Initial commit

examples/aishell/paraformer/utils/text_tokenize.sh

+#!/usr/bin/env bash
+
+
+# Begin configuration section.
+nj=32
+cmd=utils/run.pl
+
+echo "$0 $@"
+
+. utils/parse_options.sh || exit 1;
+
+# tokenize configuration
+text_dir=$1
+seg_file=$2
+logdir=$3
+output_dir=$4
+
+txt_dir=${output_dir}/txt; mkdir -p ${output_dir}/txt
+mkdir -p ${logdir}
+
+$cmd JOB=1:$nj $logdir/text_tokenize.JOB.log \
+  python utils/text_tokenize.py -t ${text_dir}/txt/text.JOB.txt \
+      -s ${seg_file} -i JOB -o ${txt_dir} \
+      || exit 1;
+
+# concatenate the text files together.
+for n in $(seq $nj); do
+  cat ${txt_dir}/text.$n.txt || exit 1
+done > ${output_dir}/text || exit 1
+
+for n in $(seq $nj); do
+  cat ${txt_dir}/len.$n || exit 1
+done > ${output_dir}/text_shape || exit 1
+
+echo "$0: Succeeded text tokenize"

examples/aishell/paraformer/utils/textnorm_zh.py

+#!/usr/bin/env python3
+# coding=utf-8
+
+# Authors:
+#   2019.5 Zhiyang Zhou (https://github.com/Joee1995/chn_text_norm.git)
+#   2019.9 Jiayu DU
+#
+# requirements:
+#   - python 3.X
+# notes: python 2.X WILL fail or produce misleading results
+
+import sys, os, argparse, codecs, string, re
+
+# ================================================================================ #
+#                                    basic constant
+# ================================================================================ #
+CHINESE_DIGIS = "零一二三四五六七八九"
+BIG_CHINESE_DIGIS_SIMPLIFIED = "零壹贰叁肆伍陆柒捌玖"
+BIG_CHINESE_DIGIS_TRADITIONAL = "零壹貳參肆伍陸柒捌玖"
+SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = "十百千万"
+SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = "拾佰仟萬"
+LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = "亿兆京垓秭穰沟涧正载"
+LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = "億兆京垓秭穰溝澗正載"
+SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = "十百千万"
+SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = "拾佰仟萬"
+
+ZERO_ALT = "〇"
+ONE_ALT = "幺"
+TWO_ALTS = ["两", "兩"]
+
+POSITIVE = ["正", "正"]
+NEGATIVE = ["负", "負"]
+POINT = ["点", "點"]
+# PLUS = [u'加', u'加']
+# SIL = [u'杠', u'槓']
+
+FILLER_CHARS = ["呃", "啊"]
+ER_WHITELIST = (
+    "(儿女|儿子|儿孙|女儿|儿媳|妻儿|"
+    "胎儿|婴儿|新生儿|婴幼儿|幼儿|少儿|小儿|儿歌|儿童|儿科|托儿所|孤儿|"
+    "儿戏|儿化|台儿庄|鹿儿岛|正儿八经|吊儿郎当|生儿育女|托儿带女|养儿防老|痴儿呆女|"
+    "佳儿佳妇|儿怜兽扰|儿无常父|儿不嫌母丑|儿行千里母担忧|儿大不由爷|苏乞儿)"
+)
+
+# 中文数字系统类型
+NUMBERING_TYPES = ["low", "mid", "high"]
+
+CURRENCY_NAMES = (
+    "(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|"
+    "里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)"
+)
+CURRENCY_UNITS = (
+    "((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)"
+)
+COM_QUANTIFIERS = (
+    "(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|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)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|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)"
+)
+
+# punctuation information are based on Zhon project (https://github.com/tsroten/zhon.git)
+CHINESE_PUNC_STOP = "！？｡。"
+CHINESE_PUNC_NON_STOP = "＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏"
+CHINESE_PUNC_LIST = CHINESE_PUNC_STOP + CHINESE_PUNC_NON_STOP
+
+
+# ================================================================================ #
+#                                    basic class
+# ================================================================================ #
+class ChineseChar(object):
+    """
+    中文字符
+    每个字符对应简体和繁体,
+    e.g. 简体 = '负', 繁体 = '負'
+    转换时可转换为简体或繁体
+    """
+
+    def __init__(self, simplified, traditional):
+        self.simplified = simplified
+        self.traditional = traditional
+        # self.__repr__ = self.__str__
+
+    def __str__(self):
+        return self.simplified or self.traditional or None
+
+    def __repr__(self):
+        return self.__str__()
+
+
+class ChineseNumberUnit(ChineseChar):
+    """
+    中文数字/数位字符
+    每个字符除繁简体外还有一个额外的大写字符
+    e.g. '陆' 和 '陸'
+    """
+
+    def __init__(self, power, simplified, traditional, big_s, big_t):
+        super(ChineseNumberUnit, self).__init__(simplified, traditional)
+        self.power = power
+        self.big_s = big_s
+        self.big_t = big_t
+
+    def __str__(self):
+        return "10^{}".format(self.power)
+
+    @classmethod
+    def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
+
+        if small_unit:
+            return ChineseNumberUnit(
+                power=index + 1,
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[1],
+                big_t=value[1],
+            )
+        elif numbering_type == NUMBERING_TYPES[0]:
+            return ChineseNumberUnit(
+                power=index + 8,
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[0],
+                big_t=value[1],
+            )
+        elif numbering_type == NUMBERING_TYPES[1]:
+            return ChineseNumberUnit(
+                power=(index + 2) * 4,
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[0],
+                big_t=value[1],
+            )
+        elif numbering_type == NUMBERING_TYPES[2]:
+            return ChineseNumberUnit(
+                power=pow(2, index + 3),
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[0],
+                big_t=value[1],
+            )
+        else:
+            raise ValueError(
+                "Counting type should be in {0} ({1} provided).".format(
+                    NUMBERING_TYPES, numbering_type
+                )
+            )
+
+
+class ChineseNumberDigit(ChineseChar):
+    """
+    中文数字字符
+    """
+
+    def __init__(self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None):
+        super(ChineseNumberDigit, self).__init__(simplified, traditional)
+        self.value = value
+        self.big_s = big_s
+        self.big_t = big_t
+        self.alt_s = alt_s
+        self.alt_t = alt_t
+
+    def __str__(self):
+        return str(self.value)
+
+    @classmethod
+    def create(cls, i, v):
+        return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
+
+
+class ChineseMath(ChineseChar):
+    """
+    中文数位字符
+    """
+
+    def __init__(self, simplified, traditional, symbol, expression=None):
+        super(ChineseMath, self).__init__(simplified, traditional)
+        self.symbol = symbol
+        self.expression = expression
+        self.big_s = simplified
+        self.big_t = traditional
+
+
+CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
+
+
+class NumberSystem(object):
+    """
+    中文数字系统
+    """
+
+    pass
+
+
+class MathSymbol(object):
+    """
+    用于中文数字系统的数学符号 (繁/简体), e.g.
+    positive = ['正', '正']
+    negative = ['负', '負']
+    point = ['点', '點']
+    """
+
+    def __init__(self, positive, negative, point):
+        self.positive = positive
+        self.negative = negative
+        self.point = point
+
+    def __iter__(self):
+        for v in self.__dict__.values():
+            yield v
+
+
+# class OtherSymbol(object):
+#     """
+#     其他符号
+#     """
+#
+#     def __init__(self, sil):
+#         self.sil = sil
+#
+#     def __iter__(self):
+#         for v in self.__dict__.values():
+#             yield v
+
+
+# ================================================================================ #
