musicgen

Maintainer: meta

Total Score

1.7K

Last updated 5/19/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

Get summaries of the top AI models delivered straight to your inbox:

Model overview

musicgen is a simple and controllable model for music generation developed by Meta. Unlike existing methods like MusicLM, musicgen doesn't require a self-supervised semantic representation and generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, the authors show they can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. musicgen was trained on 20K hours of licensed music, including an internal dataset of 10K high-quality music tracks and music data from ShutterStock and Pond5.

Model inputs and outputs

musicgen takes in a text prompt or melody and generates corresponding music. The model's inputs include a description of the desired music, an optional input audio file to influence the generated output, and various parameters to control the generation process like temperature, top-k, and top-p sampling. The output is a generated audio file in WAV format.

Inputs

  • Prompt: A description of the music you want to generate.
  • Input Audio: An optional audio file that will influence the generated music. If "continuation" is set to true, the generated music will be a continuation of the input audio. Otherwise, it will mimic the input audio's melody.
  • Duration: The duration of the generated audio in seconds.
  • Continuation Start/End: The start and end times of the input audio to use for continuation.
  • Various generation parameters: Settings like temperature, top-k, top-p, etc. to control the diversity and quality of the generated output.

Outputs

  • Generated Audio: A WAV file containing the generated music.

Capabilities

musicgen can generate a wide variety of music styles and genres based on the provided text prompt. For example, you could ask it to generate "tense, staccato strings with plucked dissonant strings, like a scary movie soundtrack" and it would produce corresponding music. The model can also continue or mimic the melody of an input audio file, allowing for more coherent and controlled music generation.

What can I use it for?

musicgen could be used for a variety of applications, such as:

  • Background music generation: Automatically generating custom music for videos, games, or other multimedia projects.
  • Music composition assistance: Helping musicians and composers come up with new musical ideas or sketches to build upon.
  • Audio creation for content creators: Allowing YouTubers, podcasters, and other content creators to easily add custom music to their projects.

Things to try

One interesting aspect of musicgen is its ability to generate music in parallel by predicting the different codebook components separately. This allows for faster generation compared to previous autoregressive music models. You could try experimenting with different generation parameters to find the right balance between generation speed, diversity, and quality for your use case.

Additionally, the model's ability to continue or mimic input audio opens up possibilities for interactive music creation workflows, where users could iterate on an initial seed melody or prompt to refine the generated output.



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

Related Models

⛏️

musicgen

aussielabs

Total Score

506

musicgen is a deployment of Meta's MusicGen model, a state-of-the-art controllable text-to-music generation system. It was developed by the team at aussielabs. musicgen can generate high-quality music from text prompts or continue and mimic existing audio. It is part of the broader AudioCraft library, which contains other impressive audio generation models like AudioGen and EnCodec. Model inputs and outputs Inputs Prompt**: A description of the music you want to generate. Input Audio**: An audio file that will influence the generated music. The generated music can either continue the audio file's melody or mimic its style. Duration**: The desired duration of the generated audio in seconds. Continuation Start/End**: The start and end times of the audio file to use for continuation. Model Version**: The specific MusicGen model to use, such as the "melody" version. Output Format**: The desired format for the generated audio, such as WAV. Normalization Strategy**: The strategy for normalizing the output audio. Temperature**: Controls the "conservativeness" of the sampling process. Top K/P**: Reduces the sampling to the most likely tokens. Classifier Free Guidance**: Increases the influence of the input on the output. Outputs Output**: The generated audio file in the specified format. Capabilities musicgen can generate diverse and high-quality musical compositions from text prompts. It can also continue and mimic existing audio, allowing for creative remixing and mashups. The model is highly controllable, with options to adjust the generated music's style, duration, and other parameters. What can I use it for? musicgen can be used for a variety of applications, such as: Generating custom background music for videos, games, or podcasts Creating unique musical compositions for personal or commercial projects Experimenting with remixing and mashups by continuing or mimicking existing tracks Exploring new musical ideas and styles through text-based prompts Things to try One interesting capability of musicgen is its ability to continue and mimic existing audio. Try providing an audio file as input and experiment with the "continuation" and "melody" options to see how the model can extend or transform the original music. You can also try adjusting the temperature and guidance settings to generate more diverse or controlled outputs.

