require 'libspeech' local TNetReader = nerv.class("nerv.TNetReader", "nerv.DataReader") function TNetReader:__init(global_conf, reader_conf) self.feat_id = reader_conf.id self.frm_ext = reader_conf.frm_ext self.gconf = global_conf self.global_transf = reader_conf.global_transf self.feat_repo = nerv.TNetFeatureRepo(reader_conf.scp_file, reader_conf.conf_file, reader_conf.frm_ext) self.lab_repo = {} for id, mlf_spec in pairs(reader_conf.mlfs) do self.lab_repo[id] = nerv.TNetLabelRepo(mlf_spec.file, mlf_spec.format, mlf_spec.format_arg, mlf_spec.dir, mlf_spec.ext) end end function TNetReader:get_data() if self.feat_repo:is_end() then return nil end local res = {} local frm_ext = self.frm_ext local step = frm_ext * 2 + 1 local feat_utter = self.feat_repo:cur_utter() local expanded = self.gconf.cumat_type(feat_utter:nrow(), feat_utter:ncol() * step) expanded:expand_frm(self.gconf.cumat_type.new_from_host(feat_utter), frm_ext) local rearranged = expanded:create() rearranged:rearrange_frm(expanded, step) local input = {rearranged} local output = {rearranged:create()} self.global_transf:init(input[1]:nrow()) self.global_transf:propagate(input, output) expanded = self.gconf.mmat_type(output[1]:nrow() - frm_ext * 2, output[1]:ncol()) output[1]:copy_toh(expanded, frm_ext, feat_utter:nrow() - frm_ext) res[self.feat_id] = expanded for id, repo in pairs(self.lab_repo) do local lab_utter = repo:get_utter(self.feat_repo, expanded:nrow()) res[id] = lab_utter end self.feat_repo:next() return res end