+#                                    basic utils
+# ================================================================================ #
+def create_system(numbering_type=NUMBERING_TYPES[1]):
+    """
+    根据数字系统类型返回创建相应的数字系统，默认为 mid
+    NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
+        low:  '兆' = '亿' * '十' = $10^{9}$,  '京' = '兆' * '十', etc.
+        mid:  '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
+        high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
+    返回对应的数字系统
+    """
+
+    # chinese number units of '亿' and larger
+    all_larger_units = zip(
+        LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED, LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL
+    )
+    larger_units = [CNU.create(i, v, numbering_type, False) for i, v in enumerate(all_larger_units)]
+    # chinese number units of '十, 百, 千, 万'
+    all_smaller_units = zip(
+        SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED, SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL
+    )
+    smaller_units = [CNU.create(i, v, small_unit=True) for i, v in enumerate(all_smaller_units)]
+    # digis
+    chinese_digis = zip(
+        CHINESE_DIGIS, CHINESE_DIGIS, BIG_CHINESE_DIGIS_SIMPLIFIED, BIG_CHINESE_DIGIS_TRADITIONAL
+    )
+    digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
+    digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
+    digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
+    digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
+
+    # symbols
+    positive_cn = CM(POSITIVE[0], POSITIVE[1], "+", lambda x: x)
+    negative_cn = CM(NEGATIVE[0], NEGATIVE[1], "-", lambda x: -x)
+    point_cn = CM(POINT[0], POINT[1], ".", lambda x, y: float(str(x) + "." + str(y)))
+    # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
+    system = NumberSystem()
+    system.units = smaller_units + larger_units
+    system.digits = digits
+    system.math = MathSymbol(positive_cn, negative_cn, point_cn)
+    # system.symbols = OtherSymbol(sil_cn)
+    return system
+
+
+def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
+
+    def get_symbol(char, system):
+        for u in system.units:
+            if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
+                return u
+        for d in system.digits:
+            if char in [d.traditional, d.simplified, d.big_s, d.big_t, d.alt_s, d.alt_t]:
+                return d
+        for m in system.math:
+            if char in [m.traditional, m.simplified]:
+                return m
+
+    def string2symbols(chinese_string, system):
+        int_string, dec_string = chinese_string, ""
+        for p in [system.math.point.simplified, system.math.point.traditional]:
+            if p in chinese_string:
+                int_string, dec_string = chinese_string.split(p)
+                break
+        return [get_symbol(c, system) for c in int_string], [
+            get_symbol(c, system) for c in dec_string
+        ]
+
+    def correct_symbols(integer_symbols, system):
+        """
+        一百八 to 一百八十
+        一亿一千三百万 to 一亿 一千万 三百万
+        """
+
+        if integer_symbols and isinstance(integer_symbols[0], CNU):
+            if integer_symbols[0].power == 1:
+                integer_symbols = [system.digits[1]] + integer_symbols
+
+        if len(integer_symbols) > 1:
+            if isinstance(integer_symbols[-1], CND) and isinstance(integer_symbols[-2], CNU):
+                integer_symbols.append(CNU(integer_symbols[-2].power - 1, None, None, None, None))
+
+        result = []
+        unit_count = 0
+        for s in integer_symbols:
+            if isinstance(s, CND):
+                result.append(s)
+                unit_count = 0
+            elif isinstance(s, CNU):
+                current_unit = CNU(s.power, None, None, None, None)
+                unit_count += 1
+
+            if unit_count == 1:
+                result.append(current_unit)
+            elif unit_count > 1:
+                for i in range(len(result)):
+                    if (
+                        isinstance(result[-i - 1], CNU)
+                        and result[-i - 1].power < current_unit.power
+                    ):
+                        result[-i - 1] = CNU(
+                            result[-i - 1].power + current_unit.power, None, None, None, None
+                        )
+        return result
+
+    def compute_value(integer_symbols):
+        """
+        Compute the value.
+        When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
+        e.g. '两千万' = 2000 * 10000 not 2000 + 10000
+        """
+        value = [0]
+        last_power = 0
+        for s in integer_symbols:
+            if isinstance(s, CND):
+                value[-1] = s.value
+            elif isinstance(s, CNU):
+                value[-1] *= pow(10, s.power)
+                if s.power > last_power:
+                    value[:-1] = list(map(lambda v: v * pow(10, s.power), value[:-1]))
+                    last_power = s.power
+                value.append(0)
+        return sum(value)
+
+    system = create_system(numbering_type)
+    int_part, dec_part = string2symbols(chinese_string, system)
+    int_part = correct_symbols(int_part, system)
+    int_str = str(compute_value(int_part))
+    dec_str = "".join([str(d.value) for d in dec_part])
+    if dec_part:
+        return "{0}.{1}".format(int_str, dec_str)
+    else:
+        return int_str
+
+
+def num2chn(
+    number_string,
+    numbering_type=NUMBERING_TYPES[1],
+    big=False,
+    traditional=False,
+    alt_zero=False,
+    alt_one=False,
+    alt_two=True,
+    use_zeros=True,
+    use_units=True,
+):
+
+    def get_value(value_string, use_zeros=True):
+
+        striped_string = value_string.lstrip("0")
+
+        # record nothing if all zeros
+        if not striped_string:
+            return []
+
+        # record one digits
+        elif len(striped_string) == 1:
+            if use_zeros and len(value_string) != len(striped_string):
+                return [system.digits[0], system.digits[int(striped_string)]]
+            else:
+                return [system.digits[int(striped_string)]]
+
+        # recursively record multiple digits
+        else:
+            result_unit = next(u for u in reversed(system.units) if u.power < len(striped_string))
+            result_string = value_string[: -result_unit.power]
+            return (
+                get_value(result_string)
+                + [result_unit]
+                + get_value(striped_string[-result_unit.power :])
+            )
+
+    system = create_system(numbering_type)
+
+    int_dec = number_string.split(".")
+    if len(int_dec) == 1:
+        int_string = int_dec[0]
+        dec_string = ""
+    elif len(int_dec) == 2:
+        int_string = int_dec[0]
+        dec_string = int_dec[1]
+    else:
+        raise ValueError(
+            "invalid input num string with more than one dot: {}".format(number_string)
+        )
+
+    if use_units and len(int_string) > 1:
+        result_symbols = get_value(int_string)
+    else:
+        result_symbols = [system.digits[int(c)] for c in int_string]
+    dec_symbols = [system.digits[int(c)] for c in dec_string]
