AI Speech to Text for LONG Files in 15 Minutes with Watson STT and Python

Nicholas Renotte
24 Sept 202017:39

TLDRIn this tutorial, viewers learn how to transcribe lengthy audio files into text using IBM Watson's Speech to Text service and Python. The process involves setting up the service with an API key, compressing and splitting large audio files into manageable MP3 chunks, and then transcribing them. The transcriptions are compiled into a single text file, making it easy to skim through lectures or meetings without listening to the entire recording. The video also covers installing necessary dependencies, using Jupyter notebooks, and the importance of file order for coherent transcription.

Takeaways

  • πŸ€– Watson STT and Python are used to transcribe long audio files into text efficiently.
  • πŸ“š The tutorial covers setting up the Watson Speech to Text service for video transcription.
  • πŸ”§ The process involves compressing large files and splitting them into manageable mp3 segments.
  • πŸ’Ύ The audio files are then transcribed into text using Watson's speech to text capabilities.
  • πŸ“ The transcriptions are compiled into a single text file for easy access and use.
  • πŸ‘¨β€πŸ« The video is educational, aimed at showing how to handle long file transcriptions.
  • πŸŽ“ Ideal for students taking notes or professionals transcribing meeting minutes.
  • πŸ”— The code and resources are available on a GitHub repository for easy reference.
  • πŸ“ˆ The use of Jupyter notebooks facilitates the transcription process with Python code.
  • 🎬 The example provided uses an audio extract from one of the creator's previous videos.
  • πŸ“ The final output is a text file that captures the essence of the long audio file.

Q & A

  • What is the main topic of the lecture?

    -The main topic of the lecture is discussing the benefits of neural networks and demonstrating how to transcribe long and large files into text using Watson Speech to Text and Python.

  • What service is used for transcribing videos in this tutorial?

    -Watson Speech to Text service is used for transcribing videos in this tutorial.

  • How are large audio files made manageable for transcription?

    -Large audio files are compressed and split into smaller mp3 files using ffmpeg, making them easier to work with.

  • What programming environment is used in the tutorial?

    -The tutorial uses Jupyter Notebooks for working with Python to transcribe the audio files.

  • What is the purpose of using the IAM Authenticator in the script?

    -The IAM Authenticator is used to authenticate against the Watson Speech to Text service, allowing the user to access and use the service.

  • How can one obtain the API key and URL for the Watson Speech to Text service?

    -The API key and URL can be obtained by creating an instance of the Speech to Text service on IBM Cloud and selecting the appropriate plan and region.

  • What is the significance of choosing the right region for the Speech to Text service?

    -Choosing the right region, preferably the one closest to the user, ensures that data travels less distance, which can result in faster transcription times.

  • How long are the audio files split into during the transcription process?

    -The audio files are split into individual mp3 files that are about 360 seconds long.

  • What is the format of the filenames for the split audio files?

    -The split audio files are formatted with 'zero zero' followed by a number, such as 'zero zero zero zero zero one', 'zero zero two zero zero three', etc.

  • How does the transcription process handle the order of files?

    -A list called 'files' is created and sorted to ensure that the files are transcribed in the correct order, preventing the speech from being jumbled up.

  • What is the final output of the transcription process?

    -The final output is a text file named 'output.txt' that contains the transcription of the entire audio file.

Outlines

00:00

🧠 Introduction to Neural Networks and Long File Transcription

The video begins with an introduction to the benefits of neural networks and the process of transcribing long and large files into text. The speaker explains that they will demonstrate how to use Watson's feature tech service to transcribe videos and convert them into manageable text files. The main focus will be on setting up the service, compressing and splitting audio files, and then transcribing them using Watson Speech to Text. The tools used will be Python and Jupyter notebooks, with ffmpeg for file compression and splitting.

05:02

πŸ”§ Setting Up Watson Speech to Text and File Compression

The speaker proceeds to guide the viewers on setting up the Watson Speech to Text service, which involves selecting a plan, choosing a region, and obtaining an API key and URL. The process of compressing and splitting the audio file into smaller, manageable mp3 files using ffmpeg is also covered. The steps include installing necessary dependencies, authenticating with the service, and preparing the audio files for transcription.

10:03

πŸ”„ Compressing and Splitting Audio Files for Transcription

This section delves into the technical details of using Python's subprocess library and ffmpeg to compress the original audio file into mp3 format and then split it into smaller segments. The speaker explains how to organize these segments in a specific order to ensure accurate transcription and provides code snippets to demonstrate the process.

15:17

πŸ“ Transcribing Audio and Generating a Text File

The final part of the video script describes the transcription process using the Watson Speech to Text service. The speaker details how to loop through each audio file, transcribe it, and store the results. The transcription results are then preprocessed and compiled into a single text file. The video concludes with a summary of the steps taken and an invitation for viewers to share how they plan to use long file transcription.

