Changing link to score.sh and moving readme into .md format.

egs/apiai_decode/s5/README.md

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+# Api.ai model decoding example scripts
+This directory contains scripts on how to use a pre-trained chain enlgish model and kaldi base code to recognize any number of wav files.
+
+IMPORTANT: wav files must be in 16kHz, 16 bit little-endian format.
+
+## Model
+English pretrained model were released by Api.ai under Creative Commons Attribution-ShareAlike 4.0 International Public License. 
+- Acustic data is mostly mobile recorded data
+- Language model is based on Assistant.ai logs and good for understanding short commands, like "Wake me up at 7 am"
+For more details, visit https://github.com/api-ai/api-ai-english-asr-model
+
+## Usage
+Ensure kaldi is compiled and this scripts are inside kaldi/egs/<subfolder>/ directory then run
+```sh
+$ ./download-model.sh # to download pretrained chain model
+$ ./recognize-wav.sh test1.wav test2.wav # to do recognition
+```
+See console output for recognition results.
+
+### Using steps/nnet3/decode.sh
+You can use kaldi steps/nnet3/decode.sh, which will decode data and calculate Word Error Rate (WER) for it.
+
+Run:
+```sh
+$ recognize-wav.sh test1.wav test2.wav
+```
+It will make data dir, calculate mfcc features for it and do decoding, you need only first two steps out of it. If you want WER then edit data/test-corpus/text and replace NO_TRANSCRIPTION with expected text transcription for every wav file. 
+
+Run for decoding:
+```sh
+$ steps/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 --cmd run.pl --nj 1 exp/api.ai-model/ data/test-corpus/ exp/api.ai-model/decode/
+```
+See exp/api.ai-model/decode/wer* files for WER and exp/api.ai-model/decode/log/ files for decoding output.
+
+### Online Decoder:
+See http://kaldi.sourceforge.net/online_decoding.html for more information about kaldi online decoding.
+
+Run:
+```sh
+$./local/create-corpus.sh data/test-corpus/ test1.wav test2.wav
+```
+If you want WER then edit data/test-corpus/text and replace NO_TRANSCRIPTION with expected text transcription for every wav file.
+
+Make config file exp/api.ai-model/conf/online.conf with following content:
+```
+--feature-type=mfcc
+--mfcc-config=exp/api.ai-model/mfcc.conf
+```
+Then run:
+```sh
+$ steps/online/nnet3/decode.sh --acwt 1.0 --post-decode-acwt 10.0 --cmd run.pl --nj 1 exp/api.ai-model/ data/test-corpus/ exp/api.ai-model/decode/
+```
+See exp/api.ai-model/decode/wer* files for WER and exp/api.ai-model/decode/log/ files for decoding output.

egs/apiai_decode/s5/local/score.sh

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-../../wsj/s5/local/score.sh
+../steps/score_kaldi.sh