+    if dec_string:
+        result_symbols += [system.math.point] + dec_symbols
+
+    if alt_two:
+        liang = CND(
+            2,
+            system.digits[2].alt_s,
+            system.digits[2].alt_t,
+            system.digits[2].big_s,
+            system.digits[2].big_t,
+        )
+        for i, v in enumerate(result_symbols):
+            if isinstance(v, CND) and v.value == 2:
+                next_symbol = result_symbols[i + 1] if i < len(result_symbols) - 1 else None
+                previous_symbol = result_symbols[i - 1] if i > 0 else None
+                if isinstance(next_symbol, CNU) and isinstance(previous_symbol, (CNU, type(None))):
+                    if next_symbol.power != 1 and (
+                        (previous_symbol is None) or (previous_symbol.power != 1)
+                    ):
+                        result_symbols[i] = liang
+
+    # if big is True, '两' will not be used and `alt_two` has no impact on output
+    if big:
+        attr_name = "big_"
+        if traditional:
+            attr_name += "t"
+        else:
+            attr_name += "s"
+    else:
+        if traditional:
+            attr_name = "traditional"
+        else:
+            attr_name = "simplified"
+
+    result = "".join([getattr(s, attr_name) for s in result_symbols])
+
+    # if not use_zeros:
+    #     result = result.strip(getattr(system.digits[0], attr_name))
+
+    if alt_zero:
+        result = result.replace(getattr(system.digits[0], attr_name), system.digits[0].alt_s)
+
+    if alt_one:
+        result = result.replace(getattr(system.digits[1], attr_name), system.digits[1].alt_s)
+
+    for i, p in enumerate(POINT):
+        if result.startswith(p):
+            return CHINESE_DIGIS[0] + result
+
+    # ^10, 11, .., 19
+    if (
+        len(result) >= 2
+        and result[1]
+        in [
+            SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
+            SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0],
+        ]
+        and result[0]
+        in [CHINESE_DIGIS[1], BIG_CHINESE_DIGIS_SIMPLIFIED[1], BIG_CHINESE_DIGIS_TRADITIONAL[1]]
+    ):
+        result = result[1:]
+
+    return result
+
+
+# ================================================================================ #
+#                          different types of rewriters
+# ================================================================================ #
+class Cardinal:
+    """
+    CARDINAL类
+    """
+
+    def __init__(self, cardinal=None, chntext=None):
+        self.cardinal = cardinal
+        self.chntext = chntext
+
+    def chntext2cardinal(self):
+        return chn2num(self.chntext)
+
+    def cardinal2chntext(self):
+        return num2chn(self.cardinal)
+
+
+class Digit:
+    """
+    DIGIT类
+    """
+
+    def __init__(self, digit=None, chntext=None):
+        self.digit = digit
+        self.chntext = chntext
+
+    # def chntext2digit(self):
+    #     return chn2num(self.chntext)
+
+    def digit2chntext(self):
+        return num2chn(self.digit, alt_two=False, use_units=False)
+
+
+class TelePhone:
+    """
+    TELEPHONE类
+    """
+
+    def __init__(self, telephone=None, raw_chntext=None, chntext=None):
+        self.telephone = telephone
+        self.raw_chntext = raw_chntext
+        self.chntext = chntext
+
+    # def chntext2telephone(self):
+    #     sil_parts = self.raw_chntext.split('<SIL>')
+    #     self.telephone = '-'.join([
+    #         str(chn2num(p)) for p in sil_parts
+    #     ])
+    #     return self.telephone
+
+    def telephone2chntext(self, fixed=False):
+
+        if fixed:
+            sil_parts = self.telephone.split("-")
+            self.raw_chntext = "<SIL>".join(
+                [num2chn(part, alt_two=False, use_units=False) for part in sil_parts]
+            )
+            self.chntext = self.raw_chntext.replace("<SIL>", "")
+        else:
+            sp_parts = self.telephone.strip("+").split()
+            self.raw_chntext = "<SP>".join(
+                [num2chn(part, alt_two=False, use_units=False) for part in sp_parts]
+            )
+            self.chntext = self.raw_chntext.replace("<SP>", "")
+        return self.chntext
+
+
+class Fraction:
+    """
+    FRACTION类
+    """
+
+    def __init__(self, fraction=None, chntext=None):
+        self.fraction = fraction
+        self.chntext = chntext
+
+    def chntext2fraction(self):
+        denominator, numerator = self.chntext.split("分之")
+        return chn2num(numerator) + "/" + chn2num(denominator)
+
+    def fraction2chntext(self):
+        numerator, denominator = self.fraction.split("/")
+        return num2chn(denominator) + "分之" + num2chn(numerator)
+
+
+class Date:
+    """
+    DATE类
+    """
+
+    def __init__(self, date=None, chntext=None):
+        self.date = date
+        self.chntext = chntext
+
+    # def chntext2date(self):
+    #     chntext = self.chntext
+    #     try:
+    #         year, other = chntext.strip().split('年', maxsplit=1)
+    #         year = Digit(chntext=year).digit2chntext() + '年'
+    #     except ValueError:
+    #         other = chntext
+    #         year = ''
+    #     if other:
+    #         try:
+    #             month, day = other.strip().split('月', maxsplit=1)
+    #             month = Cardinal(chntext=month).chntext2cardinal() + '月'
+    #         except ValueError:
+    #             day = chntext
+    #             month = ''
+    #         if day:
+    #             day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
+    #     else:
+    #         month = ''
+    #         day = ''
+    #     date = year + month + day
+    #     self.date = date
+    #     return self.date
+
+    def date2chntext(self):
+        date = self.date
+        try:
+            year, other = date.strip().split("年", 1)
+            year = Digit(digit=year).digit2chntext() + "年"
+        except ValueError:
+            other = date
+            year = ""
+        if other:
+            try:
+                month, day = other.strip().split("月", 1)
+                month = Cardinal(cardinal=month).cardinal2chntext() + "月"
+            except ValueError:
+                day = date
+                month = ""
+            if day:
+                day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
+        else:
+            month = ""
+            day = ""
+        chntext = year + month + day
+        self.chntext = chntext
+        return self.chntext
+
+
+class Money:
+    """
+    MONEY类
+    """
+
+    def __init__(self, money=None, chntext=None):
+        self.money = money
+        self.chntext = chntext
+
+    # def chntext2money(self):
+    #     return self.money
+
+    def money2chntext(self):
+        money = self.money
+        pattern = re.compile(r"(\d+(\.\d+)?)")
+        matchers = pattern.findall(money)
+        if matchers:
+            for matcher in matchers:
+                money = money.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext())
+        self.chntext = money
+        return self.chntext
+
+
+class Percentage:
+    """
+    PERCENTAGE类
+    """
+
+    def __init__(self, percentage=None, chntext=None):
