{"action":"created","issue":{"url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch/issues/4","repository_url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch","labels_url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch/issues/4/labels{/name}","comments_url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch/issues/4/comments","events_url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch/issues/4/events","html_url":"https://github.com/githubharald/CTCWordBeamSearch/issues/4","id":329595977,"node_id":"MDU6SXNzdWUzMjk1OTU5Nzc=","number":4,"title":"Usage as loss","user":{"login":"cipri-tom","id":2991890,"node_id":"MDQ6VXNlcjI5OTE4OTA=","avatar_url":"https://avatars2.githubusercontent.com/u/2991890?v=4","gravatar_id":"","url":"https://api.github.com/users/cipri-tom","html_url":"https://github.com/cipri-tom","followers_url":"https://api.github.com/users/cipri-tom/followers","following_url":"https://api.github.com/users/cipri-tom/following{/other_user}","gists_url":"https://api.github.com/users/cipri-tom/gists{/gist_id}","starred_url":"https://api.github.com/users/cipri-tom/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/cipri-tom/subscriptions","organizations_url":"https://api.github.com/users/cipri-tom/orgs","repos_url":"https://api.github.com/users/cipri-tom/repos","events_url":"https://api.github.com/users/cipri-tom/events{/privacy}","received_events_url":"https://api.github.com/users/cipri-tom/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-06-05T19:36:01Z","updated_at":"2018-06-06T15:50:20Z","closed_at":null,"author_association":"NONE","body":"Hi,\r\n\r\nThank you for such complete repo with code examples and frameworks in both Python and CPP and tensorflow ops. Really amazing work! \r\n\r\nIf I understand correctly, this is only to be used during evaluation/prediction phase, to get more meaningful ones. I was wondering if it could also be used as a loss during training, such that paths which follow the language model are more probable than those which do not.\r\n\r\nOr it is actually not relevant since CTC squashes all paths that produce the label and therefore all versions are maximised ?\r\n\r\nMany thanks, again, for making your contributions public ! "},"comment":{"url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch/issues/comments/395118381","html_url":"https://github.com/githubharald/CTCWordBeamSearch/issues/4#issuecomment-395118381","issue_url":"https://api.github.com/repos/githubharald/CTCWordBeamSearch/issues/4","id":395118381,"node_id":"MDEyOklzc3VlQ29tbWVudDM5NTExODM4MQ==","user":{"login":"githubharald","id":15148095,"node_id":"MDQ6VXNlcjE1MTQ4MDk1","avatar_url":"https://avatars2.githubusercontent.com/u/15148095?v=4","gravatar_id":"","url":"https://api.github.com/users/githubharald","html_url":"https://github.com/githubharald","followers_url":"https://api.github.com/users/githubharald/followers","following_url":"https://api.github.com/users/githubharald/following{/other_user}","gists_url":"https://api.github.com/users/githubharald/gists{/gist_id}","starred_url":"https://api.github.com/users/githubharald/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/githubharald/subscriptions","organizations_url":"https://api.github.com/users/githubharald/orgs","repos_url":"https://api.github.com/users/githubharald/repos","events_url":"https://api.github.com/users/githubharald/events{/privacy}","received_events_url":"https://api.github.com/users/githubharald/received_events","type":"User","site_admin":false},"created_at":"2018-06-06T15:50:20Z","updated_at":"2018-06-06T15:50:20Z","author_association":"OWNER","body":"Hi,\r\n\r\n> Final question: is Beam-Search decoding equivalent to char-level ngram ?\r\n\r\nchar-level n-gram is a Language Model (LM) which assigns scores to consecutive groups of n chars, e.g. n=2 for bigrams. As an example, a char-group like \"th\" is more likely than \"tx\" in the English language.\r\nBeam search decoding adds (at most) one single character at each iteration, therefore it is easy to integrate a char-level LM: each time a char is added, take the probability of the last char in the beam and the newly added char (in case of n=2) and multiply this textual score with the optical score. For a Python implementation see https://github.com/githubharald/CTCDecoder"}}
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