musicgen-medium
Maintainer: facebook
83
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
musicgen-medium is a 1.5B parameter text-to-music model developed by Facebook. It is capable of generating high-quality music samples conditioned on text descriptions or audio prompts. Unlike existing approaches like MusicLM, musicgen-medium does not require a self-supervised semantic representation and generates all 4 audio codebooks in a single pass. By introducing a small delay between the codebooks, it can predict them in parallel, reducing the number of autoregressive steps.
The model is part of a family of MusicGen checkpoints, including smaller musicgen-small and larger musicgen-large variants, as well as a musicgen-melody model focused on melody-guided generation.
Model inputs and outputs
musicgen-medium is a text-to-music model that takes in text descriptions as input and generates corresponding audio samples as output. The model is built on an autoregressive Transformer architecture and a 32kHz EnCodec tokenizer with 4 codebooks.
Inputs
- Text prompt: A text description that conditions the generated music, such as "lo-fi music with a soothing melody".
Outputs
- Audio sample: A generated 32kHz stereo audio waveform representing the music based on the text prompt.
Capabilities
musicgen-medium is capable of generating high-quality music across a variety of styles and genres based on text prompts. The model can produce samples with coherent melodies, harmonies, and rhythmic structures that match the provided descriptions. For example, it can generate "lo-fi music with a soothing melody", "happy rock", or "energetic EDM" when given the corresponding text inputs.
What can I use it for?
musicgen-medium is primarily intended for research on AI-based music generation, such as probing the model's limitations and understanding how to further improve the state of the art. It can also be used by machine learning enthusiasts to generate music guided by text or melody and gain insights into the current capabilities of generative AI models.
Things to try
One interesting aspect of musicgen-medium is its ability to generate music in parallel by predicting the 4 audio codebooks with a small delay. This allows for faster sample generation compared to autoregressive approaches that predict each audio sample sequentially. You can experiment with the generation process and observe how this parallel prediction affects the quality and coherence of the output music.
Another interesting direction is to explore prompt engineering - trying different types of text descriptions to see which ones yield the most musically satisfying results. The model's performance may vary across genres and styles, so it could be worth investigating its strengths and weaknesses in different musical domains.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The musicgen-small is a text-to-music model developed by Facebook that can generate high-quality music samples conditioned on text descriptions or audio prompts. 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 model can predict them in parallel, requiring only 50 auto-regressive steps per second of audio. MusicGen is available in different checkpoint sizes, including medium and large, as well as a melody variant trained for melody-guided music generation. These models were published in the paper Simple and Controllable Music Generation by researchers from Facebook. Model inputs and outputs Inputs Text descriptions**: MusicGen can generate music conditioned on text prompts describing the desired style, mood, or genre. Audio prompts**: The model can also be conditioned on audio inputs to guide the generation. Outputs 32kHz audio waveform**: MusicGen outputs a mono 32kHz audio waveform representing the generated music sample. Capabilities MusicGen demonstrates strong capabilities in generating high-quality, controllable music from text or audio inputs. The model can create diverse musical samples across genres like rock, pop, EDM, and more, while adhering to the provided prompts. What can I use it for? MusicGen is primarily intended for research on AI-based music generation, such as probing the model's limitations and exploring its potential applications. Hobbyists and amateur musicians may also find it useful for generating music guided by text or melody to better understand the current state of generative AI models. Things to try You can easily run MusicGen locally using the Transformers library, which provides a simple interface for generating audio from text prompts. Try experimenting with different genres, moods, and levels of detail in your prompts to see the range of musical outputs the model can produce.
