Python Speech Recognition Testing with IBM Watson Speech Recognition API | #132

Brandon Jacobson
20 Mar 202114:38

TLDRIn this video, the creator compares the performance of the standard Python speech recognition library with IBM Watson's Speech to Text API. They discuss a comment on their previous video about a homemade digital assistant and address the misconception about the speed of response. The video demonstrates how to set up and use both APIs, testing them with a simple 'test test test' command. The standard library processes the command in under a second, whereas IBM Watson takes about 2.7 seconds. The creator also covers how to install and authenticate with IBM Watson, suggesting it might be better suited for transcribing large files due to its 500 minutes free usage per month. The video invites viewers to suggest alternative speech recognition sources and encourages engagement with the channel.

Takeaways

  • πŸ˜€ The video compares the performance of the standard speech recognition library with IBM Watson's Speech to Text API.
  • πŸ” The creator received feedback on a previous video and aims to explore alternatives to improve response time.
  • πŸŽ₯ The video demonstrates the process of setting up and testing both the standard library and IBM Watson API.
  • πŸ•’ The standard speech recognition library responded in less than a second in the test.
  • πŸ—£οΈ IBM Watson's response time was approximately 2.7 seconds for the test.
  • πŸ’» The video explains how to set up the IBM Watson Speech to Text API, including obtaining an API key and service URL.
  • πŸ“š The creator suggests that IBM Watson may be better suited for large file transcriptions rather than streaming from a microphone.
  • πŸ“ˆ The video includes a step-by-step guide on how to use the IBM Watson API for speech recognition.
  • πŸ“ The script details the process of installing necessary libraries and setting up authentication for IBM Watson.
  • πŸ“± The creator mentions the potential difficulty of streaming from a microphone with IBM Watson but suggests it could be coded.
  • 🌐 The video highlights the importance of internet connectivity for using IBM Watson's API effectively.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is comparing the performance of the standard speech recognition Python library with IBM Watson's Speech to Text API.

  • What is the creator's motivation for testing IBM Watson Speech to Text API?

    -The creator wants to explore alternatives to the standard speech recognition library after receiving a comment on a previous video about the response time of his digital assistant named Shane.

  • What is the standard Python library being compared to IBM Watson Speech to Text API in the video?

    -The standard Python library being compared is the 'speech_recognition' library.

  • How does the creator plan to test the performance of the speech recognition systems?

    -The creator plans to test the performance by timing how long it takes for each system to analyze and print out a spoken command.

  • What is the issue with IBM Watson Speech to Text API when it comes to streaming from a microphone?

    -IBM Watson Speech to Text API does not have an easy way to stream from a microphone, unlike the standard speech recognition library.

  • What is the significance of the 'time' library used in the video?

    -The 'time' library is used for timing the performance of the speech recognition systems during the testing.

  • How does the creator adjust the speech recognition system for ambient noise?

    -The creator adjusts the recognizer for ambient noise by setting a threshold based on tests conducted for the real digital assistant.

  • What is the concern mentioned by the creator regarding the IBM Watson Speech to Text API?

    -The concern mentioned is that the IBM Watson Speech to Text API requires an internet connection to work, which could be a potential issue.

  • What is the free tier offering for IBM Watson Speech to Text API?

    -The free tier offers 500 minutes per month of speech to text translation.

  • How does the creator suggest handling the API key and service URL for IBM Watson Speech to Text API?

    -The creator suggests using a personal file, such as 'keys.py', to store the API key and service URL to avoid exposing them in the code.

  • What is the final step in the IBM Watson Speech to Text API testing process shown in the video?

    -The final step is to print the transcript from the nested dictionary obtained from the API's response to verify the recognized speech.

Outlines

00:00

πŸ”Š Comparing Speech Recognition Technologies

The speaker introduces a comparison between the standard speech recognition library used in previous videos and IBM Watson's Speech to Text API. They discuss a comment received on a previous video about the speed of response from their digital assistant, 'Shane'. The video aims to explore alternatives to the current system, noting that the standard library requires internet connectivity and may slow down. The speaker plans to test both technologies using a live microphone stream for the standard library and a pre-recorded sample for IBM Watson, due to the latter's lack of easy microphone streaming. The first test with the standard library is shown, taking less than a second to transcribe 'test test test'.

05:00

πŸ‘¨β€πŸ’» Setting Up IBM Watson Speech to Text API

This paragraph details the process of setting up IBM Watson's Speech to Text API. The speaker instructs the audience to upgrade to the latest version of the 'ibm-watson' Python package and to sign up for the service on IBM's website to obtain an API key and service URL. They explain the steps to install the necessary Python modules and authenticate with IBM's services using the obtained credentials. The speaker also outlines how to use the API with a pre-recorded audio file, explaining the parameters and method calls required to transcribe the audio. The process involves setting up the service URL, using the API key for authentication, and utilizing the recognize method to convert speech to text. The speaker emphasizes the complexity of setting up the IBM Watson API compared to the standard speech recognition library.

