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8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2"> Search syntax tips </a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" 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3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="Fiv0wuhI2HlK7Tqc63XDYMEiGS6yXbZNTDxAFJEW9K0aQYGT8pCxbqDxzgmLOtMXuI6kxof9oNgWzQquM59BhA==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> 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class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePianoroll RNN-NADE\u003c/h2\u003e\u003ca id=\"user-content-pianoroll-rnn-nade\" class=\"anchor\" aria-label=\"Permalink: Pianoroll RNN-NADE\" href=\"#pianoroll-rnn-nade\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis model applies language modeling to polyphonic music generation using an\nLSTM combined with a \u003ca href=\"https://arxiv.org/abs/1605.02226\" rel=\"nofollow\"\u003eNADE\u003c/a\u003e, an architecture\ncalled an \u003ca href=\"http://www-etud.iro.umontreal.ca/~boulanni/ICML2012.pdf\" rel=\"nofollow\"\u003eRNN-NADE\u003c/a\u003e.\nUnlike melody RNNs, this model needs to be capable of modeling multiple\nsimultaneous notes. It does so by representing a NoteSequence as a \"pianoroll\",\nnamed after the medium used to store scores for\n\u003ca href=\"https://en.wikipedia.org/wiki/Piano_roll\" rel=\"nofollow\"\u003eplayer pianos\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn a pianoroll, the score is represented as a binary matrix where each row\nrepresents a step and each column represents a pitch. When a column has a value\nof 1, that means the associated pitch is active at that time. When the column\nvalue is 0, the pitch is inactivate. A downside of this representation is that\nit is difficult to represent repeated legatto notes since they will appear as a\nsingle note in a pianoroll.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSince we need to output multiple pitches at each step, we cannot use a softmax.\nThe \u003ca href=\"/magenta/magenta/blob/main/models/polyphony_rnn/README.md\"\u003epolyphony_rnn\u003c/a\u003e posted previously skirted\nthis issue by representing a single time step as multiple, sequential outputs\nfrom the RNN. In this model, we instead use a Neural Autoregressive Distribution\nEstimator, or NADE to sample multiple outputs given the RNN state at each step.\nSee the original \u003ca href=\"http://www-etud.iro.umontreal.ca/~boulanni/ICML2012.pdf\" rel=\"nofollow\"\u003eRNN-NADE paper\u003c/a\u003e\nand our code for more details on how this architecture works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to Use\u003c/h2\u003e\u003ca id=\"user-content-how-to-use\" class=\"anchor\" aria-label=\"Permalink: How to Use\" href=\"#how-to-use\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFirst, set up your \u003ca href=\"/magenta/magenta/blob/main/README.md\"\u003eMagenta environment\u003c/a\u003e. Next, you can either use a pre-trained model or train your own.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePre-trained\u003c/h2\u003e\u003ca id=\"user-content-pre-trained\" class=\"anchor\" aria-label=\"Permalink: Pre-trained\" href=\"#pre-trained\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to get started right away, you can use a model that we've pre-trained:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://download.magenta.tensorflow.org/models/pianoroll_rnn_nade.mag\" rel=\"nofollow\"\u003epianoroll_rnn_nade\u003c/a\u003e: Trained\non a large corpus of piano music scraped from the web.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://download.magenta.tensorflow.org/models/pianoroll_rnn_nade-bach.mag\" rel=\"nofollow\"\u003epianoroll_rnn_nade-bach\u003c/a\u003e:\nTrained on the \u003ca href=\"https://web.archive.org/web/20150503021418/http://www.jsbchorales.net/xml.shtml\" rel=\"nofollow\"\u003eBach Chorales\u003c/a\u003e dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGenerate a pianoroll sequence\u003c/h3\u003e\u003ca id=\"user-content-generate-a-pianoroll-sequence\" class=\"anchor\" aria-label=\"Permalink: Generate a pianoroll sequence\" href=\"#generate-a-pianoroll-sequence\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"BUNDLE_PATH=\u0026lt;absolute path of .mag file\u0026gt;\n\npianoroll_rnn_nade_generate \\\n--bundle_file=${BUNDLE_PATH} \\\n--output_dir=/tmp/pianoroll_rnn_nade/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--primer_pitches=\u0026quot;[67,64,60]\u0026quot;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eBUNDLE_PATH=\u0026lt;absolute path of .mag file\u0026gt;\n\npianoroll_rnn_nade_generate \\\n--bundle_file=${BUNDLE_PATH} \\\n--output_dir=/tmp/pianoroll_rnn_nade/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--primer_pitches=\"[67,64,60]\"\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will generate a polyphonic pianoroll sequence using a C Major chord as a primer.