{"action":"opened","issue":{"url":"https://api.github.com/repos/awni/speech/issues/38","repository_url":"https://api.github.com/repos/awni/speech","labels_url":"https://api.github.com/repos/awni/speech/issues/38/labels{/name}","comments_url":"https://api.github.com/repos/awni/speech/issues/38/comments","events_url":"https://api.github.com/repos/awni/speech/issues/38/events","html_url":"https://github.com/awni/speech/issues/38","id":365760595,"node_id":"MDU6SXNzdWUzNjU3NjA1OTU=","number":38,"title":"ctc decoder with language model","user":{"login":"edchengg","id":20430102,"node_id":"MDQ6VXNlcjIwNDMwMTAy","avatar_url":"https://avatars0.githubusercontent.com/u/20430102?v=4","gravatar_id":"","url":"https://api.github.com/users/edchengg","html_url":"https://github.com/edchengg","followers_url":"https://api.github.com/users/edchengg/followers","following_url":"https://api.github.com/users/edchengg/following{/other_user}","gists_url":"https://api.github.com/users/edchengg/gists{/gist_id}","starred_url":"https://api.github.com/users/edchengg/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/edchengg/subscriptions","organizations_url":"https://api.github.com/users/edchengg/orgs","repos_url":"https://api.github.com/users/edchengg/repos","events_url":"https://api.github.com/users/edchengg/events{/privacy}","received_events_url":"https://api.github.com/users/edchengg/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-10-02T06:41:52Z","updated_at":"2018-10-02T06:41:52Z","closed_at":null,"author_association":"NONE","body":"Hi,\r\nThanks for your work!\r\nI implemented a LM with your py-arpa-lm.. The output of lm.score_tg returns a ln scale score.\r\n\r\nI just would like to check if the following is correct way to add LM:\r\n\r\n`              # *NB* this would be a good place to include an LM score. insertion penalty\r\n                lm_score = alpha * lm.score_tg(n_prefix)\r\n                ins_p = beta * np.log(len(n_prefix))\r\n                next_beam[n_prefix] = (n_p_b + lm_score + ins_p, n_p_nb + lm_score + ins_p)`\r\n\r\nThanks!"}}
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