diff --git a/README.md b/README.md
index 01eb168..32afc8f 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,7 @@ The following scripts are in the repo:
 * `wut` --- Feed it an observation ID and it returns if it is a "good", "bad", or "failed" observation.
 * `wut-compare` --- Compare an observations' current presumably human vetting with a `wut` vetting.
 * `wut-compare-all` --- Compare all the observations in `download/` with `wut` vettings.
+* `wut-compare-txmode` --- Compare all the observations in `download/` with `wut` vettings using selected encoding.
 * `wut-dl-sort` --- Populate `data/` dir with waterfalls from `download/`.
 * `wut-dl-sort-txmode` --- Populate `data/` dir with waterfalls from `download/` using selected encoding.
 * `wut-ml` --- Main machine learning Python script using Tensorflow and Keras.
diff --git a/wut-compare-txmode b/wut-compare-txmode
new file mode 100755
index 0000000..fe57bd9
--- /dev/null
+++ b/wut-compare-txmode
@@ -0,0 +1,45 @@
+#!/bin/bash
+# wut-compare-txmode
+#
+# Check the results of a prediction against vetted results
+# using a selected encoding.
+# Uses all files in download/ directory.
+# Available encodings:
+# AFSK AFSK1k2 AHRPT APT BPSK BPSK1k2 BPSK9k6 BPSK12k5 BPSK400 CERTO CW DUV
+# FFSK1k2 FM FSK1k2 FSK4k8 FSK9k6 FSK19k2 GFSK1k2 GFSK2k4 GFSK4k8 GFSK9k6
+# GFSK19k2 GFSK Rktr GMSK GMSK1k2 GMSK2k4 GMSK4k8 GMSK9k6 GMSK19k2 HRPT LRPT
+# MSK1k2 MSK2k4 MSK4k8 PSK PSK31 SSTV USB WSJT
+#
+# Usage:
+# wut-compare-txmode [Encoding]
+# Example:
+# wut-compare-txmode DUV 
+
+MAIN_DIR=`pwd`
+OBSENC="$1"
+
+cd download/ || exit
+
+CORRECT=0
+INCORRECT=0
+for OBSID in *
+do
+	cd $MAIN_DIR
+	# Get previous rating
+	VET=`cat download/$OBSID/$OBSID.json | jq --compact-output '.[0] | {vetted_status}' | cut -f 2 -d ":" | sed -e 's/}//g' -e 's/"//g'`
+	ENC=`cat download/$OBSID/$OBSID.json | jq --compact-output '.[0] | {transmitter_mode}' | cut -f 2 -d ":" | sed -e 's/}//g' -e 's/"//g'`
+        if [ $OBSENC = $ENC ] ; then
+		echo -n "$OBSID "
+		echo -n "Vet: $VET "
+		# Get Machine Learning Result
+		WUT_VET=`./wut $OBSID | cut -f 2 -d " "`
+		echo -n "Wut: $WUT_VET "
+		if [ $VET = $WUT_VET ] ; then
+		let CORRECT=$CORRECT+1
+		else
+		let INCORRECT=$INCORRECT+1
+		fi
+		echo "Correct: $CORRECT Incorrect: $INCORRECT"
+	fi
+done
+
diff --git a/wut-dl-sort-txmode b/wut-dl-sort-txmode
index b7b8a14..a290a13 100755
--- a/wut-dl-sort-txmode
+++ b/wut-dl-sort-txmode
@@ -22,7 +22,7 @@
 # * File is randomly copied to either data/train or data/val directory.
 #
 # Possible vetted_status: bad, failed, good, null, unknown.
-set -x
+
 OBSENC="$1"
 OBSIDMIN="$2"
 OBSIDMAX="$3"