This repository contains lightweight training code for MusicGen, a state-of-the-art controllable text-to-music model. MusicGen is a single stage auto-regressive Transformer model trained over a 32kHz Encodec tokenizer with 4 codebooks sampled at 50 Hz.
The aim is to provide tools to easily fine-tune and dreambooth the MusicGen model suite on small consumer GPUs, with little data and to leverage a series of optimizations and tricks to reduce resource consumption. For example, the model can be fine-tuned on a particular music genre or artist to give a checkpoint that generates in that given style. The aim is also to easily share and build on these trained checkpoints, thanks to LoRA adaptors.
Specifically, this involves:
- using as few data and resources as possible. We're talking fine-tuning with as little as 15mn on an A100 and as little as 10GB to 16GB of GPU utilization.
- easily share and build models thanks to the Hugging Face Hub.
- optionally, generate automatic music descriptions
- optionally, training MusicGen in a Dreambooth-like fashion, where one key-word triggers generation in a particular style
You first need to clone this repository before installing requirements.
git clone [email protected]:ylacombe/musicgen-dreamboothing.git
cd musicgen-dreamboothing
pip install -e .
pip install -U git+https://github.com/huggingface/transformersNote
For the time being, we're installing transformers from source, but you won't have to do once the next version is released anytime soon.
Optionally, you can create a wandb account and login to it by following this guide. wandb allows for better tracking of the experiments metrics and losses.
You also have the option to configure Accelerate by running the following command. Note that you should set the number of GPUs you wish to use for training, and also the data type (dtype) to your preferred dtype for training/inference (e.g. bfloat16 on A100 GPUs, float16 on V100 GPUs, etc.):
accelerate configLastly, you can link you Hugging Face account so that you can push model repositories on the Hub. This will allow you to save your trained models on the Hub so that you can share them with the community. Run the command:
git config --global credential.helper store
huggingface-cli loginAnd then enter an authentication token from https://huggingface.co/settings/tokens. Create a new token if you do not have one already. You should make sure that this token has "write" privileges.
The script dreambooth_musicgen.py is an end-to-end script that:
- Loads an audio dataset using the
datasetslibrary, for example this small subset of songs in the punk style derived from the royalty-free PogChamp Music Classification Competition dataset. - Loads a MusicGen checkpoint from the hub, for example the 1.5B MusicGen Melody checkpoint.
- (Optional) Generates automatic song descriptions with the
--add_metadata trueand--instance_prompt ANCHOR_STRINGflags. This will automatically computes instruments, genre, mood, tempo and key, as well as add the instance prompt to the description. In the following example, the instance prompt ispunkbecause we use a dataset made of punk songs. - (Optional) Get rid of lyrics with the flag
--audio_separation. MusicGen was trained on instrumentals only, so lyrics will only degrade the model training. - Tokenizes the text descriptions and encode the audio samples.
- Uses the Transformers' Trainer to perform training and evaluation.
Important
If you want to generate automatic song descriptions or if you want to do audio separation, you should also install optional dependencies with the following: pip install -e .[metadata]
You can learn more about the different arguments of the training script by running:
python dreambooth_musicgen.py --helpTo give a practical example, here's how to fine-tune MusicGen Melody on 27 minutes of Punk music.
python dreambooth_musicgen.py \
--use_lora \
--model_name_or_path "facebook/musicgen-melody" \
--dataset_name "ylacombe/tiny-punk" \
--dataset_config_name "default" \
--target_audio_column_name "others" \
--instance_prompt "punk" \
--train_split_name "clean" \
--eval_split_name "clean" \
--output_dir "./musicgen-melody-lora-punk" \
--do_train \
--fp16 \
--num_train_epochs 4 \
--gradient_accumulation_steps 8 \
--gradient_checkpointing \
--per_device_train_batch_size 2 \
--learning_rate 2e-4 \
--adam_beta1 0.9 \
--adam_beta2 0.99 \
--weight_decay 0.1 \
--guidance_scale 3.0 \
--do_eval \
--predict_with_generate \
--include_inputs_for_metrics \
--eval_steps 25 \
--evaluation_strategy "steps" \
--per_device_eval_batch_size 1 \
--max_eval_samples 8 \
--generation_max_length 400 \
--dataloader_num_workers 8 \
--logging_steps 1 \
--max_duration_in_seconds 30 \
--min_duration_in_seconds 1.0 \
--preprocessing_num_workers 8 \
--pad_token_id 2048 \
--decoder_start_token_id 2048 \
--seed 456 \
--overwrite_output_dir \
--push_to_hub trueUsing a few tricks, this fine-tuning run used 10GB of GPU memory and ran in under 15 minutes on an A100 GPU. The resulting punk checkpoint can be found on the Hugging Face Hub under ylacombe/musicgen-melody-lora-punk.
More specifically, those tricks are LoRA, half-precision, gradient accumulation and gradient checkpointing. The largest memory saving comes from LoRA, which is a training technique for significantly reducing the number of trainable parameters. As a result, training is faster and it is easier to store the resulting weights because they are a lot smaller (~100MBs). For more information, refer to the LoRA conceptual guide. Note that using the same parameters on MusicGen Melody large only used 16GB of GPU.
Also note that you can also use a JSON file to get your parameters. For example, punk.json:
python dreambooth_musicgen.py example_configs/punk.jsonThe JSON example above also shows to follow the training thanks to wandb (e.g of what it looks like here).