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThere are several command line options for controlling the generation process:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eprimer_pitches\u003c/strong\u003e: A string representation of a Python list of pitches that will be used as a starting chord with a quarter note duration. For example: \u003ccode\u003e\"[60, 64, 67]\"\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eprimer_pianoroll\u003c/strong\u003e: A string representation of a Python list of \u003ccode\u003enote_seq.PianorollSequence\u003c/code\u003e event values (tuples of active MIDI pitches for a sequence of steps). For example: \u003ccode\u003e\"[(55,), (54,), (55, 53), (50,), (62, 52), (), (63, 55)]\"\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eprimer_midi\u003c/strong\u003e: The path to a MIDI file containing a polyphonic track that will be used as a priming track.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFor a full list of command line options, run \u003ccode\u003epianoroll_rnn_nade_generate --help\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHere's an example that is primed with two bars of\n\u003cem\u003eTwinkle, Twinkle, Little Star\u003c/em\u003e \u003ca href=\"http://www.noteflight.com/scores/view/2bd64f53ef4a4ec692f5be310780b634b2b5d98b\" rel=\"nofollow\"\u003eset in two-voice counterpoint\u003c/a\u003e:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"BUNDLE_PATH=\u0026lt;absolute path of .mag file\u0026gt;\n\npianoroll_rnn_nade_generate \\\n--bundle_file=${BUNDLE_PATH} \\\n--output_dir=/tmp/pianoroll_rnn_nade/generated \\\n--qpm=90 \\\n--num_outputs=10 \\\n--num_steps=64 \\\n--primer_pianoroll=\u0026quot;[(55,), (54,), (55, 52), (50,), (62, 52), (57,), \u0026quot;\\\n\u0026quot;(62, 55), (59,), (64, 52), (60,), (64, 59), (57,), (62, 59), (62, 55), \u0026quot;\\\n\u0026quot;(62, 52), (62, 55)]\u0026quot;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eBUNDLE_PATH=\u0026lt;absolute path of .mag file\u0026gt;\n\npianoroll_rnn_nade_generate \\\n--bundle_file=${BUNDLE_PATH} \\\n--output_dir=/tmp/pianoroll_rnn_nade/generated \\\n--qpm=90 \\\n--num_outputs=10 \\\n--num_steps=64 \\\n--primer_pianoroll=\"[(55,), (54,), (55, 52), (50,), (62, 52), (57,), \"\\\n\"(62, 55), (59,), (64, 52), (60,), (64, 59), (57,), (62, 59), (62, 55), \"\\\n\"(62, 52), (62, 55)]\"\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTrain your own\u003c/h2\u003e\u003ca id=\"user-content-train-your-own\" class=\"anchor\" aria-label=\"Permalink: Train your own\" href=\"#train-your-own\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreate NoteSequences\u003c/h3\u003e\u003ca id=\"user-content-create-notesequences\" class=\"anchor\" aria-label=\"Permalink: Create NoteSequences\" href=\"#create-notesequences\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOur first step will be to convert a collection of MIDI or MusicXML files into NoteSequences. NoteSequences are \u003ca href=\"https://developers.google.com/protocol-buffers/\" rel=\"nofollow\"\u003eprotocol buffers\u003c/a\u003e, which is a fast and efficient data format, and easier to work with than MIDI files. See \u003ca href=\"/magenta/magenta/blob/main/magenta/scripts/README.md\"\u003eBuilding your Dataset\u003c/a\u003e for instructions on generating a TFRecord file of NoteSequences. In this example, we assume the NoteSequences were output to \u003ccode\u003e/tmp/notesequences.tfrecord\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAn example of training input is the Bach Chorales dataset, which will teach the model to generate sequences that sound like Bach. It is available either on this \u003ca href=\"https://web.archive.org/web/20150503021418/http://www.jsbchorales.net/xml.shtml\" rel=\"nofollow\"\u003earchive.org mirror\u003c/a\u003e (the \u003ca href=\"http://www.jsbchorales.net/xml.shtml\" rel=\"nofollow\"\u003eoriginal site\u003c/a\u003e seems to be down) or via the \u003ca href=\"https://github.com/cuthbertLab/music21/tree/master/music21/corpus/bach\"\u003emusic21 bach corpus\u003c/a\u003e (which also contains some additional Bach pieces).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreate SequenceExamples\u003c/h3\u003e\u003ca id=\"user-content-create-sequenceexamples\" class=\"anchor\" aria-label=\"Permalink: Create SequenceExamples\" href=\"#create-sequenceexamples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSequenceExamples are fed into the model during training and evaluation. Each SequenceExample will contain a sequence of inputs and a sequence of labels that represent a pianoroll sequence. Run the command below to extract pianoroll sequences from your NoteSequences and save them as SequenceExamples. Two collections of SequenceExamples will be generated, one for training, and one for evaluation, where the fraction of SequenceExamples in the evaluation set is determined by \u003ccode\u003e--eval_ratio\u003c/code\u003e. With an eval ratio of 0.10, 10% of the extracted polyphonic tracks will be saved in the eval collection, and 90% will be saved in the training collection.