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AI Lyrics Transcription and Correction Program Guide

Welcome to the guide for creating a program that leverages AI to transcribe audio to text, compares the transcription to original lyrics, corrects any discrepancies, and converts the corrected lyrics into the LRC format. This guide is designed for users of all levels, including beginners.

Table of Contents

Introduction

This program is your assistant in creating synchronized lyrics files for music tracks. It will help you transcribe lyrics from audio, align them with the music's timing, and ensure they are accurate when compared to the original lyrics.

Prerequisites

Before we start, you'll need:

  • Python: Make sure Python 3.x is installed on your computer. If not, download it from the Python official website.
  • Text Editor: Any basic text editor will do, but if you want suggestions, Visual Studio Code is a great option.
  • Command Line Basics: Familiarity with basic command line operations will be helpful.

Prepare Your Development Environment

Before diving into the coding part, it's essential to set up a proper development environment. This will be your workspace where you'll write code, test your program, and debug any issues that arise.

  1. Choose a Text Editor or IDE:

    A text editor is where you'll write your code. Choose one that you're comfortable with. If you're not sure where to start, here are a few options:

    • Visual Studio Code: A powerful, free editor that supports Python and many other languages.
    • Sublime Text: A fast and lightweight text editor with a lot of features.
    • Atom: An open-source text editor that's modern, approachable, and hackable to the core.

    Download and install the text editor of your choice.

  2. Install Python:

    If you haven't already, make sure Python is installed on your system. Python 3.x is recommended. You can download it from the official Python website. Follow the installation instructions for your operating system.

  3. Set Up a Project Folder:

    Create a folder on your computer where you'll store all the files related to this project. You can name it something like lyrics_transcription_project. Here's how you can do it on different operating systems:

    • Windows: Right-click in your desired directory, select "New", then "Folder", and name it.

    • macOS/Linux: Use the mkdir command in the terminal, like so:

      mkdir lyrics_transcription_project

  4. Create a Virtual Environment (Optional but Recommended):

    A virtual environment is a self-contained directory that contains a Python installation for a particular version of Python, plus a number of additional packages. This allows you to manage dependencies for your project and ensure that it's isolated from other Python projects. Here's how to create one:

    • Open your command line interface (CLI).
    • Navigate to your project folder: 
      cd path_to_your_project_folder
    • Run the following command to create a virtual environment: 
      python -m venv venv
    • To activate the virtual environment:
      • Windows: 
        venv\Scripts\activate
      • macOS/Linux: 
        source venv/bin/activate

    When the virtual environment is activated, you'll see its name in the command prompt. Now, any Python packages you install while the virtual environment is active will only affect this project.

  5. Install Required Packages:

    With your virtual environment activated, install the necessary packages using pip. For our project, we'll start with Whisper-JAX:

    pip install whisper-jax

Transcription Process

We'll start by transcribing audio files to text:

  1. Transcribe Audio:

    In your text editor, create a new Python file named transcribe.py. Here's a simple script to get you started:

    import whisper

def transcribe_audio(file_path):
    model = whisper.load_model("base")
    result = model.transcribe(file_path)
    print(result["text"])

transcribe_audio('path_to_your_audio_file.mp3')

    Replace 'path_to_your_audio_file.mp3' with the actual path to your audio file.

Detailed Instructions

  1. Extract Timing Data:

    If your transcription includes timing data, extract this to use as your timestamps. If not, you'll need to listen to the audio and create these manually.

  2. Segment the Transcribed Text:

    Break the transcribed text into segments that naturally fit the flow of the audio. Each segment will become one subtitle block.

  3. Assign Sequential Numbers:

    Each subtitle block starts with a number that increases sequentially. This number is used by video players to keep the subtitles in order.

  4. Format and Write to File:

    With all the components ready, format each subtitle block according to the SRT standard and write them to the .srt file.

Now, let's compare the AI-generated transcription with the original lyrics:

Lyrics Comparison and Correction

Once you have your AI-generated transcription, the next crucial step is to compare it with the original lyrics. This comparison is essential to ensure that the transcription matches the song's actual words and to correct any errors that the AI may have made during the transcription process.

Understanding the Need for Comparison

AI transcription is powerful, but it's not perfect. It can misinterpret words, especially when dealing with songs that have complex vocabulary, fast delivery, or background noise. Comparing the AI-generated transcription with the original lyrics allows you to ensure the accuracy of your subtitles.

Steps for Lyrics Comparison

  1. Prepare the Original Lyrics:

    Have the original lyrics ready in a text format. It's best if these are broken down line by line to correspond with the lines in your transcription.

  2. Align the Transcription with the Lyrics:

    The AI-generated transcription needs to be segmented in a way that aligns with the structure of the original lyrics. This typically means having one line of transcription for each line of lyrics.

  3. Comparison Logic:

    Develop logic that compares each line of the AI-generated transcription with the corresponding line of the original lyrics. The goal is to identify and highlight any words or phrases that don't match.

