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
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
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+#!/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=""
+
+