GPT2 Music Generation Trained

Providerhardikpatel
Categoryaudio-generation
Licensemit
Downloads11
Stars0

Overview

This model adapts the GPT-2 transformer architecture for symbolic music generation, treating musical notes and timing as tokens in a sequence. Unlike raw audio synthesis, it focuses on generating structured compositions, making it an ideal starting point for developers building MIDI-based tools, algorithmic composition plugins, or AI-assisted songwriting software. It is particularly useful for those who need a lightweight, MIT-licensed model that can be fine-tuned on specific genres or artists without the overhead of massive diffusion models. Integration is straightforward for anyone familiar with the Hugging Face ecosystem, allowing for rapid prototyping of melodic patterns and harmonic progressions.

Highlights

  • Transformer-based symbolic music sequence generation
  • Lightweight architecture suitable for local deployment
  • Permissive MIT license for commercial integration
  • Ideal for MIDI-based composition and prototyping

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("hardikpatel/GPT2_Music_Generation_Trained")
tokenizer = AutoTokenizer.from_pretrained("hardikpatel/GPT2_Music_Generation_Trained")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download hardikpatel/GPT2_Music_Generation_Trained

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download hardikpatel/GPT2_Music_Generation_Trained config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('hardikpatel/GPT2_Music_Generation_Trained')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/hardikpatel/GPT2_Music_Generation_Trained

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/hardikpatel/GPT2_Music_Generation_Trained

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('hardikpatel/GPT2_Music_Generation_Trained')
tokenizer = AutoTokenizer.from_pretrained('hardikpatel/GPT2_Music_Generation_Trained')

Full Documentation

来源: HuggingFace

---
license: mit
tags:

  • generated_from_trainer

model-index:
  • name: lmd-8bars-2048-epochs10

results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

lmd-8bars-2048-epochs10

This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:

  • Loss: 1.0086

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0005

  • train_batch_size: 8

  • eval_batch_size: 4

  • seed: 1

  • gradient_accumulation_steps: 2

  • total_train_batch_size: 16

  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

  • lr_scheduler_type: cosine

  • lr_scheduler_warmup_ratio: 0.01

  • num_epochs: 10

Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.4182 | 0.5 | 4994 | 1.4933 |
| 1.4626 | 1.0 | 9988 | 1.3082 |
| 1.3176 | 1.5 | 14982 | 1.2276 |
| 1.2604 | 2.0 | 19976 | 1.1815 |
| 1.2101 | 2.5 | 24970 | 1.1499 |
| 1.1804 | 3.0 | 29964 | 1.1260 |
| 1.1517 | 3.5 | 34958 | 1.1043 |
| 1.1349 | 4.0 | 39952 | 1.0887 |
| 1.1133 | 4.5 | 44946 | 1.0762 |
| 1.0995 | 5.0 | 49940 | 1.0618 |
| 1.0824 | 5.5 | 54934 | 1.0507 |
| 1.0713 | 6.0 | 59928 | 1.0423 |
| 1.0552 | 6.5 | 64922 | 1.0328 |
| 1.0505 | 7.0 | 69916 | 1.0279 |
| 1.0365 | 7.5 | 74910 | 1.0217 |
| 1.0307 | 8.0 | 79904 | 1.0153 |
| 1.022 | 8.5 | 84898 | 1.0107 |
| 1.0189 | 9.0 | 89892 | 1.0090 |
| 1.0129 | 9.5 | 94886 | 1.0084 |
| 1.0139 | 10.0 | 99880 | 1.0086 |

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
View official source
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