Training in progress epoch 0

README.md

@@ -1,69 +1,52 @@
 ---
 tags:
 - generated_from_keras_callback
-- music
 model-index:
 - name: juancopi81/mutopia_guitar_mmm
   results: []
-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"
 ---
 
-# 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](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).
+<!-- This model card has been generated automatically according to the information Keras had access to. You should
+probably proofread and complete it, then remove this comment. -->
 
-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). 
-I created the notebook as an adaptation of [the one created by Dr. Tristan Behrens](https://huggingface.co/TristanBehrens/js-fakes-4bars).
+# juancopi81/mutopia_guitar_mmm
 
+This model was trained from scratch on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.5837
-- Validation Loss: 1.5346
+- Train Loss: 0.5798
+- Validation Loss: 1.5411
+- Epoch: 0
 
 ## 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](https://huggingface.co/juancopi81/mutopia_guitar_dataset_tokenizer) is also in the Hugging Face hub. 
+More information needed
 
 ## 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](https://huggingface.co/course/chapter1/1) with a project that I find interesting. 
-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:
-- 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.
+More information needed
 
 ## Training and evaluation data
 
-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/). 
-The dataset mainly contains guitar music from western classical composers, such as Sor, Aguado, Carcassi, and Giuliani.
+More information needed
+
+## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- 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}
+- 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}
 - training_precision: mixed_float16
+
 ### Training results
+
 | 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          | 5     |
-| 0.5841     | 1.5263          | 6     |
-| 0.5844     | 1.5263          | 7     |
-| 0.5837     | 1.5346         | 8     |
+| 0.5798     | 1.5411          | 0     |
+
 
 ### Framework versions
-- Transformers 4.21.3
+
+- Transformers 4.22.1
 - TensorFlow 2.8.2
-- Datasets 2.4.0
+- Datasets 2.5.1
 - Tokenizers 0.12.1

config.json

@@ -32,7 +32,7 @@
       "max_length": 350
     }
   },
-  "transformers_version": "4.22.0",
+  "transformers_version": "4.22.1",
   "use_cache": true,
   "vocab_size": 588
 }

tf_model.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4f62fd02de91e2e2aab7b7f773b48ba762afd69e69b4d3c0cfaf205cb21a15c6
+oid sha256:3d5d509dbdd6d95998211d5f6f22a7c6e94e85021d3dc6a3c31f5fb690a0da8a
 size 345352296