#!/usr/bin/env bash

# Copyright 2017 Beijing Shell Shell Tech. Co. Ltd. (Authors: Hui Bu)
#           2017 Jiayu Du
#           2017 Xingyu Na
#           2017 Bengu Wu
#           2017 Hao Zheng
# Apache 2.0

# This is a shell script, but it's recommended that you run the commands one by
# one by copying and pasting into the shell.
# Caution: some of the graph creation steps use quite a bit of memory, so you
# should run this on a machine that has sufficient memory.

data=/export/a05/xna/data
data_url=www.openslr.org/resources/33

. ./cmd.sh

local/download_and_untar.sh $data $data_url data_aishell || exit 1;
local/download_and_untar.sh $data $data_url resource_aishell || exit 1;

# Lexicon Preparation,
local/aishell_prepare_dict.sh $data/resource_aishell || exit 1;

# Data Preparation,
local/aishell_data_prep.sh $data/data_aishell/wav $data/data_aishell/transcript || exit 1;

# Phone Sets, questions, L compilation
utils/prepare_lang.sh --position-dependent-phones false data/local/dict \
    "<SPOKEN_NOISE>" data/local/lang data/lang || exit 1;

# LM training
local/aishell_train_lms.sh || exit 1;

# G compilation, check LG composition
utils/format_lm.sh data/lang data/local/lm/3gram-mincount/lm_unpruned.gz \
    data/local/dict/lexicon.txt data/lang_test || exit 1;

# Now make MFCC plus pitch features.
# mfccdir should be some place with a largish disk where you
# want to store MFCC features.
mfccdir=mfcc
for x in train dev test; do
  steps/make_mfcc_pitch.sh --cmd "$train_cmd" --nj 10 data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  steps/compute_cmvn_stats.sh data/$x exp/make_mfcc/$x $mfccdir || exit 1;
  utils/fix_data_dir.sh data/$x || exit 1;
done

# Train a monophone model on delta features.
steps/train_mono.sh --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/mono || exit 1;

# Decode with the monophone model.
utils/mkgraph.sh data/lang_test exp/mono exp/mono/graph || exit 1;
steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
  exp/mono/graph data/dev exp/mono/decode_dev
steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
  exp/mono/graph data/test exp/mono/decode_test

# Get alignments from monophone system.
steps/align_si.sh --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/mono exp/mono_ali || exit 1;

# Train the first triphone pass model tri1 on delta + delta-delta features.
steps/train_deltas.sh --cmd "$train_cmd" \
 2500 20000 data/train data/lang exp/mono_ali exp/tri1 || exit 1;

# decode tri1
utils/mkgraph.sh data/lang_test exp/tri1 exp/tri1/graph || exit 1;
steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
  exp/tri1/graph data/dev exp/tri1/decode_dev
steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
  exp/tri1/graph data/test exp/tri1/decode_test

# align tri1
steps/align_si.sh --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/tri1 exp/tri1_ali || exit 1;

# train tri2 [delta+delta-deltas]
steps/train_deltas.sh --cmd "$train_cmd" \
 2500 20000 data/train data/lang exp/tri1_ali exp/tri2 || exit 1;

# decode tri2
utils/mkgraph.sh data/lang_test exp/tri2 exp/tri2/graph
steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
  exp/tri2/graph data/dev exp/tri2/decode_dev
steps/decode.sh --cmd "$decode_cmd" --config conf/decode.config --nj 10 \
  exp/tri2/graph data/test exp/tri2/decode_test

# Align training data with the tri2 model.
steps/align_si.sh --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/tri2 exp/tri2_ali || exit 1;

# Train the second triphone pass model tri3a on LDA+MLLT features.
steps/train_lda_mllt.sh --cmd "$train_cmd" \
 2500 20000 data/train data/lang exp/tri2_ali exp/tri3a || exit 1;
 
# Run a test decode with the tri3a model.
utils/mkgraph.sh data/lang_test exp/tri3a exp/tri3a/graph || exit 1;
steps/decode.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
  exp/tri3a/graph data/dev exp/tri3a/decode_dev
steps/decode.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
  exp/tri3a/graph data/test exp/tri3a/decode_test

# align tri3a with fMLLR

steps/align_fmllr.sh --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/tri3a exp/tri3a_ali || exit 1;

# Train the third triphone pass model tri4a on LDA+MLLT+SAT features.
# From now on, we start building a more serious system with Speaker
# Adaptive Training (SAT).
steps/train_sat.sh --cmd "$train_cmd" \
  2500 20000 data/train data/lang exp/tri3a_ali exp/tri4a || exit 1;

# decode tri4a
utils/mkgraph.sh data/lang_test exp/tri4a exp/tri4a/graph
steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
  exp/tri4a/graph data/dev exp/tri4a/decode_dev
steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
  exp/tri4a/graph data/test exp/tri4a/decode_test
  
# align tri4a with fMLLR
steps/align_fmllr.sh  --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/tri4a exp/tri4a_ali

# Train tri5a, which is LDA+MLLT+SAT
# Building a larger SAT system. You can see the num-leaves is 3500 and tot-gauss is 100000

steps/train_sat.sh --cmd "$train_cmd" \
  3500 100000 data/train data/lang exp/tri4a_ali exp/tri5a || exit 1;
  
# decode tri5a
utils/mkgraph.sh data/lang_test exp/tri5a exp/tri5a/graph || exit 1;
steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
   exp/tri5a/graph data/dev exp/tri5a/decode_dev || exit 1;
steps/decode_fmllr.sh --cmd "$decode_cmd" --nj 10 --config conf/decode.config \
   exp/tri5a/graph data/test exp/tri5a/decode_test || exit 1;
   
# align tri5a with fMLLR
steps/align_fmllr.sh --cmd "$train_cmd" --nj 10 \
  data/train data/lang exp/tri5a exp/tri5a_ali || exit 1;

# nnet3
local/nnet3/run_tdnn.sh

# chain
local/chain/run_tdnn.sh

# getting results (see RESULTS file)
for x in exp/*/decode_test; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null
for x in exp/*/*/decode_test; do [ -d $x ] && grep WER $x/cer_* | utils/best_wer.sh; done 2>/dev/null

exit 0;