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pianoroll_rnn_nade_create_dataset \\\n--input=/tmp/notesequences.tfrecord \\\n--output_dir=/tmp/pianoroll_rnn_nade/sequence_examples \\\n--eval_ratio=0.10\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epianoroll_rnn_nade_create_dataset \\\n--input=/tmp/notesequences.tfrecord \\\n--output_dir=/tmp/pianoroll_rnn_nade/sequence_examples \\\n--eval_ratio=0.10\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTrain and Evaluate the Model\u003c/h3\u003e\u003ca id=\"user-content-train-and-evaluate-the-model\" class=\"anchor\" aria-label=\"Permalink: Train and Evaluate the Model\" href=\"#train-and-evaluate-the-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun the command below to start a training job using the attention configuration. \u003ccode\u003e--run_dir\u003c/code\u003e is the directory where checkpoints and TensorBoard data for this run will be stored. \u003ccode\u003e--sequence_example_file\u003c/code\u003e is the TFRecord file of SequenceExamples that will be fed to the model. \u003ccode\u003e--num_training_steps\u003c/code\u003e (optional) is how many update steps to take before exiting the training loop. If left unspecified, the training loop will run until terminated manually. \u003ccode\u003e--hparams\u003c/code\u003e (optional) can be used to specify hyperparameters other than the defaults. For this example, we specify a custom batch size of 64 instead of the default batch size of 48. Using smaller batch sizes can help reduce memory usage, which can resolve potential out-of-memory issues when training larger models. We'll also use a single-layer RNN with 128 units, instead of the default of 3 layers of 128 units each. This will make our model train faster. However, if you have enough compute power, you can try using larger layer sizes for better results.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pianoroll_rnn_nade_train \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/training_pianoroll_tracks.tfrecord \\\n--hparams=\u0026quot;batch_size=48,rnn_layer_sizes=[128]\u0026quot; \\\n--num_training_steps=20000\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epianoroll_rnn_nade_train \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/training_pianoroll_tracks.tfrecord \\\n--hparams=\"batch_size=48,rnn_layer_sizes=[128]\" \\\n--num_training_steps=20000\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOptionally run an eval job in parallel. \u003ccode\u003e--run_dir\u003c/code\u003e, \u003ccode\u003e--hparams\u003c/code\u003e, and \u003ccode\u003e--num_training_steps\u003c/code\u003e should all be the same values used for the training job. \u003ccode\u003e--sequence_example_file\u003c/code\u003e should point to the separate set of eval pianoroll tracks. Include \u003ccode\u003e--eval\u003c/code\u003e to make this an eval job, resulting in the model only being evaluated without any of the weights being updated.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pianoroll_rnn_nade_train \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/eval_pianoroll_tracks.tfrecord \\\n--hparams=\u0026quot;batch_size=48,rnn_layer_sizes=[128]\u0026quot; \\\n--num_training_steps=20000 \\\n--eval\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epianoroll_rnn_nade_train \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/eval_pianoroll_tracks.tfrecord \\\n--hparams=\"batch_size=48,rnn_layer_sizes=[128]\" \\\n--num_training_steps=20000 \\\n--eval\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eRun TensorBoard to view the training and evaluation data.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"tensorboard --logdir=/tmp/pianoroll_rnn_nade/logdir\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003etensorboard --logdir=/tmp/pianoroll_rnn_nade/logdir\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen go to \u003ca href=\"http://localhost:6006\" rel=\"nofollow\"\u003ehttp://localhost:6006\u003c/a\u003e to view the TensorBoard dashboard.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGenerate Pianoroll Tracks\u003c/h3\u003e\u003ca id=\"user-content-generate-pianoroll-tracks\" class=\"anchor\" aria-label=\"Permalink: Generate Pianoroll Tracks\" href=\"#generate-pianoroll-tracks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePianoroll tracks can be generated during or after training. Run the command below to generate a set of pianoroll tracks using the latest checkpoint file of your trained model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003e--run_dir\u003c/code\u003e should be the same directory used for the training job. The \u003ccode\u003etrain\u003c/code\u003e subdirectory within \u003ccode\u003e--run_dir\u003c/code\u003e is where the latest checkpoint file will be loaded from. For example, if we use \u003ccode\u003e--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1\u003c/code\u003e. The most recent checkpoint file in \u003ccode\u003e/tmp/pianoroll_rnn_nade/logdir/run1/train\u003c/code\u003e will be used.