Music Maker

Training Data:

The model was trained on a dataset of ~11,000 piano MIDI files from the "adl-piano-midi" collection.

Model Details:

• Architecture: GPT-style transformer
• Number of layers: 12
• Hidden size: 512
• Attention heads: 8
• Tokenizer vocabulary size: 12,000

Usage:

from transformers import AutoModel
from miditok import MusicTokenizer
import torch

device = 'cuda' if torch.cuda.is_available() else 'cpu'

tokenizer = MusicTokenizer.from_pretrained('shikhr/music_maker')

model = AutoModel.from_pretrained('shikhr/music_maker', trust_remote_code=True)
model.to(device)

# Generate some music
out = model.generate(
    torch.tensor([[1]]).to(device), max_new_tokens=400, temperature=1.0, top_k=None
)

# Save the generated MIDI
tokenizer(out[0].tolist()).dump_midi("generated.mid")

Limitations and Bias:

• The model has only been trained on piano MIDI data, so its ability to generalize to other instruments may be limited.
• The generated music may exhibit some repetitive or unnatural patterns.
• The training data itself may contain certain biases or patterns reflective of its sources.

--- license: mit pipeline_tag: audio-to-audio --- ## MusicMaker - Transformer Model for Music Generation #### Overview: MusicMaker is a transformer-based model trained to generate novel musical compositions in the MIDI format. By learning from a dataset of piano MIDI files, the model can capture the intricate patterns and structures present in music and generate coherent and creative melodies. #### Key Features: - Generation of novel musical compositions in MIDI format - Trained on a dataset of piano MIDI files - Based on transformer architecture for capturing long-range dependencies - Tokenizer trained specifically on MIDI data using miditok library #### Training Data: The model was trained on a dataset of ~11,000 piano MIDI files from the "adl-piano-midi" collection. #### Model Details: - Architecture: GPT-style transformer - Number of layers: 12 - Hidden size: 512 - Attention heads: 8 - Tokenizer vocabulary size: 12,000 #### Usage: ```py from transformers import AutoModel from miditok import MusicTokenizer import torch device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = MusicTokenizer.from_pretrained('shikhr/music_maker') model = AutoModel.from_pretrained('shikhr/music_maker', trust_remote_code=True) model.to(device) # Generate some music out = model.generate( torch.tensor([[1]]).to(device), max_new_tokens=400, temperature=1.0, top_k=None ) # Save the generated MIDI tokenizer(out[0].tolist()).dump_midi("generated.mid") ``` #### Limitations and Bias: - The model has only been trained on piano MIDI data, so its ability to generalize to other instruments may be limited. - The generated music may exhibit some repetitive or unnatural patterns. - The training data itself may contain certain biases or patterns reflective of its sources.