  4. Correction:

    Once discrepancies are identified, manually review and correct them to ensure that the final product is an accurate representation of the song's lyrics.

Detailed Instructions

Comparison Logic

The comparison logic is a critical component of the lyrics transcription program. Its purpose is to identify and highlight any discrepancies between the AI-generated transcription and the original lyrics. This step is vital for ensuring the accuracy of the final output.

Purpose of Comparison Logic

The AI transcription process, while sophisticated, may not always be perfect, especially when dealing with complex audio inputs. Comparison logic allows us to:

  • Detect errors or misinterpretations made by the AI.
  • Ensure the transcribed text is faithful to the original lyrics.
  • Maintain the integrity and quality of the lyrics for the end-users.

Implementing Comparison Logic

To implement comparison logic, you will add a new function to your transcribe.py script. This function will:

  1. Read both the original lyrics and the transcribed text.
  2. Compare them line by line to find any differences.
  3. Output the discrepancies in a way that makes it easy to identify and correct them.

Step-by-Step Guide

  1. Read the Original Lyrics:

    Load the original lyrics from a file or any other source into a list or array where each element represents a line of lyrics.

  2. Read the Transcribed Text:

    Similarly, load the transcribed text into a list or array, ensuring that each line corresponds to a line from the original lyrics.

  3. Create the Comparison Function:

    Define a function in transcribe.py that takes both lists as input and iterates through them, comparing each line.

  4. Highlight Discrepancies:

    For each line that doesn't match, the function should highlight the discrepancy. This could be done by printing both lines to the console with a clear indication that there is a difference.

  5. Output the Results:

    Decide how you want to output the results. This could be a simple print to the console, or you could write the discrepancies to a file for further review.

Handling Original Lyrics

Maintaining a clean and accurate set of original lyrics is crucial for the comparison and correction process. This section will guide you through preparing and storing the original lyrics, which will serve as the reference for correcting the AI-generated transcription.

Preparing Original Lyrics

Before you begin the comparison process, ensure that you have the original lyrics in a format that is conducive to line-by-line comparison with the transcribed text.

  1. Text Format: The original lyrics should be in a plain text format, with each line of lyrics on a new line.

  2. No Timestamps: Unlike the transcribed text, the original lyrics should not contain any timestamps or other metadata. This simplifies the comparison process.

  3. Accuracy Check: Verify the accuracy of the original lyrics. This may involve cross-referencing with official sources or lyric sheets provided by the artist or publisher.

  4. Consistent Formatting: Ensure that the formatting of the original lyrics matches the formatting of the transcribed text. For example, if the transcription does not include punctuation, consider removing punctuation from the original lyrics for a more accurate comparison.

Storing Original Lyrics

Once you have prepared the original lyrics, you need to store them in a way that they can be easily accessed and compared with the transcribed text.

  1. File Storage: Save the original lyrics in a text file, with a clear and consistent naming convention. For example, original_lyrics.txt.

  2. Version Control: If you are working with multiple versions of a song, use version control to keep track of changes and ensure you are always working with the correct set of lyrics.

  3. Backup: Keep backups of your original lyrics files to prevent data loss.

Using Original Lyrics for Comparison

With the original lyrics prepared and stored, you can now use them as the basis for comparison with the AI-generated transcription.

  1. Read Original Lyrics: Write a function in your script to read the original lyrics from the file into a list or array, with each line of the song as a separate element.

  2. Comparison: Use the comparison logic outlined in previous sections to compare the original lyrics with the transcribed text.

  3. Correction: Any discrepancies found during the comparison will be corrected against this authoritative list of original lyrics.

Example Python Function for Reading Original Lyrics

def read_original_lyrics(file_path):
    with open(file_path, 'r') as file:
        original_lyrics = [line.strip() for line in file if line.strip()]
    return original_lyrics

# Example usage:
original_lyrics = read_original_lyrics('path/to/original_lyrics.txt')

Example Python Function for Comparison

Here's an example Python function that you can add to your transcribe.py script to perform the comparison:

def compare_lyrics(original_lyrics, transcribed_lyrics):
    for line_number, (original, transcribed) in enumerate(zip(original_lyrics, transcribed_lyrics), start=1):
        if original.strip() != transcribed.strip():
            print(f"Discrepancy found on line {line_number}:")
            print(f"Original: {original}")
            print(f"Transcribed: {transcribed}")
            print("\n")

# Example usage:
original_lyrics = ["Hello darkness, my old friend", "I've come to talk with you again"]
transcribed_lyrics = ["Hello darkness, my old friend", "I've come to walk with you again"]  # Notice the mistake in the second line

compare_lyrics(original_lyrics, transcribed_lyrics)

Steps for LRC Conversion

  1. Prepare Corrected Lyrics: Ensure that the corrected lyrics are paired with their corresponding timestamps.

  2. LRC Timecode Formatting: Convert the timestamps into the LRC format, which typically involves converting the hours:minutes:seconds,milliseconds format into [minutes:seconds.xx], where xx represents hundredths of a second.

