Inflect v2 Micro/Nano CoreML: fixed-shape encoder + bucketed synthesizers (fp16)

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README.md

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+---
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-to-speech
+tags:
+  - coreml
+  - tts
+  - vits
+  - apple-silicon
+  - ane
+base_model:
+  - owensong/Inflect-Micro-v2
+  - owensong/Inflect-Nano-v2
+---
+
+# Inflect v2 CoreML (Micro + Nano)
+
+Fixed-shape CoreML conversion of [Inflect-Micro-v2](https://huggingface.co/owensong/Inflect-Micro-v2)
+(9.36M params) and [Inflect-Nano-v2](https://huggingface.co/owensong/Inflect-Nano-v2) (3.97M params) —
+ultra-tiny VITS-family end-to-end English TTS, 24 kHz mono, fp16.
+
+Converted by [FluidInference](https://huggingface.co/FluidInference) (mobius);
+conversion pipeline, parity checks, and benchmark harness live in the
+`models/tts/inflect-v2/coreml/` directory of the mobius repo.
+
+## Contents
+
+```
+micro/            9.4M params, ~19 MB fp16 total per graph set
+  encoder.mlpackage             tokens+mask -> m_p, logs_p, logw   [t_text=512]
+  synthesizer_f{256,384,512,640,768,896,1024,2048}.mlpackage
+                                z_p+mask -> waveform  [bucket frames x 256 samples]
+  upstream_config.json
+nano/             4.0M params, ~8 MB fp16 per graph set (same layout)
+symbols.json      keithito-style symbol table (178 symbols, add_blank interspersal)
+LICENSE           Apache-2.0 (upstream)
+```
+
+## Inference pipeline
+
+Two deterministic models with everything stochastic or dynamically shaped on the host:
+
+1. espeak-ng `en-us` phonemization (with stress) → symbol ids → intersperse blanks (`pad_id=0`) → pad to 512.
+2. `encoder`: → `m_p`, `logs_p`, `logw`.
+3. Host: `w = ceil(exp(logw))/speed`; repeat-expand `m_p`/`logs_p` to `y_len` frames;
+   `z_p = m_p + randn * exp(logs_p) * noise_scale` (default 0.667).
+4. Pick the smallest synthesizer bucket ≥ `y_len`; zero-pad `z_p`, mask valid frames.
+5. `synthesizer`: reverse coupling flow + HiFiGAN → waveform; trim to `y_len * 256` samples.
+
+Buckets are fixed-shape for ANE compatibility; at 128-frame granularity padding
+overhead is within ~5% of exact-shape synthesis.
+
+## Benchmarks (M5 Pro, macOS 26.6, GPU, MiniMax-English 100 phrases)
+
+| Variant | Synth p50 / p95 | Agg RTFx | WER* | CER* |
+|---|---|---|---|---|
+| Micro | 25.7 / 35.5 ms | 245× | 1.43% | 0.44% |
+| Nano | 13.2 / 16.9 ms | 460× | 1.92% | 0.57% |
+
+\* Parakeet TDT v3 roundtrip, same scoring path as the
+[FluidAudio](https://github.com/FluidInference/FluidAudio) TTS benchmarks.
+fp16 parity vs the PyTorch reference: audio correlation > 0.9999, predicted
+durations bit-identical.
+
+## Notes
+
+- ANE: the synthesizer's waveform-rate tensors exceed the ANE width limit
+  (W ≤ 65536) above the 256-frame bucket; f256 reaches 77% ANE residency,
+  larger buckets run on GPU (which is faster at every size on M-series).
+- English only, one voice per variant, no cloning. Sample rate 24 kHz.
+- Upstream weights and frontend: Apache-2.0, © the Inflect authors. This repo
+  redistributes converted weights under the same license.

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