Converting Speech to Text in 10 Minutes with Python and Watson

Nicholas Renotte
7 Aug 202010:00

TLDRThis tutorial demonstrates how to convert speech to text using Python and IBM Watson in just 15 lines of code. It covers setting up the speech-to-text service, converting an MP3 file to text, and refining language models for better accuracy. The process involves using Jupyter Notebook, installing the IBM Watson SDK, and authenticating with the service. The tutorial also shows how to change language models to improve the conversion's accuracy, as demonstrated by switching from a US to an Australian model.

Takeaways

  • πŸ˜€ The tutorial demonstrates how to convert speech to text using Python and IBM Watson.
  • πŸ” The presenter used a speech-to-text converter to perfect their pitch for presentations.
  • πŸ“š The process is applicable for various purposes, such as transcribing study notes or meeting minutes.
  • πŸš€ The entire setup requires only 15 lines of code, making it a quick and efficient solution.
  • πŸ’» The tutorial is conducted within a Jupyter notebook, using Python for the conversion process.
  • πŸ“ˆ The audio file used in the example is an MP3, which will be converted to text.
  • πŸ”‘ To use IBM Watson's speech-to-text service, an API key and URL are needed, which can be obtained from IBM Cloud.
  • πŸ“ The IBM Watson SDK is the only dependency required for the project, facilitating the speech-to-text conversion.
  • 🌐 The speech is converted and the result is stored in a Python dictionary, ready for further use.
  • πŸ“ The final step is to export the converted text to a text file for additional applications or deployment.
  • πŸ—£οΈ The tutorial also covers how to refine language models to better suit specific languages or accents, enhancing accuracy.

Q & A

  • What is the main purpose of using a speech-to-text converter as described in the video?

    -The main purpose of using a speech-to-text converter is to transcribe spoken words into written text, which can help in refining pitches, converting study notes into text-based notes, or recording meeting minutes, thereby speeding up processes.

  • How many lines of code does it take to set up the speech-to-text conversion according to the video?

    -According to the video, it only takes 15 lines of code to set up the speech-to-text conversion.

  • What are the three key things covered in the video to convert speech to text?

    -The three key things covered in the video are setting up a speech-to-text service using IBM Watson, understanding the basics of converting audio to text, and refining language models to use language models specific to your language or accent.

  • Which programming environment and language are used in the video to demonstrate speech-to-text conversion?

    -The video uses Jupyter Notebook and Python to demonstrate the speech-to-text conversion process.

  • What file format is used for the audio file in the example provided in the video?

    -The example provided in the video uses an MP3 file format for the audio file.

  • How can one access the Watson Speech to Text service as mentioned in the video?

    -To access the Watson Speech to Text service, one needs to go to cloud.ibm.com/catalog, find the Speech to Text service, select it, and choose the free tier to get started.

  • What is the advantage of using the free tier of the Watson Speech to Text service for beginners?

    -The advantage of using the free tier for beginners is that it provides enough capacity to convert up to 500 minutes of speech per month, which is suitable for those just starting out.

  • What is the role of the IAM Authenticator in the speech-to-text conversion process?

    -The IAM Authenticator is used to authenticate against the speech-to-text API, ensuring that the user has the correct permissions to access and use the service.

  • How can the accuracy of the speech-to-text conversion be improved in the video?

    -The accuracy of the speech-to-text conversion can be improved by using the appropriate language model that matches the speaker's accent or language, such as switching from the US narrowband model to the Australian narrowband model.

  • What is the final step shown in the video after converting the speech to text?

    -The final step shown in the video is exporting the converted text to a text file, which can then be used in other applications or for further processing.

  • How can the confidence result of the speech-to-text conversion be accessed according to the video?

    -The confidence result of the speech-to-text conversion can be accessed by traversing the response from the conversion and changing the last key in the data structure to 'confidence'.

Outlines

00:00

πŸŽ™οΈ Speech to Text Conversion with IBM Watson

The video begins with the creator discussing their experience with numerous presentations and the desire to perfect their pitch. They introduce a speech-to-text converter tool to transcribe spoken words, which is useful for various applications like study notes or meeting minutes. The video outlines a method using only 15 lines of code to convert speech to text with the help of IBM Watson's speech-to-text service. The process involves setting up the service, converting audio to text, and refining language models to suit specific languages or accents. The tutorial is conducted within a Jupyter notebook using Python, with an mp3 file as the audio input. The steps include installing the IBM Watson SDK, setting up the speech-to-text service with an API key and URL, reading the audio file, converting it to text, and exporting the results to a text file.

