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Audio Ldm


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The audio-ldm model is a text-to-audio generation model based on latent diffusion models. It takes in a text prompt as input and generates a corresponding audio output. The model leverages latent diffusion to learn the underlying data distribution and generate high-quality audio samples. This model is designed to cater to the needs of technical users and provide them with a concise and complete summary of the model's capabilities and functionality.

Use cases

The audio-ldm model has the potential to be applied in a variety of use cases. One possible use case is in the field of entertainment, where it can be used to generate high-quality audio content for movies, video games, and virtual reality experiences. For example, it could be used to create realistic voices for characters or generate immersive soundscapes. Another possible use case is in the field of accessibility, where the model could be utilized to convert text-based content into audio format, making it more accessible to visually impaired individuals. This could include generating audio versions of books, articles, or online content. Additionally, the model could be used in the development of voice assistance technology, allowing users to interact with devices using natural language and receive audio responses. Overall, the audio-ldm model has the potential to be integrated into various products and services, enabling the generation of high-quality audio content from text inputs.



Cost per run
Avg run time
Nvidia T4 GPU

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Summary of this model and related resources.

Model NameAudio Ldm
Text-to-audio generation with latent diffusion models
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.02915
Prediction HardwareNvidia T4 GPU
Average Completion Time53 seconds