view experiments/scripts/master.sh @ 610:01986636257a

Second check-in of Alex Brandmeyer's C++ implementation of CARFAC. Addressed style issues and completed implementation of remaining functions. Still needs proper testing of the output stages against the MATLAB version, and runtime functions need improvements in efficiency.

#!/bin/bash
# Copyright 2010 Thomas Walters <[e-mail]>
#
# Run a series of experiments which compare MFCC features generated by HTK to
# AIM features generated using AIM-C using a series of syllable recogntiton
# tasks.
# This script expects to be run from within the AIM-C source tree.
# It builds the HTK binaries and AIM-C AIMCopy binary if they're not
# present.
# The following environment varaibles should be set before this script is run:
# SYLLABLES_DATABASE_URL - URL of a tar file containing the CNBH syllables
# database in FLAC format
# HTK_USERNAME and HTK_PASSWORD - username and password for the site at
# http://htk.eng.cam.ac.uk/
# NUMBER_OF_CORES - total number of machine cores

sudo apt-get -y update
sudo apt-get -y install bc subversion scons pkg-config \
                libsndfile1-dev build-essential libboost-dev \
                python sox python-matplotlib libcairo-dev

# For 64-bit systems, uncomment this line:
sudo apt-get -y install libc6-dev-i386

# Set these to be the location of your input database, and desired output
# locations. (Note: the user running this script needs write permissions on
# the $WORKING_VOLUME.)
WORKING_VOLUME=/mnt/scratch0/aim

SYLLABLES_DATABASE_TAR=$WORKING_VOLUME/001-downloaded_sounds_data/cnbh-syllables.tar
SOUNDS_ROOT=$WORKING_VOLUME/002-sounds/
FEATURES_ROOT=$WORKING_VOLUME/003-features/
HMMS_ROOT=$WORKING_VOLUME/004-hmms/
HTK_ROOT=$WORKING_VOLUME/software/htk/
AIMC_ROOT=$WORKING_VOLUME/software/aimc/

THIS_DIR=`dirname $0`
AIMCOPY_CONFIGURATION_FILE=$THIS_DIR/cnbh-syllables/feature_generation/ssi_profile_features.aimcopycfg

# Number of cores on the experimental machine. Various scripts will try to use
# this if it's set.
# NUMBER_OF_CORES=8

# Fail if any command fails
set -e

# Fail if any variable is unset
set -u

######
# Step 001 - Get the sounds database
if [ ! -e $SYLLABLES_DATABASE_TAR ]; then
  mkdir -p `dirname $SYLLABLES_DATABASE_TAR`
  wget -O $SYLLABLES_DATABASE_TAR $SYLLABLES_DATABASE_URL
fi

if [ ! -d $SOUNDS_ROOT ]; then
  mkdir -p $SOUNDS_ROOT
fi

# Untar the CNBH syllables database, and convert the files from FLAC to WAV.
if [ ! -e $SOUNDS_ROOT/.untar_db_success ]; then
  tar -x -C $SOUNDS_ROOT -f $SYLLABLES_DATABASE_TAR
  touch $SOUNDS_ROOT/.untar_db_success
fi

# Convert the database to .WAV format and place it in $SOUNDS_ROOT/clean
echo "Converting CNBH-syllables database from FLAC to WAV..."
./cnbh-syllables/feature_generation/convert_flac_to_wav.sh $SOUNDS_ROOT
#
######

#####
# Step 002 -
# Generate versions of the CNBH syllables spoke pattern with a range of
# signal-to-noise ratios (SNRs). The versions are put in the directory
# ${SOUNDS_ROOT}/${SNR}_dB/ for each SNR in $SNRS.
SNRS="45"#" 42 39 36 33" #" 30 27 24 21 18 15 12 9 6 3 0"
#SNRS="30" # For testing
./cnbh-syllables/feature_generation/pink_noise.sh $SOUNDS_ROOT/clean/ "$SNRS"

# Make the list of all feature drectories
#FEATURE_DIRS="clean"
FEATURE_DIRS=""
for SNR in $SNRS; do
  FEATURE_DIRS="$FEATURE_DIRS snr_${SNR}dB"
done