Read more

Updated Invalid Date

AI model preview image

audiogen

sepal

Total Score

36

audiogen is a model developed by Sepal that can generate sounds from text prompts. It is similar to other audio-related models like musicgen from Meta, which generates music from prompts, and styletts2 from Adirik, which generates speech from text. audiogen can be used to create a wide variety of sounds, from ambient noise to sound effects, based on the text prompt provided. Model inputs and outputs audiogen takes a text prompt as the main input, along with several optional parameters to control the output, such as duration, temperature, and output format. The model then generates an audio file in the specified format that represents the sounds described by the prompt. Inputs Prompt**: A text description of the sounds to be generated Duration**: The maximum duration of the generated audio (in seconds) Temperature**: Controls the "conservativeness" of the sampling process, with higher values producing more diverse outputs Classifier Free Guidance**: Increases the influence of the input prompt on the output Output Format**: The desired output format for the generated audio (e.g., WAV) Outputs Audio File**: The generated audio file in the specified format Capabilities audiogen can create a wide range of sounds based on text prompts, from simple ambient noise to more complex sound effects. For example, you could use it to generate the sound of a babbling brook, a thunderstorm, or even the roar of a lion. The model's ability to generate diverse and realistic-sounding audio makes it a useful tool for tasks like audio production, sound design, and even voice user interface development. What can I use it for? audiogen could be used in a variety of projects that require audio generation, such as video game sound effects, podcast or audiobook background music, or even sound design for augmented reality or virtual reality applications. The model's versatility and ease of use make it a valuable tool for creators and developers working in these and other audio-related fields. Things to try One interesting aspect of audiogen is its ability to generate sounds that are both realistic and evocative. By crafting prompts that tap into specific emotions or sensations, users can explore the model's potential to create immersive audio experiences. For example, you could try generating the sound of a cozy fireplace or the peaceful ambiance of a forest, and then incorporate these sounds into a multimedia project or relaxation app.

Read more

Updated Invalid Date

AI model preview image

musicgen-looper

andreasjansson

Total Score

46

The musicgen-looper is a Cog implementation of the MusicGen model, a simple and controllable model for music generation developed by Facebook Research. Unlike existing music generation models like MusicLM, MusicGen does not require a self-supervised semantic representation and generates all four audio codebooks in a single pass. By introducing a small delay between the codebooks, MusicGen can predict them in parallel, reducing the number of auto-regressive steps per second of audio. The model was trained on 20,000 hours of licensed music data, including an internal dataset of 10,000 high-quality tracks as well as music from ShutterStock and Pond5. The musicgen-looper model is similar to other music generation models like music-inpainting-bert, cantable-diffuguesion, and looptest in its ability to generate music from prompts. However, the key differentiator of musicgen-looper is its focus on generating fixed-BPM loops from text prompts. Model inputs and outputs The musicgen-looper model takes in a text prompt describing the desired music, as well as various parameters to control the generation process, such as tempo, seed, and sampling parameters. It outputs a WAV file containing the generated audio loop. Inputs Prompt**: A description of the music you want to generate. BPM**: Tempo of the generated loop in beats per minute. Seed**: Seed for the random number generator. If not provided, a random seed will be used. Top K**: Reduces sampling to the k most likely tokens. Top P**: Reduces sampling to tokens with cumulative probability of p. When set to 0 (default), top_k sampling is used. Temperature**: Controls the "conservativeness" of the sampling process. Higher temperature means more diversity. Classifier Free Guidance**: Increases the influence of inputs on the output. Higher values produce lower-variance outputs that adhere more closely to the inputs. Max Duration**: Maximum duration of the generated loop in seconds. Variations**: Number of variations to generate. Model Version**: Selects the model to use for generation. Output Format**: Specifies the output format for the generated audio (currently only WAV is supported). Outputs WAV file**: The generated audio loop. Capabilities The musicgen-looper model can generate a wide variety of musical styles and textures from text prompts, including tense, dissonant strings, plucked strings, and more. By controlling parameters like tempo, sampling, and classifier free guidance, users can fine-tune the generated output to match their desired style and mood. What can I use it for? The musicgen-looper model could be useful for a variety of applications, such as: Soundtrack generation**: Generating background music or sound effects for videos, games, or other multimedia projects. Music composition**: Providing a starting point or inspiration for composers and musicians to build upon. Audio manipulation**: Experimenting with different prompts and parameters to create unique and interesting musical textures. The model's ability to generate fixed-BPM loops makes it particularly well-suited for applications where a seamless, loopable audio track is required. Things to try One interesting aspect of the musicgen-looper model is its ability to generate variations on a given prompt. By adjusting the "Variations" parameter, users can explore how the model interprets and reinterprets a prompt in different ways. This could be a useful tool for composers and musicians looking to generate a diverse set of ideas or explore the model's creative boundaries. Another interesting feature is the model's use of classifier free guidance, which helps the generated output adhere more closely to the input prompt. By experimenting with different levels of classifier free guidance, users can find the right balance between adhering to the prompt and introducing their own creative flair.

