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BERTベースの句読点モデルによるWhisperの書き起こしの改善

2023/04/21に公開
AI
Python
機械学習
#
bert
Whisper
tech

概要

Whisper は、多言語音声認識システムです。その出力である書き起こしには、句読点が含まれていないことがあるため、自動翻訳の精度に悪影響を与えることがあります。そこで、Whisper の書き起こしに対して、句読点を復元するためのモデルを作りたいと思います。一般的な文章とは異なり、Whisper の書き起こしは文章が途切れていることや、話し言葉が多く含まれているなどの特徴があります。このため、句読点の復元には新しい手法が必要となります。今回は、Whisper が生成した書き起こしの中から句読点が含まれるものを自動的に抽出し、これを訓練データとして用いることで、モデルをトレーニングしたいと思います。結果として、既存の手法と比較して、Whisper の書き起こしに対して、より高い精度で句読点を復元することができることが示されました。

Whisperの書き起こしの自動翻訳での問題点

Whisper には、書き起こし機能の他にも自動翻訳機能も備えています。しかし、現在は英語への翻訳にのみ対応しています。書き起こしを日本語に翻訳する場合には、別の自動翻訳を使用する必要があります。また、Whisper が生成する書き起こしにはタイムスタンプが付いているので、タイムスタンプを利用することで字幕として利用することができます。下記に、実際に出力された書き起こしを示します：

Sam Altman - How to Succeed with a Startup

start	end	text
0	4440	 Okay, today I'm going to talk about how to succeed with a startup.
4440	9120	 Obviously, more than can be said here in 20 minutes, but I will do the best I can.
9120	14440	 The most important thing, the number one lesson we try to teach startups is that the degree
14440	18880	 to which you're successful approximates the degree to which you build a product that is
18880	23120	 so good people spontaneously tell their friends about it.
23120	25240	 Startups always ask us for the secret to success.
25240	28600	 They always want to believe it's something other than this because this is really hard
28600	31040	 to do, but this is it.

このままでは、自動翻訳できないため、句読点を利用して文章をまとめることで、より正確な自動翻訳行うことができます。しかし、以下のような書き起こしも出力されることがあります：

Let's build GPT: from scratch, in code, spelled out.

1482880	1487280	 saw a lot of this in a lot more depth in the make more series and here if i just run this
1488000	1494400	 then we currently get the predictions the scores the logits for every one of the four by eight
1494400	1498720	 positions now that we've made predictions about what comes next we'd like to evaluate the loss
1498720	1504240	 function and so in make more series we saw that a good way to measure a loss or like a quality of
1504240	1508960	 the predictions is to use the negative log likelihood loss which is also implemented in

このように、Whisper は句読点が存在しない書き起こしを生成することがあります。この場合、英語の書き起こしとしても読みにくく、また、日本語へ自動翻訳するときも、句読点を利用して文章をまとめることができないという問題があります。

既存の句読点モデル

Punctuation Model（句読点モデル）は、文章の中に句読点を自動的に挿入するためのモデルです。このモデルは、機械学習を使用して、文章の文法、文脈、および一般的な言語のパターンに基づいて、適切な句読点を自動的に挿入します。

既存の句読点モデルとしては Hugging Face に以下のようなものがあります。

これらは、完全な文章によって訓練されているため、Whisper の書き起こしは文章が途切れていることや、話し言葉が多く含まれているなどの特徴に対応することが難しいことがわかります。以下に Let's build GPT: from scratch, in code, spelled out. の書き起こしに、それぞれのモデルで句読点を自動的に挿入したものを示します：

  • Original
1482880	1487280	 saw a lot of this in a lot more depth in the make more series and here if i just run this
1488000	1494400	 then we currently get the predictions the scores the logits for every one of the four by eight
1494400	1498720	 positions now that we've made predictions about what comes next we'd like to evaluate the loss
1498720	1504240	 function and so in make more series we saw that a good way to measure a loss or like a quality of
1504240	1508960	 the predictions is to use the negative log likelihood loss which is also implemented in
  • felflare/bert-restore-punctuation
1482880	1487280	 Saw a lot of this in a lot more depth in the make More series and here if I just run this.
1488000	1494400	 Then we currently get the predictions, the scores, the logits for every one of the four by eight.
1494400	1498720	 Positions: Now that we've made predictions about what comes next, we'd like to evaluate the loss.
1498720	1504240	 Function and so in make more series. We saw that a good way to measure a loss or like a quality of.
1504240	1508960	 The predictions is to use the negative log likelihood loss which is also implemented in.
  • oliverguhr/fullstop-punctuation-multilang-large
1482880	1487280	 saw a lot of this in a lot more depth in the make more series and here, if i just run this,
1488000	1494400	 then we currently get the predictions, the scores, the logits for every one of the four by eight.
1494400	1498720	 positions. now that we've made predictions about what comes next, we'd like to evaluate the loss.
1498720	1504240	 function, and so in make more series, we saw that a good way to measure a loss or like a quality of.
1504240	1508960	 the predictions is to use the negative log likelihood loss, which is also implemented in.