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musicgen-large
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MusicGen-large is a text-to-music model developed by Facebook that can generate high-quality music samples conditioned on text descriptions or audio prompts. Unlike existing methods like MusicLM, MusicGen-large does not require a self-supervised semantic representation and generates all 4 codebooks in one pass, predicting them in parallel. This allows for faster generation at 50 auto-regressive steps per second of audio. MusicGen-large is part of a family of MusicGen models released by Facebook, including smaller and melody-focused checkpoints. Model inputs and outputs MusicGen-large is a text-to-music model, taking text descriptions or audio prompts as input and generating corresponding music samples as output. The model uses a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz, allowing it to generate all the audio information in parallel. Inputs Text descriptions**: Natural language prompts that describe the desired music Audio prompts**: Existing audio samples that the generated music should be conditioned on Outputs Music samples**: High-quality 32kHz audio waveforms representing the generated music Capabilities MusicGen-large can generate a wide variety of musical styles and genres based on text or audio prompts, demonstrating impressive quality and control. The model is able to capture complex musical structures and properties like melody, harmony, and rhythm in its outputs. By generating the audio in parallel, MusicGen-large can produce 50 seconds of music per second, making it efficient for applications. What can I use it for? The primary use cases for MusicGen-large are in music production and creative applications. Developers and artists could leverage the model to rapidly generate music for things like video game soundtracks, podcast jingles, or backing tracks for songs. The ability to control the music through text prompts also enables novel music composition workflows. Things to try One interesting thing to try with MusicGen-large is experimenting with the level of detail and specificity in the text prompts. See how changing the prompt from a broad genre descriptor to more detailed musical attributes affects the generated output. You could also try providing audio prompts and observe how the model blends the existing music with the text description.
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musicgen-melody
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musicgen-stereo-large
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musicgen-stereo-large is a 3.3B parameter text-to-music model developed by Facebook AI Research (FAIR). It is a large version of the MusicGen model, which is capable of generating high-quality music samples conditioned on text descriptions or audio prompts. Unlike existing methods like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. The musicgen-stereo-large model is a fine-tuned version of the original MusicGen model that can generate stereo audio, creating a more immersive and spatial listening experience. Compared to the smaller musicgen-small and medium musicgen-medium versions, the musicgen-stereo-large model has 3.3B parameters and can generate higher-quality and more complex musical compositions. Model inputs and outputs Inputs Text prompt**: A free-form text description of the desired music, such as "upbeat electronic dance track with a catchy melody" Outputs Stereo audio waveform**: The model generates a stereo 32kHz audio waveform based on the input text prompt. The audio has a length of up to 8 seconds. Capabilities The musicgen-stereo-large model can generate a wide variety of music styles and genres, from pop and rock to electronic and classical, by simply providing a text description. The stereo capabilities allow the model to create a more immersive and nuanced musical experience compared to mono audio. Some examples of the types of music the model can generate include: An upbeat, cinematic electronic track with a driving bassline and lush pads A melancholic piano ballad with a soaring melody A energetic rock song with heavy distorted guitars and thunderous drums What can I use it for? The primary use case for the musicgen-stereo-large model is AI-based music research and experimentation. Researchers and hobbyists can use this model to explore the current state of text-to-music generation, test different prompting strategies, and better understand the model's capabilities and limitations. Additionally, the model could be used to quickly generate musical ideas or sketches for music producers and composers. By providing a text description, users can kickstart the creative process and use the generated audio as a starting point for further development and refinement. Things to try One interesting aspect of the musicgen-stereo-large model is its ability to generate music in a stereo format. Try experimenting with prompts that leverage the spatial capabilities of the model, such as "a lush, atmospheric synth-pop track with a wide, enveloping soundscape" or "a rhythmic, percussive electronic piece with panning drums and bass." Observe how the stereo placement and imaging of the instruments and elements in the music can enhance the overall listening experience. Additionally, try providing the model with more detailed, specific prompts to see how it responds. For example, "a melancholic piano ballad in the style of Chopin, with a plaintive melody and rich, harmonically-complex chords" or "an upbeat, funk-inspired jazz track with a tight, syncopated rhythm section and improvised horn solos." The level of detail in the prompt can greatly influence the character and complexity of the generated music.
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