearrange, repeat
-from einops.layers.torch import Rearrange
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from torch.cuda.amp import autocast
-from typing import Callable, Literal
-import os, sys
-import warnings
-from torch.utils import checkpoint
-from transformers.utils import is_flash_attn_2_available
-
-try:
-    assert is_flash_attn_2_available()
-    assert torch.cuda.get_device_capability(torch.device("cuda")) >= (8, 0)
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, unpad_input, pad_input
-    assert os.environ.get("DISABLE_FLASH_ATTN",'0') != "1"
-except Exception as e:
-    flash_attn_kvpacked_func = None
-    flash_attn_func = None
-    warnings.warn("Not support flash-attn!")
-
-try:
-    import natten
-except ImportError:
-    natten = None
-
-def checkpoint(function, *args, **kwargs):
-    kwargs.setdefault("use_reentrant", False)
-    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
-
-
-# Copied and modified from https://github.com/lucidrains/x-transformers/blob/main/x_transformers/attend.py under MIT License
-# License can be found in LICENSES/LICENSE_XTRANSFORMERS.txt
-
-def create_causal_mask(i, j, device):
-    return torch.ones((i, j), device = device, dtype = torch.bool).triu(j - i + 1)
-
-def or_reduce(masks):
-    head, *body = masks
-    for rest in body:
-        head = head | rest
-    return head
-
-# positional embeddings
-
-class AbsolutePositionalEmbedding(nn.Module):
-    def __init__(self, dim, max_seq_len):
-        super().__init__()
-        self.scale = dim ** -0.5
-        self.max_seq_len = max_seq_len
-        self.emb = nn.Embedding(max_seq_len, dim)
-
-    def forward(self, x, pos = None, seq_start_pos = None):
-        seq_len, device = x.shape[1], x.device
-        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
-
-        if pos is None:
-            pos = torch.arange(seq_len, device = device)
-
-        if seq_start_pos is not None:
-            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
-
-        pos_emb = self.emb(pos)
-        pos_emb = pos_emb * self.scale
-        return pos_emb
-
-class ScaledSinusoidalEmbedding(nn.Module):
-    def __init__(self, dim, theta = 10000):
-        super().__init__()
-        assert (dim % 2) == 0, 'dimension must be divisible by 2'
-        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
-
-        half_dim = dim // 2
-        freq_seq = torch.arange(half_dim).float() / half_dim
-        inv_freq = theta ** -freq_seq
-        self.register_buffer('inv_freq', inv_freq, persistent = False)
-
-    def forward(self, x, pos = None, seq_start_pos = None):
-        seq_len, device = x.shape[1], x.device
-
-        if pos is None:
-            pos = torch.arange(seq_len, device = device)
-
-        if seq_start_pos is not None:
-            pos = pos - seq_start_pos[..., None]
-
-        emb = einsum('i, j -> i j', pos, self.inv_freq)
-        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
-        return emb * self.scale
-    
-class RotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim,
-        use_xpos = False,
-        scale_base = 512,
-        interpolation_factor = 1.,
-        base = 10000,
-        base_rescale_factor = 1.
-    ):
-        super().__init__()
-        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
-        # has some connection to NTK literature
-        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
-        base *= base_rescale_factor ** (dim / (dim - 2))
-
-        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq, persistent=False)
-
-        assert interpolation_factor >= 1.
-        self.interpolation_factor = interpolation_factor
-
-        if not use_xpos:
-            self.register_buffer('scale', None)
-        else:
-            scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
-
-            self.scale_base = scale_base
-            self.register_buffer('scale', scale)
-
-    def forward_from_seq_len(self, seq_len):
-        device = self.inv_freq.device
-
-        t = torch.arange(seq_len, device = device)
-        return self.forward(t)
-
-    @autocast(enabled = False)
-    def forward(self, t):
-        device = self.inv_freq.device
-
-        t = t.to(torch.float32)
-        seq_len = t.shape[0]
-
-        t = t / self.interpolation_factor
-
-        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
-        freqs = torch.cat((freqs, freqs), dim = -1)
-
-        if self.scale is None:
-            return freqs, 1.
-
-        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
-        scale = self.scale ** rearrange(power, 'n -> n 1')
-        scale = torch.cat((scale, scale), dim = -1)
-
-        return freqs, scale
-
-class RotaryEmbedding2D(RotaryEmbedding):
-    def __init__(self, dim, w, **kwargs):
-        super().__init__(dim // 2, **kwargs)
-        self.w = w
-        
-    
-    def forward_from_seq_len(self, seq_len):
-        device = self.inv_freq.device
-        assert seq_len % self.w == 0 , f"{seq_len} % {self.w} != 0"
-        h_len = seq_len // self.w
-        
-        t_h = torch.arange(h_len, device = device)
-        t_w = torch.arange(self.w, device = device)
-        
-        return self.forward(t_h, t_w)
-
-    @autocast(enabled = False)
-    def forward(self, t_h: torch.Tensor, t_w: torch.Tensor):
-        repeat_t_h = t_h.repeat_interleave(t_w.shape[0], dim=0)
-        repeat_t_w = t_w.repeat(t_h.shape[0])
-        freq_h, scale_h = super().forward(repeat_t_h)
-        freq_w, scale_w = super().forward(repeat_t_w)
-        freq = torch.stack([freq_h, freq_w], dim=-1) #h*w, D//2, 2
-        freq = torch.cat(torch.unbind(freq, dim=-2), dim=-1)
-        
-        if self.scale is None:
-            scale = 1.
-        else:
-            scale = torch.stack([scale_h, scale_w], dim=-1)
-            scale = torch.cat(torch.unbind(scale, dim=-2), dim=-1)
-            
-        return freq, scale
-        
-        
-    
-
-def rotate_half(x):
-    x = rearrange(x, '... (j d) -> ... j d', j = 2)
-    x1, x2 = x.unbind(dim = -2)
-    return torch.cat((-x2, x1), dim = -1)
-
-@autocast(enabled = False)
-def apply_rotary_pos_emb(t, freqs, scale = 1):
-    out_dtype = t.dtype
-
-    # cast to float32 if necessary for numerical stability
-    dtype = reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
-    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
-    freqs, t = freqs.to(dtype), t.to(dtype)
-    freqs = freqs[-seq_len:, :]
-
-    if t.ndim == 4 and freqs.ndim == 3:
-        freqs = rearrange(freqs, 'b n d -> b 1 n d')
-
-    # partial rotary embeddings, Wang et al. GPT-J
-    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
-    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
-
-    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
-    return torch.cat((t, t_unrotated), dim = -1)
-
-# norms
-class LayerNorm(nn.Module):
-    def __init__(self, dim, bias=False, fix_scale=False):
-        """
-        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
-        """
-        super().__init__()
-
-        if fix_scale:
-            self.register_buffer("gamma", torch.ones(dim))
-        else:
-            self.gamma = nn.Parameter(torch.ones(dim))
-
-        if bias:
-            self.beta = nn.Parameter(torch.zeros(dim))
-        else:
-            self.register_buffer("beta", torch.zeros(dim))
-
-
-    def forward(self, x):
-        return F.layer_norm(x, x.shape[-1:], weight=self.gamma, bias=self.beta)
-
-# feedforward
-
-class GLU(nn.Module):
-    def __init__(
-        self,
-        dim_in,
-        dim_out,
-        activation: Callable,
-        use_conv = False,
-        conv_kernel_size = 3,
-        bias = False,
-    ):
-        super().__init__()
-        self.act = activation
-        self.up_proj = nn.Linear(dim_in, dim_out, bias=bias) if not use_conv else nn.Conv1d(dim_in, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2))
-        self.gate_proj = nn.Linear(dim_in, dim_out, bias=bias) if not use_conv else nn.Conv1d(dim_in, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2))
-        self.use_conv = use_conv
-
-    def forward(self, x):
-        if self.use_conv:
-            x = rearrange(x, 'b n d -> b d n')
-            gate = self.gate_proj(x)
-            x = self.up_proj(x)
-            x = rearrange(x, 'b d n -> b n d')
-            gate = rearrange(gate, 'b d n -> b n d')
-        else:
-            gate = self.gate_proj(x)
-            x = self.up_proj(x)
-
-        return x * self.act(gate)
-
-class FeedForward(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_out = None,
-        dim_ff = None,
-        no_bias = False,
-        glu = True,
-        use_conv = False,
-        conv_kernel_size = 3,
-        zero_init_output = True,
-    ):
-        super().__init__()
-        inner_dim = dim_ff if dim_ff is not None else 4 * dim
-
-        # Default to SwiGLU
-
-        activation = nn.SiLU()
-
-        dim_out = dim if dim_out is None else dim_out
-
-        if glu:
-            linear_in = GLU(dim, inner_dim, activation, bias=not no_bias)
-        else:
-            linear_in = nn.Sequential(
-                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
-                nn.Linear(dim, inner_dim, bias = not no_bias) if not use_conv else nn.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias),
-                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
-                activation
-            )
-