10:02

πŸ“ˆ Evaluating IBM Watson's Speech Recognition Performance

The speaker evaluates the performance of IBM Watson's Speech to Text API by transcribing a pre-recorded audio file. They describe the process of setting the service URL, authenticating, and using the recognize function to obtain the transcription. The output is a dictionary containing detailed information about the speech recognition results, from which the speaker extracts the transcribed text. The IBM Watson API takes approximately 2.7 seconds to process the audio, which the speaker notes as slower than the standard library's less than a second. The speaker also discusses the potential uses of IBM Watson for transcribing large files, such as meeting recordings, and mentions the free tier of 500 minutes per month, after which users must pay for additional services. The video concludes with a call to action for viewers to suggest better speech recognition technologies and to subscribe for updates on building the digital assistant 'Shane'.

Mindmap

Keywords

πŸ’‘Speech Recognition

Speech recognition is a technology that enables software to interpret and transcribe spoken language into written text. In the context of the video, it is a core component for building a digital assistant, allowing it to understand and respond to voice commands. The script discusses testing two different speech recognition systems: a standard Python library and IBM Watson's Speech to Text API.

πŸ’‘IBM Watson Speech to Text API

The IBM Watson Speech to Text API is a cloud-based service that converts spoken language into written text. It is highlighted in the video as an alternative to the standard speech recognition library used by the creator. The API is tested for its performance and compared with the existing solution to determine if it offers any advantages, such as improved speed or accuracy.

πŸ’‘Digital Assistant

A digital assistant is a software program that can perform tasks or services for users through voice commands or text inputs. The video script revolves around the creation of a digital assistant named Shane, inspired by the character J.A.R.V.I.S. from the Iron Man movies. Speech recognition is a vital part of this digital assistant, as it allows it to process and respond to user commands.

πŸ’‘Video Editing

Video editing is the process of assembling and modifying video shots to create a finished video product. In the script, the creator mentions using video editing to make the digital assistant's responses appear faster and more seamless than they are in real-time, enhancing the viewer's experience.

πŸ’‘Streaming

Streaming in the context of the video refers to the real-time processing of audio input, such as from a microphone, by a speech recognition system. The standard Python speech recognition library is noted for its ability to easily stream from a microphone, whereas IBM Watson's API does not have an easy way to do this, as mentioned in the script.

πŸ’‘Threshold

In the context of speech recognition, a threshold is a level or value that determines when the system should start processing audio input. The script describes adjusting the threshold based on ambient noise levels to improve the digital assistant's ability to accurately pick up commands.

πŸ’‘API Key

An API key is a unique code used to authenticate requests to an API (Application Programming Interface). In the video, the creator mentions obtaining an API key when signing up for IBM Watson's Speech to Text service, which is necessary to access and use the API.

πŸ’‘Transcription

Transcription is the process of converting spoken language into written form. The script discusses the potential of IBM Watson's service for transcribing meeting minutes, suggesting that while it may not be ideal for streaming from a microphone, it could be very effective for larger transcription tasks.

πŸ’‘Timeit Library

The timeit library in Python is used to measure the execution time of small bits of code. In the video, the creator uses the timeit library to compare the performance of the standard speech recognition library and IBM Watson's API by timing how long each takes to process and analyze spoken commands.

πŸ’‘Nested Dictionaries

A nested dictionary is a dictionary within a dictionary, allowing for a structured storage of data. In the script, when using IBM Watson's API, the result is a nested dictionary from which the creator extracts the transcript of the spoken words by accessing specific keys and indices.

Highlights

Comparing the performance of the standard speech recognition library and IBM Watson's Speech to Text API.

Exploring alternatives to improve digital assistant response time.

The digital assistant named Shane is inspired by Jarvis from Iron Man movies and comics.

Using the 'time' library in Python to measure the performance of speech recognition.

Testing the speech recognition library with live microphone input.

IBM Watson requires pre-recorded samples for testing due to lack of live streaming support.

The standard speech recognition library is easy to use for streaming from a microphone.

Setting up the recognizer and microphone for speech recognition in Python.

IBM Watson's Speech to Text API may perform better for large file transcriptions.

Installing the IBM Watson Speech to Text library using pip.

Creating an IBM Cloud account to get an API key and service URL for the Speech to Text API.

Using the 'keys.py' file to store and import the IBM API key securely.

Setting up the IBM Watson Speech to Text API with the necessary authentication.

Transcribing a pre-recorded MP3 file using IBM Watson's Speech to Text API.

Accessing the nested dictionary to retrieve the transcribed text from the API response.

Timing the IBM Watson Speech to Text API to compare its performance with the standard library.

IBM Watson offers 500 minutes per month for free for speech to text translation.

Invitation for viewers to suggest better speech recognition sources in the comments.

Encouraging viewers to subscribe for updates on building the digital assistant named Shane.

Casual Browsing

Speech To Text with IBM Watson | Python - codeayan

2024-05-19 19:00:01

Watson Speech to Text - Getting Started with AI using IBM Watson

2024-05-19 22:10:02

text to speech converter/Watson IBM(2022)

2024-05-19 20:10:02

IBM Watson Speech to Text | Artificial intelligence #49

2024-05-19 20:55:02

The ultimate guide to IBM Watson text to speech

2024-05-19 21:25:01