audio-ldm

Maintainer: haoheliu - Last updated 11/10/2024

PropertyValue
Run this modelRun on Replicate
API specView on Replicate
Github linkView on Github
Paper linkView on Arxiv

Model overview

audio-ldm is a text-to-audio generation model created by Haohe Liu, a researcher at CVSSP. It uses latent diffusion models to generate audio based on text prompts. The model is similar to stable-diffusion, a widely-used latent text-to-image diffusion model, but applied to the audio domain. It is also related to models like riffusion, which generates music from text, and whisperx, which transcribes audio. However, audio-ldm is focused specifically on generating a wide range of audio content from text.

Model inputs and outputs

The audio-ldm model takes in a text prompt as input and generates an audio clip as output. The text prompt can describe the desired sound, such as "a hammer hitting a wooden surface" or "children singing". The model then produces an audio clip that matches the text prompt.

Inputs

  • Text: A text prompt describing the desired audio to generate.
  • Duration: The duration of the generated audio clip in seconds. Higher durations may lead to out-of-memory errors.
  • Random Seed: An optional random seed to control the randomness of the generation.
  • N Candidates: The number of candidate audio clips to generate, with the best one selected.
  • Guidance Scale: A parameter that controls the balance between audio quality and diversity. Higher values lead to better quality but less diversity.

Outputs

  • Audio Clip: The generated audio clip that matches the input text prompt.

Capabilities

audio-ldm is capable of generating a wide variety of audio content from text prompts, including speech, sound effects, music, and beyond. It can also perform audio-to-audio generation, where it generates a new audio clip that has similar sound events to a provided input audio. Additionally, the model supports text-guided audio-to-audio style transfer, where it can transfer the sound of an input audio clip to match a text description.

What can I use it for?

audio-ldm could be useful for various applications, such as:

  • Creative content generation: Generating audio content for use in videos, games, or other multimedia projects.
  • Audio post-production: Automating the creation of sound effects or music to complement visual content.
  • Accessibility: Generating audio descriptions for visually impaired users.
  • Education and research: Exploring the capabilities of text-to-audio generation models.

Things to try

When using audio-ldm, try providing more detailed and descriptive text prompts to get better quality results. Experiment with different random seeds to see how they affect the generation. You can also try combining audio-ldm with other audio tools and techniques, such as audio editing or signal processing, to create even more interesting and compelling audio content.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Total Score

36

Related Models

AI model preview image

llama-omni

ictnlp

Total Score

58

LLaMA-Omni is a speech-language model built upon the Llama-3.1-8B-Instruct model. It was developed by researchers from the Institute of Computing Technology, Chinese Academy of Sciences (ICTNLP). The model supports low-latency and high-quality speech interactions, allowing users to generate both text and speech responses simultaneously based on speech instructions. Compared to similar models like Meta's LLaMA-3-70B-Instruct and LLaMA-3-8B-Instruct, LLaMA-Omni is specifically designed for seamless speech interaction, leveraging the capabilities of the Llama-3.1-8B-Instruct model while adding novel speech processing components. The model can also be compared to Seamless Expressive, which focuses on multilingual speech translation while preserving the original vocal style and prosody. Model inputs and outputs Inputs input_audio**: Input audio in the form of a URI prompt**: A text prompt to guide the model's response temperature**: A value between 0 and 1 that controls the randomness of the generated output top_p**: A value between 0 and 1 that controls the diversity of the output when temperature is greater than 0 Outputs audio**: The generated audio response in the form of a URI text**: The generated text response Capabilities LLaMA-Omni is capable of engaging in seamless speech interactions, generating both text and speech responses based on the user's speech input. The model can handle a variety of tasks, such as answering questions, providing instructions, and engaging in open-ended conversations, all while maintaining low latency and high-quality speech output. What can I use it for? The LLaMA-Omni model can be used to build a wide range of applications that require natural language understanding and generation combined with speech capabilities. This could include virtual assistants, language learning tools, voice-controlled interfaces, and more. The model's ability to generate both text and speech responses simultaneously makes it particularly well-suited for applications where a natural and responsive conversational experience is essential. Things to try One interesting aspect of the LLaMA-Omni model is its low latency, with a reported latency as low as 226ms. This makes it well-suited for real-time, interactive applications where users expect a quick and responsive experience. You could try experimenting with the model's capabilities in scenarios that require rapid speech processing and generation, such as voice-controlled smart home systems or virtual meeting assistants. Another intriguing feature of the model is its ability to generate both text and speech outputs simultaneously. This could open up new possibilities for multimodal interactions, where users can seamlessly switch between text and voice input and output. You could explore how this capability can be leveraged to create more intuitive and personalized user experiences.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion

stability-ai

Total Score

109.5K

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.