+        self.percentage = percentage
+        self.chntext = chntext
+
+    def chntext2percentage(self):
+        return chn2num(self.chntext.strip().strip("百分之")) + "%"
+
+    def percentage2chntext(self):
+        return "百分之" + num2chn(self.percentage.strip().strip("%"))
+
+
+def remove_erhua(text, er_whitelist):
+    """
+    去除儿化音词中的儿:
+    他女儿在那边儿 -> 他女儿在那边
+    """
+
+    er_pattern = re.compile(er_whitelist)
+    new_str = ""
+    while re.search("儿", text):
+        a = re.search("儿", text).span()
+        remove_er_flag = 0
+
+        if er_pattern.search(text):
+            b = er_pattern.search(text).span()
+            if b[0] <= a[0]:
+                remove_er_flag = 1
+
+        if remove_er_flag == 0:
+            new_str = new_str + text[0 : a[0]]
+            text = text[a[1] :]
+        else:
+            new_str = new_str + text[0 : b[1]]
+            text = text[b[1] :]
+
+    text = new_str + text
+    return text
+
+
+# ================================================================================ #
+#                            NSW Normalizer
+# ================================================================================ #
+class NSWNormalizer:
+    def __init__(self, raw_text):
+        self.raw_text = "^" + raw_text + "$"
+        self.norm_text = ""
+
+    def _particular(self):
+        text = self.norm_text
+        pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('particular')
+            for matcher in matchers:
+                text = text.replace(matcher[0], matcher[1] + "2" + matcher[2], 1)
+        self.norm_text = text
+        return self.norm_text
+
+    def normalize(self):
+        text = self.raw_text
+
+        # 规范化日期
+        pattern = re.compile(r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('date')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
+
+        # 规范化金钱
+        pattern = re.compile(
+            r"\D+((\d+(\.\d+)?)[多余几]?" + CURRENCY_UNITS + r"(\d" + CURRENCY_UNITS + r"?)?)"
+        )
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('money')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Money(money=matcher[0]).money2chntext(), 1)
+
+        # 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
+        pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('telephone')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1
+                )
+        # 固话
+        pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('fixed telephone')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True), 1
+                )
+
+        # 规范化分数
+        pattern = re.compile(r"(\d+/\d+)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('fraction')
+            for matcher in matchers:
+                text = text.replace(matcher, Fraction(fraction=matcher).fraction2chntext(), 1)
+
+        # 规范化百分数
+        text = text.replace("％", "%")
+        pattern = re.compile(r"(\d+(\.\d+)?%)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('percentage')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], Percentage(percentage=matcher[0]).percentage2chntext(), 1
+                )
+
+        # 规范化纯数+量词
+        pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('cardinal+quantifier')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
+
+        # 规范化数字编号
+        pattern = re.compile(r"(\d{4,32})")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('digit')
+            for matcher in matchers:
+                text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
+
+        # 规范化纯数
+        pattern = re.compile(r"(\d+(\.\d+)?)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('cardinal')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1)
+
+        self.norm_text = text
+        self._particular()
+
+        return self.norm_text.lstrip("^").rstrip("$")
+
+
+def nsw_test_case(raw_text):
+    print("I:" + raw_text)
+    print("O:" + NSWNormalizer(raw_text).normalize())
+    print("")
+
+
+def nsw_test():
+    nsw_test_case("固话：0595-23865596或23880880。")
+    nsw_test_case("固话：0595-23865596或23880880。")
+    nsw_test_case("手机：+86 19859213959或15659451527。")
+    nsw_test_case("分数：32477/76391。")
+    nsw_test_case("百分数：80.03%。")
+    nsw_test_case("编号：31520181154418。")
+    nsw_test_case("纯数：2983.07克或12345.60米。")
+    nsw_test_case("日期：1999年2月20日或09年3月15号。")
+    nsw_test_case("金钱：12块5，34.5元，20.1万")
+    nsw_test_case("特殊：O2O或B2C。")
+    nsw_test_case("3456万吨")
+    nsw_test_case("2938个")
+    nsw_test_case("938")
+    nsw_test_case("今天吃了115个小笼包231个馒头")
+    nsw_test_case("有62％的概率")
+
+
+if __name__ == "__main__":
+    # nsw_test()
+
+    p = argparse.ArgumentParser()
+    p.add_argument("ifile", help="input filename, assume utf-8 encoding")
+    p.add_argument("ofile", help="output filename")
+    p.add_argument("--to_upper", action="store_true", help="convert to upper case")
+    p.add_argument("--to_lower", action="store_true", help="convert to lower case")
+    p.add_argument(
+        "--has_key", action="store_true", help="input text has Kaldi's key as first field."
+    )
+    p.add_argument(
+        "--remove_fillers", type=bool, default=True, help='remove filler chars such as "呃, 啊"'
+    )
+    p.add_argument(
+        "--remove_erhua", type=bool, default=True, help='remove erhua chars such as "这儿"'
+    )
+    p.add_argument(
+        "--log_interval", type=int, default=10000, help="log interval in number of processed lines"
+    )
+    args = p.parse_args()
+
+    ifile = codecs.open(args.ifile, "r", "utf8")
+    ofile = codecs.open(args.ofile, "w+", "utf8")
+
+    n = 0
+    for l in ifile:
+        key = ""
+        text = ""
+        if args.has_key:
+            cols = l.split(maxsplit=1)
+            key = cols[0]
+            if len(cols) == 2:
+                text = cols[1].strip()
+            else:
+                text = ""
+        else:
+            text = l.strip()
+
+        # cases
+        if args.to_upper and args.to_lower:
+            sys.stderr.write("text norm: to_upper OR to_lower?")
+            exit(1)
+        if args.to_upper:
+            text = text.upper()
+        if args.to_lower:
+            text = text.lower()
+
+        # Filler chars removal
+        if args.remove_fillers:
+            for ch in FILLER_CHARS:
+                text = text.replace(ch, "")
+
+        if args.remove_erhua:
+            text = remove_erhua(text, ER_WHITELIST)
+
+        # NSW(Non-Standard-Word) normalization
+        text = NSWNormalizer(text).normalize()
+
+        # Punctuations removal
+        old_chars = CHINESE_PUNC_LIST + string.punctuation  # includes all CN and EN punctuations
+        new_chars = " " * len(old_chars)
+        del_chars = ""
+        text = text.translate(str.maketrans(old_chars, new_chars, del_chars))
+
+        #
+        if args.has_key:
+            ofile.write(key + "\t" + text + "\n")
+        else:
+            ofile.write(text + "\n")
+
+        n += 1
+        if n % args.log_interval == 0:
+            sys.stderr.write("text norm: {} lines done.\n".format(n))
+
+    sys.stderr.write("text norm: {} lines done in total.\n".format(n))
+
+    ifile.close()
+    ofile.close()