# Generate feature sets (for the full range of SNRs in $FEATURE_DIRS)
# 1. Standard MFCC features
# 2. AIM features
# 3. MFCC features with optimal VTLN

if [ ! -d $FEATURES_ROOT ]; then
 mkdir -p $FEATURES_ROOT
fi

if [ ! -e $HTK_ROOT/.htk_installed_success ]; then
  ./HTK/install_htk.sh $HTK_ROOT
fi

if [ ! -e $AIMC_ROOT/.aimc_build_success ]; then
 ./aimc/build_aimc.sh $AIMC_ROOT
fi
export PATH=$PATH:$AIMC_ROOT/build/posix-release/

for SOURCE_SNR in $FEATURE_DIRS; do
  if [ ! -e $FEATURES_ROOT/mfcc/$SOURCE_SNR/.make_mfcc_features_success ]; then
    mkdir -p $FEATURES_ROOT/mfcc/$SOURCE_SNR/
    # Generate the list of files to convert
    ./cnbh-syllables/feature_generation/gen_hcopy_aimcopy_script.sh $FEATURES_ROOT/mfcc/$SOURCE_SNR/ $SOUNDS_ROOT/$SOURCE_SNR/ htk
    # Run the conversion
    ./cnbh-syllables/feature_generation/run_hcopy.sh $FEATURES_ROOT/mfcc/$SOURCE_SNR/ $NUMBER_OF_CORES
    touch $FEATURES_ROOT/mfcc/$SOURCE_SNR/.make_mfcc_features_success
  fi

  if [ ! -e $FEATURES_ROOT/mfcc_vtln/$SOURCE_SNR/.make_mfcc_vtln_features_success ]; then
    mkdir -p $FEATURES_ROOT/mfcc_vtln/$SOURCE_SNR/
    # Generate the file list and run the conversion (all one step, since this
    # version uses a different configuration for each talker)
    ./cnbh-syllables/feature_generation/run_mfcc_vtln_conversion.sh $FEATURES_ROOT/mfcc_vtln/$SOURCE_SNR/ $SOUNDS_ROOT/$SOURCE_SNR/
    touch $FEATURES_ROOT/mfcc_vtln/$SOURCE_SNR/.make_mfcc_vtln_features_success
  fi

  if [ ! -e $FEATURES_ROOT/aim/$SOURCE_SNR/.make_aim_features_success ]; then
    mkdir -p $FEATURES_ROOT/aim/$SOURCE_SNR/
    ./cnbh-syllables/feature_generation/gen_hcopy_aimcopy_script.sh $FEATURES_ROOT/aim/$SOURCE_SNR/ $SOUNDS_ROOT/$SOURCE_SNR/ ""
    # Run the conversion
    ./cnbh-syllables/feature_generation/run_aimcopy.sh $AIMCOPY_CONFIGURATION_FILE $FEATURES_ROOT/aim/$SOURCE_SNR/ $NUMBER_OF_CORES
    touch $FEATURES_ROOT/aim/$SOURCE_SNR/.make_aim_features_success
  fi
done

mkdir -p $HMMS_ROOT

# Now run a bunch of experiments.
# For each of the feature types, we want to run HMMs with a bunch of
# parameters.
TRAINING_ITERATIONS="0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15" # 16 17 18 19 20"
#TESTING_ITERATIONS="0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15" #" 16 17 18 19 20"
TESTING_ITERATIONS="15"
#HMM_STATES="3 4 5 6 7 8"
HMM_STATES="4"
#HMM_OUTPUT_COMPONENTS="1 2 3 4 5 6 7"
HMM_OUTPUT_COMPONENTS="4"

run_train_test () {
# TODO(tom): Make sure that the training SNR is generated first
for SOURCE_SNR in $FEATURE_DIRS; do
WORK=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/$SOURCE_SNR/$TALKERS/
mkdir -p $WORK
FEATURES_DIR=$FEATURES_ROOT/$FEATURE_CLASS/$SOURCE_SNR/
SPOKE_PATTERN_FILE=`pwd`/cnbh-syllables/run_training_and_testing/train_test_sets/gen_spoke_points/spoke_pattern.txt

