---
license: apache-2.0
datasets:
- amaai-lab/MidiCaps
tags:
- music
- text-to-music
- symbolic-music
---
# Text2midi: Generating Symbolic Music from Captions
[Demo](https://huggingface.co/spaces/amaai-lab/text2midi) | [Model](https://huggingface.co/amaai-lab/text2midi) | [Github](https://github.com/AMAAI-Lab/Text2midi) | [Examples](https://amaai-lab.github.io/Text2midi/) | [Paper](https://arxiv.org/abs/2412.16526) | [Dataset](https://huggingface.co/datasets/amaai-lab/MidiCaps)
[](https://huggingface.co/spaces/amaai-lab/text2midi)
**text2midi** is the first end-to-end model for generating MIDI files from textual descriptions. By leveraging pretrained large language models and a powerful autoregressive transformer decoder, **text2midi** allows users to create symbolic music that aligns with detailed textual prompts, including musical attributes like chords, tempo, and style. The details of the model are described in [this paper](https://arxiv.org/abs/2412.16526).
🔥 Live demo available on [HuggingFace Spaces](https://huggingface.co/spaces/amaai-lab/text2midi).
## Quickstart Guide
Generate symbolic music from a text prompt:
```python
import pickle
import torch
import torch.nn as nn
from transformers import T5Tokenizer
from model.transformer_model import Transformer
from huggingface_hub import hf_hub_download
repo_id = "amaai-lab/text2midi"
# Download the model.bin file
model_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
# Download the vocab_remi.pkl file
tokenizer_path = hf_hub_download(repo_id=repo_id, filename="vocab_remi.pkl")
if torch.cuda.is_available():
device = 'cuda'
elif torch.backends.mps.is_available():
device = 'mps'
else:
device = 'cpu'
print(f"Using device: {device}")
# Load the tokenizer dictionary
with open(tokenizer_path, "rb") as f:
r_tokenizer = pickle.load(f)
# Get the vocab size
vocab_size = len(r_tokenizer)
print("Vocab size: ", vocab_size)
model = Transformer(vocab_size, 768, 8, 2048, 18, 1024, False, 8, device=device)
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
print('Model loaded.')
# Enter the text prompt and tokenize it
src = "A melodic electronic song with ambient elements, featuring piano, acoustic guitar, alto saxophone, string ensemble, and electric bass. Set in G minor with a 4/4 time signature, it moves at a lively Presto tempo. The composition evokes a blend of relaxation and darkness, with hints of happiness and a meditative quality."
print('Generating for prompt: ' + src)
inputs = tokenizer(src, return_tensors='pt', padding=True, truncation=True)
input_ids = nn.utils.rnn.pad_sequence(inputs.input_ids, batch_first=True, padding_value=0)
input_ids = input_ids.to(device)
attention_mask =nn.utils.rnn.pad_sequence(inputs.attention_mask, batch_first=True, padding_value=0)
attention_mask = attention_mask.to(device)
# Generate the midi
output = model.generate(input_ids, attention_mask, max_len=2000,temperature = 1.0)
output_list = output[0].tolist()
generated_midi = r_tokenizer.decode(output_list)
generated_midi.dump_midi("output.mid")
```
## Installation
If you have CUDA supported machine:
```bash
git clone https://github.com/AMAAI-Lab/text2midi
cd text2midi
pip install -r requirements.txt
```
Alternatively, if you have MPS supported machine:
```bash
git clone https://github.com/AMAAI-Lab/text2midi
cd text2midi
pip install -r requirements-mac.txt
```
## Datasets
The model was trained using two datasets: [SymphonyNet](https://symphonynet.github.io/) for semi-supervised pretraining and MidiCaps for finetuning towards MIDI generation from captions.
The [MidiCaps dataset](https://huggingface.co/datasets/amaai-lab/MidiCaps) is a large-scale dataset of 168k MIDI files paired with rich text captions. These captions contain musical attributes such as key, tempo, style, and mood, making it ideal for text-to-MIDI generation tasks as described in [this paper](https://arxiv.org/abs/2406.02255).
## Inference
We spport inference on CUDA, MPS and cpu. Please make sure you have pip installed the correct requirement file (requirments.txt for CUDA, requirements-mac.txt for MPS)
```bash
python model/transformer_model.py --caption
```
## Citation
If you use text2midi in your research, please cite:
```
@inproceedings{bhandari2025text2midi,
title={text2midi: Generating Symbolic Music from Captions},
author={Keshav Bhandari and Abhinaba Roy and Kyra Wang and Geeta Puri and Simon Colton and Dorien Herremans},
booktitle={Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI 2025)},
year={2025}
}
```
## Results of the Listening Study
Each question is rated on a Likert scale from 1 (very bad) to 7 (very good). The table shows the average ratings per question for each group of participants.
| Question | MidiCaps | text2midi | MuseCoco |
|---------------------|----------|-----------|----------|
| Musical Quality | 5.79 | 4.62 | 4.40 |
| Overall Matching | 5.42 | 4.67 | 4.07 |
| Genre Matching | 5.54 | 4.98 | 4.40 |
| Mood Matching | 5.70 | 5.00 | 4.32 |
| Key Matching | 4.61 | 3.64 | 3.36 |
| Chord Matching | 3.20 | 2.50 | 2.00 |
| Tempo Matching | 5.89 | 5.42 | 4.94 |
## Objective Evaluations
| Metric | text2midi | MidiCaps | MuseCoco |
|---------------------|-----------|----------|----------|
| CR ↑ | 2.31 | 3.43 | 2.12 |
| CLAP ↑ | 0.22 | 0.26 | 0.21 |
| TB (%) ↑ | 39.70 | - | 21.71 |
| TBT (%) ↑ | 65.80 | - | 54.63 |
| CK (%) ↑ | 33.60 | - | 13.70 |
| CKD (%) ↑ | 35.60 | - | 14.59 |
**Note**:
CR = Compression ratio
CLAP = CLAP score
TB = Tempo Bin
TBT = Tempo Bin with Tolerance
CK = Correct Key
CKD = Correct Key with Duplicates
↑ = Higher score is better.
## Training
To train text2midi, we recommend using accelerate for multi-GPU support. First, configure accelerate by running:
```bash
accelerate config
```
Then, use the following command to start training:
```bash
accelerate launch train.py \
--encoder_model="google/flan-t5-large" \
--decoder_model="configs/transformer_decoder_config.json" \
--dataset_name="amaai-lab/MidiCaps" \
--pretrain_dataset="amaai-lab/SymphonyNet" \
--batch_size=16 \
--learning_rate=1e-4 \
--epochs=40 \
```