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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="qv47jgqot2pfnEvIdmFIlIyj6lRx2GTFICHqHqUFfX86oMvY5sCYpt83QrVbzhH4wTxZOh5qej9VTgxFZ8uCQQ==" /> <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"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow 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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\"\u003eMelody RNN\u003c/h2\u003e\u003ca id=\"user-content-melody-rnn\" class=\"anchor\" aria-label=\"Permalink: Melody RNN\" href=\"#melody-rnn\"\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 melody generation using an LSTM.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConfigurations\u003c/h2\u003e\u003ca id=\"user-content-configurations\" class=\"anchor\" aria-label=\"Permalink: Configurations\" href=\"#configurations\"\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\"\u003eBasic\u003c/h3\u003e\u003ca id=\"user-content-basic\" class=\"anchor\" aria-label=\"Permalink: Basic\" href=\"#basic\"\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 configuration acts as a baseline for melody generation with an LSTM model. It uses basic one-hot encoding to represent extracted melodies as input to the LSTM. For training, all examples are transposed to the MIDI pitch range [48, 84] and outputs will also be in this range.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMono\u003c/h3\u003e\u003ca id=\"user-content-mono\" class=\"anchor\" aria-label=\"Permalink: Mono\" href=\"#mono\"\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 configuration acts as a baseline for melody generation with an LSTM model. It uses basic one-hot encoding to represent extracted melodies as input to the LSTM. While \u003ccode\u003ebasic_rnn\u003c/code\u003e is trained by transposing all inputs to a narrow range, \u003ccode\u003emono_rnn\u003c/code\u003e is able to use the full 128 MIDI pitches.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLookback\u003c/h3\u003e\u003ca id=\"user-content-lookback\" class=\"anchor\" aria-label=\"Permalink: Lookback\" href=\"#lookback\"\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\"\u003eLookback RNN introduces custom inputs and labels. The custom inputs allow the model to more easily recognize patterns that occur across 1 and 2 bars. They also help the model recognize patterns related to an events position within the measure. The custom labels reduce the amount of information that the RNN’s cell state has to remember by allowing the model to more easily repeat events from 1 and 2 bars ago. This results in melodies that wander less and have a more musical structure. For more information about the custom inputs and labels, and to hear some generated sample melodies, check out the \u003ca href=\"https://magenta.tensorflow.org/2016/07/15/lookback-rnn-attention-rnn/\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e. You can also read through the \u003ccode\u003eevents_to_input\u003c/code\u003e and \u003ccode\u003eevents_to_label\u003c/code\u003e methods in \u003ccode\u003enote_seq/encoder_decoder.py\u003c/code\u003e and \u003ccode\u003enote_seq/melody_encoder_decoder.py\u003c/code\u003e to see how the custom inputs and labels are actually being encoded.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAttention\u003c/h3\u003e\u003ca id=\"user-content-attention\" class=\"anchor\" aria-label=\"Permalink: Attention\" href=\"#attention\"\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\"\u003eIn this configuration we introduce the use of attention. Attention allows the model to more easily access past information without having to store that information in the RNN cell's state. This allows the model to more easily learn longer term dependencies, and results in melodies that have longer arching themes. For an overview of how the attention mechanism works and to hear some generated sample melodies, check out the \u003ca href=\"https://magenta.tensorflow.org/2016/07/15/lookback-rnn-attention-rnn/\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e. You can also read through the \u003ccode\u003eAttentionCellWrapper\u003c/code\u003e code in Tensorflow to see what's really going on under the hood.\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 on thousands of MIDI files.