BERTベースの句読点モデルの提案

BERTとは、2018年にGoogleが発表した自然言語処理における深層学習のモデルです。BERTは、「Bidirectional Encoder Representations from Transformers」の略で、Transformerという深層学習アーキテクチャをベースにしています。

BERTは、大量のテキストデータを用いた事前学習を行うことで、文脈を考慮した単語の表現を学習します。そして、この学習済みモデルを用いて、さまざまな自然言語処理タスクを解決するためにファインチューニングを行います。

BERTは自然言語処理における様々なタスクに利用できる汎用的なモデルであり、その中の一つであるトークン分類（Token Classification）を利用することで句読点モデルを実現することができます。

まず、トークン分類は、与えられた文章の各単語や句の位置に対して、それがどのようなタグを持つかを予測するタスクです。例えば、名詞、動詞、形容詞などの品詞タグを予測する場合があります。

句読点モデルでは、各単語の後ろに句読点をどのように挿入するかを予測することになります。そのため、トークン分類においては、各単語の位置に対して、句読点を表すタグを用意します。例えば、「,」を表すタグや「.」を表すタグを用意します。

そして、BERTを用いてトークン分類のモデルを構築し、学習を行います。BERTには、事前学習済みのモデルが公開されており、それを利用することで、より高い精度での句読点予測が可能となります。

具体的には、BERTのトークン分類のモデルである BertForTokenClassification をインポートし、データセットを用いてモデルのファインチューニングを行います。その後、未知のテキストに対して予測を行い、各単語の後ろに挿入するべき句読点を予測します。

データセットの作成

Whisper から生成される文字起こしに、どのような句読点が生成されるのか調査したところ「.」や 「.」や「?」が含まれることが分かりました。そこで、各トークンに対するラベルとして「.」 「.」「?」「O」の4つのラベルを用意することにしました。

また、Whisper から生成される文字起こしには、「...」や「—」や「�」などの余計なものも含まれているために、データセットに含まれないようにする必要があります。

最後に、Whisper が正しく句読点を付けている書き起こしから、「.」や 「.」や「?」を除いた書き起こしと、それぞれの単語の後にどの句読点が来たのかのラベルを作成して、データセットを作成します。

データセットの例は以下のようになります：

id2label = {1: ',', 2: '.', 3: '?', 0: 'O'}
label2id = {',': 1, '.': 2, '?': 3, 'O': 0}

text = "Hello, my name is Andrej and I've been training deep neural networks for a bit more than a decade"
words = ['Hello', 'my', 'name', 'is', 'Andrej', 'and', "I've", 'been', 'training', 'deep', 'neural', 'networks', 'for', 'a', 'bit', 'more', 'than', 'a', 'decade']
labels = [',', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']

単語をトークナイズすると、1つの単語が複数のトークンに分割されることがあります。それぞれのトークンにラベルを付ける必要があるのですが、句読点のラベルが付いた単語が複数のトークンになった場合、最後のトークンに対して句読点のラベルを付け、それ以外には「O」のラベルを付けることにしました。

データセットは以下のようになります：

FULL Dataset: 173771
TRAIN Dataset: 139016
TEST Dataset: 34755

モデルのトレーニングと評価

ハイパーパラメータは以下のようなものになります：

MAX_LEN = 128
TRAIN_BATCH_SIZE = 4
VALID_BATCH_SIZE = 2
EPOCHS = 1
LEARNING_RATE = 1e-05
MAX_GRAD_NORM = 10

モデルは BertForTokenClassification を利用しています：

model = BertForTokenClassification.from_pretrained('bert-base-uncased', 
                                                   num_labels=len(id2label),
                                                   id2label=id2label,
                                                   label2id=label2id)

loss functionはCrossEntropyLossで、optimizerはAdamを利用しています。

Training の結果としては以下のようになりました：

Training loss epoch: 0.012938089804618604
Training accuracy epoch: 0.9565718858035926