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003e--hparams\u003c/code\u003e should be the same hyperparameters used for the training job, although some of them will be ignored, like the batch size.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003e--output_dir\u003c/code\u003e is where the generated MIDI files will be saved. \u003ccode\u003e--num_outputs\u003c/code\u003e is the number of pianoroll tracks that will be generated. \u003ccode\u003e--num_steps\u003c/code\u003e is how long each melody will be in 16th steps (128 steps = 8 bars).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee above for more information on other command line options.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pianoroll_rnn_nade_generate \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--output_dir=/tmp/pianoroll_rnn_nade/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--primer_pitches=\u0026quot;[67,64,60]\u0026quot;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epianoroll_rnn_nade_generate \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--output_dir=/tmp/pianoroll_rnn_nade/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--primer_pitches=\"[67,64,60]\"\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreating a Bundle File\u003c/h3\u003e\u003ca id=\"user-content-creating-a-bundle-file\" class=\"anchor\" aria-label=\"Permalink: Creating a Bundle File\" href=\"#creating-a-bundle-file\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ca href=\"https://github.com/magenta/note-seq/blob/main/note_seq/protobuf/generator.proto\"\u003ebundle format\u003c/a\u003e\nis a convenient way of combining the model checkpoint, metagraph, and\nsome metadata about the model into a single file.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo generate a bundle, use the\n\u003ca href=\"/magenta/magenta/blob/main/magenta/models/shared/sequence_generator.py\"\u003ecreate_bundle_file\u003c/a\u003e\nmethod within SequenceGenerator. Our generator script\nsupports a \u003ccode\u003e--save_generator_bundle\u003c/code\u003e flag that calls this method. Example:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"pianoroll_rnn_nade_generate \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--bundle_file=/tmp/pianoroll_rnn_nade.mag \\\n--save_generator_bundle\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epianoroll_rnn_nade_generate \\\n--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \\\n--bundle_file=/tmp/pianoroll_rnn_nade.mag \\\n--save_generator_bundle\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Pianoroll RNN-NADE","anchor":"pianoroll-rnn-nade","htmlText":"Pianoroll RNN-NADE"},{"level":2,"text":"How to Use","anchor":"how-to-use","htmlText":"How to Use"},{"level":2,"text":"Pre-trained","anchor":"pre-trained","htmlText":"Pre-trained"},{"level":3,"text":"Generate a pianoroll sequence","anchor":"generate-a-pianoroll-sequence","htmlText":"Generate a pianoroll sequence"},{"level":2,"text":"Train your own","anchor":"train-your-own","htmlText":"Train your own"},{"level":3,"text":"Create NoteSequences","anchor":"create-notesequences","htmlText":"Create NoteSequences"},{"level":3,"text":"Create SequenceExamples","anchor":"create-sequenceexamples","htmlText":"Create SequenceExamples"},{"level":3,"text":"Train and Evaluate the Model","anchor":"train-and-evaluate-the-model","htmlText":"Train and Evaluate the Model"},{"level":3,"text":"Generate Pianoroll Tracks","anchor":"generate-pianoroll-tracks","htmlText":"Generate Pianoroll Tracks"},{"level":3,"text":"Creating a Bundle File","anchor":"creating-a-bundle-file","htmlText":"Creating a Bundle File"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fmagenta%2Fmagenta%2Ftree%2Fmain%2Fmagenta%2Fmodels%2Fpianoroll_rnn_nade"}},"totalCount":9,"showBranchInfobar":false},"fileTree":{"magenta/models":{"items":[{"name":"arbitrary_image_stylization","path":"magenta/models/arbitrary_image_stylization","contentType":"directory"},{"name":"coconet","path":"magenta/models/coconet","contentType":"directory"},{"name":"drums_rnn","path":"magenta/models/drums_rnn","contentType":"directory"},{"name":"gansynth","path":"magenta/models/gansynth","contentType":"directory"},{"name":"image_stylization","path":"magenta/models/image_stylization","contentType":"directory"},{"name":"improv_rnn","path":"magenta/models/improv_rnn","contentType":"directory"},{"name":"latent_transfer","path":"magenta/models/latent_transfer","contentType":"directory