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