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diff --git a/dom/media/driftcontrol/plot.py b/dom/media/driftcontrol/plot.py
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+++ b/dom/media/driftcontrol/plot.py
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+#! /usr/bin/env python3
+#
+# This Source Code Form is subject to the terms of the Mozilla Public
+# License, v. 2.0. If a copy of the MPL was not distributed with this
+# file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#
+# This scripts plots graphs produced by our drift correction code.
+#
+# Install dependencies with:
+#   > pip install bokeh pandas
+#
+# Generate the csv data file with the DriftControllerGraphs log module:
+#   > MOZ_LOG=raw,sync,DriftControllerGraphs:5 \
+#   > MOZ_LOG_FILE=/tmp/driftcontrol.csv       \
+#   > ./mach gtest '*AudioDrift*StepResponse'
+#
+# Generate the graphs with this script:
+#   > ./dom/media/driftcontrol/plot.py /tmp/driftcontrol.csv.moz_log
+#
+# The script should produce a file plot.html in the working directory and
+# open it in the default browser.
+
+import argparse
+from collections import OrderedDict
+
+import pandas
+from bokeh.io import output_file, show
+from bokeh.layouts import gridplot
+from bokeh.models import TabPanel, Tabs
+from bokeh.plotting import figure
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        prog="plot.py for DriftControllerGraphs",
+        description="""Takes a csv file of DriftControllerGraphs data
+(from a single DriftController instance) and plots
+them into plot.html in the current working directory.
+
+The easiest way to produce the data is with MOZ_LOG:
+MOZ_LOG=raw,sync,DriftControllerGraphs:5 \
+MOZ_LOG_FILE=/tmp/driftcontrol.csv       \
+./mach gtest '*AudioDrift*StepResponse'""",
+    )
+    parser.add_argument("csv_file", type=str)
+    args = parser.parse_args()
+
+    all_df = pandas.read_csv(args.csv_file)
+
+    # Filter on distinct ids to support multiple plotting sources
+    tabs = []
+    for id in list(OrderedDict.fromkeys(all_df["id"])):
+        df = all_df[all_df["id"] == id]
+
+        t = df["t"]
+        buffering = df["buffering"]
+        desired = df["desired"]
+        buffersize = df["buffersize"]
+        inlatency = df["inlatency"]
+        outlatency = df["outlatency"]
+        inrate = df["inrate"]
+        outrate = df["outrate"]
+        hysteresisthreshold = df["hysteresisthreshold"]
+        corrected = df["corrected"]
+        hysteresiscorrected = df["hysteresiscorrected"]
+        configured = df["configured"]
+        p = df["p"]
+        i = df["i"]
+        d = df["d"]
+        kpp = df["kpp"]
+        kii = df["kii"]
+        kdd = df["kdd"]
+        control = df["control"]
+
+        output_file("plot.html")
+
+        fig1 = figure()
+        fig1.line(t, inlatency, color="hotpink", legend_label="In latency")
+        fig1.line(t, outlatency, color="firebrick", legend_label="Out latency")
+        fig1.line(t, buffering, color="dodgerblue", legend_label="Actual buffering")
+        fig1.line(t, desired, color="goldenrod", legend_label="Desired buffering")
+        fig1.line(t, buffersize, color="seagreen", legend_label="Buffer size")
+        fig1.varea(
+            t,
+            [d - h for (d, h) in zip(desired, hysteresisthreshold)],
+            [d + h for (d, h) in zip(desired, hysteresisthreshold)],
+            alpha=0.2,
+            color="goldenrod",
+            legend_label="Hysteresis Threshold (won't correct out rate within area)",
+        )
+
+        fig2 = figure(x_range=fig1.x_range)
+        fig2.line(t, inrate, color="hotpink", legend_label="Nominal in sample rate")
+        fig2.line(t, outrate, color="firebrick", legend_label="Nominal out sample rate")
+        fig2.line(
+            t, corrected, color="dodgerblue", legend_label="Corrected out sample rate"
+        )
+        fig2.line(
+            t,
+            hysteresiscorrected,
+            color="seagreen",
+            legend_label="Hysteresis-corrected out sample rate",
+        )
+        fig2.line(
+            t, configured, color="goldenrod", legend_label="Configured out sample rate"
+        )
+
+        fig3 = figure(x_range=fig1.x_range)
+        fig3.line(t, p, color="goldenrod", legend_label="P")
+        fig3.line(t, i, color="dodgerblue", legend_label="I")
+        fig3.line(t, d, color="seagreen", legend_label="D")
+
+        fig4 = figure(x_range=fig1.x_range)
+        fig4.line(t, kpp, color="goldenrod", legend_label="KpP")
+        fig4.line(t, kii, color="dodgerblue", legend_label="KiI")
+        fig4.line(t, kdd, color="seagreen", legend_label="KdD")
+        fig4.line(t, control, color="hotpink", legend_label="Control Signal")
+
+        fig1.legend.location = "top_left"
+        fig2.legend.location = "top_right"
+        fig3.legend.location = "bottom_left"
+        fig4.legend.location = "bottom_right"
+        for fig in (fig1, fig2, fig3, fig4):
+            fig.legend.background_fill_alpha = 0.6
+            fig.legend.click_policy = "hide"
+
+        tabs.append(
+            TabPanel(child=gridplot([[fig1, fig2], [fig3, fig4]]), title=str(id))
+        )
+
+    show(Tabs(tabs=tabs))
+
+
+if __name__ == "__main__":
+    main()