Init Fleurs

lmms_eval/tasks/fleurs/_default_template_yaml

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+dataset_path: google/fleurs
+dataset_kwargs:
+  token: True
+test_split: test
+output_type: generate_until
+doc_to_visual: !function utils.fleurs_doc_to_audio
+doc_to_text: !function utils.fleurs_doc_to_text
+doc_to_target: "transcription"
+generation_kwargs:
+  max_new_tokens: 256
+  temperature: 0
+  top_p: 1.0
+  num_beams: 1
+  do_sample: false
+process_results: !function utils.fleurs_process_result
+metric_list:
+  - metric: wer 
+    aggregation : !function utils.fleurs_wer
+    higher_is_better : false
+metadata:
+  - version: 0.0
+lmms_eval_specific_kwargs:
+  default:
+    pre_prompt: ""
+    post_prompt: ""

lmms_eval/tasks/fleurs/fleurs.yaml

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+group: fleurs 
+task:
+- fleurs_en

lmms_eval/tasks/fleurs/fleurs_en.yaml

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+dataset_name: en_us
+include: _default_template_yaml
+task: fleurs_en

lmms_eval/tasks/fleurs/utils.py

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+import os
+import re
+import unicodedata
+from collections import OrderedDict
+
+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
+
+_FLEURS_LANG_TO_ID = OrderedDict(
+    [
+        ("Afrikaans", "af"),
+        ("Amharic", "am"),
+        ("Arabic", "ar"),
+        ("Armenian", "hy"),
+        ("Assamese", "as"),
+        ("Asturian", "ast"),
+        ("Azerbaijani", "az"),
+        ("Belarusian", "be"),
+        ("Bengali", "bn"),
+        ("Bosnian", "bs"),
+        ("Bulgarian", "bg"),
+        ("Burmese", "my"),
+        ("Catalan", "ca"),
+        ("Cebuano", "ceb"),
+        ("Mandarin Chinese", "cmn_hans"),
+        ("Cantonese Chinese", "yue_hant"),
+        ("Croatian", "hr"),
+        ("Czech", "cs"),
+        ("Danish", "da"),
+        ("Dutch", "nl"),
+        ("English", "en"),
+        ("Estonian", "et"),
+        ("Filipino", "fil"),
+        ("Finnish", "fi"),
+        ("French", "fr"),
+        ("Fula", "ff"),
+        ("Galician", "gl"),
+        ("Ganda", "lg"),
+        ("Georgian", "ka"),
+        ("German", "de"),
+        ("Greek", "el"),
+        ("Gujarati", "gu"),
+        ("Hausa", "ha"),
+        ("Hebrew", "he"),
+        ("Hindi", "hi"),
+        ("Hungarian", "hu"),
+        ("Icelandic", "is"),
+        ("Igbo", "ig"),
+        ("Indonesian", "id"),
+        ("Irish", "ga"),
+        ("Italian", "it"),
+        ("Japanese", "ja"),
+        ("Javanese", "jv"),
+        ("Kabuverdianu", "kea"),
+        ("Kamba", "kam"),
+        ("Kannada", "kn"),
+        ("Kazakh", "kk"),
+        ("Khmer", "km"),
+        ("Korean", "ko"),
+        ("Kyrgyz", "ky"),
+        ("Lao", "lo"),
+        ("Latvian", "lv"),
+        ("Lingala", "ln"),
+        ("Lithuanian", "lt"),
+        ("Luo", "luo"),
+        ("Luxembourgish", "lb"),
+        ("Macedonian", "mk"),
+        ("Malay", "ms"),
+        ("Malayalam", "ml"),
+        ("Maltese", "mt"),
+        ("Maori", "mi"),
+        ("Marathi", "mr"),
+        ("Mongolian", "mn"),
+        ("Nepali", "ne"),
+        ("Northern-Sotho", "nso"),
+        ("Norwegian", "nb"),
+        ("Nyanja", "ny"),
+        ("Occitan", "oc"),
+        ("Oriya", "or"),
+        ("Oromo", "om"),
+        ("Pashto", "ps"),
+        ("Persian", "fa"),
+        ("Polish", "pl"),
+        ("Portuguese", "pt"),
+        ("Punjabi", "pa"),
+        ("Romanian", "ro"),
+        ("Russian", "ru"),
+        ("Serbian", "sr"),
+        ("Shona", "sn"),
+        ("Sindhi", "sd"),
+        ("Slovak", "sk"),
+        ("Slovenian", "sl"),
+        ("Somali", "so"),
+        ("Sorani-Kurdish", "ckb"),
+        ("Spanish", "es"),
+        ("Swahili", "sw"),
+        ("Swedish", "sv"),
+        ("Tajik", "tg"),
+        ("Tamil", "ta"),
+        ("Telugu", "te"),
+        ("Thai", "th"),
+        ("Turkish", "tr"),
+        ("Ukrainian", "uk"),
+        ("Umbundu", "umb"),
+        ("Urdu", "ur"),
+        ("Uzbek", "uz"),
+        ("Vietnamese", "vi"),
+        ("Welsh", "cy"),
+        ("Wolof", "wo"),
+        ("Xhosa", "xh"),
+        ("Yoruba", "yo"),
+        ("Zulu", "zu"),
+    ]
+)
+_FLEURS_LANG_SHORT_TO_LONG = {v: k for k, v in _FLEURS_LANG_TO_ID.items()}
+
+# 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 fleurs_doc_to_audio(doc):
+    return [doc["audio"]]
+
+
+def fleurs_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 fleurs_process_result(doc, result):
+    pred = result[0] if len(result) > 0 else ""
+
+    gt = doc["transcription"]
+    source = doc["path"]
+    language = doc["language"]
+
+    data_dict = {"gt": gt, "pred": pred, "source": source, "language": language}
+
+    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 == "cmn_hans":
+        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_hant"]:
+            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 ["cmn_hans"]:
+            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", "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 fleurs_wer(results, args):
+    refs, hyps = [], []
+    for result in results:
+        lan = _FLEURS_LANG_TO_ID[result["language"]]
+        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