README: misaki T/t output form, FluidAudio benchmark

README.md

@@ -52,7 +52,8 @@ ParadeeAcoustic  en, asr, noise -> audio [1,600F] float32, 24 kHz
 - `noise` is the harmonic source's Gaussian noise, passed in so the graph is deterministic. It is equivalent to the
   upstream in-graph noise (9 per-harmonic channels mixed by a linear layer = one scaled channel).
 - Phonemes must be written the way [misaki](https://github.com/hexgrad/misaki) writes them (Kokoro's G2P); that is
-  all Paradee saw in training.
+  all Paradee saw in training. That includes misaki's last step, flap `ɾ` → `T` and glottal stop `ʔ` → `t`:
+  lexicon-only frontends that skip it garble words like "kittens" and "satellite".
 - Use `CPU_ONLY` or `CPU_AND_NE`. **Do not use `ALL` / `CPU_AND_GPU`**: the LSTMs abort in MPSGraph
   (`GPURNNOps … JIT not supported`), the same failure Kokoro's prosody stage has.
 
@@ -68,6 +69,9 @@ paragraph, and one sentence at speed 0.8 and 1.3.
 - int8: log-mel L1 to fp32 ONNX 0.18–0.20 vs 0.21–0.31 for upstream `paradee_int8.onnx`; WER 3.81 % vs 2.97 %
   (one word of ~236).
 
+FluidAudio `tts-benchmark --corpus minimax-english` (100 phrases, Parakeet TDT round trip, Swift, CPU, includes the
+English G2P frontend): WER 1.20 % / CER 0.14 % for both int8 and fp32, RTFx ~100, 77 ms median per phrase.
+
 Conversion notes: the 1024-point phase-lock STFT is a gather + matmul + overlap-add instead of strided convolutions
 (the conv form ran ~40× slower on the Core ML CPU), and kokoro's random harmonic start phases are omitted because the
 upstream graph never reads them.