"},{"name":"melody_rnn","path":"magenta/models/melody_rnn","contentType":"directory"},{"name":"music_vae","path":"magenta/models/music_vae","contentType":"directory"},{"name":"nsynth","path":"magenta/models/nsynth","contentType":"directory"},{"name":"onsets_frames_transcription","path":"magenta/models/onsets_frames_transcription","contentType":"directory"},{"name":"performance_rnn","path":"magenta/models/performance_rnn","contentType":"directory"},{"name":"piano_genie","path":"magenta/models/piano_genie","contentType":"directory"},{"name":"pianoroll_rnn_nade","path":"magenta/models/pianoroll_rnn_nade","contentType":"directory"},{"name":"polyphony_rnn","path":"magenta/models/polyphony_rnn","contentType":"directory"},{"name":"rl_tuner","path":"magenta/models/rl_tuner","contentType":"directory"},{"name":"score2perf","path":"magenta/models/score2perf","contentType":"directory"},{"name":"shared","path":"magenta/models/shared","contentType":"directory"},{"name":"sketch_rnn","path":"magenta/models/sketch_rnn","contentType":"directory"},{"name":"svg_vae","path":"magenta/models/svg_vae","contentType":"directory"},{"name":"README.md","path":"magenta/models/README.md","contentType":"file"},{"name":"__init__.py","path":"magenta/models/__init__.py","contentType":"file"}],"totalCount":22},"magenta":{"items":[{"name":"common","path":"magenta/common","contentType":"directory"},{"name":"contrib","path":"magenta/contrib","contentType":"directory"},{"name":"interfaces","path":"magenta/interfaces","contentType":"directory"},{"name":"js","path":"magenta/js","contentType":"directory"},{"name":"models","path":"magenta/models","contentType":"directory"},{"name":"music","path":"magenta/music","contentType":"directory"},{"name":"pipelines","path":"magenta/pipelines","contentType":"directory"},{"name":"reviews","path":"magenta/reviews","contentType":"directory"},{"name":"scripts","path":"magenta/scripts","contentType":"directory"},{"name":"tensor2tensor","path":"magenta/tensor2tensor","contentType":"directory"},{"name":"testdata","path":"magenta/testdata","contentType":"directory"},{"name":"tools","path":"magenta/tools","contentType":"directory"},{"name":"video","path":"magenta/video","contentType":"directory"},{"name":"__init__.py","path":"magenta/__init__.py","contentType":"file"},{"name":"version.py","path":"magenta/version.py","contentType":"file"}],"totalCount":15},"":{"items":[{"name":".github","path":".github","contentType":"directory"},{"name":"demos","path":"demos","contentType":"directory"},{"name":"magenta","path":"magenta","contentType":"directory"},{"name":".gitignore","path":".gitignore","contentType":"file"},{"name":".isort.cfg","path":".isort.cfg","contentType":"file"},{"name":".pylintrc","path":".pylintrc","contentType":"file"},{"name":"AUTHORS","path":"AUTHORS","contentType":"file"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"conftest.py","path":"conftest.py","contentType":"file"},{"name":"magenta-logo-bg.png","path":"magenta-logo-bg.png","contentType":"file"},{"name":"setup.cfg","path":"setup.cfg","contentType":"file"},{"name":"setup.py","path":"setup.py","contentType":"file"}],"totalCount":13}},"fileTreeProcessingTime":6.5527750000000005,"foldersToFetch":[],"treeExpanded":true,"symbolsExpanded":false,"csrf_tokens":{"/magenta/magenta/branches":{"post":"MhtxVgOKWBJkjh3u7XEiYCzC-ichJt_3ObMWXLfwloYGclqSqS6GxVec9PnPo7CdALM4gs9w5Q0ZFe4iyYeDmw"},"/magenta/magenta/branches/fetch_and_merge/main":{"post":"Mq87MCnP4gphRQtBgi3U3pGS-gGy5VzoDlYQCiYWiJQMmgY34bVgCbVuLydm3YMjo5Xa_SO5I9x9mtCfIfkS4w"},"/magenta/magenta/branches/fetch_and_merge/main?discard_changes=true":{"post":"V4MDu9RaBNx9HMXNZ7Ug5cRtp-zVH29_sXAhbm_47Mhptj68HCCG36k34auDRXcY9mqHEERDEEvCvOH7aBd2vw"}}},"title":"magenta/magenta/models/pianoroll_rnn_nade 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class="types__StyledButton-sc-ws60qy-0 gAIXvS prc-Button-IconButton-szpyj" data-loading="false" data-no-visuals="true" data-size="small" aria-describedby=":R6mil9lab:-loading-announcement"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Pianoroll RNN-NADE</h2><a id="user-content-pianoroll-rnn-nade" class="anchor" aria-label="Permalink: Pianoroll RNN-NADE" href="#pianoroll-rnn-nade"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This model applies language modeling to polyphonic music generation using an LSTM combined with a <a href="https://arxiv.org/abs/1605.02226" rel="nofollow">NADE</a>, an architecture called an <a href="http://www-etud.iro.umontreal.ca/~boulanni/ICML2012.pdf" rel="nofollow">RNN-NADE</a>. Unlike melody RNNs, this model needs to be capable of modeling multiple simultaneous notes. It does so by representing a NoteSequence as a "pianoroll", named after the medium used to store scores for <a href="https://en.wikipedia.org/wiki/Piano_roll" rel="nofollow">player pianos</a>.