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PY0101EN-5.2_API_2.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Watson Speech to Text Translator\n",
"\n",
"Estimated time needed: **25** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Create Speech to Text Translator\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction\n",
"\n",
"<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>\n"
]
},
{
"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",
"</div>\n"
]
},
{
"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/a2/3c/c2cfb41db546fe98820e89017c892d73991cef61b9c48680191fe703a214/ibm-watson-4.7.1.tar.gz (385kB)\n",
"\u001b[K |████████████████████████████████| 389kB 8.9MB/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) (2.24.0)\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 19.9MB/s eta 0:00:01\n",
"\u001b[?25hCollecting ibm_cloud_sdk_core==1.7.3 (from ibm_watson)\n",
" Downloading https://files.pythonhosted.org/packages/b7/23/aa9ae242f6348a1ed28fca2e6d3e76e043c3db951f9b516e1992518fe2c3/ibm-cloud-sdk-core-1.7.3.tar.gz\n",
"Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests<3.0,>=2.0->ibm_watson) (2.10)\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) (2020.6.20)\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) (1.25.10)\n",
"Requirement already satisfied: chardet<4,>=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: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from python_dateutil>=2.5.3->ibm_watson) (1.15.0)\n",
"Collecting PyJWT>=1.7.1 (from ibm_cloud_sdk_core==1.7.3->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/6e/14/69/dbbd573a3bab3bf64984572284f13f174f430038308abdd73c\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/34/6e/58/589e0f841c2fae9dad99630d78ddc7a60c5c7663a16a39cdbb\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-1.7.3 ibm-watson-4.7.1 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>\n"
]
},
{
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"iam_apikey_s2t = \"\""
]
},
{
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ibm_watson.speech_to_text_v1_adapter.SpeechToTextV1Adapter at 0x7fbd8be285f8>"
]
},
"execution_count": 7,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-10-09 20:27:19-- 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 4.35MB/s in 0.9s \n",
"\n",
"2020-10-09 20:27:20 (4.35 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>\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"ename": "ApiException",
"evalue": "Error: Property missing or empty, Code: 400",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mApiException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-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, speech_detector_sensitivity, background_audio_suppression, **kwargs)\u001b[0m\n\u001b[1;32m 494\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m data=data)\n\u001b[0m\u001b[1;32m 497\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 498\u001b[0m \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[0;32m~/conda/envs/python/lib/python3.6/site-packages/ibm_cloud_sdk_core/base_service.py\u001b[0m in \u001b[0;36mprepare_request\u001b[0;34m(self, method, url, headers, params, data, files, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauthenticator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauthenticate\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 298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;31m# Next, we need to process the 'files' argument to try to fill in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\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/authenticators/iam_authenticator.py\u001b[0m in \u001b[0;36mauthenticate\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 104\u001b[0m \"\"\"\n\u001b[1;32m 105\u001b[0m \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'headers'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m \u001b[0mbearer_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_token\u001b[0m\u001b[0;34m(\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 107\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Authorization'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Bearer {0}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbearer_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\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/jwt_token_manager.py\u001b[0m in \u001b[0;36mget_token\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 77\u001b[0m \"\"\"\n\u001b[1;32m 78\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_token_expired\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpaced_request_token\u001b[0m\u001b[0;34m(\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 80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_token_needs_refresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\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/jwt_token_manager.py\u001b[0m in \u001b[0;36mpaced_request_token\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mrequest_active\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mtoken_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_token\u001b[0m\u001b[0;34m(\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 124\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_token_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\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/iam_token_manager.py\u001b[0m in \u001b[0;36mrequest_token\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0mauth_tuple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth_tuple\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m proxies=self.proxies)\n\u001b[0m\u001b[1;32m 115\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 116\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/jwt_token_manager.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, method, url, headers, params, data, auth_tuple, **kwargs)\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mApiException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_response\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mApiException\u001b[0m: Error: Property missing or empty, Code: 400"
]
}
],
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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-11-8e95a0f1890d>\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": 12,
"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-12-57fba478c508>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson_normalize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mjson_normalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'results'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"alternatives\"\u001b[0m\u001b[0;34m)\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": [
"from pandas.io.json import json_normalize\n",
"\n",
"json_normalize(response.result['results'],\"alternatives\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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-13-ab0153f39f33>\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'response' is not defined"
]
}
],
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"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-e5263c37ec0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrecognized_text\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'results'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"alternatives\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"transcript\"\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 2\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecognized_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'response' is not defined"
]
}
],
"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>\n"
]
},
{
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>\n"
]
},
{
"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>\n"
]
},
{
"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 language . In this case, we set it to 'en-es' or English to Spanish. We get a Detailed Response object translation_response</p>\n"
]
},
{
"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>\n"
]
},
{
"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>\n"
]
},
{
"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>\n"
]
},
{
"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>\n"
]
},
{
"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>\n"
]
},
{
"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>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" <a href=\"https://cloud.ibm.com/catalog/services/watson-studio\"><img src=\"https://ibm.box.com/shared/static/irypdxea2q4th88zu1o1tsd06dya10go.png\" width=\"750\" align=\"center\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>References</b>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[https://cloud.ibm.com/apidocs/speech-to-text?code=python](https://cloud.ibm.com/apidocs/speech-to-text?code=python&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork-19487395&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n"
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"[https://cloud.ibm.com/apidocs/language-translator?code=python](https://cloud.ibm.com/apidocs/language-translator?code=python&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork-19487395&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ)\n"
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"<hr>\n"
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"## Authors:\n",
"\n",
" [Joseph Santarcangelo](https://www.linkedin.com/in/joseph-s-50398b136?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork-19487395&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork-19487395&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ) \n",
"\n",
"Joseph Santarcangelo 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.\n",
"\n",
"## Other Contributor(s)\n",
"\n",
"<a href=\"https://www.linkedin.com/in/fanjiang0619/\">Fan Jiang</a>\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ---------- | ---------------------------------- |\n",
"| 2020-08-26 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
"| | | | |\n",
"| | | | |\n",
"\n",
"<hr/>\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
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