@@ -43,7 +43,7 @@ struct DecodableNnet3OnlineOptions {
4343 int32 max_nnet_batch_size;
4444 NnetComputeOptions compute_config;
4545 NnetOptimizeOptions optimize_config;
46-
46+
4747 DecodableNnet3OnlineOptions ():
4848 frame_subsampling_factor (1 ),
4949 acoustic_scale (0.1 ),
@@ -73,7 +73,7 @@ struct DecodableNnet3OnlineOptions {
7373 // register the compute options with the prefix "computation".
7474 ParseOptions compute_opts (" computation" , opts);
7575 compute_config.Register (&compute_opts);
76-
76+
7777 }
7878};
7979
@@ -87,29 +87,29 @@ struct DecodableNnet3OnlineOptions {
8787class DecodableNnet3SimpleOnline : public DecodableInterface {
8888 public:
8989 DecodableNnet3SimpleOnline (const AmNnetSimple &am_nnet,
90- const TransitionModel &trans_model,
91- const DecodableNnet3OnlineOptions &opts,
92- OnlineFeatureInterface *input_feats);
93-
94-
90+ const TransitionModel &trans_model,
91+ const DecodableNnet3OnlineOptions &opts,
92+ OnlineFeatureInterface *input_feats);
93+
94+
9595 // / Returns the scaled log likelihood
9696 virtual BaseFloat LogLikelihood (int32 frame, int32 index);
97-
97+
9898 virtual bool IsLastFrame (int32 frame) const ;
9999
100- virtual int32 NumFramesReady () const ;
101-
100+ virtual int32 NumFramesReady () const ;
101+
102102 // / Indices are one-based! This is for compatibility with OpenFst.
103103 virtual int32 NumIndices () const { return trans_model_.NumTransitionIds (); }
104-
104+
105105 private:
106106
107107 // / If the neural-network outputs for this frame are not cached, it computes
108108 // / them (and possibly for some succeeding frames)
109109 void ComputeForFrame (int32 frame);
110110 // corrects number of frames by frame_subsampling_factor;
111111 int32 NumSubsampledFrames (int32) const ;
112-
112+
113113 void DoNnetComputation (
114114 int32 input_t_start,
115115 const MatrixBase<BaseFloat> &input_feats,
@@ -129,11 +129,11 @@ class DecodableNnet3SimpleOnline: public DecodableInterface {
129129 int32 right_context_; // Right context of the network (cached here)
130130 int32 num_pdfs_; // Number of pdfs, equals output-dim of the network (cached
131131 // here)
132-
132+
133133 int32 begin_frame_; // First frame for which scaled_loglikes_ is valid
134134 // (i.e. the first frame of the batch of frames for
135135 // which we've computed the output).
136-
136+
137137 // scaled_loglikes_ contains the neural network pseudo-likelihoods: the log of
138138 // (prob divided by the prior), scaled by opts.acoustic_scale). We may
139139 // compute this using the GPU, but we transfer it back to the system memory
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