Document extended N1000/T2000 bucket + effective budgets (FluidAudio #924)

README.md

@@ -32,10 +32,23 @@ Each model ships as both `.mlpackage` (source) and compiled `.mlmodelc`.
 | `T3Nano-Decode-M1536-fp16-stateful` | 184 MB | Single-step AR decode, KV cache in `MLState` (**3.3 ms/step**; macOS 15+/iOS 18+) | CPU+GPU |
 | `FlowMean-N500-fp16` | 228 MB | S3Gen meanflow: 500-token bucket → 1000 mel frames, 2 plain Euler steps in-graph (0.38 s/call) | CPU+GPU |
 | `HiFT-T1000-fp16` | 40 MB | HiFTNet vocoder: mel → 24 kHz waveform (0.09 s/call) | CPU+GPU |
+| `FlowMean-N1000-fp16` | 242 MB | S3Gen meanflow, 1000-token bucket → 2000 mel frames (extended ~30 s output; FluidAudio #924) | CPU+GPU |
+| `HiFT-T2000-fp16` | 40 MB | HiFTNet vocoder for the 2000-frame extended bucket | CPU+GPU |
 | `tables/tables.safetensors` | 87 MB | text/speech embedding tables (host applies; GPT2 `wpe` is in-graph) |
 | `tables/voice-default.safetensors` | 0.7 MB | precomputed built-in voice conditioning (T3 cond embeds + S3Gen ref dict) |
 | `tokenizer/` | 1.4 MB | GPT2 BPE tokenizer (50276 ids incl. 20 paralinguistic tags: `[laugh]`, `[chuckle]`, `[sigh]`, …) |
 
+### Output budget / bucket choice
+
+The flow bucket holds `voice prompt tokens + generated speech tokens + 3
+silence tokens`. The built-in voice uses 250 prompt tokens, so `N500`
+yields **≤247 generated tokens ≈ 9.9 s of audio per call** and `N1000`
+**≤747 ≈ 29.9 s**. Likewise the T3 prefill window holds
+`voice conditioning (376) + text BPE + 1 BOS`, i.e. **≤135 text tokens**.
+Pick ONE flow/vocoder pair at load time; the buckets are static shapes, so
+the extended pair roughly doubles flow+vocoder latency even for short
+outputs.
+
 The AR stage runs at ~300 tok/s against the 25 Hz speech-token rate
 (**~12× real-time**) with the stateful decode; the full compute path is
 ~3.4× real-time with I/O-KV decode and ~6× with MLState, measured on an