{"action":"created","issue":{"url":"https://api.github.com/repos/ibab/tensorflow-wavenet/issues/161","repository_url":"https://api.github.com/repos/ibab/tensorflow-wavenet","labels_url":"https://api.github.com/repos/ibab/tensorflow-wavenet/issues/161/labels{/name}","comments_url":"https://api.github.com/repos/ibab/tensorflow-wavenet/issues/161/comments","events_url":"https://api.github.com/repos/ibab/tensorflow-wavenet/issues/161/events","html_url":"https://github.com/ibab/tensorflow-wavenet/issues/161","id":184670476,"number":161,"title":"Setting receptive field size for music training samples","user":{"login":"kaihuchen","id":13685803,"avatar_url":"https://avatars.githubusercontent.com/u/13685803?v=3","gravatar_id":"","url":"https://api.github.com/users/kaihuchen","html_url":"https://github.com/kaihuchen","followers_url":"https://api.github.com/users/kaihuchen/followers","following_url":"https://api.github.com/users/kaihuchen/following{/other_user}","gists_url":"https://api.github.com/users/kaihuchen/gists{/gist_id}","starred_url":"https://api.github.com/users/kaihuchen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kaihuchen/subscriptions","organizations_url":"https://api.github.com/users/kaihuchen/orgs","repos_url":"https://api.github.com/users/kaihuchen/repos","events_url":"https://api.github.com/users/kaihuchen/events{/privacy}","received_events_url":"https://api.github.com/users/kaihuchen/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":7,"created_at":"2016-10-23T04:17:17Z","updated_at":"2016-10-25T02:37:52Z","closed_at":null,"body":"I am trying to apply WaveNet to music samples, and per the WaveNet paper I should be training with a larger receptive field. I feel that ideally the receptive field size should be in the tens of seconds so that WaveNet can keep it coherent across a longer time frame. To keep it simple let's say I just want the receptive field to cover around 5 seconds for now, which translates into 80000 samples in the fields, which then translates into around 16 dilation layers (80000 ~= 2**16.288), which appears to be prohibitively expensive to run. \r\n\r\nDoes anybody have experience try using a fairly large receptive field size on music samples? Do you have anything to share?\r\n"},"comment":{"url":"https://api.github.com/repos/ibab/tensorflow-wavenet/issues/comments/255920369","html_url":"https://github.com/ibab/tensorflow-wavenet/issues/161#issuecomment-255920369","issue_url":"https://api.github.com/repos/ibab/tensorflow-wavenet/issues/161","id":255920369,"user":{"login":"paperkettle","id":623781,"avatar_url":"https://avatars.githubusercontent.com/u/623781?v=3","gravatar_id":"","url":"https://api.github.com/users/paperkettle","html_url":"https://github.com/paperkettle","followers_url":"https://api.github.com/users/paperkettle/followers","following_url":"https://api.github.com/users/paperkettle/following{/other_user}","gists_url":"https://api.github.com/users/paperkettle/gists{/gist_id}","starred_url":"https://api.github.com/users/paperkettle/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/paperkettle/subscriptions","organizations_url":"https://api.github.com/users/paperkettle/orgs","repos_url":"https://api.github.com/users/paperkettle/repos","events_url":"https://api.github.com/users/paperkettle/events{/privacy}","received_events_url":"https://api.github.com/users/paperkettle/received_events","type":"User","site_admin":false},"created_at":"2016-10-25T02:37:52Z","updated_at":"2016-10-25T02:37:52Z","body":"I think it'd be extremely useful if the group found a way to 44.1k with a long receptive field possible, while magically keeping it viable for small memory GPUs (aka top of the line consumer gaming Titan XP etc). Even if it means obscene training times. \r\n\r\nI've done a number of experiments training it on full mixes of music at 44.1k, but with the settings commonly used in the repo for 16k. That means my receptive field winds up being really small, and the output just sounds like large grain granular synthesis / timestretching. Which is not especially exciting (to me anyway). "}}
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