Due to our new technology, you will not have to worry anymore about running out of battery during the day. Play your music without a hitch with this smart music player.Our royalty free music samples come from artists, producers and instrumentalists across the globe, ensuring you get the best loops played and recorded at.
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Select from 15 genres and more than 200. Learn more and listen today.Sampling is a music production technique where artists take a section of existing audio and repurpose it into a new creation. Artists will use a hardware sampler such as an Akai MPC, a sampler plugin or their DAW to create a sample. These song snippets are usually looped and modified, sometimes beyond recognition.The main goal is a nice modern / html5 player that will display the track listing for each album and allow the visitor to listen to a sample of each song without allowing them to download the mp3 files directly. This MP3 Player is offline, which means you dont need the internet to use it.
Automatic music generation dates back to more than half a century. Music recognition.We already have a way to sell the physical CD product and ship it, that’s no problem.Just need a nice clean (hopefully a bit customizable) player widget that allows me to select the duration of each sample of each song, and create a separate “player box”for each album.This might seem rudimentary to some folks, but I can’t seem to find an HTML5 / MP3 player plugin that isn’t “all or nothing” and/or doesn’t prevent visitors from downloading full tracks.This topic was modified 1 year, 4 months ago by Jan Dembowski. For example, if you are using a sample in your own home and do not reproduce or sell your music, you may be able to sample freely.
Thus, to learn the high level semantics of music, a model would have to deal with extremely long-range dependencies.One way of addressing the long input problem is to use an autoencoder that compresses raw audio to a lower-dimensional space by discarding some of the perceptually irrelevant bits of information. For comparison, GPT-2 had 1,000 timesteps and OpenAI Five took tens of thousands of timesteps per game. A typical 4-minute song at CD quality (44 kHz, 16-bit) has over 10 million timesteps. Generating music at the audio level is challenging since the sequences are very long. A different approach is to model music directly as raw audio. This has led to impressive results like producing Bach chorals, polyphonic music with multiple instruments, as well as minute long musical pieces.But symbolic generators have limitations—they cannot capture human voices or many of the more subtle timbres, dynamics, and expressivity that are essential to music.
This downsampling loses much of the audio detail, and sounds noticeably noisy as we go further down the levels. To allow the model to reconstruct higher frequencies easily, we add a spectral loss that penalizes the norm of the difference of input and reconstructed spectrograms.We use three levels in our VQ-VAE, shown below, which compress the 44kHz raw audio by 8x, 32x, and 128x, respectively, with a codebook size of 2048 for each level. To maximize the use of the upper levels, we use separate decoders and independently reconstruct the input from the codes of each level. To alleviate codebook collapse common to VQ-VAE models, we use random restarts where we randomly reset a codebook vector to one of the encoded hidden states whenever its usage falls below a threshold. We modify their architecture as follows:
Each of these models has 72 layers of factorized self-attention on a context of 8192 codes, which corresponds to approximately 24 seconds, 6 seconds, and 1.5 seconds of raw audio at the top, middle and bottom levels, respectively.Once all of the priors are trained, we can generate codes from the top level, upsample them using the upsamplers, and decode them back to the raw audio space using the VQ-VAE decoder to sample novel songs. The middle and bottom upsampling priors add local musical structures like timbre, significantly improving the audio quality.We train these as autoregressive models using a simplified variant of Sparse Transformers. Like the VQ-VAE, we have three levels of priors: a top-level prior that generates the most compressed codes, and two upsampling priors that generate less compressed codes conditioned on above.The top-level prior models the long-range structure of music, and samples decoded from this level have lower audio quality but capture high-level semantics like singing and melodies.
This has two advantages: first, it reduces the entropy of the audio prediction, so the model is able to achieve better quality in any particular style second, at generation time, we are able to steer the model to generate in a style of our choosing.This t-SNE below shows how the model learns, in an unsupervised way, to cluster similar artists and genres close together, and also makes some surprising associations like Jennifer Lopez being so close to Dolly Parton!In addition to conditioning on artist and genre, we can provide more context at training time by conditioning the model on the lyrics for a song. We can provide additional information, such as the artist and genre for each song. Artist and Genre ConditioningThe top-level transformer is trained on the task of predicting compressed audio tokens. We train on 32-bit, 44.1 kHz raw audio, and perform data augmentation by randomly downmixing the right and left channels to produce mono audio. The metadata includes artist, album genre, and year of the songs, along with common moods or playlist keywords associated with each song.
Our downsampling and upsampling process introduces discernable noise. LimitationsWhile Jukebox represents a step forward in musical quality, coherence, length of audio sample, and ability to condition on artist, genre, and lyrics, there is a significant gap between these generations and human-created music.For example, while the generated songs show local musical coherence, follow traditional chord patterns, and can even feature impressive solos, we do not hear familiar larger musical structures such as choruses that repeat. After training, the model learns a more precise alignment.Lyric–music alignment learned by encoder–decoder attention layerAttention progresses from one lyric token to the next as the music progresses, with a few moments of uncertainty.
Future DirectionsOur audio team is continuing to work on generating audio samples conditioned on different kinds of priming information. Finally, we currently train on English lyrics and mostly Western music, but in the future we hope to include songs from other languages and parts of the world. Using techniques that distill the model into a parallel sampler can significantly speed up the sampling speed. It takes approximately 9 hours to fully render one minute of audio through our models, and thus they cannot yet be used in interactive applications. Our models are also slow to sample from, because of the autoregressive nature of sampling.
If you’re excited to work on these problems with us, we’re hiring.As generative modeling across various domains continues to advance, we are also conducting research into issues like bias and intellectual property rights, and are engaging with people who work in the domains where we develop tools. We expect human and model collaborations to be an increasingly exciting creative space. We hope this will improve the musicality of samples (in the way conditioning on lyrics improved the singing), and this would also be a way of giving musicians more control over the generations. Here's an example of a raw audio sample conditioned on MIDI tokens.
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