examples/aishell/transformer/README.md

+
+# Conformer Result
+
+## Training Config
+- Feature info: using 80 dims fbank, global cmvn, speed perturb(0.9, 1.0, 1.1), specaugment
+- Train info: lr 5e-4, batch_size 25000, 2 gpu(Tesla V100), acc_grad 1, 50 epochs
+- Train config: conf/train_asr_transformer.yaml
+- LM config: LM was not used
+- Model size: 46M
+
+## Results (CER)
+
+|   testset   | CER(%) |
+|:-----------:|:------:|
+|     dev     |  4.97  |
+|    test     |  5.37  |
\ No newline at end of file

examples/aishell/transformer/conf/transformer_12e_6d_2048_256.yaml

+# This is an example that demonstrates how to configure a model file.
+# You can modify the configuration according to your own requirements.
+
+# to print the register_table:
+# from funasr.register import tables
+# tables.print()
+
+# network architecture
+model: Transformer
+model_conf:
+    ctc_weight: 0.3
+    lsm_weight: 0.1     # label smoothing option
+    length_normalized_loss: false
+
+# encoder
+encoder: TransformerEncoder
+encoder_conf:
+    output_size: 256    # dimension of attention
+    attention_heads: 4
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 12      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.0
+    input_layer: conv2d # encoder architecture type
+    normalize_before: true
+
+# decoder
+decoder: TransformerDecoder
+decoder_conf:
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 6
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    self_attention_dropout_rate: 0.0
+    src_attention_dropout_rate: 0.0
+
+
+# frontend related
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 1
+    lfr_n: 1
+
+specaug: SpecAug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 40
+    num_time_mask: 2
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 150
+  keep_nbest_models: 10
+  log_interval: 50
+
+optim: adam
+optim_conf:
+   lr: 0.002
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 30000
+
+dataset: AudioDataset
+dataset_conf:
+    index_ds: IndexDSJsonl
+    batch_sampler: EspnetStyleBatchSampler
+    batch_type: length # example or length
+    batch_size: 25000 # if batch_type is example, batch_size is the numbers of samples; if length, batch_size is source_token_len+target_token_len;
+    max_token_length: 2048 # filter samples if source_token_len+target_token_len > max_token_length,
+    buffer_size: 1024
+    shuffle: True
+    num_workers: 4
+    preprocessor_speech: SpeechPreprocessSpeedPerturb
+    preprocessor_speech_conf:
+      speed_perturb: [0.9, 1.0, 1.1]
+
+tokenizer: CharTokenizer
+tokenizer_conf:
+  unk_symbol: <unk>
+
+ctc_conf:
+    dropout_rate: 0.0
+    ctc_type: builtin
+    reduce: true
+    ignore_nan_grad: true
+normalize: null

