升级到 5.1 版后，IBM watson 语音到文本出现类型错误

如何解决升级到 5.1 版后，IBM watson 语音到文本出现类型错误

我正在尝试使用 watson 语音将文件转录为文本 api。今天我使用

从 anaconda 提示 Windows 操作系统升级

pip install --upgrade "ibm-watson>=5.1.0"

但是，这样做之后，我无法再访问 api 并且收到以下错误

import os
import json
import time
# import threading
from pathlib import Path

import concurrent.futures

# from os.path import join,dirname
from ibm_watson import SpeechToTextV1
from ibm_watson.websocket import RecognizeCallback,AudioSource
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

import pandas as pd

# Replace with your api key.
my_api_key = "N_OUpGsgBvL-xx"
#my_api_key = "x"
# my_api_key = "xx-UtZsw2Q0WCwVw"

# You can add a directory path to Path() if you want to run
# the project from a different folder at some point.
directory = Path().absolute()


authenticator = IAMAuthenticator(my_api_key)

service = SpeechToTextV1(authenticator=authenticator)
service.set_service_url('https://api.us-east.speech-to-text.watson.cloud.ibm.com')
# I used this URL.
# service.set_service_url('https://stream.watsonplatform.net/speech-to-text/api') 


models = service.list_models().get_result()
#print(json.dumps(models,indent=2))

model = service.get_model('en-US_broadbandModel').get_result()
#print(json.dumps(model,indent=2))



# get data to a csv
########################RUN THIS PART SECOND#####################################


def process_data(json_data,output_path):

    print(f"Processing: {output_path.stem}")

    cols = ["transcript","confidence"]

    dfdata = [[t[cols[0]],t.get(cols[1],None)] for r in json_data.get('results') for t in r.get("alternatives")]


    df0 = pd.DataFrame(data = dfdata,columns = cols)

    df1 = pd.DataFrame(json_data.get("speaker_labels")).drop(["final","confidence"],axis=1)


    # test3 = pd.concat([df0,df1],axis=1)
    test3 = pd.merge(df0,df1,left_index = True,right_index = True)


    # sentiment
    print(f"Getting sentiment for: {output_path.stem}")
    transcript = test3["transcript"]
    transcript.dropna(inplace=True)

    analyzer = SentimentIntensityAnalyzer()
    text = transcript
    scores = [analyzer.polarity_scores(txt) for txt in text]

    # data = pd.DataFrame(text,columns = ["Text"])
    data = transcript.to_frame(name="Text")
    data2 = pd.DataFrame(scores)


    # final_dataset= pd.concat([data,data2],axis=1)
    final_dataset = pd.merge(data,data2,right_index = True)

    # test4 = pd.concat([test3,final_dataset],axis=1)
    test4 = pd.merge(test3,final_dataset,right_index = True)

    test4.drop("Text",axis=1,inplace=True)

    test4.rename(columns = {
            "neg": "Negative","pos": "Positive","neu": "Neutral",},inplace=True)

    # This is the name of the output csv file
    test4.to_csv(output_path,index = False)


def process_audio_file(filename,output_type = "csv"):

    audio_file_path = directory.joinpath(filename)

    # Update output path to consider `output_type` parameter.
    out_path = directory.joinpath(f"{audio_file_path.stem}.{output_type}")

    print(f"Current file: '{filename}'")

    with open(audio_file_path,"rb") as audio_file:
        data = service.recognize(
                audio = audio_file,speaker_labels = True,content_type = "audio/wav",inactivity_timeout = -1,smart_formatting = True,model = "en-US_NarrowbandModel",continuous = True,).get_result()

    print(f"Speech-to-text complete for: '{filename}'")

    # Return data and output path as collection.
    return [data,out_path]


def main():
    print("Running main()...")

    # Default num. workers == min(32,os.cpu_count() + 4)
    n_workers = os.cpu_count() + 2

    # Create generator for all .wav files in folder (and subfolders).
    file_gen = directory.glob("**/*.wav")

    with concurrent.futures.ThreadPoolExecutor(max_workers = n_workers) as executor:
        futures = {executor.submit(process_audio_file,f) for f in file_gen}
        for future in concurrent.futures.as_completed(futures):
            pkg = future.result()
            process_data(*pkg)


if __name__ == "__main__":

    print(f"Program to process audio files has started.")

    t_start = time.perf_counter()

    main()

    t_stop = time.perf_counter()
    print(f"Done! Processing completed in {t_stop - t_start} seconds.")
    

TypeError：init() 缺少 7 个必需的仅限关键字的参数：“url”、“client_id”、“client_secret”、“disable_ssl_verification”、“headers”、“proxies”和“scope” '

我该如何解决这个问题？我需要添加这些新参数吗？如果是这样，在我代码的哪一部分？