Read more

Updated Invalid Date

AI model preview image

tango

declare-lab

Total Score

21

Tango is a latent diffusion model (LDM) for text-to-audio (TTA) generation, capable of generating realistic audios including human sounds, animal sounds, natural and artificial sounds, and sound effects from textual prompts. It uses the frozen instruction-tuned language model Flan-T5 as the text encoder and trains a UNet-based diffusion model for audio generation. Compared to current state-of-the-art TTA models, Tango performs comparably across both objective and subjective metrics, despite training on a dataset 63 times smaller. The maintainer has released the model, training, and inference code for the research community. Tango 2 is a follow-up to Tango, built upon the same foundation but with additional alignment training using Direct Preference Optimization (DPO) on the Audio-alpaca dataset, a pairwise text-to-audio preference dataset. This helps Tango 2 generate higher-quality and more aligned audio outputs. Model inputs and outputs Inputs Prompt**: A textual description of the desired audio to be generated. Steps**: The number of steps to use for the diffusion-based audio generation process, with more steps typically producing higher-quality results at the cost of longer inference time. Guidance**: The guidance scale, which controls the trade-off between sample quality and sample diversity during the audio generation process. Outputs Audio**: The generated audio clip corresponding to the input prompt, in WAV format. Capabilities Tango and Tango 2 can generate a wide variety of realistic audio clips, including human sounds, animal sounds, natural and artificial sounds, and sound effects. For example, they can generate sounds of an audience cheering and clapping, rolling thunder with lightning strikes, or a car engine revving. What can I use it for? The Tango and Tango 2 models can be used for a variety of applications, such as: Audio content creation**: Generating audio clips for videos, games, podcasts, and other multimedia projects. Sound design**: Creating custom sound effects for various applications. Music composition**: Generating musical elements or accompaniment for songwriting and composition. Accessibility**: Generating audio descriptions for visually impaired users. Things to try You can try generating various types of audio clips by providing different prompts to the Tango and Tango 2 models, such as: Everyday sounds (e.g., a dog barking, water flowing, a car engine revving) Natural phenomena (e.g., thunderstorms, wind, rain) Musical instruments and soundscapes (e.g., a piano playing, a symphony orchestra) Human vocalizations (e.g., laughter, cheering, singing) Ambient and abstract sounds (e.g., a futuristic machine, alien landscapes) Experiment with the number of steps and guidance scale to find the right balance between sample quality and generation time for your specific use case.

Read more

Updated Invalid Date

AI model preview image

sdxl-lightning-4step

bytedance

Total Score

548.9K

sdxl-lightning-4step is a fast text-to-image model developed by ByteDance that can generate high-quality images in just 4 steps. It is similar to other fast diffusion models like AnimateDiff-Lightning and Instant-ID MultiControlNet, which also aim to speed up the image generation process. Unlike the original Stable Diffusion model, these fast models sacrifice some flexibility and control to achieve faster generation times. Model inputs and outputs The sdxl-lightning-4step model takes in a text prompt and various parameters to control the output image, such as the width, height, number of images, and guidance scale. The model can output up to 4 images at a time, with a recommended image size of 1024x1024 or 1280x1280 pixels. Inputs Prompt**: The text prompt describing the desired image Negative prompt**: A prompt that describes what the model should not generate Width**: The width of the output image Height**: The height of the output image Num outputs**: The number of images to generate (up to 4) Scheduler**: The algorithm used to sample the latent space Guidance scale**: The scale for classifier-free guidance, which controls the trade-off between fidelity to the prompt and sample diversity Num inference steps**: The number of denoising steps, with 4 recommended for best results Seed**: A random seed to control the output image Outputs Image(s)**: One or more images generated based on the input prompt and parameters Capabilities The sdxl-lightning-4step model is capable of generating a wide variety of images based on text prompts, from realistic scenes to imaginative and creative compositions. The model's 4-step generation process allows it to produce high-quality results quickly, making it suitable for applications that require fast image generation. What can I use it for? The sdxl-lightning-4step model could be useful for applications that need to generate images in real-time, such as video game asset generation, interactive storytelling, or augmented reality experiences. Businesses could also use the model to quickly generate product visualization, marketing imagery, or custom artwork based on client prompts. Creatives may find the model helpful for ideation, concept development, or rapid prototyping. Things to try One interesting thing to try with the sdxl-lightning-4step model is to experiment with the guidance scale parameter. By adjusting the guidance scale, you can control the balance between fidelity to the prompt and diversity of the output. Lower guidance scales may result in more unexpected and imaginative images, while higher scales will produce outputs that are closer to the specified prompt.

Read more

Updated Invalid Date