Read more

Updated Invalid Date

AI model preview image

musicgen-choral

fofr

Total Score

4

musicgen-choral is a version of the MusicGen model that has been fine-tuned on chamber choir music. It allows users to generate music influenced by this specific genre. Compared to the original MusicGen model, musicgen-choral has been adapted to excel at producing choral-style compositions. The model can generate new music or continue an existing audio file, and offers various configuration options to control the output. Model inputs and outputs The musicgen-choral model takes several inputs to generate music, including a prompt describing the desired output, an optional input audio file for continuation or mimicking, and various parameters to control the generation process. The generated audio is output as a URI that can be accessed and downloaded. Inputs Prompt**: A description of the music you want to generate. Input Audio**: An audio file that will influence the generated music. If continuation is True, the generated music will be a continuation of the audio file. Otherwise, the generated music will mimic the audio file's melody. Duration**: The duration of the generated audio in seconds. Continuation Start/End**: The start and end times of the audio file to use for continuation. Multi Band Diffusion**: Whether to use multi-band diffusion when decoding the EnCodec tokens. Normalization Strategy**: The strategy for normalizing the output audio. Temperature**: Controls the 'conservativeness' of the sampling process. Higher temperature means more diversity. Classifier Free Guidance**: Increases the influence of inputs on the output. Seed**: The seed for the random number generator. Top K/Top P**: Reduces sampling to the most likely tokens. Outputs Audio URI**: The generated audio is output as a URI that can be accessed and downloaded. Capabilities The musicgen-choral model is capable of generating high-quality choral-style music based on a provided prompt or input audio file. It can continue an existing audio clip or mimic its melody, and offers various parameters to control the generation process. The model was fine-tuned on a dataset of chamber choir music, allowing it to capture the nuances and characteristics of this genre. What can I use it for? The musicgen-choral model can be useful for a variety of music-related applications, such as: Composing original choral music for film, TV, or video game soundtracks Generating background music or accompaniment for choral performances Experimenting with different styles and moods of choral music Continuing or remixing existing choral recordings Things to try Some interesting things to try with the musicgen-choral model include: Experimenting with different prompts to see how the model generates varied choral compositions Trying the model's continuation and mimicking capabilities by providing input audio files Adjusting the various generation parameters, such as temperature and classifier free guidance, to produce different styles of choral music Comparing the output of musicgen-choral to the original MusicGen model to see the differences in the generated music

Read more

Updated Invalid Date