Validation の結果としては以下のようになりました：

Validation Loss: 0.01050175565781492
Validation Accuracy: 0.9633911385449255

各クラスの適合率、再現率、F1スコアおよびサポート数は以下のようになりました：

              precision    recall  f1-score   support

           ,       0.78      0.73      0.75     29186
           .       0.77      0.88      0.82     24984
           ?       0.84      0.73      0.78      3297
           O       0.99      0.99      0.99    406983

    accuracy                           0.96    464450
   macro avg       0.85      0.83      0.84    464450
weighted avg       0.96      0.96      0.96    464450
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Training loss per 100 training steps: 0.013345798218339122
Training loss per 100 training steps: 0.013334597012183069
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Training loss per 100 training steps: 0.013316246173233364
Training loss per 100 training steps: 0.013308072595824927
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Training loss per 100 training steps: 0.013290648266230939
Training loss per 100 training steps: 0.013285202157923863
Training loss per 100 training steps: 0.013279819804158837
Training loss per 100 training steps: 0.01327259806905168
Training loss per 100 training steps: 0.013262805661925201
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Training loss per 100 training steps: 0.013068199906587887
Training loss per 100 training steps: 0.013058789726817184
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Training loss per 100 training steps: 0.013044924239800951
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Training loss per 100 training steps: 0.01303022196898285
Training loss per 100 training steps: 0.013026768948216024
Training loss per 100 training steps: 0.013019551307574413
Training loss per 100 training steps: 0.013015335379261276
Training loss per 100 training steps: 0.013009730962988526
Training loss per 100 training steps: 0.01299993139345828
Training loss per 100 training steps: 0.012996370525922186
Training loss per 100 training steps: 0.012986185602213216
Training loss per 100 training steps: 0.012978728016598733
Training loss per 100 training steps: 0.012974297417596734
Training loss per 100 training steps: 0.012968579755834143
Training loss per 100 training steps: 0.012962411443168792
Training loss per 100 training steps: 0.012959617032179746
Training loss per 100 training steps: 0.012953593669965473
Training loss per 100 training steps: 0.012947324074313173
Training loss per 100 training steps: 0.012946335298171501
Training loss per 100 training steps: 0.012940867656289384
Training loss epoch: 0.012938089804618604
Training accuracy epoch: 0.9565718858035926

既存手法との比較

Whisper の書き起こしをデータセットとして訓練したモデルで、Let's build GPT: from scratch, in code, spelled out. の書き起こしに句読点を自動的に挿入したものを示します：

  • Original
1482880	1487280	 saw a lot of this in a lot more depth in the make more series and here if i just run this
1488000	1494400	 then we currently get the predictions the scores the logits for every one of the four by eight
1494400	1498720	 positions now that we've made predictions about what comes next we'd like to evaluate the loss
1498720	1504240	 function and so in make more series we saw that a good way to measure a loss or like a quality of
1504240	1508960	 the predictions is to use the negative log likelihood loss which is also implemented in
  • Our model
1482880	1487280	 saw a lot of this in a lot more depth in the make more series. and here, if i just run this
1488000	1494400	 then we currently get the predictions, the scores, the logits for every one of the four by eight
1494400	1498720	 positions. now that we've made predictions about what comes next, we'd like to evaluate the loss
1498720	1504240	 function. and so in make more series, we saw that a good way to measure a loss or like a quality of
1504240	1508960	 the predictions is to use the negative log likelihood loss, which is also implemented in

既存のモデルでは 「saw a lot of this in a lot more depth in the make more series and here, if i just run this,」や「Saw a lot of this in a lot more depth in the make More series and here if I just run this.」のように、完全な文章で訓練している影響を受けていたのに比べて、今回作成した「saw a lot of this in a lot more depth in the make more series. and here, if i just run this」のように、文章が途切れていることや、話し言葉が多く含まれているなどの特徴に対応しているように思います。

今後の課題と展望

  • データセットの追加
    現状ではデータセットの追加によって精度が向上しているため、データセットを追加していきたいと思います。
  • モデルのチューニング
    データセットが整ってきたら、モデルのチューニングに移りたいと思います。
  • コードの公開
    再現性のために実験が一定の水準まで進んだらコードを公開したいと思います。

おわりに

Whisper の書き起こしには、句読点が含まれていないことがあるため、自動翻訳の精度に悪影響を与えることがあります。そこで、Whisper の書き起こしに対して、句読点を復元するためのモデルを作成しました。一般的な文章とは異なり、Whisper の書き起こしは文章が途切れていることや、話し言葉が多く含まれているなどの特徴があるため、句読点の復元には新しい手法が必要となります。今回は、Whisper が生成した書き起こしの中から句読点が含まれるものを自動的に抽出し、これを訓練データとして用いることで、モデルをトレーニングしました。結果として、既存の手法と比較して、Whisper の書き起こしに対して、より高い精度で句読点を復元することができることが示されました。

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