-        linear_out = nn.Linear(inner_dim, dim_out, bias = not no_bias) if not use_conv else nn.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias)
-
-        # init last linear layer to 0
-        if zero_init_output:
-            nn.init.zeros_(linear_out.weight)
-            if not no_bias:
-                nn.init.zeros_(linear_out.bias)
-
-
-        self.ff = nn.Sequential(
-            linear_in,
-            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
-            linear_out,
-            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
-        )
-
-    def forward(self, x):
-        return self.ff(x)
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_heads = 64,
-        dim_context = None,
-        causal = False,
-        zero_init_output=True,
-        qk_norm: Literal['l2', 'ln', 'none'] = 'none',
-        natten_kernel_size = None
-    ):
-        super().__init__()
-        self.dim = dim
-        self.dim_heads = dim_heads
-        self.causal = causal
-
-        dim_kv = dim_context if dim_context is not None else dim
-        
-        self.num_heads = dim // dim_heads
-        self.kv_heads = dim_kv // dim_heads
-
-        if dim_context is not None:
-            self.to_q = nn.Linear(dim, dim, bias=False)
-            self.to_kv = nn.Linear(dim_kv, dim_kv * 2, bias=False)
-        else:
-            self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
-
-        self.to_out = nn.Linear(dim, dim, bias=False)
-
-        if zero_init_output:
-            nn.init.zeros_(self.to_out.weight)
-
-        self.qk_norm = qk_norm
-
-        if self.qk_norm == "ln":
-            self.q_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
-            self.k_norm = nn.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6)
-
-        # Using 1d neighborhood attention
-        self.natten_kernel_size = natten_kernel_size
-        if natten_kernel_size is not None:
-            return
-
-        self.use_pt_flash = torch.cuda.is_available() and version.parse(torch.__version__) >= version.parse('2.0.0')
-
-        self.use_fa_flash = torch.cuda.is_available() and flash_attn_func is not None
-
-        self.sdp_kwargs = dict(
-            enable_flash = True,
-            enable_math = True,
-            enable_mem_efficient = True
-        )
-
-    def flash_attn(
-            self,
-            q, 
-            k, 
-            v,
-            mask = None,
-            causal = None
-    ):
-        batch, heads, q_len, _, k_len, device = *q.shape, k.shape[-2], q.device
-        kv_heads = k.shape[1]
-        # Recommended for multi-query single-key-value attention by Tri Dao
-        # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
-
-        if heads != kv_heads:
-            # Repeat interleave kv_heads to match q_heads
-            heads_per_kv_head = heads // kv_heads
-            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
-
-        if k.ndim == 3:
-            k = rearrange(k, 'b ... -> b 1 ...').expand_as(q)
-
-        if v.ndim == 3:
-            v = rearrange(v, 'b ... -> b 1 ...').expand_as(q)
-
-        causal = self.causal if causal is None else causal
-
-        if q_len == 1 and causal:
-            causal = False
-        
-        if mask is not None:
-            assert mask.ndim == 4
-            mask = mask.expand(batch, heads, q_len, k_len)
-
-        # handle kv cache - this should be bypassable in updated flash attention 2
-
-        if k_len > q_len and causal:
-            causal_mask = self.create_causal_mask(q_len, k_len, device = device)
-            if mask is None:
-                mask = ~causal_mask
-            else:
-                mask = mask & ~causal_mask
-            causal = False
-
-        # manually handle causal mask, if another mask was given
-
-        row_is_entirely_masked = None
-
-        if mask is not None and causal:
-            causal_mask = self.create_causal_mask(q_len, k_len, device = device)
-            mask = mask & ~causal_mask
-
-            # protect against an entire row being masked out
-
-            row_is_entirely_masked = ~mask.any(dim = -1)
-            mask[..., 0] = mask[..., 0] | row_is_entirely_masked
-
-            causal = False
-        
-        with torch.backends.cuda.sdp_kernel(**self.sdp_kwargs):
-            out = F.scaled_dot_product_attention(
-                q, k, v,
-                attn_mask = mask,
-                is_causal = causal
-            )
-
-        # for a row that is entirely masked out, should zero out the output of that row token
-
-        if row_is_entirely_masked is not None:
-            out = out.masked_fill(row_is_entirely_masked[..., None], 0.)
-
-        return out
-
-    def forward(
-        self,
-        x,
-        context = None,
-        mask = None,
-        context_mask = None,
-        rotary_pos_emb = None,
-        causal = None
-    ):
-        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
-
-        kv_input = context if has_context else x
-
-        if hasattr(self, 'to_q'):
-            # Use separate linear projections for q and k/v
-            q = self.to_q(x)
-            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
-
-            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
-            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
-        else:
-            # Use fused linear projection
-            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
-        
-        # Normalize q and k for cosine sim attention
-        if self.qk_norm == "l2":
-            q = F.normalize(q, dim=-1)
-            k = F.normalize(k, dim=-1)
-        elif self.qk_norm == "ln":
-            q = self.q_norm(q)
-            k = self.k_norm(k)
-
-        if rotary_pos_emb is not None and not has_context:
-            freqs, _ = rotary_pos_emb
-
-            q_dtype = q.dtype
-            k_dtype = k.dtype
-
-            q = q.to(torch.float32)
-            k = k.to(torch.float32)
-            freqs = freqs.to(torch.float32)
-
-            q = apply_rotary_pos_emb(q, freqs)
-            k = apply_rotary_pos_emb(k, freqs)
-
-            q = q.to(q_dtype)
-            k = k.to(k_dtype)
-        
-        # TODO 这里这俩都是 [B, k/Q_len]这样的格式
-        # context mask也许应该改成 [B, Q_len, K_len]
-        # 并且下面flash_attn 默认假设attn靠左部分全为1
-        input_mask = context_mask # cross-attn
-        if input_mask is None and not has_context: # self-attn
-            input_mask = mask
-
-        # determine masking
-        masks = []
-        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
-
-        if input_mask is not None:
-            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
-            masks.append(~input_mask)
-
-        # Other masks will be added here later
-
-        if len(masks) > 0:
-            final_attn_mask = ~or_reduce(masks)
-
-        n, device = q.shape[-2], q.device
-
-        causal = self.causal if causal is None else causal
-        if n == 1 and causal:
-            causal = False
-        if self.natten_kernel_size is not None:
-            if natten is None:
-                raise ImportError('natten not installed, please install natten to use neighborhood attention')
-            
-            dtype_in = q.dtype
-            q, k, v = map(lambda t: t.to(torch.float32), (q, k, v))
-
-            attn = natten.functional.natten1dqk(q, k, kernel_size = self.natten_kernel_size, dilation=1)
-
-            if final_attn_mask is not None:
-                attn = attn.masked_fill(final_attn_mask, -torch.finfo(attn.dtype).max)
-
-            attn = F.softmax(attn, dim=-1, dtype=torch.float32)
-
-            out = natten.functional.natten1dav(attn, v, kernel_size = self.natten_kernel_size, dilation=1).to(dtype_in)
-
-        # Prioritize Flash Attention 2
-        elif self.use_fa_flash:
-            fa_dtype_in = q.dtype
-            if q.dtype in [torch.float, torch.float32]:
-                target_dtype = self.to_out.weight.dtype if self.to_out.weight.dtype not in [torch.float, torch.float32] else torch.float16
-                warnings.warn(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-                )
-                q, k, v = map(lambda t: t.to(target_dtype), (q, k, v))
-            q, k, v = map(lambda t: rearrange(t, 'b h n d -> b n h d'), (q, k, v))
-            # out = flash_attn_func(q, k, v, causal = causal) 
-            if final_attn_mask is not None:
-                # Check if the mask meets the requirement of FlashAttn
-                kv_seq_mask = final_attn_mask.squeeze(dim=[1,2])
-                kv_reallens = kv_seq_mask.sum(dim=-1, dtype=torch.int32)
-                first_zero_indices = torch.argmax((kv_seq_mask == 0).int(), dim=1).masked_fill(kv_seq_mask[:,-1] != 0, kv_seq_mask.shape[1])
-                assert (kv_reallens == first_zero_indices).all(), f'{kv_reallens} , {first_zero_indices}'
-                
-                batch_size, kv_seq_len, num_key_value_heads, head_dim = k.shape
-                unpad_k, indices_k, cu_seqlens_k, max_seqlen_in_batch_k = unpad_input(k, kv_seq_mask)
-                unpad_v = index_first_axis(
-                    v.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
-                )
-                q_seq_len = q.shape[1]
-                unpad_q, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(q, torch.ones((batch_size, q_seq_len), device=q.device, dtype=torch.bool))
-                # print(q.shape, k.shape)
-                # print(cu_seqlens_q, cu_seqlens_k)
-                # breakpoint()
