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--- |
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tags: |
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- generated_from_keras_callback |
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- music |
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model-index: |
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- name: juancopi81/mutopia_guitar_mmm |
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results: [] |
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datasets: |
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- juancopi81/mutopia_guitar_dataset |
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widget: |
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- text: "PIECE_START TIME_SIGNATURE=4_4 BPM=90 TRACK_START INST=0 DENSITY=2 BAR_START NOTE_ON=43" |
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example_title: "Time signature 4/4, BPM=90, NOTE=G2" |
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--- |
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# juancopi81/mutopia_guitar_mmm |
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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](https://github.com/AI-Guru/MMM-JSB) of Dr. Tristan Beheren of the paper: [MMM: Exploring Conditional Multi-Track Music Generation with the Transformer](https://arxiv.org/abs/2008.06048). |
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This model is a fine-tuned version of [gpt2](https://huggingface.co./gpt2) on the [Mutopia Guitar Dataset](https://huggingface.co./datasets/juancopi81/mutopia_guitar_dataset). Use the widget to generate your piece, and then use [this notebook](https://colab.research.google.com/drive/14vlJwCvDmNH6SFfVuYY0Y18qTbaHEJCY?usp=sharing) to listen to the results (work in progress). |
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I created the notebook as an adaptation of [the one created by Dr. Tristan Behrens](https://huggingface.co./TristanBehrens/js-fakes-4bars). |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.5773 |
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- Validation Loss: 1.5440 |
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## Model description |
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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 |
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`WhitespaceSplit` pre-tokenizer. The [tokenizer](https://huggingface.co./juancopi81/mutopia_guitar_dataset_tokenizer) is also in the Hugging Face hub. |
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## Intended uses & limitations |
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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](https://huggingface.co./course/chapter1/1) with a project that I find interesting. |
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The main intention of this model is educational. I am creating a [series of notebooks](https://github.com/juancopi81/MMM_Mutopia_Guitar) where I show every step of the process: |
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- Collecting the data |
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- Pre-processing the data |
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- Training a tokenizer from scratch |
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- Fine-tuning a GPT-2 model |
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- Building a Gradio app for the model |
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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. |
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## Training and evaluation data |
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I am training the model with [Mutopia Guitar Dataset](https://huggingface.co./datasets/juancopi81/mutopia_guitar_dataset). This dataset consists of the soloist guitar pieces of the [Mutopia Project](https://www.mutopiaproject.org/). |
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The dataset mainly contains guitar music from western classical composers, such as Sor, Aguado, Carcassi, and Giuliani. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: mixed_float16 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 1.0705 | 1.3590 | 0 | |
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| 0.8889 | 1.3702 | 1 | |
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| 0.7588 | 1.3974 | 2 | |
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| 0.7294 | 1.4813 | 3 | |
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| 0.6263 | 1.5263 | 5 | |
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| 0.5841 | 1.5263 | 6 | |
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| 0.5844 | 1.5263 | 7 | |
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| 0.5837 | 1.5346 | 8 | |
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| 0.5798 | 1.5411 | 9 | |
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| 0.5773 | 1.5440 | 10 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- TensorFlow 2.8.2 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
