Long-form music generation with latent diffusion

🌈 Abstract

The article discusses the recent advancements in audio-based generative models for music and presents a model that can produce long-form music of up to 4 minutes and 45 seconds with coherent musical structure. The key points are:

• Existing music generation models have been limited to producing relatively short music segments (10-30 seconds), and struggle to generate longer pieces with coherent structure.
• The proposed model uses a diffusion-transformer operating on a highly downsampled continuous latent representation to generate long-form music.
• The model is evaluated quantitatively and qualitatively, showing state-of-the-art performance in audio quality, text-prompt alignment, and the ability to generate full-length music with coherent structure.
• The model can also perform other creative tasks like audio-to-audio style transfer and short-form audio generation.

🙋 Q&A

[01] Introduction

1. What are the key challenges in generating long-form music with coherent structure using deep learning? The key challenges are:

• Existing music generation models have typically been trained on relatively short music segments (10-30 seconds) and struggle to generate longer pieces with coherent musical structure.
• Most models rely on semantic tokens to provide guidance on the overall characteristics and evolution of the music, which can be challenging to obtain for long-form generation.
• Generating an entire music piece at once (full-context generation) is computationally challenging due to the VRAM limitations of current GPUs.

2. How does the proposed model address these challenges? The proposed model addresses these challenges by:

• Using a highly compressed continuous latent representation to enable training and generation on longer temporal contexts (up to 4 minutes and 45 seconds).
• Employing a diffusion-transformer architecture that can generate the entire music piece at once without relying on semantic tokens.
• Utilizing techniques like efficient block-wise attention and gradient checkpointing to make the transformer-based model viable for the long temporal contexts.

[02] Latent Diffusion Architecture

1. What are the main components of the proposed model? The model consists of three main components:

• An autoencoder that compresses waveforms into a manageable sequence length
• A contrastive text-audio embedding model (CLAP) for text conditioning
• A transformer-based diffusion model that operates in the latent space of the autoencoder

2. How does the autoencoder work and what are the key objectives used in training it? The autoencoder uses a convolutional encoder-decoder structure with downsampling and upsampling blocks. It is trained using a reconstruction loss based on a perceptually weighted multi-resolution STFT loss, as well as an adversarial loss using a convolutional discriminator.

3. What is the key difference between the proposed diffusion-transformer (DiT) and the commonly used convolutional U-Net structure? The key difference is that the proposed model uses a diffusion-transformer architecture instead of the convolutional U-Net. This allows the model to effectively operate over the longer temporal contexts required for full-length music generation.

[03] Training Setup

1. How is the model trained in a multi-stage process? The training process is divided into multiple stages:

1. The autoencoder and CLAP model are trained first.
2. The diffusion model is then pre-trained on sequences up to 3 minutes and 10 seconds.
3. The pre-trained diffusion model is then fine-tuned on sequences up to 4 minutes and 45 seconds.

2. What is the rationale behind the multi-stage training approach? The multi-stage training approach is used to gradually scale the model to handle the longer temporal contexts required for full-length music generation. The pre-training on shorter sequences helps the model learn the necessary capabilities before fine-tuning on the full 4 minute and 45 second target length.

3. What are some of the key hyperparameters and techniques used in training the model? Key training details include:

• Using the AdamW optimizer with a base learning rate of 1e-4 and an exponential ramp-up and decay scheduler.
• Maintaining an exponential moving average of the weights for improved inference.
• Employing weight decay with a coefficient of 0.001.
• Training the diffusion model to predict a noise increment from noised ground-truth latents, following the v-objective approach.
• Using DPM-Solver++ for sampling with classifier-free guidance.

[04] Experiments

1. How does the proposed model perform compared to the MusicGen-large-stereo baseline in quantitative evaluations? The proposed model outperforms the MusicGen-large-stereo baseline on all evaluated metrics (Fréchet distance on OpenL3 embeddings, KL-divergence on PaSST tags, and distance in LAION-CLAP space) at both the 2-minute and 3-minute 10-second lengths. The proposed model also generates the music significantly faster than MusicGen.

2. What are the key findings from the qualitative listening test evaluation? The key findings from the qualitative listening test are:

• The generations from the proposed system are comparable to the ground-truth in most aspects (audio quality, text alignment, musicality) and superior to the MusicGen baseline.
• The 4-minute 45-second generations from the proposed model achieve good (4 out of 5) mean opinion scores across the evaluated qualities.
• The 2-minute generations from the proposed model have slightly lower scores for musical structure, potentially due to the relative scarcity of full-structured music at that length in the training data.
• The proposed model achieves over 95% stereo correctness, while the MusicGen baseline only achieves around 60% stereo correctness.

3. How does the proposed model perform in terms of memorization of the training data? The authors conducted a comprehensive memorization analysis and could not find any instances of memorization in the model's generations, even when specifically targeting potential memorization candidates.

[05] Additional Creative Capabilities

1. What other creative capabilities does the proposed model exhibit beyond text-conditioned long-form music generation? The model exhibits the following additional creative capabilities:

• Audio-to-audio style transfer: The model can modify the aesthetics of an existing audio recording based on a given text prompt, while maintaining the reference audio's structure.
• Vocal music generation: When prompted for vocals, the model generates vocal-like melodies without intelligible words, which can have artistic and textural value.
• Short-form audio generation: The model can also generate shorter sounds like sound effects or instrument samples when prompted appropriately.