\nWe host .mag bundle files for each of the configurations described above at these links:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"http://download.magenta.tensorflow.org/models/basic_rnn.mag\" rel=\"nofollow\"\u003ebasic_rnn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://download.magenta.tensorflow.org/models/mono_rnn.mag\" rel=\"nofollow\"\u003emono_rnn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://download.magenta.tensorflow.org/models/lookback_rnn.mag\" rel=\"nofollow\"\u003elookback_rnn\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://download.magenta.tensorflow.org/models/attention_rnn.mag\" rel=\"nofollow\"\u003eattention_rnn\u003c/a\u003e\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 melody\u003c/h3\u003e\u003ca id=\"user-content-generate-a-melody\" class=\"anchor\" aria-label=\"Permalink: Generate a melody\" href=\"#generate-a-melody\"\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;\nCONFIG=\u0026lt;one of 'basic_rnn', 'lookback_rnn', or 'attention_rnn', matching the bundle\u0026gt;\n\nmelody_rnn_generate \\\n--config=${CONFIG} \\\n--bundle_file=${BUNDLE_PATH} \\\n--output_dir=/tmp/melody_rnn/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--primer_melody=\u0026quot;[60]\u0026quot;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eBUNDLE_PATH=\u0026lt;absolute path of .mag file\u0026gt;\nCONFIG=\u0026lt;one of 'basic_rnn', 'lookback_rnn', or 'attention_rnn', matching the bundle\u0026gt;\n\nmelody_rnn_generate \\\n--config=${CONFIG} \\\n--bundle_file=${BUNDLE_PATH} \\\n--output_dir=/tmp/melody_rnn/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--primer_melody=\"[60]\"\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will generate a melody starting with a middle C. If you'd like, you can also supply a priming melody using a string representation of a Python list. The values in the list should be ints that follow the melodies_lib.Melody format (-2 = no event, -1 = note-off event, values 0 through 127 = note-on event for that MIDI pitch). For example \u003ccode\u003e--primer_melody=\"[60, -2, 60, -2, 67, -2, 67, -2]\"\u003c/code\u003e would prime the model with the first four notes of \u003cem\u003eTwinkle Twinkle Little Star\u003c/em\u003e. Instead of using \u003ccode\u003e--primer_melody\u003c/code\u003e, we can use \u003ccode\u003e--primer_midi\u003c/code\u003e to prime our model with a melody stored in a MIDI file. For example, \u003ccode\u003e--primer_midi=\u0026lt;absolute path to magenta/models/melody_rnn/primer.mid\u0026gt;\u003c/code\u003e will prime the model with the melody in that MIDI file.\u003c/p\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 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\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 melody. Run the command below to extract melodies from our 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 melodies 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=\"melody_rnn_create_dataset \\\n--config=\u0026lt;one of 'basic_rnn', 'mono_rnn', lookback_rnn', or 'attention_rnn'\u0026gt; \\\n--input=/tmp/notesequences.tfrecord \\\n--output_dir=/tmp/melody_rnn/sequence_examples \\\n--eval_ratio=0.10\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emelody_rnn_create_dataset \\\n--config=\u0026lt;one of 'basic_rnn', 'mono_rnn', lookback_rnn', or 'attention_rnn'\u0026gt; \\\n--input=/tmp/notesequences.tfrecord \\\n--output_dir=/tmp/melody_rnn/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 128. 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 2-layer RNN with 64 units each, instead of the default of 2 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. You can also adjust how many previous steps the attention mechanism looks at by changing the \u003ccode\u003eattn_length\u003c/code\u003e hyperparameter. For this example we leave it at the default value of 40 steps (2.5 bars).\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"melody_rnn_train \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--sequence_example_file=/tmp/melody_rnn/sequence_examples/training_melodies.tfrecord \\\n--hparams=\u0026quot;batch_size=64,rnn_layer_sizes=[64,64]\u0026quot; \\\n--num_training_steps=20000\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emelody_rnn_train \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--sequence_example_file=/tmp/melody_rnn/sequence_examples/training_melodies.tfrecord \\\n--hparams=\"batch_size=64,rnn_layer_sizes=[64,64]\" \\\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 melodies. 