Recently, text-to-music generation models have achieved unprecedented results in synthesizing high-quality and diverse music samples from a given text prompt. Despite these advances, it remains unclear how one can generate personalized, user-specific musical concepts, manipulate them, and combine them with existing ones. For example, can one generate a rock song using their personal guitar playing style or a specific ethnic instrument? Motivated by the computer vision literature, we investigate text-to-music personalization by exploring two established methods, namely Textual Inversion and DreamBooth.

• Release code!

• Example code for training and evaluation

• DreamBooth with AudioLDM2

• Gradio app!

• Release code for Personalized Style Transfer

Use python 3.10.13

pip install -r requirements.txt
git clone https://huggingface.co/cvssp/audioldm-m-full

You need Git Large File Storage for cloning the huggingface model.

To train the personalization methods for e.g. a short collection of guitar recordings, you can chose "guitar" or "string instrument" as a class, and a not commonly used word like "sks" as an instance word.

export MODEL_NAME="audioldm-m-full"
export DATA_DIR="path/to/concept/audios"
export OUTPUT_DIR="path/to/output/dir"
accelerate launch dreambooth_audioldm.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--instance_word="sks" \
--object_class="guitar" \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=300 \
--learning_rate=1.0e-06 \
--output_dir=$OUTPUT_DIR \
--num_vectors=1 \
--save_as_full_pipeline 

In textual inversion you just need to specify a placeholder token, that can be any rearely used string. Here we use "" as a placeholder token.

export MODEL_NAME="audioldm-m-full"
export DATA_DIR="path/to/concept/audios"
export OUTPUT_DIR="path/to/output/dir"
accelerate launch textual_inversion_audioldm.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATA_DIR \
  --learnable_property="object" \
  --placeholder_token="<guitar>" \
  --validation_prompt="a recording of a <guitar>" \
  --initializer="mean" \
  --initializer_token="" \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --max_train_steps=3000 \
  --learning_rate=5.0e-04 --scale_lr \
  --output_dir=$OUTPUT_DIR \

For Textual inversion, you have to use the placeholder token used in training in the prompts, after loading the learned embeddings in the base model. For Dreambooth, you have to load the fine-tuned model and use [instance word] [class-word] in the inference prompt.

from pipeline.pipeline_audioldm import AudioLDMPipeline


#Textual Inversion

pipe = AudioLDMPipeline.from_pretrained("audioldm-m-full", torch_dtype=torch.float16).to("cuda")
learned_embedding = "path/to/learnedembedding"
prompt = "A recording of <guitar>"
pipe.load_textual_inversion(learned_embedding)
waveform = pipe(prompt).audios

#DreamBooth
pipeline = AudioLDMPipeline.from_pretrained("path/to/dreambooth/model", torch_dtype=torch.float16).to("cuda")
prompt = "A recording of a sks guitar"
waveform = pipe(prompt).audios

To train AudioLDM2 DreamBooth:

export MODEL_NAME="cvssp/audioldm2"
export DATA_DIR="dataset/concepts/oud"
export OUTPUT_DIR="oud_ldm2_db_string_instrument"
accelerate launch dreambooth_audioldm2.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--instance_word="sks" \
--object_class="string instrument" \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=300 \
--learning_rate=1.0e-05 \
--output_dir=$OUTPUT_DIR \
--validation_steps=50 \
--num_validation_audio_files=3 \
--num_vectors=1 \

And for inference:

from pipeline.pipeline_audioldm2 import AudioLDM2Pipeline

pipeline = AudioLDM2Pipeline.from_pretrained("path/to/dreambooth/model", torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")

prompt="a recording of a sks string instrument"
waveform=pipeline(prompt,num_inference_steps=50,num_waveforms_per_prompt=1,audio_length_in_s=5.12).audios[0]

If you use this code please cite:

@article{plitsis2023investigating,
  title={Investigating Personalization Methods in Text to Music Generation},
  author={Plitsis, Manos and Kouzelis, Theodoros and Paraskevopoulos, Georgios and Katsouros, Vassilis and Panagakis, Yannis},
  journal={arXiv preprint arXiv:2309.11140},
  year={2023}
}

This code is heavily based on AudioLDM and Diffusers.