Here, we need to have utt2spk, text, wav.scp files.

• text
  Format of text file:

<utt_id> <word1> <word2> ... <wordN>

• utt2spk
  Format of utt2spk file: For datasets without spk_id, we can directly mark spk_id as utt_id

<utt_id> <spk_id>

• wav.scp
  Format of wav.scp

<utt_id> <path_to_audio_file>

Note: Make sure that you change this path when shifting your code to server

• spk2gender [optional file]
  Format of spk2gender file:

<spk_id> <gender>

• corpus.txt
  This is used to train our language model (in this script SRILM with ngram order 3 is used)
  Format of text file:

<word1> <word2> ... <wordN>

Here, we store all the lexicon related files i.e lexicon.txt, nonsilence_phones.txt, optional_silence.txt, silence_phones.txt, extra_questions.txt (this is optional)

• lexicon.txt
  Format of text file:

<word> <phone1> <phone2> ... <phoneN>

To make this file, first extract all the unique words and then run a g2p model on those words. This could be Sequitr or CMUdict. For a small dataset like this you can directly use the word itself instead of <phone1> <phone2> .. <phoneN>.

NOTE: Make sure to enter !SIL SIL and <UNK> SIL rows into the lexicon.txt file.

• nonsilence_phones.txt
  All unique phones in lexicon.txt are entered here -each in one line.

• silence_phones.txt
  A phone representing silence is entered (usually 'SIL') and any special noise or OOVs phones.

• optional_silence
  Just silence phone 'SIL' is entered in this file.

From here on, we can use various Kaldi scripts since we have got to a point where everything is formatted in a way Kaldi takes its inputs. So, the script itself will take care from here.

• Copy the local folder from voxforge recipe
• Copy the conf folder from any recipe

• Make a symbolic link with steps folder in wsj recipe
• Make a symbolic link with utils folder in wsj recipe

NOTE: Change the path of KALDI_ROOT in path.sh to where your Kaldi is installed.

./run.sh
NOTE: Make sure that you give full permissions to run.sh

./best_score.sh
You can find the results in RESULTS file.

NOTE: This should be done in addition to HMM-GMM Model

• change run_tdnn variable from 0 to 1 in run.sh
• copy minilibrispeech local folder and place it in the current folder.
• Remove the file local/chain/run_tdnn.sh and copy run_tdnn.sh and paste it in local/chain folder. I have made minute changes so that it fits my directory structure etc.
• ./run.sh

• cd exp/chain/tdnn1h_sp/decode_test && cat wer_* | ../../../../utils/best_wer.sh