-                out_unpad = flash_attn_varlen_func(
-                    unpad_q,
-                    unpad_k,
-                    unpad_v,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    causal=causal,
-                )
-                out = pad_input(out_unpad, indices_q, batch_size, q_seq_len)
-            else:
-                out = flash_attn_func(q, k, v, causal = causal)
-                
-                
-            out = rearrange(out.to(fa_dtype_in), 'b n h d -> b h n d')
-        # Fall back to PyTorch implementation
-        elif self.use_pt_flash:
-            out = self.flash_attn(q, k, v, causal = causal, mask = final_attn_mask)
-
-        else:
-            # Fall back to custom implementation
-
-            if h != kv_h:
-                # Repeat interleave kv_heads to match q_heads
-                heads_per_kv_head = h // kv_h
-                k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
-
-            scale = 1. / (q.shape[-1] ** 0.5)
-
-            kv_einsum_eq = 'b j d' if k.ndim == 3 else 'b h j d'
-
-            dots = einsum(f'b h i d, {kv_einsum_eq} -> b h i j', q, k) * scale
-            
-            i, j, dtype = *dots.shape[-2:], dots.dtype
-
-            mask_value = -torch.finfo(dots.dtype).max
-
-            if final_attn_mask is not None:
-                dots = dots.masked_fill(~final_attn_mask, mask_value)
-
-            if causal:
-                causal_mask = self.create_causal_mask(i, j, device = device)
-                dots = dots.masked_fill(causal_mask, mask_value)
-
-            attn = F.softmax(dots, dim=-1, dtype=torch.float32)
-            attn = attn.type(dtype)
-
-            out = einsum(f'b h i j, {kv_einsum_eq} -> b h i d', attn, v)
-
-        # merge heads
-        out = rearrange(out, ' b h n d -> b n (h d)')
-
-        # Communicate between heads
-        
-        # with autocast(enabled = False):
-        #     out_dtype = out.dtype
-        #     out = out.to(torch.float32)
-        #     out = self.to_out(out).to(out_dtype)
-        out = self.to_out(out)
-
-        if mask is not None:
-            mask = rearrange(mask, 'b n -> b n 1')
-            out = out.masked_fill(~mask, 0.)
-
-        return out
-
-class ConformerModule(nn.Module):
-    def __init__(
-        self,
-        dim,
-        norm_kwargs = {},
-    ):     
-
-        super().__init__()
-
-        self.dim = dim
-        
-        self.in_norm = LayerNorm(dim, **norm_kwargs)
-        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
-        self.glu = GLU(dim, dim, nn.SiLU())
-        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
-        self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
-        self.swish = nn.SiLU()
-        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        x = self.in_norm(x)
-        x = rearrange(x, 'b n d -> b d n')
-        x = self.pointwise_conv(x)
-        x = rearrange(x, 'b d n -> b n d')
-        x = self.glu(x)
-        x = rearrange(x, 'b n d -> b d n')
-        x = self.depthwise_conv(x)
-        x = rearrange(x, 'b d n -> b n d')
-        x = self.mid_norm(x)
-        x = self.swish(x)
-        x = rearrange(x, 'b n d -> b d n')
-        x = self.pointwise_conv_2(x)
-        x = rearrange(x, 'b d n -> b n d')
-
-        return x
-
-class TransformerBlock(nn.Module):
-    def __init__(
-            self,
-            dim,
-            dim_heads = 64,
-            cross_attend = False,
-            dim_context = None,
-            global_cond_dim = None,
-            causal = False,
-            zero_init_branch_outputs = True,
-            conformer = False,
-            layer_ix = -1,
-            remove_norms = False,
-            attn_kwargs = {},
-            ff_kwargs = {},
-            norm_kwargs = {}
-    ):
-        
-        super().__init__()
-        self.dim = dim
-        self.dim_heads = dim_heads
-        self.cross_attend = cross_attend
-        self.dim_context = dim_context
-        self.causal = causal
-
-        self.pre_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
-
-        self.self_attn = Attention(
-            dim,
-            dim_heads = dim_heads,
-            causal = causal,
-            zero_init_output=zero_init_branch_outputs,
-            **attn_kwargs
-        )
-
-        if cross_attend:
-            self.cross_attend_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
-            self.cross_attn = Attention(
-                dim,
-                dim_heads = dim_heads,
-                dim_context=dim_context,
-                causal = causal,
-                zero_init_output=zero_init_branch_outputs,
-                **attn_kwargs
-            )
-        
-        self.ff_norm = LayerNorm(dim, **norm_kwargs) if not remove_norms else nn.Identity()
-        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, **ff_kwargs)
-
-        self.layer_ix = layer_ix
-
-        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
-
-        self.global_cond_dim = global_cond_dim
-
-        if global_cond_dim is not None:
-            self.to_scale_shift_gate = nn.Sequential(
-                nn.SiLU(),
-                nn.Linear(global_cond_dim, dim * 6, bias=False)
-            )
-
-            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
-            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
-
-    def forward(
-        self,
-        x,
-        mask = None,
-        global_cond=None,
-        context = None,
-        context_mask = None,
-        rotary_pos_emb = None
-    ):
-        if self.global_cond_dim is not None:
-            assert global_cond is not None
-            # scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = checkpoint(self.to_scale_shift_gate, global_cond).unsqueeze(1).chunk(6, dim = -1)
-            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
-
-            # self-attention with adaLN
-            residual = x
-            x = self.pre_norm(x)
-            x = x * (1 + scale_self) + shift_self
-            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
-            x = x * torch.sigmoid(1 - gate_self)
-            x = x + residual
-
-            if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
-
-            if self.conformer is not None:
-                x = x + self.conformer(x)
-
-            # feedforward with adaLN
-            residual = x
-            x = self.ff_norm(x)
-            x = x * (1 + scale_ff) + shift_ff
-            x = self.ff(x)
-            x = x * torch.sigmoid(1 - gate_ff)
-            x = x + residual
-
-        else:
-            assert global_cond is None
-            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
-
-            if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
-
-            if self.conformer is not None:
-                x = x + self.conformer(x)
-
-            x = x + self.ff(self.ff_norm(x))
-
-        return x
-        
-class ContinuousTransformer(nn.Module):
-    def __init__(
-        self,
-        dim,
-        depth,
-        *,
-        dim_in = None,
-        dim_out = None,
-        dim_heads = 64,
-        cross_attend=False,
-        cross_atten_layer_idx=None,
-        cond_token_dim=None,
-        global_cond_dim=None,
-        causal=False,
-        rotary_pos_emb=True,
-        zero_init_branch_outputs=True,
-        conformer=False,
-        use_sinusoidal_emb=False,
-        use_abs_pos_emb=False,
-        abs_pos_emb_max_length=10000,
-        pos_emb_2d_size=1,
-        rotary_base_val=10000,
-        init_std=0.02,
-        **kwargs
-        ):
-
-        super().__init__()
-
-        self.dim = dim
-        self.depth = depth
-        self.causal = causal
-        self.layers = nn.ModuleList([])
-
-        self.project_in = nn.Linear(dim_in, dim, bias=False) if dim_in is not None else nn.Identity()
-        self.project_out = nn.Linear(dim, dim_out, bias=False) if dim_out is not None else nn.Identity()
-
-        if rotary_pos_emb:
-            if pos_emb_2d_size == 1:
-                self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), base=rotary_base_val)
-            else:
-                self.rotary_pos_emb = RotaryEmbedding2D(max(dim_heads // 2, 32), pos_emb_2d_size, base=rotary_base_val)
-        else:
-            self.rotary_pos_emb = None
-
-        self.use_sinusoidal_emb = use_sinusoidal_emb
-        if use_sinusoidal_emb:
-            if pos_emb_2d_size != 1:
-                raise NotImplementedError
-            self.pos_emb = ScaledSinusoidalEmbedding(dim)
-
-        self.use_abs_pos_emb = use_abs_pos_emb
-        if use_abs_pos_emb:
-            if pos_emb_2d_size != 1:
-                raise NotImplementedError
-            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
-
-        
-        if cross_atten_layer_idx is None:
-            cross_atten_layer_idx = list(range(depth))
-        for i in range(depth):
-            self.layers.append(
-                TransformerBlock(
-                    dim,
-                    dim_heads = dim_heads,
-                    cross_attend = cross_attend and (i in cross_atten_layer_idx),
-                    dim_context = cond_token_dim,
-                    global_cond_dim = global_cond_dim,
-                    causal = causal,
-                    zero_init_branch_outputs = zero_init_branch_outputs,
-                    conformer=conformer,
-                    layer_ix=i,
-                    **kwargs
-                )
-            )
