Finalize v2 model card, CPU evidence, API contract, and release docs

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Changelog
 
-## 2.0.0 - 2026-07-22
+## 2.0.0 - 2026-07-24
 
 - Release Inflect-Micro-v2 as a complete 24 kHz English text-to-waveform package.
 - Add punctuation-preserving normalization, long-text chunking, deterministic seeds, speed control, and variation control.

CITATION.cff

@@ -6,6 +6,6 @@ authors:
   - family-names: Song
     given-names: Owen
 version: 2.0.0
-date-released: 2026-07-22
+date-released: 2026-07-24
 url: 'https://huggingface.co/owensong/Inflect-Micro-v2'
 license: Apache-2.0

README.md

@@ -27,18 +27,18 @@ inference: false
 24 kHz English TTS with the neural waveform decoder already inside the model.</p>
 
 <p align="center">
-  <a href="https://github.com/owenawsong/Inflect"><img alt="GitHub" height="40" src="assets/nav/github.svg"></a>
-  <a href="https://huggingface.co/owensong/Inflect-Micro-v2"><img alt="Inflect Micro v2" height="40" src="assets/nav/micro.svg"></a>
-  <a href="https://huggingface.co/owensong/Inflect-Nano-v2"><img alt="Inflect Nano v2" height="40" src="assets/nav/nano.svg"></a>
-  <a href="https://huggingface.co/spaces/owensong/Inflect-v2"><img alt="Live playground" height="40" src="assets/nav/playground.svg"></a>
-  <a href="https://huggingface.co/collections/owensong/inflect-v2-6a619e820808eccf361a2948"><img alt="Inflect v2 collection" height="40" src="assets/nav/collection.svg"></a>
+  <a href="https://github.com/owenawsong/Inflect"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-0B2A54?style=for-the-badge&logo=github&logoColor=white"></a>
+  <a href="https://huggingface.co/owensong/Inflect-Micro-v2"><img alt="Inflect Micro v2" src="https://img.shields.io/badge/Micro_9.36M-1769E0?style=for-the-badge&logo=huggingface&logoColor=white"></a>
+  <a href="https://huggingface.co/owensong/Inflect-Nano-v2"><img alt="Inflect Nano v2" src="https://img.shields.io/badge/Nano_3.97M-FFFFFF?style=for-the-badge&logo=huggingface&logoColor=1769E0"></a>
+  <a href="https://huggingface.co/spaces/owensong/Inflect-v2"><img alt="Live playground" src="https://img.shields.io/badge/Playground-1769E0?style=for-the-badge&logo=huggingface&logoColor=white"></a>
+  <a href="https://huggingface.co/collections/owensong/inflect-v2-6a619e820808eccf361a2948"><img alt="Inflect v2 collection" src="https://img.shields.io/badge/Collection-0B2A54?style=for-the-badge&logo=huggingface&logoColor=white"></a>
 </p>
 
 <p align="center"><strong>9.36M complete parameters</strong> · <strong>37.53 MB FP32 weights</strong> · <strong>one fixed English voice</strong> · <strong>no external vocoder</strong></p>
 
 ---
 
-> **Complete means complete.** The published parameter count includes text encoding, duration prediction, latent synthesis, and the integrated 24 kHz waveform decoder. Inflect does not hide a second vocoder, server model, or inference-time teacher.
+> **Small enough to ship. Complete enough to stand alone.** Every published parameter is part of the local text-to-waveform path: text encoding, duration prediction, latent synthesis, and the integrated 24 kHz waveform decoder. There is no second vocoder download, server model, or inference-time teacher.
 
 <details>
 <summary><strong>Explore this model card</strong></summary>
@@ -48,8 +48,9 @@ inference: false
 | [Listen](#listen) | [Architecture](#architecture-and-parameter-budget) |
 | [Evaluation](#evaluation) | [Controls and long text](#controls-determinism-and-long-text) |
 | [Choose Micro or Nano](#choose-the-right-inflect) | [Data and adaptation](#data-voice-and-adaptation-status) |
-| [Run locally](#run-locally) | [Package map](#package-map) |
-| [Limitations](#limitations) | [Evaluation protocol](docs/EVALUATION.md) |
+| [Run locally](#run-locally) | [Exports and quantization](docs/EXPORTS.md) |
+| [Package map](#package-map) | [Evaluation and raw protocol](docs/EVALUATION.md) |
+| [Limitations](#limitations) | [Deployment guide](docs/DEPLOYMENT.md) |
 