</p> <p dir="auto">In a pianoroll, the score is represented as a binary matrix where each row represents a step and each column represents a pitch. When a column has a value of 1, that means the associated pitch is active at that time. When the column value is 0, the pitch is inactivate. A downside of this representation is that it is difficult to represent repeated legatto notes since they will appear as a single note in a pianoroll.</p> <p dir="auto">Since we need to output multiple pitches at each step, we cannot use a softmax. The <a href="/magenta/magenta/blob/main/models/polyphony_rnn/README.md">polyphony_rnn</a> posted previously skirted this issue by representing a single time step as multiple, sequential outputs from the RNN. In this model, we instead use a Neural Autoregressive Distribution Estimator, or NADE to sample multiple outputs given the RNN state at each step. See the original <a href="http://www-etud.iro.umontreal.ca/~boulanni/ICML2012.pdf" rel="nofollow">RNN-NADE paper</a> and our code for more details on how this architecture works.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to Use</h2><a id="user-content-how-to-use" class="anchor" aria-label="Permalink: How to Use" href="#how-to-use"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">First, set up your <a href="/magenta/magenta/blob/main/README.md">Magenta environment</a>. Next, you can either use a pre-trained model or train your own.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Pre-trained</h2><a id="user-content-pre-trained" class="anchor" aria-label="Permalink: Pre-trained" href="#pre-trained"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you want to get started right away, you can use a model that we've pre-trained:</p> <ul dir="auto"> <li><a href="http://download.magenta.tensorflow.org/models/pianoroll_rnn_nade.mag" rel="nofollow">pianoroll_rnn_nade</a>: Trained on a large corpus of piano music scraped from the web.</li> <li><a href="http://download.magenta.tensorflow.org/models/pianoroll_rnn_nade-bach.mag" rel="nofollow">pianoroll_rnn_nade-bach</a>: Trained on the <a href="https://web.archive.org/web/20150503021418/http://www.jsbchorales.net/xml.shtml" rel="nofollow">Bach Chorales</a> dataset.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Generate a pianoroll sequence</h3><a id="user-content-generate-a-pianoroll-sequence" class="anchor" aria-label="Permalink: Generate a pianoroll sequence" href="#generate-a-pianoroll-sequence"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="BUNDLE_PATH=<absolute path of .mag file> pianoroll_rnn_nade_generate \ --bundle_file=${BUNDLE_PATH} \ --output_dir=/tmp/pianoroll_rnn_nade/generated \ --num_outputs=10 \ --num_steps=128 \ --primer_pitches="[67,64,60]""><pre class="notranslate"><code>BUNDLE_PATH=<absolute path of .mag file> pianoroll_rnn_nade_generate \ --bundle_file=${BUNDLE_PATH} \ --output_dir=/tmp/pianoroll_rnn_nade/generated \ --num_outputs=10 \ --num_steps=128 \ --primer_pitches="[67,64,60]" </code></pre></div> <p dir="auto">This will generate a polyphonic pianoroll sequence using a C Major chord as a primer.</p> <p dir="auto">There are several command line options for controlling the generation process:</p> <ul dir="auto"> <li><strong>primer_pitches</strong>: A string representation of a Python list of pitches that will be used as a starting chord with a quarter note duration. For example: <code>"[60, 64, 67]"</code>.</li> <li><strong>primer_pianoroll</strong>: A string representation of a Python list of <code>note_seq.PianorollSequence</code> event values (tuples of active MIDI pitches for a sequence of steps). For example: <code>"[(55,), (54,), (55, 53), (50,), (62, 52), (), (63, 55)]"</code>.</li> <li><strong>primer_midi</strong>: The path to a MIDI file containing a polyphonic track that will be used as a priming track.</li> </ul> <p dir="auto">For a full list of command line options, run <code>pianoroll_rnn_nade_generate --help</code>.</p> <p dir="auto">Here's an example that is primed with two bars of <em>Twinkle, Twinkle, Little Star</em> <a href="http://www.noteflight.com/scores/view/2bd64f53ef4a4ec692f5be310780b634b2b5d98b" rel="nofollow">set in two-voice counterpoint</a>:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="BUNDLE_PATH=<absolute path of .mag file> pianoroll_rnn_nade_generate \ --bundle_file=${BUNDLE_PATH} \ --output_dir=/tmp/pianoroll_rnn_nade/generated \ --qpm=90 \ --num_outputs=10 \ --num_steps=64 \ --primer_pianoroll="[(55,), (54,), (55, 52), (50,), (62, 52), (57,), "\ "(62, 55), (59,), (64, 52), (60,), (64, 59), (57,), (62, 59), (62, 55), "\ "(62, 52), (62, 55)]""><pre class="notranslate"><code>BUNDLE_PATH=<absolute path of .mag file> pianoroll_rnn_nade_generate \ --bundle_file=${BUNDLE_PATH} \ --output_dir=/tmp/pianoroll_rnn_nade/generated \ --qpm=90 \ --num_outputs=10 \ --num_steps=64 \ --primer_pianoroll="[(55,), (54,), (55, 52), (50,), (62, 52), (57,), "\ "(62, 55), (59,), (64, 52), (60,), (64, 59), (57,), (62, 59), (62, 55), "\ "(62, 52), (62, 55)]" </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Train your own</h2><a id="user-content-train-your-own" class="anchor" aria-label="Permalink: Train your own" href="#train-your-own"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Create NoteSequences</h3><a id="user-content-create-notesequences" class="anchor" aria-label="Permalink: Create NoteSequences" href="#create-notesequences"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Our first step will be to convert a collection of MIDI or MusicXML files into NoteSequences. NoteSequences are <a href="https://developers.google.com/protocol-buffers/" rel="nofollow">protocol buffers</a>, which is a fast and efficient data format, and easier to work with than MIDI files. See <a href="/magenta/magenta/blob/main/magenta/scripts/README.md">Building your Dataset</a> for instructions on generating a TFRecord file of NoteSequences. In this example, we assume the NoteSequences were output to <code>/tmp/notesequences.tfrecord</code>.</p> <p dir="auto">An example of training input is the Bach Chorales dataset, which will teach the model to generate sequences that sound like Bach. It is available either on this <a href="https://web.archive.org/web/20150503021418/http://www.jsbchorales.net/xml.shtml" rel="nofollow">archive.org mirror</a> (the <a href="http://www.jsbchorales.net/xml.shtml" rel="nofollow">original site</a> seems to be down) or via the <a href="https://github.com/cuthbertLab/music21/tree/master/music21/corpus/bach">music21 bach corpus</a> (which also contains some additional Bach pieces).</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Create SequenceExamples</h3><a id="user-content-create-sequenceexamples" class="anchor" aria-label="Permalink: Create SequenceExamples" href="#create-sequenceexamples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">SequenceExamples are fed into the model during training and evaluation. Each SequenceExample will contain a sequence of inputs and a sequence of labels that represent a pianoroll sequence. Run the command below to extract pianoroll sequences from your NoteSequences and save them as SequenceExamples. Two collections of SequenceExamples will be generated, one for training, and one for evaluation, where the fraction of SequenceExamples in the evaluation set is determined by <code>--eval_ratio</code>. With an eval ratio of 0.10, 10% of the extracted polyphonic tracks will be saved in the eval collection, and 90% will be saved in the training collection.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pianoroll_rnn_nade_create_dataset \ --input=/tmp/notesequences.tfrecord \ --output_dir=/tmp/pianoroll_rnn_nade/sequence_examples \ --eval_ratio=0.10"><pre class="notranslate"><code>pianoroll_rnn_nade_create_dataset \ --input=/tmp/notesequences.tfrecord \ --output_dir=/tmp/pianoroll_rnn_nade/sequence_examples \ --eval_ratio=0.10 </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Train and Evaluate the Model</h3><a id="user-content-train-and-evaluate-the-model" class="anchor" aria-label="Permalink: Train and Evaluate the Model" href="#train-and-evaluate-the-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Run the command below to start a training job using the attention configuration. <code>--run_dir</code> is the directory where checkpoints and TensorBoard data for this run will be stored. <code>--sequence_example_file</code> is the TFRecord file of SequenceExamples that will be fed to the model. <code>--num_training_steps</code> (optional) is how many update steps to take before exiting the training loop. If left unspecified, the training loop will run until terminated manually. <code>--hparams</code> (optional) can be used to specify hyperparameters other than the defaults. For this example, we specify a custom batch size of 64 instead of the default batch size of 48. Using smaller batch sizes can help reduce memory usage, which can resolve potential out-of-memory issues when training larger models. We'll also use a single-layer RNN with 128 units, instead of the default of 3 layers of 128 units each. This will make our model train faster. However, if you have enough compute power, you can try using larger layer sizes for better results.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pianoroll_rnn_nade_train \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/training_pianoroll_tracks.tfrecord \ --hparams="batch_size=48,rnn_layer_sizes=[128]" \ --num_training_steps=20000"><pre class="notranslate"><code>pianoroll_rnn_nade_train \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/training_pianoroll_tracks.tfrecord \ --hparams="batch_size=48,rnn_layer_sizes=[128]" \ --num_training_steps=20000 </code></pre></div> <p dir="auto">Optionally run an eval job in parallel. <code>--run_dir</code>, <code>--hparams</code>, and <code>--num_training_steps</code> should all be the same values used for the training job. <code>--sequence_example_file</code> should point to the separate set of eval pianoroll tracks. Include <code>--eval</code> to make this an eval job, resulting in the model only being evaluated without any of the weights being updated.