./cnbh-syllables/run_training_and_testing/train_test_sets/generate_train_test_lists.sh \
    $TALKERS \
    $WORK \
    $FEATURES_DIR \
    $FEATURE_SUFFIX

if [ $TRAINING_SNR == 'random' ]; then
  TRAINING_SCRIPT=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/clean/$TALKERS/training_script
  TRAINING_MASTER_LABEL_FILE=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/clean/$TALKERS/training_master_label_file
  RANDOMIZED_TRAINING_SCRIPT=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/clean/$TALKERS/training_script_randomized
  RANDOMIZED_TRAINING_MASTER_LABEL_FILE=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/clean/$TALKERS/training_master_label_file_randomized
  ./cnbh-syllables/run_training_and_testing/train_test_sets/randomize_snrs.py -s clean -f $TRAINING_SCRIPT -m $TRAINING_MASTER_LABEL_FILE -o $RANDOMIZED_TRAINING_SCRIPT -p $RANDOMIZED_TRAINING_MASTER_LABEL_FILE
  TRAINING_SCRIPT=$RANDOMIZED_TRAINING_SCRIPT
  TRAINING_MASTER_LABEL_FILE=$RANDOMIZED_TRAINING_MASTER_LABEL_FILE
else
  TRAINING_SCRIPT=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/$TRAINING_SNR/$TALKERS/training_script
  TRAINING_MASTER_LABEL_FILE=$HMMS_ROOT/$FEATURE_CLASS/$FEATURE_SUFFIX/$TRAINING_SNR/$TALKERS/training_master_label_file
fi

TESTING_SCRIPT=$WORK/testing_script
TESTING_MASTER_LABEL_FILE=$WORK/testing_master_label_file

./cnbh-syllables/run_training_and_testing/gen_htk_base_files.sh $WORK

./cnbh-syllables/run_training_and_testing/test_features.sh \
    "$WORK" \
    "$FEATURES_ROOT/$FEATURE_CLASS/$SOURCE_SNR/" \
    "$FEATURE_SUFFIX" \
    "$HMM_STATES" \
    "$HMM_OUTPUT_COMPONENTS" \
    "$TRAINING_ITERATIONS" \
    "$TESTING_ITERATIONS" \
    "$FEATURE_SIZE" \
    "$FEATURE_TYPE" \
    "$TRAINING_SCRIPT" \
    "$TESTING_SCRIPT" \
    "$TRAINING_MASTER_LABEL_FILE" \
    "$TESTING_MASTER_LABEL_FILE" \
    "$SPOKE_PATTERN_FILE"
done
}

########################
# Standard MFCCs
FEATURE_CLASS=mfcc
FEATURE_SUFFIX=htk
FEATURE_SIZE=39
FEATURE_TYPE=MFCC_0_D_A
TALKERS=inner_talkers
TRAINING_SNR=random
run_train_test
########################

########################
# Standard MFCCs
# Train on extrema
FEATURE_CLASS=mfcc
FEATURE_SUFFIX=htk
FEATURE_SIZE=39
FEATURE_TYPE=MFCC_0_D_A
TALKERS=outer_talkers
TRAINING_SNR=clean
#run_train_test
########################

########################
# MFCCs with VTLN
FEATURE_CLASS=mfcc_vtln
FEATURE_SUFFIX=htk
FEATURE_SIZE=39
FEATURE_TYPE=MFCC_0_D_A
TALKERS=inner_talkers
TRAINING_SNR=random
run_train_test
########################

########################
# MFCCs with VTLN
# Train on extrema
FEATURE_CLASS=mfcc_vtln
FEATURE_SUFFIX=htk
FEATURE_SIZE=39
FEATURE_TYPE=MFCC_0_D_A
TALKERS=outer_talkers
TRAINING_SNR=random
#run_train_test
########################

AIM_FEATURE_SUFFIXES="slice_1_no_cutoff ssi_profile_no_cutoff slice_1_cutoff ssi_profile_cutoff smooth_nap_profile"
for f in $AIM_FEATURE_SUFFIXES
do
########################
# AIM Features
# Inner talkers
FEATURE_CLASS=aim
FEATURE_SUFFIX=$f
FEATURE_SIZE=12
FEATURE_TYPE=USER_E_D_A
TALKERS=inner_talkers
TRAINING_SNR=random
run_train_test
########################

########################
# AIM Features
# Inner talkers
FEATURE_CLASS=aim
FEATURE_SUFFIX=$f
FEATURE_SIZE=12
FEATURE_TYPE=USER_E_D_A
TALKERS=outer_talkers
TRAINING_SNR=clean
#run_train_test
########################
done