The world of music generation is evolving rapidly with the advent of AI technology. One such remarkable tool is the MusicLang Chord Predictor, a model that allows users to create original chord progressions in an intuitive format. This blog will guide you through the installation and usage of this powerful model, ensuring even beginners can harness its capabilities.

What is MusicLang Chord Predictor?

The MusicLang Chord Predictor is designed for various applications in music creation, including:

• Predicting a chord progression from scratch (with a fixed number of chords).
• Continuing an existing chord progression using a MusicLang prompt.

If you’re primarily interested in generating chord progressions in a readable format, consider checking out the text chord predictor.

Installation

To get started, you’ll need to install the MusicLang Predict package via pip. Simply run:

pip install musiclang-predict

How to Use MusicLang Chord Predictor?

Now that you have everything installed, it’s time to start generating some chord progressions! Here’s how to approach it:

Generating a 4 Chord Progression in Few Lines

Imagine you are a composer reaching for a magic box of musical ideas. You simply need to input your preferences, and the box generates a unique chord sequence. Here’s how you can do it in Python:

from musiclang_predict import predict_chords, MusicLangTokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer
from musiclang.library import *

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("musiclang/musiclang-chord-v2-4k")
tokenizer = AutoTokenizer.from_pretrained("musiclang/musiclang-chord-v2-4k")

# Generate chord progression
soundtrack = predict_chords(model, tokenizer, nb_chords=4, temperature=1.0)

# Give simple voicing and save to midi
soundtrack = soundtrack(b0, b1, b2, b3)
soundtrack.to_midi("song.mid", tempo=120, time_signature=(4, 4))

Using a MusicLang Prompt

Now, suppose you already have a riff in mind, but you need something more to build on it. Using prompts is like providing context to your magic box; it shapes the chord landscape with your existing ideas:

prompt = (I % I.M) + (V % I.M)[6].o(-1)

# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained("musiclang/musiclang-chord-v2-4k")
tokenizer = AutoTokenizer.from_pretrained("musiclang/musiclang-chord-v2-4k")

# Generate chord progression using prompt
soundtrack = predict_chords(model, tokenizer, nb_chords=4, prompt=prompt)

# Give simple voicing and save to midi
soundtrack = soundtrack(b0, b1, b2, b3)
soundtrack.to_midi("song.mid", tempo=120, time_signature=(4, 4))

Troubleshooting

If you encounter any issues while using the MusicLang Chord Predictor, here are some troubleshooting tips:

• Ensure that you have installed all necessary dependencies. Sometimes, missing packages can cause errors.
• Check if the model and tokenizer are correctly loaded. Misnaming can lead to problems with predictions.
• Try adjusting the ‘temperature’ parameter during generation to control the randomness of the output; this might help if the results seem too chaotic.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.