-        self.gradient_checkpointing = False
-        
-        self.apply(partial(self._init_weights,init_std=init_std))
-        
-    def forward(
-        self,
-        x,
-        mask = None,
-        prepend_embeds = None,
-        prepend_mask = None,
-        global_cond = None,
-        return_info = False,
-        **kwargs
-    ):
-        batch, seq, device = *x.shape[:2], x.device
-
-        info = {
-            "hidden_states": [],
-        }
-
-        x = self.project_in(x)
-
-        if prepend_embeds is not None:
-            prepend_length, prepend_dim = prepend_embeds.shape[1:]
-
-            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
-
-            x = torch.cat((prepend_embeds, x), dim = -2)
-
-            if prepend_mask is not None or mask is not None:
-                mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
-                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
-
-                mask = torch.cat((prepend_mask, mask), dim = -1)
-
-        # Attention layers 
-
-        if self.rotary_pos_emb is not None:
-            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1])
-        else:
-            rotary_pos_emb = None
-
-        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
-            x = x + self.pos_emb(x)
-
-        # Iterate over the transformer layers
-        context, context_mask = kwargs.pop('context', None), kwargs.pop("context_mask", None)
-
-        for layer_idx, layer in enumerate(self.layers):
-            if layer.cross_attend:  
-                # x = layer(x, mask, global_cond=global_cond, rotary_pos_emb=rotary_pos_emb, context=context, context_mask=context_mask,**kwargs)
-                if self.gradient_checkpointing:
-                    x = checkpoint(layer, x, mask, global_cond, context, context_mask, rotary_pos_emb=rotary_pos_emb, **kwargs)
-                else:
-                    x = layer(x, mask, global_cond, context, context_mask, rotary_pos_emb=rotary_pos_emb, **kwargs)
-            else:
-                # x = layer(x, mask, global_cond=global_cond, rotary_pos_emb=rotary_pos_emb, **kwargs)
-                if self.gradient_checkpointing:
-                    x = checkpoint(layer, x, mask, global_cond, rotary_pos_emb=rotary_pos_emb, **kwargs)
-                else:
-                    x = layer(x, mask, global_cond, rotary_pos_emb=rotary_pos_emb, **kwargs)
-            if return_info:
-                info["hidden_states"].append(x)
-
-        x = self.project_out(x)
-
-        if return_info:
-            return x, info
-        
-        return x
-
-    def gradient_checkpointing_enable(self):
-        self.gradient_checkpointing = True
-        
-        
-    def _init_weights(self, module, init_std=0.02):
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=init_std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=init_std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()

SongBloom/models/vae_frontend/__init__.py

@@ -1,96 +0,0 @@
-import torch
-from torch import nn
-import typing as tp
-import torchaudio
-import einops
-from abc import ABC, abstractmethod
-
-
-class AbstractVAE(ABC, nn.Module):
-
-    @property
-    @abstractmethod
-    def frame_rate(self) -> float:
-        ...
-
-    @property
-    @abstractmethod
-    def orig_sample_rate(self) -> int:
-        ...
-    
-
-    @property
-    @abstractmethod
-    def channel_dim(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def split_bands(self) -> int:
-        ...
-
-    @property
-    @abstractmethod
-    def input_channel(self) -> int:
-        ...
-
-
-    def encode(self, wav) -> torch.Tensor:
-        ...
-        
-    def decode(self, latents) -> torch.Tensor:
-        ...
-
-
-from .autoencoders import create_autoencoder_from_config, AudioAutoencoder
-class StableVAE(AbstractVAE):
-    def __init__(self, vae_ckpt, vae_cfg, sr=48000) -> None:
-        super().__init__()
-        import json
-        with open(vae_cfg) as f:
-            config = json.load(f)
-        self.vae: AudioAutoencoder = create_autoencoder_from_config(config)
-        self.vae.load_state_dict(torch.load(vae_ckpt)['state_dict'])
-        self.sample_rate = sr
-        self.rsp48k = torchaudio.transforms.Resample(sr, self.orig_sample_rate) if sr != self.orig_sample_rate else nn.Identity()
-       
-    @torch.no_grad()
-    def encode(self, wav: torch.Tensor, sample=True) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]:
-        wav = self.rsp48k(wav)
-        if wav.shape[-1] < 2048:
-            return torch.zeros((wav.shape[0], self.channel_dim, 0), device=wav.device, dtype=wav.dtype)
-        if wav.ndim == 2:
-            wav = wav.unsqueeze(1)
-        if wav.shape[1] == 1:
-            wav = wav.repeat(1, self.vae.in_channels, 1)
-        latent = self.vae.encode_audio(wav) # B, 64, T
-        return latent
-            
-
-        
-    def decode(self, latents: torch.Tensor, **kwargs):
-        # B, 64, T
-        with torch.no_grad():
-            audio_recon = self.vae.decode_audio(latents, **kwargs)
-            
-        return audio_recon
-        
-    @property
-    def frame_rate(self) -> float:
-        return float(self.vae.sample_rate) / self.vae.downsampling_ratio
-
-    @property
-    def orig_sample_rate(self) -> int:
-        return self.vae.sample_rate
-
-    @property
-    def channel_dim(self) -> int:
-        return self.vae.latent_dim
-
-    @property
-    def split_bands(self) -> int:
-        return 1
-    
-    @property
-    def input_channel(self) -> int:
-        return self.vae.in_channels

SongBloom/models/vae_frontend/autoencoders.py

@@ -1,657 +0,0 @@
-# https://github.com/Stability-AI/stable-audio-tools/tree/main/stable_audio_tools/models
-
-import torch
-import math
-import numpy as np
-
-from torch import nn
-from torch.nn import functional as F
-from torchaudio import transforms as T
-from dac.nn.layers import WNConv1d, WNConvTranspose1d
-from typing import Literal, Dict, Any
-import os,sys
-
-sys.path.insert(0, os.path.dirname(__file__))
-from bottleneck import create_bottleneck_from_config
-
-class Bottleneck(nn.Module):
-    def __init__(self, is_discrete: bool = False):
-        super().__init__()
-
-        self.is_discrete = is_discrete
-
-    def encode(self, x, return_info=False, **kwargs):
-        raise NotImplementedError
-
-    def decode(self, x):
-        raise NotImplementedError
-
-class DiscreteBottleneck(Bottleneck):
-    def __init__(self, num_quantizers, codebook_size, tokens_id):
-        super().__init__(is_discrete=True)
-
-        self.num_quantizers = num_quantizers
-        self.codebook_size = codebook_size
-        self.tokens_id = tokens_id
-
-    def decode_tokens(self, codes, **kwargs):
-        raise NotImplementedError
-    
-
-def checkpoint(function, *args, **kwargs):
-    kwargs.setdefault("use_reentrant", False)
-    return torch.utils.checkpoint.checkpoint(function, *args, **kwargs)
-
-
-
-# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
-# License available in LICENSES/LICENSE_NVIDIA.txt
-def snake_beta(x, alpha, beta):
-    return x + (1.0 / (beta + 1e-9)) * pow(torch.sin(x * alpha), 2)
-
-class SnakeBeta(nn.Module):
-
-    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
-        super(SnakeBeta, self).__init__()
-        self.in_features = in_features
-
-        # initialize alpha
-        self.alpha_logscale = alpha_logscale
-        if self.alpha_logscale: # log scale alphas initialized to zeros
-            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
-            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
-        else: # linear scale alphas initialized to ones
-            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
-            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
-
-        self.alpha.requires_grad = alpha_trainable
-        self.beta.requires_grad = alpha_trainable
-
-        self.no_div_by_zero = 1e-9
-
-    def forward(self, x):
-        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
-        beta = self.beta.unsqueeze(0).unsqueeze(-1)
-        if self.alpha_logscale:
-            alpha = torch.exp(alpha)
-            beta = torch.exp(beta)
-        x = snake_beta(x, alpha, beta)
-
-        return x
-    
-def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
-    if activation == "elu":
-        act = nn.ELU()
-    elif activation == "snake":
-        act = SnakeBeta(channels)
-    elif activation == "none":
-        act = nn.Identity()
-    else:
-        raise ValueError(f"Unknown activation {activation}")
-    
-    if antialias:
-        from alias_free_torch import Activation1d
-        act = Activation1d(act)
-    
-    return act
-
-class ResidualUnit(nn.Module):
-    def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
-        super().__init__()
-        
-        self.dilation = dilation
-
-        padding = (dilation * (7-1)) // 2
-
-        self.layers = nn.Sequential(
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
-            WNConv1d(in_channels=in_channels, out_channels=out_channels,
-                      kernel_size=7, dilation=dilation, padding=padding),
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
-            WNConv1d(in_channels=out_channels, out_channels=out_channels,
-                      kernel_size=1)
-        )
-
-    def forward(self, x):
-        res = x
-        
-        #x = checkpoint(self.layers, x)
-        x = self.layers(x)
-
-        return x + res
-
