Whisperを用いた音声認識の文字起こし精度

from google.colab import drive
drive.mount('/content/drive')

!pip install git+https://github.com/openai/whisper.git

!whisper "{音声ファイルへのpath}" --model large --language Japanese -o result

! pip install -U ginza ja-ginza
! pip install spacy
! pip install sacrebleu=="1.4.5"
! pip install Pillow==8.0
! pip install mecab-python3==0.996.5
! pip install unidic-lite
! pip install bert_score
! pip install sumeval

import pandas as pd
from bert_score import score
from sumeval.metrics.bleu import BLEUCalculator
from sumeval.metrics.rouge import RougeCalculator

def calc_bert_score(cands, refs):
    """ BERTスコアの算出

    Args:
        cands ([List[str]]): Pillow[比較元の文]
        refs ([List[str]]): [比較対象の文]

    Returns:
        [(List[float], List[float], List[float])]: [(Precision, Recall, F1スコア)]
    """
    Precision, Recall, F1 = score(cands, refs, lang="ja", verbose=True)
    return Precision.numpy().tolist(), Recall.numpy().tolist(), F1.numpy().tolist()

def evaluate_all(predict_file, correct_file, model_name):
    bert_score_p = []
    bert_score_r = []
    bert_score_f = []
    rouge_1_score = []
    rouge_2_score = []
    rouge_L_score = []
    rouge_BE_score = []
    blue_scores = []

    with open(predict_file,encoding="utf-8") as f:
        predicts = f.readlines()
    with open(correct_file,encoding="utf-8") as f:
        corrects = f.readlines()

    P, R, F1 = calc_bert_score(predicts, corrects)
    for p,r, f1 in zip(P, R, F1):
        bert_score_p.append(p)
        bert_score_r.append(r)
        bert_score_f.append(f1)

    for i in range(len(predicts)):
        rouge = RougeCalculator(lang="ja")

        rouge_1 = rouge.rouge_n(
                    summary=predicts[i],
                    references=corrects[i],
                    n=1)

        rouge_2 = rouge.rouge_n(
                    summary=predicts[i],
                    references=corrects[i],
                    n=2)

        rouge_l = rouge.rouge_l(
                    summary=predicts[i],
                    references=corrects[i])

        rouge_be = rouge.rouge_be(
                    summary=predicts[i],
                    references=corrects[i])

        rouge_1_score.append(rouge_1)
        rouge_2_score.append(rouge_2)
        rouge_L_score.append(rouge_l)
        rouge_BE_score.append(rouge_be)


    for i in range(len(predicts)):
        bleu_ja = BLEUCalculator(lang="ja")
        blue_score = bleu_ja.bleu(predicts[i], corrects[i])
        blue_scores.append(blue_score)

    # 出力用のDataFrameを作成
    df = pd.DataFrame(
      list(zip(bert_score_p, bert_score_r, bert_score_f, rouge_1_score, rouge_2_score, rouge_L_score, rouge_BE_score, blue_scores)),
      columns = ["bert_score_p", "bert_score_r","bert_score_f","rouge_1_score","rouge_2_score","rouge_L_score","rouge_BE_score","blue_scores"]
    )

    # 出力
    df.to_csv(f"score/{model_name}_score.csv", mode="w", encoding="utf-8")

# モデル名
model_name = "whisper_large_Interview_1.mp4"

# 正解の文字起こしテキスト
correct_file = "correct.txt"

# 音声認識による文字起こしテキスト
predict_file = f"{model_name}.txt"

evaluate_all(predict_file, correct_file, model_name)