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AndFran/PY0101EN-5.2_API_2.ipynb

Created January 30, 2020 14:28
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PY0101EN-5.2_API_2.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://cognitiveclass.ai/\">\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/CCLog.png\" width=\"200\" align=\"center\">\n",
"</a>\n",
"\n",
"<h1 align=\"center\"><font size=\"5\"><b>Application Programming Interface</b></font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>In this notebook, you will learn to convert an audio file of an English speaker to text using a Speech to Text API. Then you will translate the English version to a Spanish version using a Language Translator API. <b>Note:</b> You must obtain the API keys and enpoints to complete the lab.</p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
" <a href=\"https://cocl.us/topNotebooksPython101Coursera\">\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Ad/TopAd.png\" width=\"750\" align=\"center\">\n",
" </a>\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"<h2>Table of Contents</h2>\n",
"<ul>\n",
" <li><a href=\"#ref0\">Speech To Text</a></li>\n",
" <li><a href=\"#ref1\">Language Translator</a></li>\n",
" <li><a href=\"#ref2\">Exercise</a></li>\n",
"</ul>\n",
"<br>\n",
"<p>Estimated Time Needed: <strong>25 min</strong></p>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting ibm_watson\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/da/f4/7e256026ee22c75a630c6de53eb45b6fef4840ac6728b80a92dd2e523a1a/ibm-watson-4.2.1.tar.gz (348kB)\n",
"\u001b[K |████████████████████████████████| 358kB 31.4MB/s eta 0:00:01\n",
"\u001b[?25hCollecting wget\n",
" Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
"Requirement already satisfied: requests<3.0,>=2.0 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from ibm_watson) (2.22.0)\n",
"Requirement already satisfied: python_dateutil>=2.5.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from ibm_watson) (2.8.1)\n",
"Collecting websocket-client==0.48.0 (from ibm_watson)\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/8a/a1/72ef9aa26cfe1a75cee09fc1957e4723add9de098c15719416a1ee89386b/websocket_client-0.48.0-py2.py3-none-any.whl (198kB)\n",
"\u001b[K |████████████████████████████████| 204kB 19.4MB/s eta 0:00:01\n",
"\u001b[?25hCollecting ibm_cloud_sdk_core==1.5.1 (from ibm_watson)\n",
" Downloading https://files.pythonhosted.org/packages/b7/f6/10d5271c807d73d236e6ae07b68035fed78b28b5ab836704d34097af3986/ibm-cloud-sdk-core-1.5.1.tar.gz\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0,>=2.0->ibm_watson) (1.25.7)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0,>=2.0->ibm_watson) (3.0.4)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0,>=2.0->ibm_watson) (2.8)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0,>=2.0->ibm_watson) (2019.9.11)\n",
"Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from python_dateutil>=2.5.3->ibm_watson) (1.13.0)\n",
"Collecting PyJWT>=1.7.1 (from ibm_cloud_sdk_core==1.5.1->ibm_watson)\n",
" Downloading https://files.pythonhosted.org/packages/87/8b/6a9f14b5f781697e51259d81657e6048fd31a113229cf346880bb7545565/PyJWT-1.7.1-py2.py3-none-any.whl\n",
"Building wheels for collected packages: ibm-watson, wget, ibm-cloud-sdk-core\n",
" Building wheel for ibm-watson (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /home/jupyterlab/.cache/pip/wheels/ce/4d/6e/ae352b7c7acdddf073aeb06617fbfeefaea9fcb6d7ae98800b\n",
" Building wheel for wget (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /home/jupyterlab/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
" Building wheel for ibm-cloud-sdk-core (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /home/jupyterlab/.cache/pip/wheels/6a/42/50/f96888116b329578304f9dda4693cef6f3e76e18272d22cb6c\n",
"Successfully built ibm-watson wget ibm-cloud-sdk-core\n",
"Installing collected packages: websocket-client, PyJWT, ibm-cloud-sdk-core, ibm-watson, wget\n",
"Successfully installed PyJWT-1.7.1 ibm-cloud-sdk-core-1.5.1 ibm-watson-4.2.1 websocket-client-0.48.0 wget-3.2\n"
]
}
],
"source": [
"#you will need the following library \n",
"!pip install ibm_watson wget"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2 id=\"ref0\">Speech to Text</h2>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>First we import <code>SpeechToTextV1</code> from <code>ibm_watson</code>.For more information on the API, please click on this <a href=\"https://cloud.ibm.com/apidocs/speech-to-text?code=python\">link</a></p>"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from ibm_watson import SpeechToTextV1 \n",
"import json\n",
"from ibm_cloud_sdk_core.authenticators import IAMAuthenticator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The service endpoint is based on the location of the service instance, we store the information in the variable URL. To find out which URL to use, view the service credentials.</p>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"url_s2t = \"https://stream.watsonplatform.net/speech-to-text/api\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>You require an API key, and you can obtain the key on the <a href=\"https://cloud.ibm.com/resources\">Dashboard </a>.</p>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"iam_apikey_s2t = \"W2x_MXATpv3SnijIZALu2ORYCNplUdeia-2UEHgmL0NG\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>You create a <a href=\"http://watson-developer-cloud.github.io/python-sdk/v0.25.0/apis/watson_developer_cloud.speech_to_text_v1.html\">Speech To Text Adapter object</a> the parameters are the endpoint and API key.</p>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ibm_watson.speech_to_text_v1_adapter.SpeechToTextV1Adapter at 0x7fdc44230198>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"authenticator = IAMAuthenticator(iam_apikey_s2t)\n",
