Separate voice from music
Spleeter is the Deezer source separation library with pretrained models written in Python and uses Tensorflow. It makes it easy to train source separation model (assuming you have a dataset of isolated sources), and provides already trained state of the art model for performing various flavor of separation:
* vocals (singing voice) / accompaniment separation (2 stems)
* vocals / drums / bass / other separation (4 stems)
* vocals / drums / bass / piano / other separation (5 stems)
Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU
blog: https://deezer.io/releasing-spleeter-deezer-r-d-source-separation-engine-2b88985e797e
paper: http://archives.ismir.net/ismir2019/latebreaking/000036.pdf
github: https://github.com/deezer/spleeter
#voice #music #tf
Spleeter is the Deezer source separation library with pretrained models written in Python and uses Tensorflow. It makes it easy to train source separation model (assuming you have a dataset of isolated sources), and provides already trained state of the art model for performing various flavor of separation:
* vocals (singing voice) / accompaniment separation (2 stems)
* vocals / drums / bass / other separation (4 stems)
* vocals / drums / bass / piano / other separation (5 stems)
Spleeter is also very fast as it can perform separation of audio files to 4 stems 100x faster than real-time when run on a GPU
blog: https://deezer.io/releasing-spleeter-deezer-r-d-source-separation-engine-2b88985e797e
paper: http://archives.ismir.net/ismir2019/latebreaking/000036.pdf
github: https://github.com/deezer/spleeter
#voice #music #tf
Listen to Transformer
It is an open source ML model from the Magenta research group at Google that can generate musical performances with some long-term structure. The authors find it interesting to see what these models can and can’t do, so they made this app to make it easier to explore and curate the model’s output.
The models were trained on an exciting data source: piano recordings on YouTube transcribed using Onsets and Frames. They trained each Transformer model on hundreds of thousands of piano recordings, with a total length of over 10k hours. As described in the Wave2Midi2Wave approach, using such transcriptions allows training symbolic music models on a representation that carries the expressive performance characteristics from the original recordings.
Also, the artwork for each song is algorithmically generated based on the notes in the song itself – while the notes are represented by random shapes, the opacity represents the velocity, and the size represents the duration of each note
paper: https://arxiv.org/abs/1809.04281
blog post: https://magenta.tensorflow.org/listen-to-transformer
github: https://github.com/magenta/listen-to-transformer
demos: https://magenta.github.io/listen-to-transformer/#a1_650.mid
#transformer #listen #music
It is an open source ML model from the Magenta research group at Google that can generate musical performances with some long-term structure. The authors find it interesting to see what these models can and can’t do, so they made this app to make it easier to explore and curate the model’s output.
The models were trained on an exciting data source: piano recordings on YouTube transcribed using Onsets and Frames. They trained each Transformer model on hundreds of thousands of piano recordings, with a total length of over 10k hours. As described in the Wave2Midi2Wave approach, using such transcriptions allows training symbolic music models on a representation that carries the expressive performance characteristics from the original recordings.
Also, the artwork for each song is algorithmically generated based on the notes in the song itself – while the notes are represented by random shapes, the opacity represents the velocity, and the size represents the duration of each note
paper: https://arxiv.org/abs/1809.04281
blog post: https://magenta.tensorflow.org/listen-to-transformer
github: https://github.com/magenta/listen-to-transformer
demos: https://magenta.github.io/listen-to-transformer/#a1_650.mid
#transformer #listen #music
🎙🎶Improved audio generative model from OpenAI
Wow! OpenAI just released Jukebox – neural net and service that generates music from genre, artist name, and some lyrics that you can supply. It is can generate even some singing like from corrupted magnet compact cassette.
Some of the sounds seem it is from hell. Agonizing Michel Jakson for example or Creepy Eminiem or Celien Dion
#OpenAI 's approach is to use 3 levels of quantized variational autoencoders VQVAE-2 to learn discrete representations of audio and compress audio by 8x, 32x, and 128x and use the spectral loss to reconstruct spectrograms. And after that, they use sparse transformers conditioned on lyrics to generate new patterns and upsample it to higher discrete samples and decode it to the song.
The net can even learn and generates some solo parts during the track.
explore some creepy songs: https://jukebox.openai.com/
code: https://github.com/openai/jukebox/
paper: https://cdn.openai.com/papers/jukebox.pdf
blog: https://openai.com/blog/jukebox/
#openAI #music #sound #cool #fan #creepy #vae #audiolearning #soundlearning
Wow! OpenAI just released Jukebox – neural net and service that generates music from genre, artist name, and some lyrics that you can supply. It is can generate even some singing like from corrupted magnet compact cassette.
Some of the sounds seem it is from hell. Agonizing Michel Jakson for example or Creepy Eminiem or Celien Dion
#OpenAI 's approach is to use 3 levels of quantized variational autoencoders VQVAE-2 to learn discrete representations of audio and compress audio by 8x, 32x, and 128x and use the spectral loss to reconstruct spectrograms. And after that, they use sparse transformers conditioned on lyrics to generate new patterns and upsample it to higher discrete samples and decode it to the song.
The net can even learn and generates some solo parts during the track.
explore some creepy songs: https://jukebox.openai.com/
code: https://github.com/openai/jukebox/
paper: https://cdn.openai.com/papers/jukebox.pdf
blog: https://openai.com/blog/jukebox/
#openAI #music #sound #cool #fan #creepy #vae #audiolearning #soundlearning
Lo-Fi Player
The team from the magenta project, that does research about deep learning and music powered by TensorFlow in Google, obviously, release a new fun project lofi-player powered by their open-source library magenta.js.
So it's basically a lo-fi music generator which popular genre on youtube streams and other kinds of stuff. You can customize the vibe on your manner and wish from sad to moody, slow to fast, etc.
It is based on their earlier work MusicVae to sample latent space of music and MelodyRNN to generate music sequences from different instruments. The project is not about new research, but to show what can do with an already done library in a creative way.
They also create a stream on youtube to listen lo-fi generated by that application and users in chat can together tune lo-fi player in stream :)
#magenta #lo-fi #music #google #tensorflow #fun
The team from the magenta project, that does research about deep learning and music powered by TensorFlow in Google, obviously, release a new fun project lofi-player powered by their open-source library magenta.js.
So it's basically a lo-fi music generator which popular genre on youtube streams and other kinds of stuff. You can customize the vibe on your manner and wish from sad to moody, slow to fast, etc.
It is based on their earlier work MusicVae to sample latent space of music and MelodyRNN to generate music sequences from different instruments. The project is not about new research, but to show what can do with an already done library in a creative way.
They also create a stream on youtube to listen lo-fi generated by that application and users in chat can together tune lo-fi player in stream :)
#magenta #lo-fi #music #google #tensorflow #fun
Lo-Fi Player
Interactive lofi beat player.