Mercurial > hg > aimc
diff trunk/experiments/scripts/master.sh @ 335:71c438f9daf7
- Scripts for running recognition experiments using AIM-C and HTK to compare MFCCs against features generated with AIM-C
author | tomwalters |
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date | Wed, 04 Aug 2010 06:41:56 +0000 |
parents | |
children | b6b6b2082760 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/trunk/experiments/scripts/master.sh Wed Aug 04 06:41:56 2010 +0000 @@ -0,0 +1,95 @@ +#!/bin/bash +# Copyright 2010 Thomas Walters <tom@acousticscale.org> +# +# 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 the HTK binaries and AIM-C AIMCopy binary to be present +# in the PATH. + +# Set these to be the location of your input database, and desired output +# locations. +SYLLABLES_DATABASE_TAR=/media/sounds/cnbh-syllables.tar +SOUNDS_ROOT=/mnt/experiments/sounds/ +FEATURES_ROOT=/mnt/experiments/features/ +HMMS_ROOT=/mnt/experiments/hmms/ + +# Number of cores on the experimental machine. Various scripts will try to use +# this if it's set. +NUMBER_OF_CORES=2 + +# Fail if any command fails +set -e + +# Fail if any variable is unset +set -u + +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 +./cnbh-syllables/convert_flac_to_wave.sh $SOUNDS_ROOT + +# 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="30 27 24 21 18 15 12 9 6 3 0" +./cnbh-syllables/pink_noise.sh $SOUNDS_ROOT/clean/ $SNRS + +# Make the list of all feature drectories +FEATURE_DIRS="clean" +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 +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/ + # 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 + done + + 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 configuraiton 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 + done + + 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 $FEATURES_ROOT/aim/$SOURCE_SNR/ $NUMBER_OF_CORES + touch $FEATURES_ROOT/aim/$SOURCE_SNR/.make_aim_features_success + done +done + +# 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="1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20" +HMM_STATES="3 4 5 6 7 8" +HMM_OUTPUT_COMPONENTS="" + +