The Future of Music Production
Using AI to automate music production workflow, and AI’s impact on the future of the music industry
The music industry is always changing, and one of the biggest changes in recent years has been the rise of artificial intelligence (AI). AI is being used in a variety of ways to automate tasks that were once done manually, and the music production process is no exception.
In this blog post, we’ll discuss how AI can be used to automate the following tasks:
- Transcribing audio to text
- Generating engaging text
- Creating music artwork
I will also provide a brief demonstration of how to use three different AI models to achieve these objectives.
Transcribing Audio to Text
One of the most time-consuming tasks in music production is transcribing audio to text. This involves listening to a recording and then manually typing out the lyrics. AI can automate this process by using speech recognition technology.
There are a number of different speech-to-text models available, one of the popular models is Whisper. Whisper is an Open Source, automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. By being trained on such a variety of data, Whisper can accurately transcribe a wide range of accents and dialects. Whisper is a powerful ASR system especially for the music industry because it is can differentiate between background noise and words, which allows it to accurately transcribe noisy, long-form audio.
Generating Engaging Text
Adding personalized details to music is a great way to connect with listeners on a deeper level and build a stronger relationship with them. This involves writing text that is interesting and attention-grabbing. AI can automate this process by using natural language generation technology.
There are a number of different natural language generation models available. One of the popular models is LLAMA. LLAMA is a Large Language Model (LLM) that can be used to generate personalized details for a given song. This can make a song more relatable, engaging, unique, and fun for listeners. For example, it can be used to generate a personalized verse about the listener’s name, favorite hobbies, or recent experiences. This can help artists create songs that are truly one-of-a-kind and that connect with listeners on a deeper level.
Creating Music artwork
Another tedious task in music production is creating music cover artwork. Music artwork is a visual representation of music, it should be eye-catching enough that music listeners are eager to listen to the music. The process of creating music/album covers involves listening to a recording and designing a cover image. AI can automate this process by using image generation technology.
There are a number of different image generation models available. One of the popular models is Stable Diffusion. Stable Diffusion is a powerful AI art generator that can create unique music artwork that is visually appealing and consistent with your music style. It is easy to use and can generate high-quality images in a relatively short amount of time.
Use Case Overview
The objective of the example below is to build a quick demo to automate the music production process explained above using AI models. The three AI models used in this demo are Whisper , Stable diffusion, and LLAMA.
Flow
Prerequisite:
You should have access to an AWS account with the appropriate permissions, and have a basic understanding of Sagemaker, Jumpstart models and IAM Role.
Create Amazon SageMaker domain:
- Login to the AWS Web console.
- Navigate to Amazon SageMaker
- Create a Amazon SageMaker domain (it may take few minutes)
- Login to domain
Deploy Models:
- Once you are in SageMaker Studio, search for each of the models: Whisper ,LLAMA, and Stable diffusion
- Review the license’s terms and conditions
- Deploy the model
- Note down the endpoints
Deploy a sample WebApp
Once the models are deployed, you can use them to build a variety of applications. In this scenario, I will be running a streamlit app to build an app. It will allow the user to upload any music audio (mp3, mp4 or wav form). The app will transcribe the audio to text/lyrics . The same text will be used to generate artwork and description.
The source code is in this git page.
AI can also be used in the following ways:
- To check for profanity or hate language in text or speech.
- To generate different versions of a song, such as a karaoke version or a version with different lyrics.
- To personalize music for individual listeners by recommending songs that they are likely to enjoy based on their listening history.
If you have any questions or suggestions for future posts, please feel free to contact me.