examples/aishell/transformer/demo_infer.sh

+../paraformer/demo_infer.sh
\ No newline at end of file

examples/aishell/transformer/demo_train_or_finetune.sh

+../paraformer/demo_train_or_finetune.sh
\ No newline at end of file

examples/aishell/transformer/local/aishell_data_prep.sh

+#!/bin/bash
+
+# Copyright 2017 Xingyu Na
+# Apache 2.0
+
+#. ./path.sh || exit 1;
+
+if [ $# != 3 ]; then
+  echo "Usage: $0 <audio-path> <text-path> <output-path>"
+  echo " $0 /export/a05/xna/data/data_aishell/wav /export/a05/xna/data/data_aishell/transcript data"
+  exit 1;
+fi
+
+aishell_audio_dir=$1
+aishell_text=$2/aishell_transcript_v0.8.txt
+output_dir=$3
+
+train_dir=$output_dir/data/local/train
+dev_dir=$output_dir/data/local/dev
+test_dir=$output_dir/data/local/test
+tmp_dir=$output_dir/data/local/tmp
+
+mkdir -p $train_dir
+mkdir -p $dev_dir
+mkdir -p $test_dir
+mkdir -p $tmp_dir
+
+# data directory check
+if [ ! -d $aishell_audio_dir ] || [ ! -f $aishell_text ]; then
+  echo "Error: $0 requires two directory arguments"
+  exit 1;
+fi
+
+# find wav audio file for train, dev and test resp.
+find $aishell_audio_dir -iname "*.wav" > $tmp_dir/wav.flist
+n=`cat $tmp_dir/wav.flist | wc -l`
+[ $n -ne 141925 ] && \
+  echo Warning: expected 141925 data data files, found $n
+
+grep -i "wav/train" $tmp_dir/wav.flist > $train_dir/wav.flist || exit 1;
+grep -i "wav/dev" $tmp_dir/wav.flist > $dev_dir/wav.flist || exit 1;
+grep -i "wav/test" $tmp_dir/wav.flist > $test_dir/wav.flist || exit 1;
+
+rm -r $tmp_dir
+
+# Transcriptions preparation
+for dir in $train_dir $dev_dir $test_dir; do
+  echo Preparing $dir transcriptions
+  sed -e 's/\.wav//' $dir/wav.flist | awk -F '/' '{print $NF}' > $dir/utt.list
+  paste -d' ' $dir/utt.list $dir/wav.flist > $dir/wav.scp_all
+  utils/filter_scp.pl -f 1 $dir/utt.list $aishell_text > $dir/transcripts.txt
+  awk '{print $1}' $dir/transcripts.txt > $dir/utt.list
+  utils/filter_scp.pl -f 1 $dir/utt.list $dir/wav.scp_all | sort -u > $dir/wav.scp
+  sort -u $dir/transcripts.txt > $dir/text
+done
+
+mkdir -p $output_dir/data/train $output_dir/data/dev $output_dir/data/test
+
+for f in wav.scp text; do
+  cp $train_dir/$f $output_dir/data/train/$f || exit 1;
+  cp $dev_dir/$f $output_dir/data/dev/$f || exit 1;
+  cp $test_dir/$f $output_dir/data/test/$f || exit 1;
+done
+
+echo "$0: AISHELL data preparation succeeded"
+exit 0;

examples/aishell/transformer/local/download_and_untar.sh

+#!/usr/bin/env bash
+
+# Copyright   2014  Johns Hopkins University (author: Daniel Povey)
+#             2017  Xingyu Na
+# Apache 2.0
+
+remove_archive=false
+
+if [ "$1" == --remove-archive ]; then
+  remove_archive=true
+  shift
+fi
+
+if [ $# -ne 3 ]; then
+  echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
+  echo "e.g.: $0 /export/a05/xna/data www.openslr.org/resources/33 data_aishell"
+  echo "With --remove-archive it will remove the archive after successfully un-tarring it."
+  echo "<corpus-part> can be one of: data_aishell, resource_aishell."
+fi
+
+data=$1
+url=$2
+part=$3
+
+if [ ! -d "$data" ]; then
+  echo "$0: no such directory $data"
+  exit 1;
+fi
+
+part_ok=false
+list="data_aishell resource_aishell"
+for x in $list; do
+  if [ "$part" == $x ]; then part_ok=true; fi
+done
+if ! $part_ok; then
+  echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
+  exit 1;
+fi
+
+if [ -z "$url" ]; then
+  echo "$0: empty URL base."
+  exit 1;
+fi
+
+if [ -f $data/$part/.complete ]; then
+  echo "$0: data part $part was already successfully extracted, nothing to do."
+  exit 0;
+fi
+
+# sizes of the archive files in bytes.
+sizes="15582913665 1246920"
+
+if [ -f $data/$part.tgz ]; then
+  size=$(/bin/ls -l $data/$part.tgz | awk '{print $5}')
+  size_ok=false
+  for s in $sizes; do if [ $s == $size ]; then size_ok=true; fi; done
+  if ! $size_ok; then
+    echo "$0: removing existing file $data/$part.tgz because its size in bytes $size"
+    echo "does not equal the size of one of the archives."
+    rm $data/$part.tgz
+  else
+    echo "$data/$part.tgz exists and appears to be complete."
+  fi
+fi
+
+if [ ! -f $data/$part.tgz ]; then
+  if ! command -v wget >/dev/null; then
+    echo "$0: wget is not installed."
+    exit 1;
+  fi
+  full_url=$url/$part.tgz
+  echo "$0: downloading data from $full_url.  This may take some time, please be patient."
+
+  cd $data || exit 1
+  if ! wget --no-check-certificate $full_url; then
+    echo "$0: error executing wget $full_url"
+    exit 1;
+  fi
+fi
+
+cd $data || exit 1
+
+if ! tar -xvzf $part.tgz; then
+  echo "$0: error un-tarring archive $data/$part.tgz"
+  exit 1;
+fi
+
+touch $data/$part/.complete
+
+if [ $part == "data_aishell" ]; then
+  cd $data/$part/wav || exit 1
+  for wav in ./*.tar.gz; do
+    echo "Extracting wav from $wav"
+    tar -zxf $wav && rm $wav
+  done
+fi
+
+echo "$0: Successfully downloaded and un-tarred $data/$part.tgz"
+
+if $remove_archive; then
+  echo "$0: removing $data/$part.tgz file since --remove-archive option was supplied."
+  rm $data/$part.tgz
+fi
+
+exit 0;

