File size: 6,138 Bytes
b0c0df0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
import os
import re
import unicodedata
import editdistance as ed
import zhconv
from lmms_eval.tasks.librispeech.cn_tn import TextNorm
from lmms_eval.tasks.librispeech.whisper_normalizer.basic import BasicTextNormalizer
from lmms_eval.tasks.librispeech.whisper_normalizer.english import EnglishTextNormalizer
# ImportError: To support decoding audio files, please install 'librosa' and 'soundfile'.
english_normalizer = EnglishTextNormalizer()
chinese_normalizer = TextNorm(
to_banjiao=False,
to_upper=False,
to_lower=False,
remove_fillers=False,
remove_erhua=False,
check_chars=False,
remove_space=False,
cc_mode="",
)
basic_normalizer = BasicTextNormalizer()
dir_name = os.path.dirname(os.path.abspath(__file__))
def common_voice_15_doc_to_audio(doc):
return [doc["audio"]]
def common_voice_15_doc_to_text(doc, lmms_eval_specific_kwargs):
pre_prompt = lmms_eval_specific_kwargs["pre_prompt"]
post_prompt = lmms_eval_specific_kwargs["post_prompt"]
return f"{pre_prompt}Please recognize the speech and only output the recognized content:{post_prompt}"
def common_voice_15_process_result(doc, result):
pred = result[0] if len(result) > 0 else ""
gt = doc["sentence"]
source = doc["path"]
task = doc["locale"]
data_dict = {"gt": gt, "pred": pred, "source": source, "task": task}
return {"wer": data_dict}
PUNCS = "!,.?;:"
def remove_sp(text, language):
gt = re.sub(r"<\|.*?\|>", " ", text)
gt = re.sub(rf"\s+", r" ", gt) # Replace consecutive spaces in the text with a single space.
gt = re.sub(f" ?([{PUNCS}])", r"\1", gt)
gt = gt.lstrip(" ")
if language == "zh-CN":
gt = re.sub(rf"\s+", r"", gt)
return gt
class EvaluationTokenizer(object):
"""A generic evaluation-time tokenizer, which leverages built-in tokenizers
in sacreBLEU (https://github.com/mjpost/sacrebleu). It additionally provides
lowercasing, punctuation removal and character tokenization, which are
applied after sacreBLEU tokenization.
Args:
tokenizer_type (str): the type of sacreBLEU tokenizer to apply.
lowercase (bool): lowercase the text.
punctuation_removal (bool): remove punctuation (based on unicode
category) from text.
character_tokenization (bool): tokenize the text to characters.
"""
SPACE = chr(32)
SPACE_ESCAPE = chr(9601)
# ALL_TOKENIZER_TYPES = ChoiceEnum(["none", "13a", "intl", "zh", "ja-mecab"])
def __init__(
self,
tokenizer_type: str = "13a",
lowercase: bool = False,
punctuation_removal: bool = False,
character_tokenization: bool = False,
):
from sacrebleu.tokenizers.tokenizer_13a import Tokenizer13a
from sacrebleu.tokenizers.tokenizer_char import TokenizerChar
from sacrebleu.tokenizers.tokenizer_intl import TokenizerV14International
from sacrebleu.tokenizers.tokenizer_ja_mecab import TokenizerJaMecab
from sacrebleu.tokenizers.tokenizer_none import NoneTokenizer
from sacrebleu.tokenizers.tokenizer_zh import TokenizerZh
TOKENIZERS = {
"none": NoneTokenizer,
"13a": Tokenizer13a,
"intl": TokenizerV14International,
"zh": TokenizerZh,
"ja-mecab": TokenizerJaMecab,
"char": TokenizerChar,
}
assert tokenizer_type in TOKENIZERS, f"{tokenizer_type}, {TOKENIZERS}"
self.lowercase = lowercase
self.punctuation_removal = punctuation_removal
self.character_tokenization = character_tokenization
self.tokenizer = TOKENIZERS[tokenizer_type]
# self.tokenizer = tokenizer_none
@classmethod
def remove_punctuation(cls, sent: str):
"""Remove punctuation based on Unicode category."""
return cls.SPACE.join(t for t in sent.split(cls.SPACE) if not all(unicodedata.category(c)[0] == "P" for c in t))
def tokenize(self, sent: str):
tokenized = self.tokenizer()(sent)
if self.punctuation_removal:
tokenized = self.remove_punctuation(tokenized)
if self.character_tokenization:
tokenized = self.SPACE.join(list(tokenized.replace(self.SPACE, self.SPACE_ESCAPE)))
if self.lowercase:
tokenized = tokenized.lower()
return tokenized
def compute_wer(refs, hyps, language):
distance = 0
ref_length = 0
tokenizer = EvaluationTokenizer(
tokenizer_type="none",
lowercase=True,
punctuation_removal=True,
character_tokenization=False,
)
for i in range(len(refs)):
ref = refs[i]
pred = hyps[i]
if language in ["yue"]:
ref = zhconv.convert(ref, "zh-cn")
pred = zhconv.convert(pred, "zh-cn")
if language in ["en"]:
ref = english_normalizer(ref)
pred = english_normalizer(pred)
if language in ["zh-CN"]:
ref = chinese_normalizer(ref)
pred = chinese_normalizer(pred)
else:
ref = basic_normalizer(ref)
pred = basic_normalizer(pred)
ref_items = tokenizer.tokenize(ref).split()
pred_items = tokenizer.tokenize(pred).split()
if language in ["zh-CN", "yue"]:
ref_items = [x for x in "".join(ref_items)]
pred_items = [x for x in "".join(pred_items)]
if i == 0:
print(f"ref: {ref}")
print(f"pred: {pred}")
print(f"ref_items:\n{ref_items}\n{len(ref_items)}\n{ref_items[0]}")
print(f"pred_items:\n{pred_items}\n{len(ref_items)}\n{ref_items[0]}")
distance += ed.eval(ref_items, pred_items)
ref_length += len(ref_items)
return distance / ref_length
def common_voice_15_wer(results, args):
refs, hyps = [], []
for result in results:
lan = result["task"]
gt = result["gt"]
response = result["pred"]
gt = remove_sp(gt, lan)
response = remove_sp(response, lan)
refs.append(gt)
hyps.append(response)
wer = compute_wer(refs, hyps, lan)
return wer * 100
|