"s2t = SpeechToTextV1(authenticator=authenticator)\n",
"s2t.set_service_url(url_s2t)\n",
"s2t"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>Lets download the audio file that we will use to convert into text.</p>"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-01-30 14:25:46-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/PolynomialRegressionandPipelines.mp3\n",
"Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 4234179 (4.0M) [audio/mpeg]\n",
"Saving to: ‘PolynomialRegressionandPipelines.mp3’\n",
"\n",
"PolynomialRegressio 100%[===================>] 4.04M 2.95MB/s in 1.4s \n",
"\n",
"2020-01-30 14:25:48 (2.95 MB/s) - ‘PolynomialRegressionandPipelines.mp3’ saved [4234179/4234179]\n",
"\n"
]
}
],
"source": [
"!wget -O PolynomialRegressionandPipelines.mp3 https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/PolynomialRegressionandPipelines.mp3\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We have the path of the wav file we would like to convert to text</p>"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"filename='PolynomialRegressionandPipelines.mp3'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We create the file object <code>wav</code> with the wav file using <code>open</code> ; we set the <code>mode</code> to \"rb\" , this is similar to read mode, but it ensures the file is in binary mode.We use the method <code>recognize</code> to return the recognized text. The parameter audio is the file object <code>wav</code>, the parameter <code>content_type</code> is the format of the audio file.</p>"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ERROR:root:<html>\n",
"<head><title>413 Request Entity Too Large</title></head>\n",
"<body bgcolor=\"white\">\n",
"<center><h1>413 Request Entity Too Large</h1></center>\n",
"<hr><center>nginx</center>\n",
"</body>\n",
"</html>\n",
"Traceback (most recent call last):\n",
" File \"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ibm_cloud_sdk_core/base_service.py\", line 229, in send\n",
" response.status_code, error_message, http_response=response)\n",
"ibm_cloud_sdk_core.api_exception.ApiException: Error: <html>\n",
"<head><title>413 Request Entity Too Large</title></head>\n",
"<body bgcolor=\"white\">\n",
"<center><h1>413 Request Entity Too Large</h1></center>\n",
"<hr><center>nginx</center>\n",
"</body>\n",
"</html>\n",
", Code: 413\n"
]
},
{
"ename": "ApiException",
"evalue": "Error: <html>\r\n<head><title>413 Request Entity Too Large</title></head>\r\n<body bgcolor=\"white\">\r\n<center><h1>413 Request Entity Too Large</h1></center>\r\n<hr><center>nginx</center>\r\n</body>\r\n</html>\r\n, Code: 413",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mApiException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-f84fab0fe42d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mwav\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms2t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecognize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maudio\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwav\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'audio/mp3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/ibm_watson/speech_to_text_v1.py\u001b[0m in \u001b[0;36mrecognize\u001b[0;34m(self, audio, content_type, model, language_customization_id, acoustic_customization_id, base_model_version, customization_weight, inactivity_timeout, keywords, keywords_threshold, max_alternatives, word_alternatives_threshold, word_confidence, timestamps, profanity_filter, smart_formatting, speaker_labels, customization_id, grammar_name, redaction, audio_metrics, end_of_phrase_silence_time, split_transcript_at_phrase_end, **kwargs)\u001b[0m\n\u001b[1;32m 463\u001b[0m data=data)\n\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 466\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/conda/envs/python/lib/python3.6/site-packages/ibm_cloud_sdk_core/base_service.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;34m'invalid credentials'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m raise ApiException(\n\u001b[0;32m--> 229\u001b[0;31m response.status_code, error_message, http_response=response)\n\u001b[0m\u001b[1;32m 230\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSLError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0mlogging\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mERROR_MSG_DISABLE_SSL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mApiException\u001b[0m: Error: <html>\r\n<head><title>413 Request Entity Too Large</title></head>\r\n<body bgcolor=\"white\">\r\n<center><h1>413 Request Entity Too Large</h1></center>\r\n<hr><center>nginx</center>\r\n</body>\r\n</html>\r\n, Code: 413"
]
}
],
"source": [
"with open(filename, mode=\"rb\") as wav:\n",
" response = s2t.recognize(audio=wav, content_type='audio/mp3')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The attribute result contains a dictionary that includes the translation:</p>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'response' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-8e95a0f1890d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'response' is not defined"
]
}
],
"source": [
"response.result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pandas.io.json import json_normalize\n",
"\n",
