AudioLDM¶
AudioLDM was proposed in AudioLDM: Text-to-Audio Generation with Latent Diffusion Models by Haohe Liu et al. Inspired by Stable Diffusion, AudioLDM is a text-to-audio latent diffusion model (LDM) that learns continuous audio representations from CLAP latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.
The abstract from the paper is:
Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the continuous audio representations from contrastive language-audio pretraining (CLAP) latents. The pretrained CLAP models enable us to train LDMs with audio embedding while providing text embedding as a condition during sampling. By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves state-of-the-art TTA performance measured by both objective and subjective metrics (e.g., frechet distance). Moreover, AudioLDM is the first TTA system that enables various text-guided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at this https URL.
The original codebase can be found at haoheliu/AudioLDM.
Tips¶
When constructing a prompt, keep in mind:
- Descriptive prompt inputs work best; you can use adjectives to describe the sound (for example, "high quality" or "clear") and make the prompt context specific (for example, "water stream in a forest" instead of "stream").
- It's best to use general terms like "cat" or "dog" instead of specific names or abstract objects the model may not be familiar with.
During inference:
- The quality of the predicted audio sample can be controlled by the
num_inference_steps
argument; higher steps give higher quality audio at the expense of slower inference. - The length of the predicted audio sample can be controlled by varying the
audio_length_in_s
argument.
Tip
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.
mindone.diffusers.AudioLDMPipeline
¶
Bases: DiffusionPipeline
, StableDiffusionMixin
Pipeline for text-to-audio generation using AudioLDM.
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.).
PARAMETER | DESCRIPTION |
---|---|
vae |
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
TYPE:
|
text_encoder |
Frozen text-encoder (
TYPE:
|
tokenizer |
A [
TYPE:
|
unet |
A
TYPE:
|
scheduler |
A scheduler to be used in combination with
TYPE:
|
vocoder |
Vocoder of class
TYPE:
|
Source code in mindone/diffusers/pipelines/audioldm/pipeline_audioldm.py
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mindone.diffusers.AudioLDMPipeline.__call__(prompt=None, audio_length_in_s=None, num_inference_steps=10, guidance_scale=2.5, negative_prompt=None, num_waveforms_per_prompt=1, eta=0.0, generator=None, latents=None, prompt_embeds=None, negative_prompt_embeds=None, return_dict=True, callback=None, callback_steps=1, cross_attention_kwargs=None, output_type='np')
¶
The call function to the pipeline for generation.
PARAMETER | DESCRIPTION |
---|---|
prompt |
The prompt or prompts to guide audio generation. If not defined, you need to pass
TYPE:
|
audio_length_in_s |
The length of the generated audio sample in seconds.
TYPE:
|
num_inference_steps |
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the expense of slower inference.
TYPE:
|
guidance_scale |
A higher guidance scale value encourages the model to generate audio that is closely linked to the text
TYPE:
|
negative_prompt |
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to
pass
TYPE:
|
num_waveforms_per_prompt |
The number of waveforms to generate per prompt.
TYPE:
|
eta |
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the [
TYPE:
|
generator |
A
TYPE:
|
latents |
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
TYPE:
|
prompt_embeds |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the
TYPE:
|
negative_prompt_embeds |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided,
TYPE:
|
return_dict |
Whether or not to return a [
TYPE:
|
callback |
A function that calls every
TYPE:
|
callback_steps |
The frequency at which the
TYPE:
|
cross_attention_kwargs |
A kwargs dictionary that if specified is passed along to the [
TYPE:
|
output_type |
The output format of the generated image. Choose between
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
Source code in mindone/diffusers/pipelines/audioldm/pipeline_audioldm.py
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mindone.diffusers.pipelines.AudioPipelineOutput
dataclass
¶
Bases: BaseOutput
Output class for audio pipelines.
Source code in mindone/diffusers/pipelines/pipeline_utils.py
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