05:01

πŸ”„ Converting Speech to Text and Refining Language Models

The second paragraph delves into the specifics of setting up the speech-to-text service on IBM Cloud, obtaining the API key and URL, and initializing the service with these credentials. The process of converting the audio file 'untitled.mp3' containing the phrase 'hello world' is demonstrated. Initially, the conversion might not be accurate due to the use of a U.S. narrowband model, which may not cater well to an Australian accent. The video then shows how to improve accuracy by switching to an Australian language model. The conversion results, including the transcript and confidence interval, are extracted and can be exported to a text file for further use. The tutorial concludes with a reminder of the steps taken and an invitation for viewers to engage with the content by liking, subscribing, and commenting with their questions or suggestions for using the speech-to-text service.

Mindmap

Keywords

πŸ’‘Speech to Text Converter

A speech to text converter is a software tool that transcribes spoken language into written text. In the context of the video, the converter is used to improve the presenter's pitch by creating a transcript of their spoken words. This allows for better analysis and refinement of the presentation content.

πŸ’‘IBM Watson

IBM Watson is a suite of artificial intelligence technologies developed by IBM. In the video, IBM Watson's speech to text service is highlighted as the tool used for converting audio files into written text. This service is crucial for the process demonstrated in the tutorial.

πŸ’‘Jupyter Notebook

A Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. The video script mentions using a Jupyter Notebook for interactively coding and converting speech to text.

πŸ’‘API Key

An API key is a unique code transmitted along with the request to an API to identify the requester and control access to the service. In the script, obtaining an API key from IBM's Watson Speech to Text service is a necessary step to authenticate and use the speech to text functionality.

πŸ’‘Language Models

Language models in the context of speech to text conversion refer to the algorithms and data sets used to predict the probability of a sequence of words appearing in a given context. The video explains how to refine these models to better suit specific languages or accents, enhancing the accuracy of the conversion.

πŸ’‘MP3

MP3 is a popular audio file format for storing digital audio. The script uses an MP3 file named 'untitled.mp3' as an example to demonstrate the process of converting audio speech into text using the speech to text service.

πŸ’‘Transcript

A transcript is a written version of spoken language, typically used for reference or study. In the video, the goal is to output a transcript of the audio file to ensure the pitch and content of the presentation are accurate and effective.

πŸ’‘Narrowband Model

A narrowband model in speech recognition refers to a type of language model that is optimized for voice frequencies typical of telephone lines. The video script mentions using a U.S. narrowband model and then switching to an Australian one to improve the accuracy of speech recognition.

πŸ’‘Confidence Interval

In the context of speech to text conversion, a confidence interval represents the level of certainty the system has in its transcription. The script describes how to extract this metric from the conversion results to gauge the reliability of the transcribed text.

πŸ’‘Output File

An output file is the result of a process, in this case, the transcribed text from the speech to text conversion. The video script details how to export the transcribed text into a text file named 'output.txt' for further use or analysis.

Highlights

This week, the presenter found themselves doing numerous presentations and sought to refine their pitch using a speech to text converter.

A speech to text service is introduced to convert audio into text for various applications such as study notes or meeting minutes.

The process will only require 15 lines of code to convert speech to text using IBM Watson's speech to text service.

The presenter will demonstrate setting up the speech to text service using IBM Watson and converting an MP3 file to text.

The tutorial will also cover how to refine language models to better suit specific languages or accents.

The process will be conducted within Jupyter, using Python to read in an audio file and convert it to text.

The converted text will be stored in a Python dictionary and then exported to a text file for further use.

An MP3 file named 'untitled.mp3' containing the phrase 'hello world' will be used for the demonstration.

IBM Watson's speech to text service is accessed via cloud.ibm.com/catalog to set up an account and obtain an API key and URL.

The presenter guides on selecting the free tier for the Watson speech to text service, suitable for up to 500 minutes of speech per month.

The IBM Watson SDK is installed and imported to facilitate the speech to text conversion process.

The API key and URL obtained from IBM Cloud are used to authenticate and set up the speech to text service.

The audio file 'untitled.mp3' is opened and converted to text using the speech to text service.

The initial conversion may not be accurate due to the use of the U.S. narrowband model instead of an Australian model.

The conversion result can be accessed from the response and the confidence interval can also be extracted.

The converted text and confidence results can be exported to a text file for further use.

The language model can be changed to the Australian narrowband model for more accurate conversions.

The tutorial concludes with a successful demonstration of converting speech to text using the correct language model.

The presenter encourages viewers to like, subscribe, and turn on notifications for future videos.

Questions and comments are welcomed in the comments section for further interaction and assistance.

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