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Update README file - Epocj 8

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  ---
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  tags:
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  - generated_from_keras_callback
 
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  model-index:
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  - name: juancopi81/mutopia_guitar_mmm
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  results: []
 
 
 
 
 
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  ---
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- <!-- This model card has been generated automatically according to the information Keras had access to. You should
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- probably proofread and complete it, then remove this comment. -->
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-
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  # juancopi81/mutopia_guitar_mmm
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- This model was trained from scratch on an unknown dataset.
 
 
 
 
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  It achieves the following results on the evaluation set:
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  - Train Loss: 0.5837
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  - Validation Loss: 1.5346
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- - Epoch: 0
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  ## Model description
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- More information needed
 
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  ## Intended uses & limitations
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- More information needed
 
 
 
 
 
 
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- ## Training and evaluation data
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- More information needed
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- ## Training procedure
 
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'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': 2363, '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}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
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  - training_precision: mixed_float16
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-
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  ### Training results
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-
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  | Train Loss | Validation Loss | Epoch |
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  |:----------:|:---------------:|:-----:|
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- | 0.5837 | 1.5346 | 0 |
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-
 
 
 
 
 
 
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  ### Framework versions
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-
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- - Transformers 4.22.0
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  - TensorFlow 2.8.2
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  - Datasets 2.4.0
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  - Tokenizers 0.12.1
 
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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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+
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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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+
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  It achieves the following results on the evaluation set:
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  - Train Loss: 0.5837
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  - Validation Loss: 1.5346
 
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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-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 9089, '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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  ### Framework versions
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+ - Transformers 4.21.3
 
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  - TensorFlow 2.8.2
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  - Datasets 2.4.0
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  - Tokenizers 0.12.1