-class EncoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
-        super().__init__()
-
-        self.layers = nn.Sequential(
-            ResidualUnit(in_channels=in_channels,
-                         out_channels=in_channels, dilation=1, use_snake=use_snake),
-            ResidualUnit(in_channels=in_channels,
-                         out_channels=in_channels, dilation=3, use_snake=use_snake),
-            ResidualUnit(in_channels=in_channels,
-                         out_channels=in_channels, dilation=9, use_snake=use_snake),
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
-            WNConv1d(in_channels=in_channels, out_channels=out_channels,
-                      kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-class DecoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
-        super().__init__()
-
-        if use_nearest_upsample:
-            upsample_layer = nn.Sequential(
-                nn.Upsample(scale_factor=stride, mode="nearest"),
-                WNConv1d(in_channels=in_channels,
-                        out_channels=out_channels, 
-                        kernel_size=2*stride,
-                        stride=1,
-                        bias=False,
-                        padding='same')
-            )
-        else:
-            upsample_layer = WNConvTranspose1d(in_channels=in_channels,
-                               out_channels=out_channels,
-                               kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
-
-        self.layers = nn.Sequential(
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
-            upsample_layer,
-            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
-                         dilation=1, use_snake=use_snake),
-            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
-                         dilation=3, use_snake=use_snake),
-            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
-                         dilation=9, use_snake=use_snake),
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-class OobleckEncoder(nn.Module):
-    def __init__(self, 
-                 in_channels=2, 
-                 channels=128, 
-                 latent_dim=32, 
-                 c_mults = [1, 2, 4, 8], 
-                 strides = [2, 4, 8, 8],
-                 use_snake=False,
-                 antialias_activation=False
-        ):
-        super().__init__()
-          
-        c_mults = [1] + c_mults
-
-        self.depth = len(c_mults)
-
-        layers = [
-            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
-        ]
-        
-        for i in range(self.depth-1):
-            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
-
-        layers += [
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
-            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
-        ]
-
-        self.layers = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class OobleckDecoder(nn.Module):
-    def __init__(self, 
-                 out_channels=2, 
-                 channels=128, 
-                 latent_dim=32, 
-                 c_mults = [1, 2, 4, 8], 
-                 strides = [2, 4, 8, 8],
-                 use_snake=False,
-                 antialias_activation=False,
-                 use_nearest_upsample=False,
-                 final_tanh=True):
-        super().__init__()
-
-        c_mults = [1] + c_mults
-        
-        self.depth = len(c_mults)
-
-        layers = [
-            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
-        ]
-        
-        for i in range(self.depth-1, 0, -1):
-            layers += [DecoderBlock(
-                in_channels=c_mults[i]*channels, 
-                out_channels=c_mults[i-1]*channels, 
-                stride=strides[i-1], 
-                use_snake=use_snake, 
-                antialias_activation=antialias_activation,
-                use_nearest_upsample=use_nearest_upsample
-                )
-            ]
-
-        layers += [
-            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
-            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
-            nn.Tanh() if final_tanh else nn.Identity()
-        ]
-
-        self.layers = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-
-class AudioAutoencoder(nn.Module):
-    def __init__(
-        self,
-        encoder,
-        decoder,
-        latent_dim,
-        downsampling_ratio,
-        sample_rate,
-        io_channels=2,
-        bottleneck = None,
-        pretransform = None,
-        in_channels = None,
-        out_channels = None,
-        soft_clip = False
-    ):
-        super().__init__()
-
-        self.downsampling_ratio = downsampling_ratio
-        self.sample_rate = sample_rate
-
-        self.latent_dim = latent_dim
-        self.io_channels = io_channels
-        self.in_channels = io_channels
-        self.out_channels = io_channels
-
-        self.min_length = self.downsampling_ratio
-
-        if in_channels is not None:
-            self.in_channels = in_channels
-
-        if out_channels is not None:
-            self.out_channels = out_channels
-
-        self.bottleneck = bottleneck
-
-        self.encoder = encoder
-
-        self.decoder = decoder
-
-        self.pretransform = pretransform
-
-        self.soft_clip = soft_clip
- 
-        self.is_discrete = self.bottleneck is not None and self.bottleneck.is_discrete
-
-    def encode(self, audio, return_info=False, skip_pretransform=False, iterate_batch=False, **kwargs):
-
-        info = {}
-
-        if self.pretransform is not None and not skip_pretransform:
-            if self.pretransform.enable_grad:
-                if iterate_batch:
-                    audios = []
-                    for i in range(audio.shape[0]):
-                        audios.append(self.pretransform.encode(audio[i:i+1]))
-                    audio = torch.cat(audios, dim=0)
-                else:
-                    audio = self.pretransform.encode(audio)
-            else:
-                with torch.no_grad():
-                    if iterate_batch:
-                        audios = []
-                        for i in range(audio.shape[0]):
-                            audios.append(self.pretransform.encode(audio[i:i+1]))
-                        audio = torch.cat(audios, dim=0)
-                    else:
-                        audio = self.pretransform.encode(audio)
-
-        if self.encoder is not None:
-            if iterate_batch:
-                latents = []
-                for i in range(audio.shape[0]):
-                    latents.append(self.encoder(audio[i:i+1]))
-                latents = torch.cat(latents, dim=0)
-            else:
-                latents = self.encoder(audio)
-        else:
-            latents = audio
-
-        if self.bottleneck is not None:
-            # TODO: Add iterate batch logic, needs to merge the info dicts
-            latents, bottleneck_info = self.bottleneck.encode(latents, return_info=True, **kwargs)
-
-            info.update(bottleneck_info)
-        
-        if return_info:
-            return latents, info
-
-        return latents
-
-    def decode(self, latents, iterate_batch=False, **kwargs):
-
-        if self.bottleneck is not None:
-            if iterate_batch:
-                decoded = []
-                for i in range(latents.shape[0]):
-                    decoded.append(self.bottleneck.decode(latents[i:i+1]))
-                latents = torch.cat(decoded, dim=0)
-            else:
-                latents = self.bottleneck.decode(latents)
-
-        if iterate_batch:
-            decoded = []
-            for i in range(latents.shape[0]):
-                decoded.append(self.decoder(latents[i:i+1]))
-            decoded = torch.cat(decoded, dim=0)
-        else:
-            decoded = self.decoder(latents, **kwargs)
-
-        if self.pretransform is not None:
-            if self.pretransform.enable_grad:
-                if iterate_batch:
-                    decodeds = []
-                    for i in range(decoded.shape[0]):
-                        decodeds.append(self.pretransform.decode(decoded[i:i+1]))
-                    decoded = torch.cat(decodeds, dim=0)
-                else:
-                    decoded = self.pretransform.decode(decoded)
-            else:
-                with torch.no_grad():
-                    if iterate_batch:
-                        decodeds = []
-                        for i in range(latents.shape[0]):
-                            decodeds.append(self.pretransform.decode(decoded[i:i+1]))
-                        decoded = torch.cat(decodeds, dim=0)
-                    else:
-                        decoded = self.pretransform.decode(decoded)
-
-        if self.soft_clip:
-            decoded = torch.tanh(decoded)
-        
-        return decoded
-          
-    def decode_tokens(self, tokens, **kwargs):
-        '''
-        Decode discrete tokens to audio
-        Only works with discrete autoencoders
-        '''
-
-        assert isinstance(self.bottleneck, DiscreteBottleneck), "decode_tokens only works with discrete autoencoders"
-
-        latents = self.bottleneck.decode_tokens(tokens, **kwargs)
-
-        return self.decode(latents, **kwargs)
-        
-    
-
-    def encode_audio(self, audio, chunked=False, overlap=32, chunk_size=128, **kwargs):
-        '''
-        Encode audios into latents. Audios should already be preprocesed by preprocess_audio_for_encoder.
