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mutopia_guitar_mmm

juancopi81 / mutopia_guitar_mmm

Run on huggingface.co
huggingface.co
Total runs: 49
24-hour runs: 0
7-day runs: 18
30-day runs: 25
Model's Last Updated: September 15 2023
text-generation

Introduction of mutopia_guitar_mmm

Model Details of mutopia_guitar_mmm

juancopi81/mutopia_guitar_mmm

Music generation could be approached similarly to language generation. There are many ways to represent music as text and then use a language model to create a model capable of music generation. For encoding MIDI files as text, I am using the excellent implementation of Dr. Tristan Beheren of the paper: MMM: Exploring Conditional Multi-Track Music Generation with the Transformer .

This model is a fine-tuned version of gpt2 on the Mutopia Guitar Dataset . Use the widget to generate your piece, and then use this notebook to listen to the results (work in progress). I created the notebook as an adaptation of the one created by Dr. Tristan Behrens .

It achieves the following results on the evaluation set:

  • Train Loss: 0.5365
  • Validation Loss: 1.5482
Model description

The model is GPT-2 loaded with the GPT2LMHeadModel architecture from Hugging Face. The context size is 256, and the vocabulary size is 588. The model uses a WhitespaceSplit pre-tokenizer. The tokenizer is also in the Hugging Face hub.

Intended uses & limitations

I built this model to learn more about how to use Hugging Face. I am implementing some of the parts of the Hugging Face course with a project that I find interesting. The main intention of this model is educational. I am creating a series of notebooks where I show every step of the process:

  • Collecting the data
  • Pre-processing the data
  • Training a tokenizer from scratch
  • Fine-tuning a GPT-2 model
  • Building a Gradio app for the model

I trained the model using the free version of Colab with a small dataset. Right now, it is heavily overfitting. My idea is to have a more extensive dataset of Guitar Music from Latinoamerica to train a new model similar to the Mutopia Guitar Model, using more GPU resources.

Training and evaluation data

I am training the model with Mutopia Guitar Dataset . This dataset consists of the soloist guitar pieces of the Mutopia Project . The dataset mainly contains guitar music from western classical composers, such as Sor, Aguado, Carcassi, and Giuliani.

For the first epochs of training, I transposed the notes by raising and lowering the pitches using the twelve semi-tones of an entire octave. Later, I trained the model without transposing the pieces so that generation shows better results of a real guitar piece.

Training hyperparameters
Click to expand

The following hyperparameters were used during training (with transposition):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 5726, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition - first round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, ' passive_serialization ': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition - second round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, ' passive_serialization ': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition, new tokenizer - third round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition, new tokenizer - fourth round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition, new tokenizer - fifth round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, ' passive_serialization ': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition, new tokenizer - sixth round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-07, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-07, 'decay_steps': 350, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

The following hyperparameters were used during training (without transposition, new tokenizer - seventh round):

  • optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0005, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 1025, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'passive_serialization': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}

  • training_precision: mixed_float16

Training results
Click to expand Using transposition: | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0705 | 1.3590 | 0 | | 0.8889 | 1.3702 | 1 | | 0.7588 | 1.3974 | 2 | | 0.7294 | 1.4813 | 3 | | 0.6263 | 1.5263 | 4 | | 0.5841 | 1.5263 | 5 | | 0.5844 | 1.5263 | 6 | | 0.5837 | 1.5346 | 7 | | 0.5798 | 1.5411 | 8 | | 0.5773 | 1.5440 | 9 |

Without transposition (first round):

Train Loss Validation Loss Epoch
0.5503 1.5436 0
0.5503 1.5425 1
0.5476 1.5425 2
0.5467 1.5425 3
0.5447 1.5431 4
0.5418 1.5447 5
0.5418 1.5451 6
0.5401 1.5472 7
0.5386 1.5479 8
0.5365 1.5482 9

Without transposition (second round):

Train Loss Validation Loss Epoch
0.5368 1.5482 0
0.5355 1.5480 1
0.5326 1.5488 2
0.5363 1.5493 3
0.5346 1.5488 4
0.5329 1.5502 5
0.5329 1.5514 6
0.5308 1.5514 7
0.5292 1.5536 8
0.5272 1.5543 9

Without transposition (third round - new tokenizer):

Train Loss Validation Loss Epoch
6.1361 6.4569 0
5.6383 5.8249 1
4.9125 4.8956 2
4.2013 4.2778 3
3.8665 4.0330 4
3.7106 3.8956 5
3.6041 3.7995 6
3.5301 3.7485 7
3.4973 3.7323 8
3.4909 3.7323 9

Without transposition (fourth round - new tokenizer):

Train Loss Validation Loss Epoch
3.4879 3.7206 0
3.4667 3.6874 1
3.4229 3.6373 2
3.3680 3.5751 3
3.2998 3.5026 4
3.2208 3.4240 5
3.1385 3.3397 6
3.0580 3.2587 7
2.9949 3.2118 8
2.9646 3.1958 9

Without transposition (fifth round - new tokenizer):

Train Loss Validation Loss Epoch
2.9562 3.1902 0
2.9457 3.1751 1
2.9266 3.1512 2
2.9039 3.1176 3
2.8705 3.0775 4
2.8291 3.0295 5
2.7872 2.9811 6
2.7394 2.9321 7
2.6996 2.9023 8
2.6819 2.8927 9

Without transposition (sixth round - new tokenizer):

Train Loss Validation Loss Epoch
2.6769 2.8894 0
2.6719 2.8791 1
2.6612 2.8638 2
2.6465 2.8439 3
2.6242 2.8174 4
2.6006 2.7877 5
2.5679 2.7554 6
2.5387 2.7223 7
2.5115 2.7029 8
2.5011 2.6970 9

Without transposition (seventh round - new tokenizer):

Train Loss Validation Loss Epoch
2.2881 2.2059 0
1.7702 1.8533 1
1.4625 1.6948 2
1.2876 1.6865 3
1.1926 1.6414 4
1.1329 1.6360 5
1.1069 1.6448 6
1.0408 1.6207 7
0.8939 1.5837 8
0.7265 1.5901 9
0.5902 1.6261 10
0.4489 1.7007 11
0.3223 1.7940 12
0.2158 1.9032 13
0.1448 1.9892 14
Framework versions
  • Transformers 4.22.1
  • TensorFlow 2.8.2
  • Datasets 2.5.1
  • Tokenizers 0.12.1

Runs of juancopi81 mutopia_guitar_mmm on huggingface.co

49
Total runs
0
24-hour runs
11
3-day runs
18
7-day runs
25
30-day runs

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Url of mutopia_guitar_mmm

mutopia_guitar_mmm huggingface.co Url

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Total runs: 12
Run Growth: -1
Growth Rate: -10.00%
Updated:March 22 2023