试图使IBM助手根据输入语音说出不同的输出。我该怎么办？

Question

实际上，这是我先前提出的问题的扩展。这是将文本转换为音频文件中的语音的工作块。

import json
        from symbol import parameters
        from ibm_watson import TextToSpeechV1
        from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
        from ibm_watson import ApiException
        import speech_recognition as sr

        try:
            ()  # Invoke a Text to Speech method
        except ApiException as ex:
            print("Method failed with status code " + str(ex.code) + ": " + ex.message)

        authenticator = IAMAuthenticator("(API KEY)")
        text_to_speech = TextToSpeechV1(
            authenticator=authenticator
        )

        text_to_speech.set_service_url(
            'https://api.us-south.text-to-speech.watson.cloud.ibm.com/instances/113cd664-f07b-44fe-a11d-a46cc50caf84')


        with open('hello_world.wav', 'wb') as audio_file:
            audio_file.write(
                text_to_speech.synthesize(
                    'Hello world',
                    voice='en-US_AllisonVoice',
                    accept='audio/wav'        
                ).get_result().content)

使用上面提供的代码，我如何将IBM api与Python结合使用，并用我的声音输入问题，并根据我的声音输入从IBM声音中获得不同的响应？现在，输入的文本是“ hello world”所在的位置，即被发送到IBM的内容，然后使用选定的Allison Voice，它将保存一个Wav文件，该文本由Allison读取。如果有人对我该怎么做有想法，请留下一段代码作为示例。我对python缩进不太满意。

[这是我尝试将代码从Text转换为语音和从Speech to Text合并IBM api的尝试。这是非常有缺陷的，我不知道该怎么办。任何帮助都会很棒。

import json
import os
from symbol import parameters
import wikipedia
import pyjokes
from ibm_watson import TextToSpeechV1
from ibm_watson import SpeechToTextV1
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson import ApiException

try:
    ()  # Invoke a Text to Speech method
except ApiException as ex:
    print("Method failed with status code " + str(ex.code) + ": " + ex.message)

authenticator = IAMAuthenticator("API Key")
text_to_speech = TextToSpeechV1(
    authenticator=authenticator
)

authenticator = IAMAuthenticator('API KEY')
speech_to_text = SpeechToTextV1(
    authenticator=authenticator
)

voice='en-US_AllisonVoice'

speech_to_text.set_service_url('https://api.us-south.speech-to-text.watson.cloud.ibm.com/instances/7393db4a-82d8-40f8-a86d-09cb948589e2')

text_to_speech.set_service_url('https://api.us-south.text-to-speech.watson.cloud.ibm.com/instances/113cd664-f07b-44fe-a11d-a46cc50caf84')

def takeCommand(recognize_using_websocket)

    with dict recognize_using_websocket.Microphone() as source:

        print("Listening...")
        r.pause_threshold = .5
        audio = r.listen(source)

    try:
        print("Recognizing...")
        query = r.recognize_google(audio, language='en-us')
        print("User said: {query}\n")

    except Exception as e:
        print(e)
        text_to_speech.synthesize("I can't hear you sir.")
        print("I can't hear you sir.")
        return "None"

    return query

if __name__ == '__main__':
    clear = lambda: os.system('cls')

    # This Function will clean any
    # command before execution of this python file
    clear()

    while True:

        query = takeCommand().lower()

        # All the commands said by user will be
        # stored here in 'query' and will be
        # converted to lower case for easily
        # recognition of command
        if 'wikipedia' in query:
            text_to_speech.synthesize('Searching Wikipedia...')
            query = query.replace("wikipedia", "")
            results = wikipedia.summary(query, sentences=3)
            text_to_speech.synthesize("According to Wikipedia")
            print(results)
            text_to_speech.synthesize(results)


        elif "who made you" in query or "who created you" in query:
    text_to_speech.synthesize("I have been created by you sir.")

        elif 'tell me a joke' in query or "make me laugh" in query:
    text_to_speech.synthesize(pyjokes.get_joke()

我试图在这里建立一个简单的问与答系统。有些只是问而已，就像Wikipedia一样弹出网站。我对此很陌生，因此非常感谢您的帮助。我会悬赏以帮助一些人，但我才刚开始使用StackOverflow。

Answer 1

我建议您看一下项目TJBot。这是一个基于Raspberry Pi的硬纸板机器人，带有麦克风和扬声器。可以使用所谓的recipes与麦克风和扬声器进行交互，以构建具有语音功能的机器人。他们中的许多人都使用Watson STT，WA和TTS。我几年前使用的一个较旧的示例是“ Tell the time”。另一个配方“ Conversation”使用相同的服务。