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=\"melody_rnn_train \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--sequence_example_file=/tmp/melody_rnn/sequence_examples/eval_melodies.tfrecord \\\n--hparams=\u0026quot;batch_size=64,rnn_layer_sizes=[64,64]\u0026quot; \\\n--num_training_steps=20000 \\\n--eval\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emelody_rnn_train \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--sequence_example_file=/tmp/melody_rnn/sequence_examples/eval_melodies.tfrecord \\\n--hparams=\"batch_size=64,rnn_layer_sizes=[64,64]\" \\\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/melody_rnn/logdir\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003etensorboard --logdir=/tmp/melody_rnn/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 Melodies\u003c/h3\u003e\u003ca id=\"user-content-generate-melodies\" class=\"anchor\" aria-label=\"Permalink: Generate Melodies\" href=\"#generate-melodies\"\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\"\u003eMelodies can be generated during or after training. Run the command below to generate a set of melodies 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/melody_rnn/logdir/run1\u003c/code\u003e. The most recent checkpoint file in \u003ccode\u003e/tmp/melody_rnn/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 melodies 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\"\u003eAt least one note needs to be fed to the model before it can start generating consecutive notes. We can use \u003ccode\u003e--primer_melody\u003c/code\u003e to specify a priming melody using a string representation of a Python list. The values in the list should be ints that follow the melodies_lib.Melody format (-2 = no event, -1 = note-off event, values 0 through 127 = note-on event for that MIDI pitch). For example \u003ccode\u003e--primer_melody=\"[60, -2, 60, -2, 67, -2, 67, -2]\"\u003c/code\u003e would prime the model with the first four notes of Twinkle Twinkle Little Star. Instead of using \u003ccode\u003e--primer_melody\u003c/code\u003e, we can use \u003ccode\u003e--primer_midi\u003c/code\u003e to prime our model with a melody stored in a MIDI file. For example, \u003ccode\u003e--primer_midi=\u0026lt;absolute path to magenta/models/shared/primer.mid\u0026gt;\u003c/code\u003e will prime the model with the melody in that MIDI file. If neither \u003ccode\u003e--primer_melody\u003c/code\u003e nor \u003ccode\u003e--primer_midi\u003c/code\u003e are specified, a random note from the model's note range will be chosen as the first note, then the remaining notes will be generated by the model. In the example below we prime the melody with \u003ccode\u003e--primer_melody=\"[60]\"\u003c/code\u003e, a single note-on event for the note C4.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"melody_rnn_generate \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--output_dir=/tmp/melody_rnn/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--hparams=\u0026quot;batch_size=64,rnn_layer_sizes=[64,64]\u0026quot; \\\n--primer_melody=\u0026quot;[60]\u0026quot;\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003emelody_rnn_generate \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--output_dir=/tmp/melody_rnn/generated \\\n--num_outputs=10 \\\n--num_steps=128 \\\n--hparams=\"batch_size=64,rnn_layer_sizes=[64,64]\" \\\n--primer_melody=\"[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/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. All of our melody model generator scripts\nsupport a \u003ccode\u003e--save_generator_bundle\u003c/code\u003e flag that calls this method. Example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"melody_rnn_generate \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--hparams=\u0026quot;batch_size=64,rnn_layer_sizes=[64,64]\u0026quot; \\\n--bundle_file=/tmp/attention_rnn.mag \\\n--save_generator_bundle\"\u003e\u003cpre\u003emelody_rnn_generate \\\n--config=attention_rnn \\\n--run_dir=/tmp/melody_rnn/logdir/run1 \\\n--hparams=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ebatch_size=64,rnn_layer_sizes=[64,64]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \\\n--bundle_file=/tmp/attention_rnn.mag \\\n--save_generator_bundle\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Melody RNN","anchor":"melody-rnn","htmlText":"Melody RNN"},{"level":2,"text":"Configurations","anchor":"configurations","htmlText":"Configurations"},{"level":3,"text":"Basic","anchor":"basic","htmlText":"Basic"},{"level":3,"text":"Mono","anchor":"mono","htmlText":"Mono"},{"level":3,"text":"Lookback","anchor":"lookback","htmlText":"Lookback"},{"level":3,"text":"Attention","anchor":"attention","htmlText":"Attention"},{"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 melody","anchor":"generate-a-melody","htmlText":"Generate a melody"},{"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 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