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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. We can then train a model to generate audio in this compressed space, and upsample back to the raw audio space.[^reference-25][^reference-17]

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We chose to work on music because we want to continue to push the boundaries of generative models. Our previous work on MuseNet explored synthesizing music based on large amounts of MIDI data. Now in raw audio, our models must learn to tackle high diversity as well as very long range structure, and the raw audio domain is particularly unforgiving of errors in short, medium, or long term timing.

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. This downsampling loses much of the audio detail, and sounds noticeably noisy as we go further down the levels. However, it retains essential information about the pitch, timbre, and volume of the audio.

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. 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.[^reference-29][^reference-30] 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.

To train this model, we crawled the web to curate a new dataset of 1.2 million songs (600,000 of which are in English), paired with the corresponding lyrics and metadata from LyricWiki. The metadata includes artist, album genre, and year of the songs, along with common moods or playlist keywords associated with each song. 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 top-level transformer is trained on the task of predicting compressed audio tokens. We can provide additional information, such as the artist and genre for each song. 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.

To match audio portions to their corresponding lyrics, we begin with a simple heuristic that aligns the characters of the lyrics to linearly span the duration of each song, and pass a fixed-size window of characters centered around the current segment during training. While this simple strategy of linear alignment worked surprisingly well, we found that it fails for certain genres with fast lyrics, such as hip hop. To address this, we use Spleeter[^reference-32] to extract vocals from each song and run NUS AutoLyricsAlign[^reference-33] on the extracted vocals to obtain precise word-level alignments of the lyrics. We chose a large enough window so that the actual lyrics have a high probability of being inside the window.

While 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. Our downsampling and upsampling process introduces discernable noise. Improving the VQ-VAE so its codes capture more musical information would help reduce this. Our models are also slow to sample from, because of the autoregressive nature of sampling. 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. Using techniques[^reference-27][^reference-34] that distill the model into a parallel sampler can significantly speed up the sampling speed. 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.

We collect a larger and more diverse dataset of songs, with labels for genres and artists. Model picks up artist and genre styles more consistently with diversity, and at convergence can also produce full-length songs with long-range coherence.

We scale our VQ-VAE from 22 to 44kHz to achieve higher quality audio. We also scale top-level prior from 1B to 5B to capture the increased information. We see better musical quality, clear singing, and long-range coherence. We also make novel completions of real songs.

One of the unique features of Wynk Music is that it offers users the ability to stream music in multiple regional languages, including Hindi, Punjabi, Bengali, Tamil, Telugu, and more. Also, users of the app can download MP3 songs for offline listening. This online music platform provides access to additional features such as offline listening, high-quality audio, and exclusive content.

This song mashup maker will automatically analyze your music file and identify places to split, cut, and apply remix effects. The mixing positions are marked on the audio track timeline. Then, of course, you can select different effects to adjust them accordingly.

To get the call audio i start a random song in spotify and mute the spotify audio (otherwise the music would be distracting the call) and suddenly i can hear the Teams audio. When i pause the song again, the call audio stops as well.

I have issues playing songs that are generally over 5-6 minutes long. Come 5-6 minutes the audio for the song will just stop, but the timer continues to run. I will then sit in silence for the remainder of the song, until the next one begins.

SAME!!! longer songs stop around 5-6 minutes. Pink Floyd, Metallica, for examples. Desktop, Android, all my Xfinity cable boxes, all do it. It's been doing it for a few years and I can't get a response. I have a paid account. Please help

HII am new to Blackberry.I am developing an application to get the song name from the live audio stream. I am able to get the mp3 stream bytes from the particular radioserver.To get the song name I add the flag "Icy-metadata:1".So I am getting the header from the stream.To get the mp3 block size I use "Icy-metaInt".How to recognize the metadatablocks with this mp3 block size.I am using the following code.can anyone help me to get it...Here the b[off+k] is the bytes that are from the server...I am converting whole stream in to charArray which is wrong, but how to recognize the metadataHeaders according to the mp3 block size.. 2351a5e196