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import os
import re
import unicodedata
from sacrebleu import corpus_bleu
from lmms_eval.tasks.gigaspeech.whisper_normalizer.basic import BasicTextNormalizer
from lmms_eval.tasks.gigaspeech.whisper_normalizer.english import EnglishTextNormalizer
from lmms_eval.tasks.librispeech.cn_tn import TextNorm
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()
PUNCS = "!,.?;:"
def covost2_doc_to_audio(doc):
return [doc["audio"]]
def covost2_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}{post_prompt}"
def covost2_zh_en_process_result(doc, result):
pred = result[0] if len(result) > 0 else ""
gt = doc["gt"]
data_dict = {"gt": gt, "pred": pred}
return {"bleu": data_dict}
def covost2_en_zh_process_result(doc, result):
pred = result[0] if len(result) > 0 else ""
gt = doc["translation"]
data_dict = {"gt": gt, "pred": pred}
return {"bleu": data_dict}
def covost2_bleu(results, args):
refs, hyps = [], []
for result in results:
gt = result["gt"]
response = result["pred"]
gt = remove_sp(gt)
response = remove_sp(response)
refs.append(gt)
hyps.append(response)
bleu = compute_bleu_zh(refs, hyps)
return round(bleu, 5)
def covost2_bleu_en(results, args):
refs, hyps = [], []
for result in results:
gt = result["gt"]
response = result["pred"]
gt = remove_sp(gt)
response = remove_sp(response)
refs.append(gt)
hyps.append(response)
bleu = compute_bleu_en(refs, hyps)
return round(bleu, 5)
def remove_sp(text):
gt = re.sub(r"<\|.*?\|>", " ", text)
gt = re.sub(rf"\s+", r" ", gt)
gt = re.sub(f" ?([{PUNCS}])", r"\1", gt)
gt = gt.lstrip(" ")
return gt
def compute_bleu_en(refs, hyps):
tokenizer = EvaluationTokenizer(
tokenizer_type="none",
lowercase=True,
punctuation_removal=True,
character_tokenization=False,
)
refs = [tokenizer.tokenize(english_normalizer(ref)) for ref in refs]
hyps = [tokenizer.tokenize(english_normalizer(hyp)) for hyp in hyps]
bleu_score = corpus_bleu(hyps, [refs], tokenize="13a")
return bleu_score.score
def compute_bleu_zh(refs, hyps):
tokenizer = EvaluationTokenizer(
tokenizer_type="zh",
lowercase=True,
punctuation_removal=True,
character_tokenization=False,
)
refs = [tokenizer.tokenize(chinese_normalizer(ref)) for ref in refs]
hyps = [tokenizer.tokenize(chinese_normalizer(hyp)) for hyp in hyps]
bleu_score = corpus_bleu(hyps, [refs], tokenize="zh")
return bleu_score.score
class EvaluationTokenizer(object):
SPACE = chr(32)
SPACE_ESCAPE = chr(9601)
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
self.lowercase = lowercase
self.punctuation_removal = punctuation_removal
self.character_tokenization = character_tokenization
self.tokenizer = TOKENIZERS[tokenizer_type]
@classmethod
def remove_punctuation(cls, sent: str):
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
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