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TJBot zeroと会話しよう!

More than 1 year has passed since last update.

TJBot zeroと音声でやり取りをしたい。そんな人向けの拡張コンテンツです。
TJBot zeroの公式サイトのガイドや、BMXUG主催のWorkshopなどでTJBot zeroを製作した方を対象にしています。

オリジナルのTJBotのNode.jsプログラムでは、WatsonのSpeech to TextとWatsonAssistantそしてText to Speechの組み合わせで会話できる仕組みが作られていますが、会話がない時もずっとSpeech to Textが動き続けているので、あっという間にライトアカウントの無償枠を使い切ってします。(現在は、タイムアウトの設定も追加されています)
そこで、TJBot zreoにプシュスイッチを追加して、ボタンが押された後に3秒間命令を聞きとった後で、命令をWatson Assistantで解釈し、アクションするフローのインポートと、Watson AssitantのSkillのインポート方法などについて解説します。

動画エラーのようです。違うブラウザでお試しください
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ハードウェアの準備

TJBot zeroにプッシュスイッチを追加します。
image.png
⬇︎今回使用したプッシュスイッチ
image.png
⬇︎TJBot zeroの後ろ側にプッシュスイッチをつけた様子
image.png
プッシュスイッチには、モーメンタリーとオルタネイトという2種類のスイッチがあり、今回はモーメンタリー・スイッチを使っています。この2種類のスイッチの違いは、モーメンタリーは押している間だけオンになり、離すとオフになります。一方オルタネイトは、一度押すとロックがかかりオンになりっぱなしになり、再度押すとロックが外れオフになるスイッチです。
新しく買う場合は、モーメンタリー・スイッチを選んでください。

追加しておく機能(ノード)とWatsonの機能

今回のシナリオ(フロー)で特有な機能として、Text to Speechから直接スピーカーを鳴らす、node-red-contrib-speakerpiがありますので、node-red-contrib-speakerpiのページに記載されている下記のコマンドでRaspbery PiのNode-Red用にインストールをしておきます。

$cd ~/.node-red
$sudo npm install node-red-contrib-speakerpi

⬆︎Raspberry Piのコマンドプロンプトから下記のコマンドを使用してnode-red-contrib-speakerpiをインストールする。

今回のフローで使用するWatsonの機能としては、Speech to Text, Text to Speech, Visual Recognition, Assitantなどがあります。Assitantは設定なども必要なので後ほど手順を紹介しますが、それ以外の機能がIBM Cloudのダッシュボード上で使えるようになっていることを確認しておいてください。設定方法は、TJBot zeroの公式サイトにあるインストールガイドをご参照ください。

フローの読み込みと設定

Raspberry Pi上のNode-Redエディターの画面右上にある三本線メニューから、読み込み=>クリップボードの順でクリックし、下記のフロー・コードをカット・アンド・ペーストして読み込みます。
image.png

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読み込みに成功すると、下記のようなフローが作成されます。デプロイする前にいくつか準備することがありますので、デプロイは少々お待ちください。
image.png

Watson Assitantの準備とAPIキー

IBM CloudのダッシュボードにカタログからWatson Assistantを追加します。2018年の終盤に東京リージョンでもWatson Assistantが利用可能となりましたので、迷わず東京を選んでみましょう。

IBM Cloudのダッシュボードからカタログをクリックします。
image.png

AIカテゴリーからWatson Assitant(旧称Comversation)を選びます。
image.png

ロケーションで東京を選び、画面下の作成ボタンを押し、少し待ちます。
image.png

下記の画面に移ったら、ツールを起動します。下記の画面は後ほどAPI鍵のコピーに使いますので、閉じないでおていください。
image.png

ツールが起動すると、下記のような英語の画面になりますが、迷わずSkillsをクリックします。
image.png

下記の画面に移動したら、Create Newをクリックします。
image.png

ここからWatson AssistantのSkillのJSONファイルをダウンロードしておきます。
下記の画面が出たら、Import Skillをクリックし、Choose Json Fileを選び、先ほどダウンロードしたJSONファイルを選択して読み込みます。

image.png

読み込みが成功すると、下記のような画面が出て来ます。次に画面左上のSlillsをクリックします。
image.png

Skillsの画面で、縦にドットが3つ並んでいるところをクリックし、出て来たメニューのView API Detailsをクリックします。
image.png

Skill Datails画面に、Workspace IDが表示されますので、これをクリップボードにコピーします。
image.png

Node-Redに読み込んだ、先ほどのフローの中にあるAssistantノードをダブルクリックし、ノードの編集画面のWorkspace IDの欄に先ほどクリップボードに入れたWorkspace IDを貼り付けます。
image.png

Assitantのツールを起動させた画面を開き、API鍵とURLをそれぞれ前の画面のAPI KeyとSource Endpointにカット・アンド・ペーストします。Source Endpointが表示されていない場合は、Use Deault Service Endpointのチェックを外します。
image.png

同様に、Speech to Text、Text to SpeechそしてVisual RecognitionのAPI KeyとURLをセットします。(まだ海外のサイトを使用している場合は、これを機会に東京で作り直すことをお勧めします)

フローをデプロイする

これでやっと今回のフローをデプロイすることができるようになりましたので、Node-Redのエディター画面の右上にあるデプロイボタンを押して、デプロイします。

話しかけてみましょう

下記は、全体のフローの一部分ですが、PIN:↑22のノードでプッシュスイッチの状態を読み取っています。↑は、PIN22がプルアップされていて、通常時は"Hi"または、"1"のステータスになっていることを表しています。このノードは、PINの状態が変わると次のノードへ信号を伝達します。スイッチを押すと状態が、1から0に変化し、スイッチを話すと0から1に変化します。次にあるswitchノードでは、状態が1になった時に1のポートに出力を出すように設定していますので、スイッチを押した時は何もせず、離した時に次のノードへ信号を出します。そして、3秒間頭を緑色にして、3秒間録音をする命令が実行されます。
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スイッチを押してから離し、頭が緑色の間に話しかけます。
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Shibu-Shin
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