Some take-aways from the different experiments we've done:
- to fine-tune and keep model ability it's essential to have a low number of epochs.
- for small datasets, a learning rate of 2e-4 gave great results.
- it doesn't actually matter to have the training loss going down, it's always better to actually listen to the output samples.
- you can get quickly get a sense of how and if the model learned by comparing the samples before and after fine-tuning on wandb (e.g here).
- If you're not using a melody checkpoint and get
nanerrors, you might want to setguidance_scaleto 1.0, check this FAQ response.
The following snippet indicates how to do inference with LoRA fine-tuned MusicGen model:
from peft import PeftConfig, PeftModel
from transformers import AutoModelForTextToWaveform, AutoProcessor
import torch
import soundfile as sf
device = torch.device("cuda:0" if torch.cuda.device_count()>0 else "cpu")
repo_id = "ylacombe/musicgen-melody-punk-lora"
config = PeftConfig.from_pretrained(repo_id)
model = AutoModelForTextToWaveform.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(model, repo_id).to(device)
processor = AutoProcessor.from_pretrained(repo_id)
inputs = processor(
text=["80s punk and pop track with bassy drums and synth", "punk bossa nova"],
padding=True,
return_tensors="pt",
).to(device)
audio_values = model.generate(**inputs, do_sample=True, guidance_scale=3, max_new_tokens=256)
sampling_rate = model.config.audio_encoder.sampling_rate
audio_values = audio_values.cpu().float().numpy()
sf.write("musicgen_out_0.wav", audio_values[0].T, sampling_rate)
sf.write("musicgen_out_1.wav", audio_values[1].T, sampling_rate)The above snippet simply load the base weights from the original pre-trained checkpoint and then load on-top of it the 100MB of adaptors from the LoRA fine-tuned checkpoint.
Then, the rest of the code simply use the model it in the same way you'd use MusicGen or MusicGen Melody (refers to the transformers docs here and here respectively).
Note that this training code also allows fine-tuning without LoRA, in which case you can refer to the transformers docs here for MusicGen and here for MusicGen Melody, in order to do inference on the newly fine-tuned checkpoint.
Here is a quick summary of the MusicGen models that have been trained and released by Meta, and which are compatible with this training code:
| Model | Task | Model size |
|---|---|---|
| MusicGen Melody | Melody-guided | 1.5B |
| MusicGen Melody Large | Melody-guided | 3.3B |
| MusicGen Small | Text-to-music | 300M |
| MusicGen Medium | Text-to-music | 1.5B |
| MusicGen Large | Text-to-music | 3.3B |
Additionally, you can track MusicGen models on the hub here. You'll find some additional checkpoints trained and released by the community, which you can use for inference straight away.
We recommend using the MusicGen Melody checkpoints, as those are the ones which gave the best results for us.
I'm currently considering adapting the training script to:
- A hands-on gradio demo that will require no code.
- A notebook, with detailed steps and some explanations, that will require some Python knowledge.
Of course, I welcome all contributions from the community to speed up the implementation of these projects!
We rely on the datasets library, which is optimized for speed and efficiency, and is deeply integrated with the HuggingFace Hub which allows easy sharing and loading.
In order to use this repository, you need an audio dataset from datasets with at least one audio column. You can set target_audio_column_name with this column name.
Audio samples must be less than 30 seconds long and contain no lyrics (instrumentals only).
Tip
If you have songs with lyrics, simply set the flag --audio_separation, this will performs audio separation thanks to demucs.
This is what I've done for the some of my datasets. I've got inspired from this script to do audio separation with datasets and demucs.
You can also use your own set of descriptions instead of automatically generated ones. In that case, you also need a text column with those descriptions. You can set text_column_name with this column name.
If you song files in your computer, and want to create a dataset from datasets with those, you can use easily do this.
- You first need to create a csv file that contains the full paths to the audio. The column name for those audio files could be for example
audio. - Once you have this csv file, you can load it to a dataset like this:
from datasets import DatasetDict
dataset = DatasetDict.from_csv({"train": PATH_TO_CSV_FILE})- You then need to convert the audio column name to
Audioso thatdatasetsunderstand that it deals with audio files.
from datasets import Audio
dataset = dataset.cast_column("audio", Audio())- You can then save the datasets locally or push it to the hub:
dataset.push_to_hub(REPO_ID)Note that you can make the dataset private by passing private=True to the push_to_hub method. Find other possible arguments here.
There seems to be an error using guidance scale with some musicgen checkpoints. If that happens, I recommend setting guidance_scale to 1.0 in the training parameters.
I haven't tested yet the training script with stereo MusicGen models. I welcome all contributions from the community to test and correct the training script on those!
The code in this repository is released under the Apache license as found in the LICENSE file. The pre-trained MusicGen weights are licenced under CC-BY-NC 4.0.
This library builds on top of a number of open-source giants, to whom we'd like to extend our warmest thanks for providing these tools!
Special thanks to:
- The MusicGen team from Meta AI and their audiocraft repository.
- Nathan Raw for his support and advice.
- the many libraries used, to name but a few: 🤗 datasets, 🤗 accelerate, wandb, and 🤗 transformers.
- Hugging Face 🤗 for providing compute resources and time to explore!
If you found this repository useful, please consider citing the original MusicGen paper:
@misc{copet2024simple,
title={Simple and Controllable Music Generation},
author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
year={2024},
eprint={2306.05284},
archivePrefix={arXiv},
primaryClass={cs.SD}
}