Add benchmark harness and compare against 11 other STT backends

Measures median latency, strict exact-match accuracy, digit formatting and
silence behaviour across 48 clips (16 Home Assistant commands x 3 macOS TTS
voices), on an M4 Mac mini.

Headline: Parakeet v2 at 102ms median is 5x faster than the best whisper.cpp
configuration and lands within one clip of it on accuracy. mlx-whisper
large-v3 is the only backend to score 48/48, at 11x the latency. Moonshine is
2x faster again but gives up real accuracy (35/48).

Also quantifies the reason this defaults to v2 over v3: v3 returned digits
for only 10 of 21 number-bearing commands, against 21/21 for v2, which is
most of the gap between their exact-match scores.

The harness feeds audio to every backend as an array rather than a path --
mlx-whisper and moonshine otherwise shell out to ffmpeg, which this project
deliberately does not require.

Clips are gitignored; bench/make_clips.sh regenerates them. Caveats are
documented in the README: this is clean synthetic TTS, so it measures latency
rigorously and accuracy only as a domain smoke test, and faster-whisper is
CPU-only on Apple Silicon because CTranslate2 has no Metal backend.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

bench/make_clips.sh

@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+# Render the benchmark corpus to 16kHz mono WAV using macOS TTS, across
+# several voices. Synthetic speech is clean and accent-consistent, so treat
+# the accuracy numbers as a domain smoke test, not a WER benchmark.
+set -euo pipefail
+
+DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+OUT="$DIR/clips"
+VOICES=(Daniel Samantha Karen)
+
+rm -rf "$OUT"; mkdir -p "$OUT"
+
+i=0
+while IFS=$'\t' read -r phrase _expected _has_number; do
+    [[ "$phrase" =~ ^#  || -z "$phrase" ]] && continue
+    i=$((i + 1))
+    for voice in "${VOICES[@]}"; do
+        if ! say -v "$voice" -o "$OUT/${i}_${voice}.aiff" "$phrase" 2>/dev/null; then
+            say -o "$OUT/${i}_${voice}.aiff" "$phrase"
+        fi
+        afconvert -f WAVE -d LEI16@16000 -c 1 \
+            "$OUT/${i}_${voice}.aiff" "$OUT/${i}_${voice}.wav"
+        rm -f "$OUT/${i}_${voice}.aiff"
+    done
+done < "$DIR/corpus.tsv"
+
+python3 - "$OUT/silence.wav" <<'PY'
+import sys, wave
+w = wave.open(sys.argv[1], "wb")
+w.setparams((1, 2, 16000, 0, "NONE", "NONE"))
+w.writeframes(b"\x00\x00" * 16000 * 3)
+w.close()
+PY
+
+echo "Wrote $(ls "$OUT"/*.wav | wc -l | tr -d ' ') clips to $OUT"