Yannick Estève / ONTRAC-Kaldi

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  # Api.ai model decoding example scripts
  This directory contains scripts on how to use a pre-trained chain english 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. 
  - Acoustic 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-asr.org/doc/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.