  3. Write LRC File: Combine the timecodes and lyrics into the LRC format and write them to a .lrc file.

Writing the LRC Conversion Function

The LRC conversion function will take the corrected lyrics with timestamps and format them into the LRC standard.

  1. Define the Function: Create a function named convert_to_lrc that accepts the corrected lyrics with timestamps.

  2. Format Each Line: For each line of lyrics, format the timestamp according to the LRC standard and append it to the lyric line.

  3. Output the LRC File: Write the formatted lines to a .lrc file, ensuring each line is properly encoded and structured.

Example Python Function for LRC Conversion

Here's an example Python function that demonstrates how to convert corrected lyrics with timestamps into the LRC format:

def convert_to_lrc(corrected_lyrics_with_timestamps, output_file):
    with open(output_file, 'w', encoding='utf-8') as file:
        for timestamp, lyric in corrected_lyrics_with_timestamps:
            # Convert SRT timestamp to LRC timestamp
            hours, minutes, seconds = timestamp.split(':')
            seconds, milliseconds = seconds.split(',')
            lrc_timestamp = f"[{int(hours) * 60 + int(minutes)}:{seconds}.{int(milliseconds)//10}]"
            
            # Write the LRC-formatted line to the file
            file.write(f"{lrc_timestamp} {lyric}\n")

# Example usage:
corrected_lyrics_with_timestamps = [
    ("00:00:01,000", "Hello darkness, my old friend"),
    ("00:00:04,000", "I've come to talk with you again")
]

convert_to_lrc(corrected_lyrics_with_timestamps, 'song.lrc')

Testing

Thorough testing is a critical phase in the development of your lyrics transcription program. It ensures that your application performs accurately and reliably under various conditions.

Run Tests

  1. Transcription Accuracy:

    • Test the transcription feature with audio files of varying quality, genres, and languages to ensure the transcription is as accurate as possible.
    • Include audio files with different background noise levels to evaluate the robustness of the noise cancellation features.

  2. Synchronization:

    • Verify that the timestamps in the transcribed lyrics match the corresponding segments in the audio files.
    • Test with songs of different tempos and rhythms to ensure the synchronization works well across different beat patterns.

  3. Correction Efficiency:

    • Assess how well the program corrects misheard words by comparing the output with a set of pre-verified lyrics.
    • Measure the time taken to process and correct lyrics to ensure the program operates efficiently.

  4. LRC Format Compatibility:

    • Check the compatibility of the LRC files with various media players to confirm that the timestamps trigger the correct lyric display.
    • Validate the encoding and formatting of the LRC files to prevent any display issues.

Automated Testing

Consider implementing automated tests that can run through a batch of audio files and report on the accuracy and efficiency of each feature. Automated tests help in identifying issues early and streamline the testing process.

Deployment

Once you are satisfied with the testing phase, the next step is to deploy your program for users to access and use.

Deployment Options

  1. Web-Based Application:

    • If you opt for a web-based application, set up a server and domain, and ensure your application is secure and scalable.
    • Consider using cloud services for hosting, which can provide scalability and ease of maintenance.

  2. Standalone Program:

    • For a standalone application, package your program for the target operating systems (Windows, macOS, Linux).
    • Ensure that all dependencies are included in the package or are easily installable.

  3. Cross-Platform Compatibility:

    • Test the application on different platforms to ensure consistent functionality.
    • Address any platform-specific bugs or issues that may arise during testing.

User Experience

Focus on creating a seamless installation process and a user-friendly interface. The easier it is to get started with your program, the more likely users are to have a positive experience.

Support and Troubleshooting

Providing support and troubleshooting resources is essential for user retention and satisfaction.

Documentation

  1. User Manual:

    • Create a comprehensive user manual that guides users through each feature of the program.
    • Include screenshots and step-by-step instructions to help users navigate the application.

  2. FAQs and Troubleshooting Guide:

    • Compile a list of frequently asked questions and common issues that users may encounter.
    • Provide clear and concise solutions or workarounds for these issues.

  3. Contact Information:

    • Provide contact information for further support, whether it's an email address, a support ticket system, or a phone number.

Feedback Loop

Establish a feedback loop that allows users to report bugs, request features, or offer suggestions. This can be done through:

  • An integrated feedback form within the application.
  • A community forum where users can discuss and help each other.
  • Regular surveys to gather user opinions and experiences.

Updates and Maintenance

  • Plan for regular updates to the program to fix bugs, improve features, and add new functionality.
  • Keep the user informed about upcoming updates and what changes they can expect.

By following these expanded guidelines for Testing, Deployment, and Support, you will enhance the reliability, accessibility, and user-friendliness of your lyrics transcription program.

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