examples/aishell/transformer/run.sh

+#!/usr/bin/env bash
+
+
+CUDA_VISIBLE_DEVICES="0,1"
+
+# general configuration
+feats_dir="../DATA" #feature output dictionary
+exp_dir=`pwd`
+lang=zh
+token_type=char
+stage=0
+stop_stage=5
+
+# feature configuration
+nj=32
+
+inference_device="cuda" #"cpu"
+inference_checkpoint="model.pt.avg10"
+inference_scp="wav.scp"
+inference_batch_size=1
+
+# data
+raw_data=../raw_data
+data_url=www.openslr.org/resources/33
+
+# exp tag
+tag="exp1"
+workspace=`pwd`
+
+master_port=12345
+
+. utils/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+train_set=train
+valid_set=dev
+test_sets="dev test"
+
+config=transformer_12e_6d_2048_256.yaml
+model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
+
+
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    echo "stage -1: Data Download"
+    mkdir -p ${raw_data}
+    local/download_and_untar.sh ${raw_data} ${data_url} data_aishell
+    local/download_and_untar.sh ${raw_data} ${data_url} resource_aishell
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    echo "stage 0: Data preparation"
+    # Data preparation
+    local/aishell_data_prep.sh ${raw_data}/data_aishell/wav ${raw_data}/data_aishell/transcript ${feats_dir}
+    for x in train dev test; do
+        cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
+        paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
+            > ${feats_dir}/data/${x}/text
+        utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
+        mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
+
+        # convert wav.scp text to jsonl
+        scp_file_list_arg="++scp_file_list='[\"${feats_dir}/data/${x}/wav.scp\",\"${feats_dir}/data/${x}/text\"]'"
+        python ../../../funasr/datasets/audio_datasets/scp2jsonl.py \
+        ++data_type_list='["source", "target"]' \
+        ++jsonl_file_out=${feats_dir}/data/${x}/audio_datasets.jsonl \
+        ${scp_file_list_arg}
+    done
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    echo "stage 1: Feature and CMVN Generation"
+    python ../../../funasr/bin/compute_audio_cmvn.py \
+    --config-path "${workspace}/conf" \
+    --config-name "${config}" \
+    ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
+    ++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
+
+fi
+
+token_list=${feats_dir}/data/${lang}_token_list/$token_type/tokens.txt
+echo "dictionary: ${token_list}"
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    echo "stage 2: Dictionary Preparation"
+    mkdir -p ${feats_dir}/data/${lang}_token_list/$token_type/
+
+    echo "make a dictionary"
+    echo "<blank>" > ${token_list}
+    echo "<s>" >> ${token_list}
+    echo "</s>" >> ${token_list}
+    utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
+        | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
+    echo "<unk>" >> ${token_list}
+fi
+
+# LM Training Stage
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "stage 3: LM Training"
+fi
+
+# ASR Training Stage
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+  echo "stage 4: ASR Training"
+
+  mkdir -p ${exp_dir}/exp/${model_dir}
+  current_time=$(date "+%Y-%m-%d_%H-%M")
+  log_file="${exp_dir}/exp/${model_dir}/train.log.txt.${current_time}"
+  echo "log_file: ${log_file}"
+
+  export CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES
+  gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  torchrun \
+  --nnodes 1 \
+  --nproc_per_node ${gpu_num} \
+  --master_port ${master_port} \
+  ../../../funasr/bin/train.py \
+  --config-path "${workspace}/conf" \
+  --config-name "${config}" \
+  ++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
+  ++valid_data_set_list="${feats_dir}/data/${valid_set}/audio_datasets.jsonl" \
+  ++tokenizer_conf.token_list="${token_list}" \
+  ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
+  ++output_dir="${exp_dir}/exp/${model_dir}" &> ${log_file}
+fi
+
+
+
+# Testing Stage
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+  echo "stage 5: Inference"
+
+  if [ ${inference_device} == "cuda" ]; then
+      nj=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+  else
+      inference_batch_size=1
+      CUDA_VISIBLE_DEVICES=""
+      for JOB in $(seq ${nj}); do
+          CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"-1,"
+      done
+  fi
+
+  for dset in ${test_sets}; do
+
+    inference_dir="${exp_dir}/exp/${model_dir}/inference-${inference_checkpoint}/${dset}"
+    _logdir="${inference_dir}/logdir"
+    echo "inference_dir: ${inference_dir}"
+
+    mkdir -p "${_logdir}"
+    data_dir="${feats_dir}/data/${dset}"
+    key_file=${data_dir}/${inference_scp}
+
+    split_scps=
+    for JOB in $(seq "${nj}"); do
+        split_scps+=" ${_logdir}/keys.${JOB}.scp"
+    done
+    utils/split_scp.pl "${key_file}" ${split_scps}
+
+    gpuid_list_array=(${CUDA_VISIBLE_DEVICES//,/ })
+    for JOB in $(seq ${nj}); do
+        {
+          id=$((JOB-1))
+          gpuid=${gpuid_list_array[$id]}
+
+          export CUDA_VISIBLE_DEVICES=${gpuid}
+          python ../../../funasr/bin/inference.py \
+          --config-path="${exp_dir}/exp/${model_dir}" \
+          --config-name="config.yaml" \
+          ++init_param="${exp_dir}/exp/${model_dir}/${inference_checkpoint}" \
+          ++tokenizer_conf.token_list="${token_list}" \
+          ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
+          ++input="${_logdir}/keys.${JOB}.scp" \
+          ++output_dir="${inference_dir}/${JOB}" \
+          ++device="${inference_device}" \
+          ++ncpu=1 \
+          ++disable_log=true \
+          ++batch_size="${inference_batch_size}" &> ${_logdir}/log.${JOB}.txt
+        }&
+
+    done
+    wait
+
+    mkdir -p ${inference_dir}/1best_recog
+    for f in token score text; do
+        if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
+          for JOB in $(seq "${nj}"); do
+              cat "${inference_dir}/${JOB}/1best_recog/${f}"
+          done | sort -k1 >"${inference_dir}/1best_recog/${f}"
+        fi
+    done
+
+    echo "Computing WER ..."
+    python utils/postprocess_text_zh.py ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
+    python utils/postprocess_text_zh.py  ${data_dir}/text ${inference_dir}/1best_recog/text.ref
+    python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
+    tail -n 3 ${inference_dir}/1best_recog/text.cer
+  done
+
+fi
\ No newline at end of file