Mindmap

Keywords

πŸ’‘Neural Networks

Neural networks are a foundational concept in artificial intelligence, inspired by the human brain. They are composed of interconnected nodes that mimic neurons, designed to process complex information through a system of weighted connections. In the video, the benefits of neural networks are discussed, likely referring to their ability to learn from data, recognize patterns, and make predictions or decisions. Neural networks are central to the theme as they are the underlying technology for the speech-to-text service being explored.

πŸ’‘Transcribe

Transcribing refers to the process of converting spoken language into written form. In the context of the video, the term is used to describe the conversion of audio files into text using IBM Watson's Speech to Text service. This is a key process in the video, as it allows for the creation of text files from long audio recordings, which can then be easily reviewed and analyzed.

πŸ’‘Watson Speech to Text

Watson Speech to Text is a cloud-based service provided by IBM that employs advanced machine learning algorithms to convert spoken language into written text. It is a crucial component in the video's demonstration, as the service is used to transcribe lengthy audio files. The video outlines how to set up and use this service, highlighting its utility for various applications such as transcribing meeting minutes or university lectures.

πŸ’‘Jupyter Notebooks

Jupyter Notebooks are open-source web applications that allow users to create and share documents containing live code, equations, visualizations, and narrative text. They are widely used in data science and machine learning. In the video, Jupyter Notebooks are the platform of choice for executing Python code to set up the speech-to-text service, compress and split audio files, and manage the transcription process.

πŸ’‘FFmpeg

FFmpeg is a free and open-source software project that can handle multimedia data, including conversion between different audio and video formats. In the video, FFmpeg is used to compress large audio files into MP3 format and split them into smaller, more manageable chunks. This preprocessing step is essential before the audio files can be transcribed by the Watson Speech to Text service.

πŸ’‘API Key

An API key is a unique identifier used to authenticate a user, developer, or calling program to an API. It is a critical component for accessing the Watson Speech to Text service. The video demonstrates how to obtain an API key by creating an instance of the service on IBM Cloud, which then allows the user to make requests to the transcription service.

πŸ’‘Language Model

A language model in the context of speech recognition is a system that predicts the probability of a sequence of words occurring in a language. The video mentions using the Australian narrowband model, which is tailored for the Australian accent. This choice ensures that the transcription is as accurate as possible, given the specific linguistic nuances.

πŸ’‘Continuous Transcription

Continuous transcription refers to the ongoing process of converting speech into text in real-time or as the audio is being processed. The video discusses setting this feature to ensure that as the audio plays, the transcription service continuously updates the text output, providing a complete and timely transcription of the spoken content.

πŸ’‘Inactivity Timeout

Inactivity timeout is a parameter that specifies how long the transcription service should wait for silence before ending the transcription. It is an important setting when dealing with long audio files, as it can help to manage the length of the transcription and prevent unnecessary processing time after the audio source has stopped.

πŸ’‘Text File Output

The final output of the transcription process is a text file, which contains the transcribed content from the audio files. In the video, the text file is created by compiling the results from each transcribed audio segment. This output format is useful for various purposes, such as reviewing meeting notes, creating summaries, or further analysis.

πŸ’‘Pre-processing

Pre-processing in data analysis refers to the initial steps of transforming raw data into a format suitable for analysis. In the video, pre-processing involves organizing the transcription results into a structured format before writing them to a text file. This step ensures that the final text file is coherent and that the transcriptions from different audio segments are properly aligned and ordered.

Highlights

Today's lecture discusses the benefits of neural networks and how to build them.

The video demonstrates how to transcribe long and large files into text using Watson STT and Python.

Setting up Watson's feature tech service allows transcription of videos.

Large files are compressed into mp3 format for easier handling.

Transcription is done using Watson Speech to Text, outputting to a text file.

The process primarily uses Python and Jupyter notebooks.

Audio files are split using ffmpeg for efficient transcription.

A large audio file is chunked into smaller audio files for transcription.

The transcription process involves installing and importing dependencies like IBM Watson.

An API key and URL are required to set up the Speech to Text service through IBM's Watson.

The Watson Speech to Text service is authenticated using the IAM authenticator.

The audio file is compressed from WAV to MP3 to reduce size.

Individual MP3 files are split to be approximately 360 seconds long for transcription.

Files are ordered correctly to maintain the coherence of the transcription.

The STT.recognize function is used to transcribe each audio file.

Transcription results are stored in an array for further processing.

Pre-processing of results involves extracting the transcript and removing white space.

The final transcription is compiled into a single text file for easy access.

The method allows for efficient review of long audio files without the need to listen to them in full.

The video provides a practical application for transcribing university notes or meeting minutes.

The transcription process is demonstrated with an example using an Australian English language model.

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