-        If chunked is True, split the audio into chunks of a given maximum size chunk_size, with given overlap.
-        Overlap and chunk_size params are both measured in number of latents (not audio samples) 
-        # and therefore you likely could use the same values with decode_audio. 
-        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
-        Every autoencoder will have a different receptive field size, and thus ideal overlap.
-        You can determine it empirically by diffing unchunked vs chunked output and looking at maximum diff.
-        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
-        Smaller chunk_size uses less memory, but more compute.
-        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
-        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
-        '''
-        if not chunked:
-            # default behavior. Encode the entire audio in parallel
-            return self.encode(audio, **kwargs)
-        else:
-            # CHUNKED ENCODING
-            # samples_per_latent is just the downsampling ratio (which is also the upsampling ratio)
-            samples_per_latent = self.downsampling_ratio
-            total_size = audio.shape[2] # in samples
-            batch_size = audio.shape[0]
-            chunk_size *= samples_per_latent # converting metric in latents to samples
-            overlap *= samples_per_latent # converting metric in latents to samples
-            hop_size = chunk_size - overlap
-            chunks = []
-            for i in range(0, total_size - chunk_size + 1, hop_size):
-                chunk = audio[:,:,i:i+chunk_size]
-                chunks.append(chunk)
-            if i+chunk_size != total_size:
-                # Final chunk
-                chunk = audio[:,:,-chunk_size:]
-                chunks.append(chunk)
-            chunks = torch.stack(chunks)
-            num_chunks = chunks.shape[0]
-            # Note: y_size might be a different value from the latent length used in diffusion training
-            # because we can encode audio of varying lengths
-            # However, the audio should've been padded to a multiple of samples_per_latent by now.
-            y_size = total_size // samples_per_latent
-            # Create an empty latent, we will populate it with chunks as we encode them
-            y_final = torch.zeros((batch_size,self.latent_dim,y_size)).to(audio.device)
-            for i in range(num_chunks):
-                x_chunk = chunks[i,:]
-                # encode the chunk
-                y_chunk = self.encode(x_chunk)
-                # figure out where to put the audio along the time domain
-                if i == num_chunks-1:
-                    # final chunk always goes at the end
-                    t_end = y_size
-                    t_start = t_end - y_chunk.shape[2]
-                else:
-                    t_start = i * hop_size // samples_per_latent
-                    t_end = t_start + chunk_size // samples_per_latent
-                #  remove the edges of the overlaps
-                ol = overlap//samples_per_latent//2
-                chunk_start = 0
-                chunk_end = y_chunk.shape[2]
-                if i > 0:
-                    # no overlap for the start of the first chunk
-                    t_start += ol
-                    chunk_start += ol
-                if i < num_chunks-1:
-                    # no overlap for the end of the last chunk
-                    t_end -= ol
-                    chunk_end -= ol
-                # paste the chunked audio into our y_final output audio
-                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
-            return y_final
-    
-    def decode_audio(self, latents, chunked=False, overlap=32, chunk_size=128, **kwargs):
-        '''
-        Decode latents to audio. 
-        If chunked is True, split the latents into chunks of a given maximum size chunk_size, with given overlap, both of which are measured in number of latents. 
-        A overlap of zero will cause discontinuity artefacts. Overlap should be => receptive field size. 
-        Every autoencoder will have a different receptive field size, and thus ideal overlap.
-        You can determine it empirically by diffing unchunked vs chunked audio and looking at maximum diff.
-        The final chunk may have a longer overlap in order to keep chunk_size consistent for all chunks.
-        Smaller chunk_size uses less memory, but more compute.
-        The chunk_size vs memory tradeoff isn't linear, and possibly depends on the GPU and CUDA version
-        For example, on a A6000 chunk_size 128 is overall faster than 256 and 512 even though it has more chunks
-        '''
-        if not chunked:
-            # default behavior. Decode the entire latent in parallel
-            return self.decode(latents, **kwargs)
-        else:
-            # chunked decoding
-            hop_size = chunk_size - overlap
-            total_size = latents.shape[2]
-            batch_size = latents.shape[0]
-            chunks = []
-            for i in range(0, total_size - chunk_size + 1, hop_size):
-                chunk = latents[:,:,i:i+chunk_size]
-                chunks.append(chunk)
-            if i+chunk_size != total_size:
-                # Final chunk
-                chunk = latents[:,:,-chunk_size:]
-                chunks.append(chunk)
-            chunks = torch.stack(chunks)
-            num_chunks = chunks.shape[0]
-            # samples_per_latent is just the downsampling ratio
-            samples_per_latent = self.downsampling_ratio
-            # Create an empty waveform, we will populate it with chunks as decode them
-            y_size = total_size * samples_per_latent
-            y_final = torch.zeros((batch_size,self.out_channels,y_size)).to(latents.device)
-            for i in range(num_chunks):
-                x_chunk = chunks[i,:]
-                # decode the chunk
-                y_chunk = self.decode(x_chunk)
-                # figure out where to put the audio along the time domain
-                if i == num_chunks-1:
-                    # final chunk always goes at the end
-                    t_end = y_size
-                    t_start = t_end - y_chunk.shape[2]
-                else:
-                    t_start = i * hop_size * samples_per_latent
-                    t_end = t_start + chunk_size * samples_per_latent
-                #  remove the edges of the overlaps
-                ol = (overlap//2) * samples_per_latent
-                chunk_start = 0
-                chunk_end = y_chunk.shape[2]
-                if i > 0:
-                    # no overlap for the start of the first chunk
-                    t_start += ol
-                    chunk_start += ol
-                if i < num_chunks-1:
-                    # no overlap for the end of the last chunk
-                    t_end -= ol
-                    chunk_end -= ol
-                # paste the chunked audio into our y_final output audio
-                y_final[:,:,t_start:t_end] = y_chunk[:,:,chunk_start:chunk_end]
-            return y_final
-
-        
-# AE factories
-
-def create_encoder_from_config(encoder_config: Dict[str, Any]):
-    encoder_type = encoder_config.get("type", None)
-    assert encoder_type is not None, "Encoder type must be specified"
-
-    if encoder_type == "oobleck":
-        encoder = OobleckEncoder(
-            **encoder_config["config"]
-        )
-    
-    elif encoder_type == "seanet":
-        from encodec.modules import SEANetEncoder
-        seanet_encoder_config = encoder_config["config"]
-
-        #SEANet encoder expects strides in reverse order
-        seanet_encoder_config["ratios"] = list(reversed(seanet_encoder_config.get("ratios", [2, 2, 2, 2, 2])))
-        encoder = SEANetEncoder(
-            **seanet_encoder_config
-        )
-    else:
-        raise ValueError(f"Unknown encoder type {encoder_type}")
-    
-    requires_grad = encoder_config.get("requires_grad", True)
-    if not requires_grad:
-        for param in encoder.parameters():
