MusicLDM was proposed in MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies by Ke Chen, Yusong Wu, Haohe Liu, Marianna Nezhurina, Taylor Berg-Kirkpatrick, Shlomo Dubnov. MusicLDM takes a text prompt as input and predicts the corresponding music sample.
Inspired by Stable Diffusion and AudioLDM, MusicLDM is a text-to-music latent diffusion model (LDM) that learns continuous audio representations from CLAP latents.
MusicLDM is trained on a corpus of 466 hours of music data. Beat-synchronous data augmentation strategies are applied to the music samples, both in the time domain and in the latent space. Using beat-synchronous data augmentation strategies encourages the model to interpolate between the training samples, but stay within the domain of the training data. The result is generated music that is more diverse while staying faithful to the corresponding style.
The abstract of the paper is the following:
In this paper, we present MusicLDM, a state-of-the-art text-to-music model that adapts Stable Diffusion and AudioLDM architectures to the music domain. We achieve this by retraining the contrastive language-audio pretraining model (CLAP) and the Hifi-GAN vocoder, as components of MusicLDM, on a collection of music data samples. Then, we leverage a beat tracking model and propose two different mixup strategies for data augmentation: beat-synchronous audio mixup and beat-synchronous latent mixup, to encourage the model to generate music more diverse while still staying faithful to the corresponding style.
This pipeline was contributed by sanchit-gandhi.
When constructing a prompt, keep in mind:
During inference:
num_inference_steps
argument; higher steps give higher quality audio at the expense of slower inference.num_waveforms_per_prompt
to a value greater than 1 to enable. Automatic scoring will be performed between the generated waveforms and prompt text, and the audios ranked from best to worst accordingly.audio_length_in_s
argument.Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
( vae: AutoencoderKL text_encoder: typing.Union[transformers.models.clap.modeling_clap.ClapTextModelWithProjection, transformers.models.clap.modeling_clap.ClapModel] tokenizer: typing.Union[transformers.models.roberta.tokenization_roberta.RobertaTokenizer, transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast] feature_extractor: typing.Optional[transformers.models.clap.feature_extraction_clap.ClapFeatureExtractor] unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers vocoder: SpeechT5HifiGan )
Parameters
ClapTextModel
), specifically the
laion/clap-htsat-unfused variant. PreTrainedTokenizer
) —
A RobertaTokenizer to tokenize text. UNet2DConditionModel
to denoise the encoded audio latents. unet
to denoise the encoded audio latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. SpeechT5HifiGan
. Pipeline for text-to-audio generation using MusicLDM.
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).
( prompt: typing.Union[str, typing.List[str]] = None audio_length_in_s: typing.Optional[float] = None num_inference_steps: int = 200 guidance_scale: float = 2.0 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_waveforms_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None output_type: typing.Optional[str] = 'np' ) → AudioPipelineOutput or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide audio generation. If not defined, you need to pass prompt_embeds
. int
, optional, defaults to 10.24) —
The length of the generated audio sample in seconds. int
, optional, defaults to 200) —
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference. float
, optional, defaults to 2.0) —
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
prompt
at the expense of lower sound quality. Guidance scale is enabled when guidance_scale > 1
. str
or List[str]
, optional) —
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
pass negative_prompt_embeds
instead. Ignored when not using guidance (guidance_scale < 1
). int
, optional, defaults to 1) —
The number of waveforms to generate per prompt. If num_waveforms_per_prompt > 1
, the text encoding
model is a joint text-audio model (ClapModel), and the tokenizer is a
[~transformers.ClapProcessor]
, then automatic scoring will be performed between the generated outputs
and the input text. This scoring ranks the generated waveforms based on their cosine similarity to text
input in the joint text-audio embedding space. float
, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. torch.Generator
or List[torch.Generator]
, optional) —
A torch.Generator
to make
generation deterministic. torch.FloatTensor
, optional) —
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random generator
. torch.FloatTensor
, optional) —
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds
are generated from the negative_prompt
input argument. bool
, optional, defaults to True
) —
Whether or not to return a AudioPipelineOutput instead of a plain tuple. Callable
, optional) —
A function that calls every callback_steps
steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor)
. int
, optional, defaults to 1) —
The frequency at which the callback
function is called. If not specified, the callback is called at
every step. dict
, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor
as defined in
self.processor
. str
, optional, defaults to "np"
) —
The output format of the generated audio. Choose between "np"
to return a NumPy np.ndarray
or
"pt"
to return a PyTorch torch.Tensor
object. Set to "latent"
to return the latent diffusion
model (LDM) output. Returns
AudioPipelineOutput or tuple
If return_dict
is True
, AudioPipelineOutput is returned, otherwise a tuple
is
returned where the first element is a list with the generated audio.
The call function to the pipeline for generation.
Examples:
>>> from diffusers import MusicLDMPipeline
>>> import torch
>>> import scipy
>>> repo_id = "cvssp/audioldm-s-full-v2"
>>> pipe = MusicLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
>>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
>>> # save the audio sample as a .wav file
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
Disable sliced VAE decoding. If enable_vae_slicing
was previously enabled, this method will go back to
computing decoding in one step.
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to enable_sequential_cpu_offload
, this method moves one whole model at a time to the GPU when its forward
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
enable_sequential_cpu_offload
, but performance is much better due to the iterative execution of the unet
.
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.