 </details>
 
@@ -115,7 +116,45 @@ For Inflect-Micro-v2, the individual results are **2.52% Qwen3-ASR**, **5.45% Ne
 
 </details>
 
-### 4. Complete weight footprint
+<details>
+<summary><strong>Open evaluator robustness and error-category diagnostics</strong></summary>
+
+![ASR evaluator robustness](assets/evidence/asr-robustness.svg)
+
+![Semantic WER by prompt category](assets/evidence/category-semantic-wer.svg)
+
+These views are diagnostics, not additional leaderboards. They show where the
+recognizers disagree and which prompt categories still produce recoverable
+transcription errors.
+
+</details>
+
+### 4. Measured local CPU throughput
+
+![Warm CPU throughput](assets/evidence/cpu-throughput.svg)
+
+![Predicted quality and CPU throughput](assets/evidence/quality-vs-cpu-speed.svg)
+
+All systems use the same host, frozen prompt list, three warmups, isolated
+processes, and end-to-end timing boundary. Runtime includes each system's text
+frontend and waveform generation. The table and figures report audio seconds
+generated per wall-clock second; **1.0× is real time**.
+
+| System | Warm throughput ↑ | RTF ↓ | Median latency | p95 latency | Cold load |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| **Inflect-Micro-v2** | **1.58×** | 0.635 | 2.402 s | 7.281 s | 2.774 s |
+| **Inflect-Nano-v2** | **1.59×** | 0.630 | 1.797 s | 7.207 s | 0.192 s |
+| KittenTTS Nano · 2-voice mean | 7.85× | 0.127 | 0.740 s | 1.074 s | 3.830 s |
+| Piper Low · 2-voice mean | 18.58× | 0.054 | 0.219 s | 0.364 s | 3.172 s |
+| Supertonic 3 · James · 3-step | 5.51× | 0.181 | 0.994 s | 1.494 s | 1.318 s |
+| Supertonic 3 · James · 8-step | 2.51× | 0.398 | 2.267 s | 3.181 s | 1.601 s |
+
+These measurements used an AMD Ryzen 9 3900X with 12 configured CPU threads,
+48 identical prompts, and `CUDA_VISIBLE_DEVICES` empty. They compare the
+released software paths, not architecture alone: Inflect runs PyTorch, while
+the competitor packages use their published ONNX Runtime paths.
+
+### 5. Complete weight footprint
 
 ![Complete deployable model footprint](assets/evidence/model-footprint.svg)
 
@@ -132,13 +171,15 @@ Voice variants sharing the same weights are merged. Inflect totals include the i
 - The Modern400 corpus SHA-256 is `b7504ce2dce44a2da82770a6a5dfd2a034fe17e2113980f8a69663ade417a34c`.
 - Prompts, hypotheses, compressed row-level reports, and summaries ship under `evaluation/final/`.
 
-The retained CPU snapshot used one isolated process on an AMD EPYC 7K62, 50 prompts, three warmups, and runtime-default CPU threading:
+The retained CPU snapshot used one isolated process, 48 matched prompts, three
+warmups, and 12 configured CPU threads on an AMD Ryzen 9 3900X:
 
 | RTF ↓ | Audio generated / wall time ↑ | Median utterance | p95 utterance | Cold load |
 | ---: | ---: | ---: | ---: | ---: |
-| 0.743 | 1.35× real time | 3.205 s | 4.681 s | 1.35 s |
+| 0.635 | 1.58× real time | 2.402 s | 7.281 s | 2.774 s |
 
-RTF is wall time divided by generated audio duration. This is a host-specific engineering snapshot, not a portable cross-system speed claim. A matched local CPU rerun is tracked before the public release.
+RTF is wall time divided by generated audio duration. This is a host-specific
+engineering snapshot, not a portable cross-system speed claim.
 
