
Muzaic.ai is a browser-based tool that scores video automatically. You give it a cut (or a folder of cuts); it returns a finished audio mix — music, SFX, ambience and voiceover as separate layers — mastered to broadcast spec, in roughly 30 seconds per clip.
What it actually does
The pipeline runs in four stages. First it analyses the video: scenes, objects, pacing, colour and sentiment. From that it builds an audio plan and commits each concept to a clear direction (the site's example: a string quartet under a battle scene) rather than averaging toward something inoffensive. Then it composes every layer to fit the picture and the other layers — SFX land on the action, ambience ducks under speech, the voiceover is drafted from your brief rather than read out. Finally it mixes and masters the result. Existing audio in the source can be kept, replaced or mixed under.
Output is three concepts per clip, exported as MP3 or WAV. Layer balance is adjusted inside Muzaic before export; the deliverable is the finished mix, not separate stems.
The commercial angle
Pricing is metered in minutes of finished audio, not per track or per download; downloads are unlimited. Personal is free for 10 minutes a month, non-commercial only. Creator ($45/month) covers your own brand and paid ads. Studio ($249/month) is for agencies: the licence travels to the client and the legal cover extends to them too. Enterprise adds TV, radio, cinema and OOH, plus a custom contract, DPA and SLA.
What it is not
It is not a music-generation toy. The free tier exists to test the workflow on your own footage; nothing from it may be published. The target user is a team producing hundreds of ad variants a month, where the audio step is the bottleneck.
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Most AI video tools hand you a black box: closed weights, a subscription, and no way to see what is happening under the hood. LTX takes the opposite approach. Built by Lightricks, LTX is an open foundation model that generates and simulates across video, audio, and the physical world, and it puts the weights, the code, and the control in your hands.
At the center of the model is LTX-2.5, a 22B-parameter dual-stream diffusion transformer that produces native 4K video at up to 50 frames per second, with audio and video generated together in a single pass rather than stitched together afterward. Artificial Analysis, an independent benchmarking group, currently ranks LTX among the top three AI video models in the world.
You choose how you want to use it. Download the open weights and run LTX-2.5 on your own hardware. License the model for on-premise deployment backed by enterprise support. Or build directly on LTX Studio, the production suite that turns the model into a full creative workflow. Companies like ElevenLabs, Asteria Film Co., Magnopus, and NVIDIA already rely on LTX for their own work.
LTX is not built for one-off social clips. It is infrastructure for teams that generate motion, audio, and physical environments as part of their own products and pipelines.
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MusicGen
Meta's MusicGen is an open-source deep-learning model designed to create short musical compositions based on textual descriptions. Trained on 20,000 hours of music, encompassing complete tracks and single instrument samples, this model produces 12 seconds of audio in response to user prompts. Additionally, users can submit reference audio to extract a general melody, which the model will incorporate alongside the provided description. All generated samples utilize the melody model, ensuring consistency. Furthermore, users have the option to run the model on their own GPUs or utilize Google Colab by following the guidelines available in the repository. MusicGen features a single-stage transformer architecture combined with efficient token interleaving techniques, which streamline the process by eliminating the need for multiple cascading models. This innovative approach enables MusicGen to generate high-quality audio samples that are responsive to both textual inputs and musical characteristics, allowing users to exert greater control over the final output. The combination of these features positions MusicGen as a versatile tool for music creation and exploration.
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OpenAI Jukebox
We are excited to unveil Jukebox, a cutting-edge neural network designed to create music, including basic vocalization, in diverse genres and artistic expressions as raw audio. Alongside the release of the model weights and code, we are offering a tool to help users explore the music samples generated by Jukebox. By inputting genre, artist, and lyrics, users can receive entirely new music pieces crafted from the ground up. Jukebox is capable of producing a vast array of musical and vocal styles, and it can also generalize to lyrics that were not part of the training dataset. The lyrics included here have been collaboratively crafted by researchers at OpenAI and a language model. When provided with lyrics from its training set, Jukebox generates songs that diverge significantly from the originals, showcasing its creative capabilities. Users can input a 12-second audio clip for Jukebox to build upon, with the final output reflecting a desired style. Our focus on music stems from a desire to advance the potential of generative models further. Utilizing a quantization-based approach called VQ-VAE, Jukebox’s autoencoder model effectively compresses audio into a discrete latent space, enabling innovative sound generation. As we continue to refine these technologies, we look forward to the creative possibilities that lie ahead.
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