31:T2245,checkpoints = [ { "id": "drums_2bar_lokl_small", "model": "MusicVAE", "description": "A 2-bar, 9-class onehot drum model with a strong prior (low KL divergence), which is better for sampling. Less accurate, but smaller in size than full model.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/drums_2bar_lokl_small", "size_mb": 18.5 }, { "id": "drums_2bar_hikl_small", "model": "MusicVAE", "description": "A 2-bar, 9-class onehot drum model with a weak prior (higher KL divergence), which is better for reconstructions and interpolations. Less accurate, but smaller in size than full model.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/drums_2bar_hikl_small", "size_mb": 18.5 }, { "id": "drums_2bar_nade_9_q2", "model": "MusicVAE", "description": "A 2-bar, 9-class multilabel drum model with a NADE decoder. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/drums_2bar_nade_9_q2", "size_mb": 27.6 }, { "id": "drums_4bar_med_q2", "model": "MusicVAE", "description": "A medium-sized 2-bar, 9-class onehot drum model with a weak prior (higher KL divergence), which is better for reconstructions and interpolations. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/drums_4bar_med_q2", "size_mb": 68.2 }, { "id": "drums_4bar_med_lokl_q2", "model": "MusicVAE", "description": "A medium-sized 2-bar, 9-class onehot drum model with a strong prior (lower KL divergence), which is better for sampling. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/drums_4bar_med_lokl_q2", "size_mb": 68.2 }, { "id": "mel_2bar_small", "model": "MusicVAE", "description": "A 2-bar, 90-class onehot melody model. Less accurate, but smaller in size than full model.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_2bar_small", "size_mb": 17.7 }, { "id": "mel_4bar_med_q2", "model": "MusicVAE", "description": "A medium-sized 4-bar, 90-class onehot melody model. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_4bar_med_q2", "size_mb": 65.4 }, { "id": "mel_4bar_med_lokl_q2", "model": "MusicVAE", "description": "A medium-sized 4-bar, 90-class onehot melody model. Trained with a strong prior (low KL divergence), which is better for sampling. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_4bar_med_lokl_q2", "size_mb": 65.4 }, { "id": "mel_4bar_small_q2", "model": "MusicVAE", "description": "A 4-bar, 90-class onehot melody model. Less accurate, but smaller in size than full model. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_4bar_small_q2", "size_mb": 26.5 }, { "id": "mel_chords", "model": "MusicVAE", "description": "A 2-bar, 90-class onehot melody model with chord conditioning. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_chords", "size_mb": 17.6 }, { "id": "mel_16bar_small_q2", "model": "MusicVAE", "description": "A 16-bar, 90-class onehot melody model with a 16-step conductor level. Less accurate, but smaller in size than full model. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/mel_16bar_small_q2", "size_mb": 23.5 }, { "id": "trio_4bar_lokl_small_q1", "model": "MusicVAE", "description": "A 4-bar, 'trio' model for melody, bass, and drums, with a 4-step conductor level. Trained with a strong prior (low KL divergence), which is better for sampling. Less accurate, but smaller in size than full model. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/trio_4bar", "size_mb": 17.6 }, { "id": "multitrack", "model": "MusicVAE", "description": "A 1-bar multitrack model, trained with 64 free bits. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/multitrack", "size_mb": 26.4 }, { "id": "multitrack_med", "model": "MusicVAE", "description": "A larger 1-bar multitrack model, trained with 64 free bits. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/multitrack_med", "size_mb": 95.9 }, { "id": "multitrack_med_fb256", "model": "MusicVAE", "description": "A larger 1-bar multitrack model, trained with 256 free bits. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/multitrack_med_fb256", "size_mb": 95.9 }, { "id": "multitrack_chords", "model": "MusicVAE", "description": "A 1-bar chord-conditioned multitrack model, trained with 64 free bits. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/multitrack_chords", "size_mb": 26.9 }, { "id": "multitrack_med_chords", "model": "MusicVAE", "description": "A larger 1-bar chord-conditioned multitrack model, trained with 64 free bits. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/multitrack_med_chords", "size_mb": 96.9 }, { "id": "multitrack_med_chords_fb256", "model": "MusicVAE", "description": "A larger 1-bar chord-conditioned multitrack model, trained with 256 free bits. Quantized to 1-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/multitrack_med_chords_fb256", "size_mb": 96.9 }, { "id": "groovae_2bar_humanize", "model": "MusicVAE", "description": "A 2-bar GrooVAE model that converts a quantized, constant-velocity drum pattern into a 'humanized' groove.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/groovae_2bar_humanize", "size_mb": 15.8 }, { "id": "groovae_2bar_tap2drum", "model": "MusicVAE", "description": "A 2-bar GrooVAE model that converts a constant-velocity single-drum 'tap' pattern into a groove.