 </details>
 
@@ -260,7 +301,7 @@ This release is inference-first. New-voice and new-language adaptation are **not
 | `runtime/` | Self-contained model implementation |
 | `samples/` | Held-out example generations |
 | `evaluation/final/` | Frozen benchmark prompts, reports, and protocol artifacts |
-| `docs/` | API, deployment, evaluation, data, and adaptation documentation |
+| `docs/` | API, deployment, evaluation, adaptation, and export documentation |
 | `release_manifest.json` | File sizes and SHA-256 hashes |
 
 ## Limitations

docs/DEPLOYMENT.md

@@ -31,11 +31,12 @@ from huggingface_hub import snapshot_download
 
 model_dir = snapshot_download(
     "owensong/Inflect-Micro-v2",
-    revision="<release-commit-sha>",
+    revision="v2.0.0",
 )
 ```
 
-Replace the placeholder with the final immutable Hub revision after launch.
+The `v2.0.0` tag points to the frozen initial release. Use `main` only when you
+intentionally want later documentation or runtime fixes.
 
 ## Reproducibility
 

docs/EVALUATION.md

@@ -9,7 +9,7 @@ Modern400 intelligibility results use 400 identical unseen English prompts per s
 - Inflect-Micro-v2 and Inflect-Nano-v2, fixed release checkpoints.
 - KittenTTS Nano, Bruno and Hugo reported separately.
 - Piper low, Ryan and Danny reported separately.
-- Supertonic 3 v1.3.1, official voice M2 at 3 and 8 steps, reported separately.
+- Supertonic 3, James (`M2` preset) at 3 and 8 steps, reported separately.
 
 Kitten and Piper voices are reported separately. The two Supertonic step counts are never pooled.
 
@@ -25,18 +25,18 @@ UTMOS22 is run over every generated clip with 10,000-sample bootstrap intervals.
 
 ## Runtime
 
-The retained launch snapshot uses one named host, the same 50-prompt subset,
-warm synthesis, one isolated process per system, runtime-default thread
-behavior, and separate model-load timing. RTF, audio-seconds per wall-second,
-median latency, p95 latency, and per-prompt rows are retained.
+The release CPU snapshot uses an AMD Ryzen 9 3900X (12 physical cores, 24
+logical processors), a fixed 12-thread policy, the same 48-prompt subset, three
+warmups, one isolated process per system, and separate model-load timing.
+`CUDA_VISIBLE_DEVICES` is empty; active providers are captured in every report.
+The timed boundary includes each public text frontend and complete waveform
+generation. RTF, audio-seconds per wall-second, median latency, p95 latency, and
+per-prompt rows are retained.
 
-This snapshot is explicitly **host-specific engineering evidence**, not a
-controlled cross-framework CPU leaderboard. The host exposed 192 logical CPUs,
-thread counts were not pinned, and framework endpoints differed. The model card
-therefore explains Inflect's own RTF but does not use the old competitor runtime
-chart as headline evidence. A publication-grade rerun requires fixed affinity
-and thread counts, active-provider capture, equivalent waveform endpoints,
-multiple randomized trials, and confidence intervals.
+This remains **host-specific engineering evidence**, not a universal speed
+ranking. Frameworks use different execution engines and output sample rates.
+The raw reports disclose those boundaries so results can be reproduced rather
+than silently generalized to unrelated hardware.
 
 ## Human evidence
 

docs/EXPORTS.md

@@ -0,0 +1,34 @@
+# Export formats and quantization
+
+## Release formats
+
+| Format | Release status | Notes |
+| --- | --- | --- |
+| PyTorch FP32 | Supported | Canonical weights and the fully tested runtime. |
+| PyTorch FP16/BF16 | Not released | Smaller in memory, but not validated for waveform quality or CPU compatibility. |
+| ONNX | Not released | Dynamic text length, stochastic latent sampling, and custom model operations require a dedicated export/runtime path. |
+| INT8 / INT4 | Not released | Naive weight quantization can audibly damage the integrated waveform decoder. |
+| GGUF | Not applicable today | GGUF targets transformer/LLM runtimes and is not a drop-in container for this VITS-family convolutional waveform model. |
+| Core ML / TFLite | Not released | Requires separate conversion and target-device validation. |
+
+## Why only FP32 ships initially
+
+The model is already small: 37.53 MB for Micro and 15.97 MB for Nano. A format
+is useful only if it is smaller or faster **and** preserves pronunciation,
+stability, and waveform quality. An export that loads but produces degraded
+speech is not release-ready.
+
+The canonical `model.pth` remains the source of truth. Future exports must:
+
+1. reproduce deterministic fixed-seed outputs within a declared tolerance;
+2. pass matched semantic-WER and signal-diagnostic gates;