-            param.requires_grad = False
-
-    return encoder
-
-def create_decoder_from_config(decoder_config: Dict[str, Any]):
-    decoder_type = decoder_config.get("type", None)
-    assert decoder_type is not None, "Decoder type must be specified"
-
-    if decoder_type == "oobleck":
-        decoder = OobleckDecoder(
-            **decoder_config["config"]
-        )
-    elif decoder_type == "seanet":
-        from encodec.modules import SEANetDecoder
-
-        decoder = SEANetDecoder(
-            **decoder_config["config"]
-        )
-    else:
-        raise ValueError(f"Unknown decoder type {decoder_type}")
-    
-    requires_grad = decoder_config.get("requires_grad", True)
-    if not requires_grad:
-        for param in decoder.parameters():
-            param.requires_grad = False
-
-    return decoder
-
-def create_autoencoder_from_config(config: Dict[str, Any]):
-    
-    # print(config)
-    ae_config = config["model"]
-
-    encoder = create_encoder_from_config(ae_config["encoder"])
-    decoder = create_decoder_from_config(ae_config["decoder"])
-
-    bottleneck = ae_config.get("bottleneck", None)
-
-    latent_dim = ae_config.get("latent_dim", None)
-    assert latent_dim is not None, "latent_dim must be specified in model config"
-    downsampling_ratio = ae_config.get("downsampling_ratio", None)
-    assert downsampling_ratio is not None, "downsampling_ratio must be specified in model config"
-    io_channels = ae_config.get("io_channels", None)
-    assert io_channels is not None, "io_channels must be specified in model config"
-    sample_rate = config.get("sample_rate", None)
-    assert sample_rate is not None, "sample_rate must be specified in model config"
-
-    in_channels = ae_config.get("in_channels", None)
-    out_channels = ae_config.get("out_channels", None)
-
-    pretransform = ae_config.get("pretransform", None)
-
-    if pretransform is not None:
-        from stable_audio_tools.models.factory import create_pretransform_from_config
-        pretransform = create_pretransform_from_config(pretransform, sample_rate)
-
-    if bottleneck is not None:
-        bottleneck = create_bottleneck_from_config(bottleneck)
-
-    soft_clip = ae_config["decoder"].get("soft_clip", False)
-
-    return AudioAutoencoder(
-        encoder,
-        decoder,
-        io_channels=io_channels,
-        latent_dim=latent_dim,
-        downsampling_ratio=downsampling_ratio,
-        sample_rate=sample_rate,
-        bottleneck=bottleneck,
-        pretransform=pretransform,
-        in_channels=in_channels,
-        out_channels=out_channels,
-        soft_clip=soft_clip
-    )
-
-
-
-if __name__ == "__main__":
-    import json
-    import torchaudio
-    config_path = 'modelzoo/stable_audio_vae/stable_audio_2_0_vae.json'
-    with open(config_path) as f:
-        config = json.load(f)
-    with torch.no_grad():
-        vae_model = create_autoencoder_from_config(config).cuda()
-        model_ckpt_path = 'modelzoo/stable_audio_vae/autoencoder.ckpt'
-        vae_model.load_state_dict(torch.load(model_ckpt_path)['state_dict'])
-        
-        
-        input_audios, sr = torchaudio.load("music_example/加勒比海盗 主题.wav")
-        input_audios = torchaudio.functional.resample(input_audios, sr, 48000)[...,:2048]
-        input_audios = input_audios.unsqueeze(1).repeat(1, 2, 1).cuda()
-        latents = vae_model.encode_audio(input_audios)
-        recover_audio = vae_model.decode_audio(latents)
-        print(recover_audio)
-    
-    breakpoint()

SongBloom/models/vae_frontend/bottleneck.py

@@ -1,417 +0,0 @@
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from einops import rearrange
-from vector_quantize_pytorch import ResidualVQ, FSQ
-from dac.nn.quantize import ResidualVectorQuantize as DACResidualVQ
-
-
-def create_bottleneck_from_config(bottleneck_config):
-    bottleneck_type = bottleneck_config.get('type', None)
-
-    assert bottleneck_type is not None, 'type must be specified in bottleneck config'
-
-    if bottleneck_type == 'tanh':
-        bottleneck = TanhBottleneck()
-    elif bottleneck_type == 'vae':
-        bottleneck = VAEBottleneck()
-    elif bottleneck_type == 'rvq':
-
-        quantizer_params = {
-            "dim": 128,
-            "codebook_size": 1024,
-            "num_quantizers": 8,
-            "decay": 0.99,
-            "kmeans_init": True,
-            "kmeans_iters": 50,
-            "threshold_ema_dead_code": 2,
-        }
-
-        quantizer_params.update(bottleneck_config["config"])
-
-        bottleneck = RVQBottleneck(**quantizer_params)
-    elif bottleneck_type == "dac_rvq":
-        bottleneck = DACRVQBottleneck(**bottleneck_config["config"])
-    
-    elif bottleneck_type == 'rvq_vae':
-
-        quantizer_params = {
-            "dim": 128,
-            "codebook_size": 1024,
-            "num_quantizers": 8,
-            "decay": 0.99,
-            "kmeans_init": True,
-            "kmeans_iters": 50,
-            "threshold_ema_dead_code": 2,
-        }
-
-        quantizer_params.update(bottleneck_config["config"])
-
-        bottleneck = RVQVAEBottleneck(**quantizer_params)
-        
-    elif bottleneck_type == 'dac_rvq_vae':
-        bottleneck = DACRVQVAEBottleneck(**bottleneck_config["config"])
-    elif bottleneck_type == 'l2_norm':
-        bottleneck = L2Bottleneck()
-    elif bottleneck_type == "wasserstein":
-        bottleneck = WassersteinBottleneck(**bottleneck_config.get("config", {}))
-    elif bottleneck_type == "fsq":
-        bottleneck = FSQBottleneck(**bottleneck_config["config"])
-    else:
-        raise NotImplementedError(f'Unknown bottleneck type: {bottleneck_type}')
-    
-    requires_grad = bottleneck_config.get('requires_grad', True)
-    if not requires_grad:
-        for param in bottleneck.parameters():
-            param.requires_grad = False
-
-    return bottleneck
-
-class Bottleneck(nn.Module):
-    def __init__(self, is_discrete: bool = False):
-        super().__init__()
-
-        self.is_discrete = is_discrete
-
-    def encode(self, x, return_info=False, **kwargs):
-        raise NotImplementedError
-
-    def decode(self, x):
-        raise NotImplementedError
-
-class DiscreteBottleneck(Bottleneck):
-    def __init__(self, num_quantizers, codebook_size, tokens_id):
-        super().__init__(is_discrete=True)
-
-        self.num_quantizers = num_quantizers
-        self.codebook_size = codebook_size
-        self.tokens_id = tokens_id
-
-    def decode_tokens(self, codes, **kwargs):
-        raise NotImplementedError
-    
-class TanhBottleneck(Bottleneck):
-    def __init__(self):
-        super().__init__(is_discrete=False)
-        self.tanh = nn.Tanh()
-
-    def encode(self, x, return_info=False):
-        info = {}
-
-        x = torch.tanh(x)
-
-        if return_info:
-            return x, info
-        else:
-            return x
-
-    def decode(self, x):
-        return x
-
-def vae_sample(mean, scale):
-        stdev = nn.functional.softplus(scale) + 1e-4
-        var = stdev * stdev
-        logvar = torch.log(var)
-        latents = torch.randn_like(mean) * stdev + mean
-
-        kl = (mean * mean + var - logvar - 1).sum(1).mean()
-
-        return latents, kl
-
-class VAEBottleneck(Bottleneck):
-    def __init__(self):
-        super().__init__(is_discrete=False)
-
-    def encode(self, x, return_info=False, **kwargs):
-        info = {}
-
-        mean, scale = x.chunk(2, dim=1)
-
-        x, kl = vae_sample(mean, scale)
-
-        info["kl"] = kl
-
-        if return_info:
-            return x, info
-        else:
-            return x
-
-    def decode(self, x):
-        return x
-
-def compute_mean_kernel(x, y):
-        kernel_input = (x[:, None] - y[None]).pow(2).mean(2) / x.shape[-1]
-        return torch.exp(-kernel_input).mean()
-
-def compute_mmd(latents):
-    latents_reshaped = latents.permute(0, 2, 1).reshape(-1, latents.shape[1])
-    noise = torch.randn_like(latents_reshaped)
-
-    latents_kernel = compute_mean_kernel(latents_reshaped, latents_reshaped)
-    noise_kernel = compute_mean_kernel(noise, noise)