"json_normalize(response.result['results'],\"alternatives\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can obtain the recognized text and assign it to the variable <code>recognized_text</code>:</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"recognized_text=response.result['results'][0][\"alternatives\"][0][\"transcript\"]\n",
"type(recognized_text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2 id=\"ref1\">Language Translator</h2>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>First we import <code>LanguageTranslatorV3</code> from ibm_watson. For more information on the API click <a href=\"https://cloud.ibm.com/apidocs/speech-to-text?code=python\"> here</a></p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ibm_watson import LanguageTranslatorV3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The service endpoint is based on the location of the service instance, we store the information in the variable URL. To find out which URL to use, view the service credentials.</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"url_lt='https://gateway.watsonplatform.net/language-translator/api'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>You require an API key, and you can obtain the key on the <a href=\"https://cloud.ibm.com/resources\">Dashboard</a>.</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"apikey_lt=''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>API requests require a version parameter that takes a date in the format version=YYYY-MM-DD. This lab describes the current version of Language Translator, 2018-05-01</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"version_lt='2018-05-01'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>we create a Language Translator object <code>language_translator</code>:</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"authenticator = IAMAuthenticator(apikey_lt)\n",
"language_translator = LanguageTranslatorV3(version=version_lt,authenticator=authenticator)\n",
"language_translator.set_service_url(url_lt)\n",
"language_translator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can get a Lists the languages that the service can identify.\n",
"The method Returns the language code. For example English (en) to Spanis (es) and name of each language.</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from pandas.io.json import json_normalize\n",
"\n",
"json_normalize(language_translator.list_identifiable_languages().get_result(), \"languages\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can use the method <code>translate</code> this will translate the text. The parameter text is the text. Model_id is the type of model we would like to use use we use list the the langwich . In this case, we set it to 'en-es' or English to Spanish. We get a Detailed Response object translation_response</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"translation_response = language_translator.translate(\\\n",
" text=recognized_text, model_id='en-es')\n",
"translation_response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>The result is a dictionary.</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"translation=translation_response.get_result()\n",
"translation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can obtain the actual translation as a string as follows:</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spanish_translation =translation['translations'][0]['translation']\n",
"spanish_translation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can translate back to English</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"translation_new = language_translator.translate(text=spanish_translation ,model_id='es-en').get_result()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can obtain the actual translation as a string as follows:</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"translation_eng=translation_new['translations'][0]['translation']\n",
"translation_eng"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p>We can convert it to french as well:</p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"French_translation=language_translator.translate(\n",
" text=translation_eng , model_id='en-fr').get_result()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"French_translation['translations'][0]['translation']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>Language Translator</h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" <a href=\"http://cocl.us/NotebooksPython101bottom\"><img src=\"https://ibm.box.com/shared/static/irypdxea2q4th88zu1o1tsd06dya10go.png\" width=\"750\" align=\"center\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>References</b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://cloud.ibm.com/apidocs/speech-to-text?code=python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://cloud.ibm.com/apidocs/language-translator?code=python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h4>About the Author:</h4>\n",
"<p><a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.</p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Other contributor: <a href=\"https://www.linkedin.com/in/fanjiang0619/\">Fan Jiang</a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright &copy; 2019 [cognitiveclass.ai](https:cognitiveclass.ai). This notebook and its source code are released under the terms of the [MIT License](cognitiveclass.ai)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
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"pygments_lexer": "ipython3",
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