+3. complete a held-out listening comparison against FP32;
+4. report target hardware, runtime version, memory, latency, and file size;
+5. remain compatible with punctuation-aware long-text synthesis.
+
+## Practical deployment today
+
+Use the FP32 package and keep one `InflectTTS` instance loaded. Nano is the
+recommended footprint-first deployment; Micro is the quality-first deployment.
+Install the CPU or CUDA PyTorch build appropriate for the target platform before
+installing the remaining requirements.

docs/FINETUNING.md

@@ -1,34 +1,95 @@
-# Adaptation status
-
-`Inflect-Micro-v2` is an open-weight, inference-first release. The repository does not ship the private corpus-construction recipe, teacher setup, discriminator checkpoints, optimizer state, or a supported training command.
-
-## What is supported
-
-- Local inference from the released generator checkpoint.
-- Deterministic regression testing through a fixed text, seed, speed, and variation value.
-- Inspection of the complete deployable architecture and frontend.
-- Inspection of the released generator as a starting point for independent
-  research in a separately reconstructed VITS-compatible training stack.
-
-## What is not yet supported
-
-- A one-command new-voice trainer.
-- Zero-shot voice cloning or speaker embeddings.
-- Language adaptation without rebuilding the symbol inventory and frontend.
-- Resuming the release checkpoint as if it contained discriminators or optimizer state.
-- Adding a selectable second speaker to this single-speaker architecture.
-
-## Data contract for future adapters
-
-A defensible voice-adaptation toolkit must require a UTF-8 manifest with one audio path and one verified transcript per row; mono, consistently sampled, unclipped speech; held-out prompts that never enter training; speaker-identity and transcript audits; and an inference-only export that removes all training-only state. Duration, silence, clipping, and ASR checks must run before training rather than after a failed model.
-
-## Evaluation contract
-
-Any adapted checkpoint should be compared with the frozen release on identical unseen prompts, a fixed seed policy, semantic WER, signal diagnostics, a learned quality predictor, and a blinded human listening test. Improving speaker similarity while reducing intelligibility is not a successful adaptation.
-
-The released checkpoint may be technically reusable inside a reconstructed
-training system, but new-voice and new-language adaptation have not been
-validated. This file intentionally does not turn theoretical compatibility into
-a supported feature. A public adapter will be documented separately only if it
-can reproduce a usable checkpoint from a clean environment and pass the same
-package and evaluation gates as the release models.
+# Voice and language adaptation
+
+Inflect v2 is an open-weight, inference-first release. The public checkpoint is
+the complete deployable generator, not a resumable training snapshot: it does
+not contain optimizer state, discriminators, training manifests, or the private
+corpus-construction pipeline.
+
+This document separates what is technically possible from what is supported.
+
+## Current support level
+
+| Task | Status | What that means |
+| --- | --- | --- |
+| Local inference | Supported | Tested through the packaged Python API and CLI. |
+| Continue generator training | Research use | The released generator can initialize a compatible VITS-family training stack. |
+| Replace the fixed voice | Research use | Requires a new single-speaker corpus and careful decoder adaptation. |
+| Add a selectable second voice | Not supported | The release architecture has no speaker-ID or speaker-embedding interface. |
+| Add a language | Not supported | Requires a new frontend, symbol inventory, embeddings, alignments, and data. |
+| Zero-shot cloning | Not supported | There is no reference encoder or cloning interface. |
+
+## What a compatible adaptation stack needs
+
+The release config describes the deployable generator:
+
+- 24 kHz mono waveform output;
+- 16,384-sample training segments;
+- English phoneme input with interspersed blanks;
+- stochastic latent synthesis and monotonic alignment;
+- an integrated waveform decoder.
+
+A separately reconstructed trainer must add the training-only components that
+are intentionally absent from the release:
+
+1. waveform and period discriminators compatible with the decoder;
+2. optimizer and learning-rate schedule state;
+3. mel, KL, duration, feature-matching, and adversarial losses;
+4. data loading, segmentation, and alignment handling;
+5. validation synthesis and checkpoint export.
+