examples/aishell/transformer/utils

+../paraformer/utils
\ No newline at end of file

examples/common_voice/whisper_lid/demo_funasr.py

+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+multilingual_wavs = [
+    "example_zh-CN.mp3",
+    "example_en.mp3",
+    "example_ja.mp3",
+    "example_ko.mp3",
+]
+
+model = AutoModel(model="iic/speech_whisper-large_lid_multilingual_pytorch")
+for wav_id in multilingual_wavs:
+    wav_file = f"{model.model_path}/examples/{wav_id}"
+    res = model.generate(input=wav_file, data_type="sound", inference_clip_length=250)
+    print("detect sample {}: {}".format(wav_id, res))

examples/common_voice/whisper_lid/demo_modelscope.py

+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+multilingual_wavs = [
+    "https://www.modelscope.cn/api/v1/models/iic/speech_whisper-large_lid_multilingual_pytorch/repo?Revision=master&FilePath=examples/example_zh-CN.mp3",
+    "https://www.modelscope.cn/api/v1/models/iic/speech_whisper-large_lid_multilingual_pytorch/repo?Revision=master&FilePath=examples/example_en.mp3",
+    "https://www.modelscope.cn/api/v1/models/iic/speech_whisper-large_lid_multilingual_pytorch/repo?Revision=master&FilePath=examples/example_ja.mp3",
+    "https://www.modelscope.cn/api/v1/models/iic/speech_whisper-large_lid_multilingual_pytorch/repo?Revision=master&FilePath=examples/example_ko.mp3",
+]
+
+inference_pipeline = pipeline(
+    task=Tasks.auto_speech_recognition, model="iic/speech_whisper-large_lid_multilingual_pytorch"
+)
+
+for wav in multilingual_wavs:
+    rec_result = inference_pipeline(input=wav, inference_clip_length=250)
+    print(rec_result)

examples/deepspeed_conf/ds_stage0.json

+{
+  "train_micro_batch_size_per_gpu": 1,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 100,
+  "gradient_clipping": 5,
+  "fp16": {
+    "enabled": false,
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "consecutive_hysteresis": false,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+   "enabled": true
+  },
+  "zero_force_ds_cpu_optimizer": false,
+  "zero_optimization": {
+    "stage": 0,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients": true,
+    "round_robin_gradients": true
+  }
+}

examples/deepspeed_conf/ds_stage1.json

+{
+  "train_micro_batch_size_per_gpu": 1,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 100,
+  "gradient_clipping": 5,
+  "fp16": {
+    "enabled": false,
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "consecutive_hysteresis": false,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+   "enabled": true
+  },
+  "zero_force_ds_cpu_optimizer": false,
+  "zero_optimization": {
+    "stage": 1,
+    "offload_optimizer": {
+      "device": "none",
+      "pin_memory": true
+    },
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients" : true
+  }
+}

examples/deepspeed_conf/ds_stage2.json

+{
+  "train_micro_batch_size_per_gpu": 1,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 100,
+  "gradient_clipping": 5,
+  "fp16": {
+    "enabled": false,
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "consecutive_hysteresis": false,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+   "enabled": true
+  },
+  "zero_force_ds_cpu_optimizer": false,
+  "zero_optimization": {
+    "stage": 2,
+    "offload_optimizer": {
+      "device": "none",
+      "pin_memory": true
+    },
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": false,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients" : true
+  }
+}

examples/deepspeed_conf/ds_stage3.json

+{
+  "train_micro_batch_size_per_gpu": 1,
+  "gradient_accumulation_steps": 1,
+  "steps_per_print": 100,
+  "gradient_clipping": 5,
+  "fp16": {
+    "enabled": false,
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "consecutive_hysteresis": false,
+    "min_loss_scale": 1
+  },
+  "bf16": {
+   "enabled": true
+  },
+  "zero_force_ds_cpu_optimizer": false,
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "none",
+      "pin_memory": true
+    },
+    "offload_param": {
+      "device": "none",
+      "pin_memory": true
+    },
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "overlap_comm": true,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "contiguous_gradients" : true,
+    "stage3_max_live_parameters": 1e9,
+    "stage3_max_reuse_distance": 1e9,
+    "stage3_prefetch_bucket_size": 5e8,
+    "stage3_param_persistence_threshold": 1e5
+  }
+}

examples/industrial_data_pretraining/bicif_paraformer/demo.py

+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+    vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+    punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large",
+    # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
+)
+
+res = model.generate(
+    input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav",
+    batch_size_s=300,
+    batch_size_threshold_s=60,
+)
+print(res)

examples/industrial_data_pretraining/bicif_paraformer/demo.sh

+
+model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
+#punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
+punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large"
+spk_model="iic/speech_campplus_sv_zh-cn_16k-common"
+
+python funasr/bin/inference.py \
++model=${model} \
++vad_model=${vad_model} \
++punc_model=${punc_model} \
++spk_model=${spk_model} \
++input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav" \
++output_dir="./outputs/debug" \
++device="cpu" \
++batch_size_s=300 \
++batch_size_threshold_s=60
+

examples/industrial_data_pretraining/bicif_paraformer/export.py

+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method1, inference from model hub
+
+from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+    device="cpu",
+)
+
+res = model.export(type="torchscript", quantize=False)
+print(res)
+
+
+# # method2, inference from local path
+# from funasr import AutoModel
+
+# model = AutoModel(
+#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+#     device="cpu",
+# )
+
+# res = model.export(type="onnx", quantize=False)
+# print(res)

examples/industrial_data_pretraining/bicif_paraformer/export.sh

+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+# method1, inference from model hub
+export HYDRA_FULL_ERROR=1
+
+
+model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+
+python -m funasr.bin.export \
+++model=${model} \
+++type="onnx" \
+++quantize=false \
+++device="cpu"
+
+# method2, inference from local path
+model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+
+python -m funasr.bin.export \
+++model=${model} \
+++type="onnx" \
+++quantize=false \
+++device="cpu"
\ No newline at end of file