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juancopi81
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mutopia_guitar_mmm

Text Generation
Transformers
TensorFlow
gpt2
generated_from_keras_callback
music
Inference Endpoints
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mutopia_guitar_mmm / README.md
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metadata
tags:
  - generated_from_keras_callback
  - music
datasets:
  - juancopi81/mutopia_guitar_dataset
widget:
  - text: >-
      PIECE_START TIME_SIGNATURE=4_4 BPM=90 TRACK_START INST=0 DENSITY=2
      BAR_START NOTE_ON=43
    example_title: Time signature 4/4, BPM=90, NOTE=G2
base_model: gpt2
model-index:
  - name: juancopi81/mutopia_guitar_mmm
    results: []

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