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pianoroll_rnn_nade_train \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/eval_pianoroll_tracks.tfrecord \ --hparams="batch_size=48,rnn_layer_sizes=[128]" \ --num_training_steps=20000 \ --eval"><pre class="notranslate"><code>pianoroll_rnn_nade_train \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --sequence_example_file=/tmp/pianoroll_rnn_nade/sequence_examples/eval_pianoroll_tracks.tfrecord \ --hparams="batch_size=48,rnn_layer_sizes=[128]" \ --num_training_steps=20000 \ --eval </code></pre></div> <p dir="auto">Run TensorBoard to view the training and evaluation data.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="tensorboard --logdir=/tmp/pianoroll_rnn_nade/logdir"><pre class="notranslate"><code>tensorboard --logdir=/tmp/pianoroll_rnn_nade/logdir </code></pre></div> <p dir="auto">Then go to <a href="http://localhost:6006" rel="nofollow">http://localhost:6006</a> to view the TensorBoard dashboard.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Generate Pianoroll Tracks</h3><a id="user-content-generate-pianoroll-tracks" class="anchor" aria-label="Permalink: Generate Pianoroll Tracks" href="#generate-pianoroll-tracks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Pianoroll tracks can be generated during or after training. Run the command below to generate a set of pianoroll tracks using the latest checkpoint file of your trained model.</p> <p dir="auto"><code>--run_dir</code> should be the same directory used for the training job. The <code>train</code> subdirectory within <code>--run_dir</code> is where the latest checkpoint file will be loaded from. For example, if we use <code>--run_dir=/tmp/pianoroll_rnn_nade/logdir/run1</code>. The most recent checkpoint file in <code>/tmp/pianoroll_rnn_nade/logdir/run1/train</code> will be used.</p> <p dir="auto"><code>--hparams</code> should be the same hyperparameters used for the training job, although some of them will be ignored, like the batch size.</p> <p dir="auto"><code>--output_dir</code> is where the generated MIDI files will be saved. <code>--num_outputs</code> is the number of pianoroll tracks that will be generated. <code>--num_steps</code> is how long each melody will be in 16th steps (128 steps = 8 bars).</p> <p dir="auto">See above for more information on other command line options.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pianoroll_rnn_nade_generate \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --output_dir=/tmp/pianoroll_rnn_nade/generated \ --num_outputs=10 \ --num_steps=128 \ --primer_pitches="[67,64,60]""><pre class="notranslate"><code>pianoroll_rnn_nade_generate \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --output_dir=/tmp/pianoroll_rnn_nade/generated \ --num_outputs=10 \ --num_steps=128 \ --primer_pitches="[67,64,60]" </code></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Creating a Bundle File</h3><a id="user-content-creating-a-bundle-file" class="anchor" aria-label="Permalink: Creating a Bundle File" href="#creating-a-bundle-file"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <a href="https://github.com/magenta/note-seq/blob/main/note_seq/protobuf/generator.proto">bundle format</a> is a convenient way of combining the model checkpoint, metagraph, and some metadata about the model into a single file.</p> <p dir="auto">To generate a bundle, use the <a href="/magenta/magenta/blob/main/magenta/models/shared/sequence_generator.py">create_bundle_file</a> method within SequenceGenerator. Our generator script supports a <code>--save_generator_bundle</code> flag that calls this method. Example:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="pianoroll_rnn_nade_generate \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --bundle_file=/tmp/pianoroll_rnn_nade.mag \ --save_generator_bundle"><pre class="notranslate"><code>pianoroll_rnn_nade_generate \ --run_dir=/tmp/pianoroll_rnn_nade/logdir/run1 \ --bundle_file=/tmp/pianoroll_rnn_nade.mag \ --save_generator_bundle </code></pre></div> </article></div></div></div></div></div></div></div><div class="Box-sc-g0xbh4-0"></div></div></div></div></div><div id="find-result-marks-container" class="Box-sc-g0xbh4-0 cCoXib"></div><button hidden="" data-testid="" data-hotkey-scope="read-only-cursor-text-area"></button><button hidden=""></button></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-app> </turbo-frame> </div> </turbo-frame> </main> </div> </div> <footer 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