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/groovae_2bar_tap2drum", "size_mb": 15.8 }, { "id": "groovae_4bar", "model": "MusicVAE", "description": "A 4-bar GrooVAE autoencoder.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_vae/groovae_4bar", "size_mb": 15.8 }, { "id": "basic_rnn", "model": "MusicRNN", "description": "A 36-class onehot MelodyRNN model. Converted from http://download.magenta.tensorflow.org/models/basic_rnn.mag.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_rnn/basic_rnn", "size_mb": 13.0 }, { "id": "melody_rnn", "model": "MusicRNN", "description": "A 128-class onehot MelodyRNN model.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_rnn/melody_rnn", "size_mb": 13.4 }, { "id": "drum_kit_rnn", "model": "MusicRNN", "description": "A 9-class onehot DrumsRNN model. Converted from http://download.magenta.tensorflow.org/models/drum_kit_rnn.mag.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_rnn/drum_kit_rnn", "size_mb": 11.9 }, { "id": "chord_pitches_improv", "model": "MusicRNN", "description": "A 36-class onehot melody ImprovRNN model conditioned on chords as described at https://github.com/tensorflow/magenta/tree/master/magenta/models/improv_rnn#chord-pitches-improv. Converted from http://download.magenta.tensorflow.org/models/chord_pitches_improv.mag.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/music_rnn/chord_pitches_improv", "size_mb": 5.6 }, { "id": "onsets_frames_uni", "model": "OnsetsAndFrames", "description": "A unidirectional piano transcription model.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/transcription/onsets_frames_uni", "size_mb": 60 }, { "id": "onsets_frames_uni_q2", "model": "OnsetsAndFrames", "description": "A unidirectional piano transcription model. Quantized to 2-byte weights.", "url": "https://storage.googleapis.com/magentadata/js/checkpoints/transcription/onsets_frames_uni_q2", "size_mb": 30 } ]32:[] 2f:{"title":"Using @magenta/music as a MIDI player","cells":[{"id":0,"value":"md`# Using @magenta/music as a MIDI player\n\nhttps://tensorflow.github.io/magenta-js/music \nhttps://bitmidi.com `","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"viewof select = {\n const select = html`\n \n `\n return select\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"viewof soundfont = radio({\n description: 'Use a sound font?',\n options: [\n { value: '', label: 'None' },\n { value: 'https://storage.googleapis.com/magentadata/js/soundfonts/salamander', label: 'Salamander Grand Piano' },\n { value: 'https://storage.googleapis.com/magentadata/js/soundfonts/sgm_plus', label: 'SGM Plus' },\n ],\n value: ''\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"{\n player.resumeContext()\n player.stop()\n\n yield html`Loading...`\n\n if (player.loadSamples)\n await player.loadSamples(sequence)\n\n const play = '▶︎ Play'\n const stop = '■ Stop'\n\n const button = html``\n button.onclick = async () => {\n if (player.isPlaying()) {\n button.innerText = play\n player.stop()\n } else {\n button.innerText = stop\n await player.start(sequence)\n player.stop()\n button.innerText = play\n }\n }\n\n yield button\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":225,"value":"viewof visualizer = {\n const div = html`

`\n div.value = new mm.PianoRollSVGVisualizer(sequence, div.querySelector('svg'))\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":271,"value":"sequence = {\n const sequence = await mm.urlToNoteSequence(select)\n return mm.sequences.quantizeNoteSequence({\n ...sequence,\n notes: sequence.notes.map(({program, ...rest}) => rest) // program throws off the soundfont?\n }, 8)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"player = {\n const player = soundfont ? new mm.SoundFontPlayer(soundfont) : new mm.Player()\n player.callbackObject = {\n run(note) { visualizer.redraw(note, true) },\n stop() {}\n }\n return player\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":442,"value":"md`Now to play with MusicRNN…`","pinned":false,"hidden":false,"mode":"ojs"},{"id":424,"value":"quantizedSequence = mm.sequences.quantizeNoteSequence(sequence, 4)","pinned":true,"hidden":false,"mode":"ojs"},{"id":358,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":356,"value":"// rnn = new mm.MusicRNN(checkpoints.filter(c => c.id === 'melody_rnn')[0].url)","pinned":true,"hidden":false,"mode":"ojs"},{"id":373,"value":"// continued = rnn.continueSequence(quantizedSequence, 100, 1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":408,"value":"/* {\n if (player.loadSamples)\n await player.loadSamples(continued)\n \n const button = html``\n button.onclick = async () => {\n if (player.isPlaying()) {\n button.innerText = 'Play'\n player.stop()\n } else {\n button.innerText = 'Stop'\n player.stop()\n await player.start(continued)\n player.stop()\n button.innerText = 'Play'\n }\n }\n \n return button\n} */","pinned":true,"hidden":false,"mode":"ojs"},{"id":42,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"mm = (await import('https://cdn.skypack.dev/@magenta/music?min')).default","pinned":true,"hidden":false,"mode":"ojs"},{"id":327,"value":"import { radio } from '@jashkenas/inputs'","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W32","stdlib":"1","titling":"auto"}