IBM Watson Studio is an integrated environment designed to develop, train, manage models, and deploy AI-powered applications and is a Software as a Service (SaaS) solution delivered on the IBM Cloud. The IBM Cloud provides lots of services like Speech To Text, Text To Speech, Visual Recognition, Natural Language Classifier, Language Translator, etc.
The Speech to Text service transcribes audio to text to enable speech transcription capabilities for applications.
Create an instance of the service
- Go to the Speech to Text page in the IBM Cloud Catalog.
- Sign up for a free IBM Cloud account or log in.
- Click Create.
Copy the Credentials to Authenticate to your service instance
- From the IBM Cloud Resource list, click on your Speech to Text service instance to go to the Speech to Text service dashboard page.
- On the Manage page, click Show Credentials to view your credentials.
- Copy the API Key and URL values.
Module Needed:
- Json
- ibm_watson: This module does not comes pre-defined with Python. To install it type the below command in the terminal.
pip install ibm_watson
Now you’re ready to use the IBM Cloud Services.
Below code illustrates the use of IBM Watson studio’s Speech To Text Service using Python and web socket interface
#Python Program To Use IBM Watson # Studio's Speech To Text Below Code # Accepts only .mp3 Format of Audio # File import json 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 # Insert API Key in place of # 'YOUR UNIQUE API KEY' authenticator = IAMAuthenticator('YOUR UNIQUE API KEY') service = SpeechToTextV1(authenticator = authenticator) #Insert URL in place of 'API_URL' service.set_service_url('API_URL') # Insert local mp3 file path in # place of 'LOCAL FILE PATH' with open(join(dirname('__file__'), r'LOCAL FILE PATH'), 'rb') as audio_file: dic = json.loads( json.dumps( service.recognize( audio=audio_file, content_type='audio/flac', model='en-US_NarrowbandModel', continuous=True).get_result(), indent=2)) # Stores the transcribed text str = "" while bool(dic.get('results')): str = dic.get('results').pop().get('alternatives').pop().get('transcript')+str[:] print(str) |
Output
The Output will be Transcript (Text) of audio file.
