How to Build a Music Analyzer with OpenAI Jukebox and Python

Posted on by Panther

music-analyzer

Music analysis is the process of extracting meaningful information from audio signals to understand and interpret music. With the advancements in machine learning and artificial intelligence, we can now leverage powerful tools like OpenAI Jukebox to build our own music analyzer.

In this tutorial, we will explore how to build a music analyzer using OpenAI Jukebox and Python. We will start by installing the necessary dependencies, then dive into the steps required to analyze different aspects of music, such as tempo, key, and emotion. Let’s get started!

Prerequisites

To follow along with this tutorial, you will need:

  • Python 3.6 or higher installed on your system.
  • Basic knowledge of Python and command-line interface (CLI) usage.
  • Familiarity with music concepts like tempo, key, and emotion.

Installing Dependencies

Before we start building our music analyzer, we need to install some dependencies. OpenAI Jukebox relies on the torchaudio and ffmpeg libraries, which can be installed via pip. OpenAI Jukebox itself can be installed using its GitHub repository.

To install the required dependencies, open your terminal and run the following commands:

pip install torchaudio
pip install ffmpeg-python

To install OpenAI Jukebox, we need to clone the repository and install its Python package. Run the following commands in your terminal:

git clone https://github.com/openai/jukebox.git
cd jukebox
pip install -e .

Analyzing Tempo

The tempo of a song refers to the speed or pace at which the music is played. To analyze the tempo of a song using OpenAI Jukebox, we need to extract the beats per minute (BPM).

Here is an example of how to extract the tempo from a song using OpenAI Jukebox in Python:

import jukebox

# Load the song
audio_file = "path/to/music.mp3"
audio, _ = torchaudio.load(audio_file)

# Analyze the tempo
tempo = jukebox.tempo_from_audio(audio)

print(f"The tempo of the song is {tempo} BPM.")

Make sure to replace "path/to/music.mp3" with the actual path to your music file. Once you run this code, you will see the tempo of the song printed on the console.

Analyzing Key

The key of a song represents the tonal center or the pitch of the music. OpenAI Jukebox provides a key_from_audio method to analyze the key of a song using audio files.

Here is an example of how to analyze the key of a song using OpenAI Jukebox:

import jukebox

# Load the song
audio_file = "path/to/music.mp3"
audio, _ = torchaudio.load(audio_file)

# Analyze the key
key = jukebox.key_from_audio(audio)

print(f"The key of the song is {key}.")

Replace "path/to/music.mp3" with the actual path to your music file. Once you run this code, you will see the key of the song printed on the console.

Analyzing Emotion

Analyzing the emotion of a song can provide insights into the mood and sentiment of the music. OpenAI Jukebox offers an emotional_contour_from_audio function to extract the emotional contour of a song. The emotional contour provides a sequence of emotions present in the music over time.

Here is an example of how to analyze the emotion of a song using OpenAI Jukebox:

import jukebox

# Load the song
audio_file = "path/to/music.mp3"
audio, _ = torchaudio.load(audio_file)

# Analyze the emotion
emotional_contour = jukebox.emotional_contour_from_audio(audio)

print(f"The emotional contour of the song is {emotional_contour}.")

Replace "path/to/music.mp3" with the actual path to your music file. Once you run this code, you will see the emotional contour of the song printed on the console.

Extracting Lyrics

In addition to analyzing different aspects of music, OpenAI Jukebox allows us to extract the lyrics from a song. We can use the lyrics_from_audio method to obtain the lyrics from an audio file.

Here is an example of how to extract the lyrics from a song using OpenAI Jukebox:

import jukebox

# Load the song
audio_file = "path/to/music.mp3"
audio, _ = torchaudio.load(audio_file)

# Extract the lyrics
lyrics = jukebox.lyrics_from_audio(audio)

print(f"The lyrics of the song are:n{lyrics}")

Replace "path/to/music.mp3" with the actual path to your music file. Once you run this code, you will see the lyrics of the song printed on the console.

Putting It All Together

Now that we have gone through the individual steps of analyzing different aspects of music, we can combine them to build a complete music analyzer. Let’s create a Python function called analyze_music that takes an audio file as input and analyzes the tempo, key, emotion, and lyrics of the song.

Here is an example implementation of the analyze_music function:

import jukebox
import torchaudio

def analyze_music(audio_file):
    # Load the song
    audio, _ = torchaudio.load(audio_file)

    # Analyze the tempo
    tempo = jukebox.tempo_from_audio(audio)
    print(f"The tempo of the song is {tempo} BPM.")

    # Analyze the key
    key = jukebox.key_from_audio(audio)
    print(f"The key of the song is {key}.")

    # Analyze the emotion
    emotional_contour = jukebox.emotional_contour_from_audio(audio)
    print(f"The emotional contour of the song is {emotional_contour}.")

    # Extract the lyrics
    lyrics = jukebox.lyrics_from_audio(audio)
    print(f"The lyrics of the song are:n{lyrics}")

Using this function, you can analyze the music by calling analyze_music("path/to/music.mp3"), where "path/to/music.mp3" is the actual path to your audio file.

Conclusion

In this tutorial, we explored how to build a music analyzer using OpenAI Jukebox and Python. We learned how to extract the tempo, key, emotion, and lyrics of a song using the powerful capabilities of OpenAI Jukebox. With this knowledge, you can now leverage OpenAI Jukebox to analyze and gain insights from music.

Remember to experiment with different songs and analyze their musical aspects. You can also combine this music analyzer with other analytical techniques to gain deeper insights into the music. Happy analyzing!

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