-    latents_noise_kernel = compute_mean_kernel(latents_reshaped, noise)
-    
-    mmd = latents_kernel + noise_kernel - 2 * latents_noise_kernel
-    return mmd.mean()
-
-class WassersteinBottleneck(Bottleneck):
-    def __init__(self, noise_augment_dim: int = 0, bypass_mmd: bool = False):
-        super().__init__(is_discrete=False)
-
-        self.noise_augment_dim = noise_augment_dim
-        self.bypass_mmd = bypass_mmd
-    
-    def encode(self, x, return_info=False):
-        info = {}
-
-        if self.training and return_info:
-            if self.bypass_mmd:
-                mmd = torch.tensor(0.0)
-            else:
-                mmd = compute_mmd(x)
-                
-            info["mmd"] = mmd
-        
-        if return_info:
-            return x, info
-        
-        return x
-
-    def decode(self, x):
-
-        if self.noise_augment_dim > 0:
-            noise = torch.randn(x.shape[0], self.noise_augment_dim,
-                                x.shape[-1]).type_as(x)
-            x = torch.cat([x, noise], dim=1)
-
-        return x
-
-class L2Bottleneck(Bottleneck):
-    def __init__(self):
-        super().__init__(is_discrete=False)
-    
-    def encode(self, x, return_info=False):
-        info = {}
-
-        x = F.normalize(x, dim=1)
-
-        if return_info:
-            return x, info
-        else:
-            return x
-        
-    def decode(self, x):
-        return F.normalize(x, dim=1)
-        
-class RVQBottleneck(DiscreteBottleneck):
-    def __init__(self, **quantizer_kwargs):
-        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
-        self.quantizer = ResidualVQ(**quantizer_kwargs)
-        self.num_quantizers = quantizer_kwargs["num_quantizers"]
-
-    def encode(self, x, return_info=False, **kwargs):
-        info = {}
-
-        x = rearrange(x, "b c n -> b n c")
-        x, indices, loss = self.quantizer(x)
-        x = rearrange(x, "b n c -> b c n")
-
-        info["quantizer_indices"] = indices
-        info["quantizer_loss"] = loss.mean()
-
-        if return_info:
-            return x, info
-        else:
-            return x
-        
-    def decode(self, x):
-        return x
-    
-    def decode_tokens(self, codes, **kwargs):
-        latents = self.quantizer.get_outputs_from_indices(codes)
-
-        return self.decode(latents, **kwargs)
-    
-class RVQVAEBottleneck(DiscreteBottleneck):
-    def __init__(self, **quantizer_kwargs):
-        super().__init__(num_quantizers = quantizer_kwargs["num_quantizers"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "quantizer_indices")
-        self.quantizer = ResidualVQ(**quantizer_kwargs)
-        self.num_quantizers = quantizer_kwargs["num_quantizers"]
-
-    def encode(self, x, return_info=False):
-        info = {}
-
-        x, kl = vae_sample(*x.chunk(2, dim=1))
-
-        info["kl"] = kl
-
-        x = rearrange(x, "b c n -> b n c")
-        x, indices, loss = self.quantizer(x)
-        x = rearrange(x, "b n c -> b c n")
-
-        info["quantizer_indices"] = indices
-        info["quantizer_loss"] = loss.mean()
-
-        if return_info:
-            return x, info
-        else:
-            return x
-        
-    def decode(self, x):
-        return x
-    
-    def decode_tokens(self, codes, **kwargs):
-        latents = self.quantizer.get_outputs_from_indices(codes)
-
-        return self.decode(latents, **kwargs)
-
-class DACRVQBottleneck(DiscreteBottleneck):
-    def __init__(self, quantize_on_decode=False, noise_augment_dim=0, **quantizer_kwargs):
-        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
-        self.quantizer = DACResidualVQ(**quantizer_kwargs)
-        self.num_quantizers = quantizer_kwargs["n_codebooks"]
-        self.quantize_on_decode = quantize_on_decode
-        self.noise_augment_dim = noise_augment_dim
-
-    def encode(self, x, return_info=False, **kwargs):
-        info = {}
-
-        info["pre_quantizer"] = x
-
-        if self.quantize_on_decode:
-            return x, info if return_info else x
-
-        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, **kwargs)
-
-        output = {
-            "z": z,
-            "codes": codes,
-            "latents": latents,
-            "vq/commitment_loss": commitment_loss,
-            "vq/codebook_loss": codebook_loss,
-        }
-
-        output["vq/commitment_loss"] /= self.num_quantizers
-        output["vq/codebook_loss"] /= self.num_quantizers
-
-        info.update(output)
-
-        if return_info:
-            return output["z"], info
-        
-        return output["z"]
-    
-    def decode(self, x):
-
-        if self.quantize_on_decode:
-            x = self.quantizer(x)[0]
-
-        if self.noise_augment_dim > 0:
-            noise = torch.randn(x.shape[0], self.noise_augment_dim,
-                                x.shape[-1]).type_as(x)
-            x = torch.cat([x, noise], dim=1)
-
-        return x
-    
-    def decode_tokens(self, codes, **kwargs):
-        latents, _, _ = self.quantizer.from_codes(codes)
-
-        return self.decode(latents, **kwargs)
-
-class DACRVQVAEBottleneck(DiscreteBottleneck):
-    def __init__(self, quantize_on_decode=False, **quantizer_kwargs):
-        super().__init__(num_quantizers = quantizer_kwargs["n_codebooks"], codebook_size = quantizer_kwargs["codebook_size"], tokens_id = "codes")
-        self.quantizer = DACResidualVQ(**quantizer_kwargs)
-        self.num_quantizers = quantizer_kwargs["n_codebooks"]
-        self.quantize_on_decode = quantize_on_decode
-
-    def encode(self, x, return_info=False, n_quantizers: int = None):
-        info = {}
-
-        mean, scale = x.chunk(2, dim=1)
-
-        x, kl = vae_sample(mean, scale)
-
-        info["pre_quantizer"] = x
-        info["kl"] = kl
-
-        if self.quantize_on_decode:
-            return x, info if return_info else x
-
-        z, codes, latents, commitment_loss, codebook_loss = self.quantizer(x, n_quantizers=n_quantizers)
-
-        output = {
-            "z": z,
-            "codes": codes,
-            "latents": latents,
-            "vq/commitment_loss": commitment_loss,
-            "vq/codebook_loss": codebook_loss,
-        }
-
-        output["vq/commitment_loss"] /= self.num_quantizers
-        output["vq/codebook_loss"] /= self.num_quantizers
-
-        info.update(output)
-
-        if return_info:
-            return output["z"], info
-        
-        return output["z"]
-    
-    def decode(self, x):
-
-        if self.quantize_on_decode:
-            x = self.quantizer(x)[0]
-
-        return x
-
-    def decode_tokens(self, codes, **kwargs):
-        latents, _, _ = self.quantizer.from_codes(codes)
-
-        return self.decode(latents, **kwargs)
-    
-class FSQBottleneck(DiscreteBottleneck):
-    def __init__(self, noise_augment_dim=0, **kwargs):
-        super().__init__(num_quantizers = kwargs.get("num_codebooks", 1), codebook_size = np.prod(kwargs["levels"]), tokens_id = "quantizer_indices")
-
-        self.noise_augment_dim = noise_augment_dim
-
-        self.quantizer = FSQ(**kwargs, allowed_dtypes=[torch.float16, torch.float32, torch.float64])
-
-    def encode(self, x, return_info=False):
-        info = {}
-
-        orig_dtype = x.dtype
-        x = x.float()
-
-        x = rearrange(x, "b c n -> b n c")
-        x, indices = self.quantizer(x)
-        x = rearrange(x, "b n c -> b c n")
-
-        x = x.to(orig_dtype)
-
-        # Reorder indices to match the expected format
-        indices = rearrange(indices, "b n q -> b q n")
-
-        info["quantizer_indices"] = indices
-
-        if return_info:
-            return x, info
-        else:
-            return x
-        
-    def decode(self, x):
-
-        if self.noise_augment_dim > 0:
-            noise = torch.randn(x.shape[0], self.noise_augment_dim,
-                                x.shape[-1]).type_as(x)
-            x = torch.cat([x, noise], dim=1)
-
-        return x
-    
-    def decode_tokens(self, tokens, **kwargs):
-        latents = self.quantizer.indices_to_codes(tokens)
-
-        return self.decode(latents, **kwargs)