+Loading `model.pth` as the generator initialization is reasonable. Treating it
+as a complete resumable checkpoint is not.
+
+## Data contract
+
+Use a UTF-8 manifest with one verified transcript and one audio path per row.
+The audio should be:
+
+- mono, consistently sampled, and free of clipping;
+- one speaker with stable recording conditions;
+- trimmed without cutting consonants or sentence endings;
+- diverse in phonemes, punctuation, sentence shape, numbers, and names;
+- split into train and held-out evaluation sets before training.
+
+Run transcript verification, duration checks, silence checks, clipping checks,
+and speaker-consistency checks before training. Voice adaptation quality is
+usually limited by data coverage and decoder mismatch before it is limited by
+the number of optimization steps.
+
+## Conservative warm-start sequence
+
+This is research guidance, not a guaranteed recipe:
+
+1. Load the public generator and freshly initialize training-only components.
+2. Begin with a low generator learning rate.
+3. Temporarily freeze most of the waveform decoder while the text and latent
+   paths adapt to the new speaker.
+4. Unfreeze the decoder gradually and monitor high-frequency buzz, sibilance,
+   identity drift, and intelligibility.
+5. Export inference-only candidates frequently.
+6. Select by held-out listening and matched evaluation, not training loss.
+
+Do not publish an adapted voice as equivalent to the release voice without a
+matched unseen-text evaluation.
+
+## Required evaluation
+
+Compare every candidate with the frozen release on identical prompts and seeds:
+
+- semantic WER with more than one ASR evaluator;
+- clipping, silence, loudness, and ending diagnostics;
+- a learned quality predictor used only as a diagnostic;
+- speaker consistency for voice replacement;
+- blinded human listening on unseen text.
+
+An adaptation that improves speaker similarity while reducing intelligibility,
+stability, or audio quality is not a successful adaptation.
+
+## Why there is no one-command trainer
+
+A nominal script would be easy to publish; a trustworthy trainer is not. The
+project will call adaptation supported only after a clean environment can
+produce a usable checkpoint, export it correctly, and pass the same package and
+evaluation gates as the release models. Until then, the honest interface is the
+tested inference package plus the architecture/configuration needed for
+independent research.

evaluation/final/runtime_snapshot/inflect-micro-v2_cpu.json

@@ -1,435 +1,419 @@
-{
-  "format": "inflect_tts_runtime_v2",
-  "system": "inflect-micro-v2",
-  "provider": "cpu",
-  "hardware": {
-    "platform": "Linux-5.15.0-164-generic-x86_64-with-glibc2.39",
-    "processor": "AMD EPYC 7K62 48-Core Processor",
-    "logical_cpus_visible": 192,
-    "process_cpu_affinity_count": 192
-  },
-  "runtime_protocol": {
-    "python": "3.12.13",
-    "single_process": true,
-    "concurrent_synthesis_requests": 1,
-    "warmup_utterances": 3,
-    "thread_policy": "runtime default",
-    "environment_threads": {
-      "OMP_NUM_THREADS": null,
-      "MKL_NUM_THREADS": null,
-      "OPENBLAS_NUM_THREADS": null
-    }
-  },
-  "utterances": 50,
-  "load_seconds": 1.3450956749729812,
-  "peak_process_rss_bytes_observed": 1374117888,
-  "total_latency_seconds": 164.47246361640282,
-  "total_audio_seconds": 221.32266666666666,
-  "rtf": 0.7431342938955017,
-  "realtime_multiple": 1.3456517997009816,
-  "latency_mean_seconds": 3.2894492723280564,
-  "latency_median_seconds": 3.204997018503491,
-  "latency_p95_seconds": 4.681012376293073,
-  "rows": [
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+}

evaluation/final/runtime_snapshot/inflect-nano-v2_cpu.json

@@ -1,435 +1,419 @@
-{
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+}

evaluation/final/runtime_snapshot/piper-danny-low_cpu.json

@@ -1,435 +1,419 @@
-{
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evaluation/final/runtime_snapshot/supertonic3-james-8step_cpu.json

@@ -1,435 +1,419 @@
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+    {
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+    },
+    {
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+      "rtf": 0.3216601796026144
+    }
+  ]
+}

inference.py

@@ -112,7 +112,7 @@ class InflectTTS:
         speed: float = 1.0,
         variation: float = 0.667,
         seed: int = 0,
-    ) -> np.ndarray:
+    ) -> tuple[int, np.ndarray]:
         normalized = " ".join(text.split())
         if not normalized:
             raise ValueError("Text must not be empty.")
@@ -143,12 +143,14 @@ class InflectTTS:
                 max_len=4000,
             )[0][0, 0].float().cpu().numpy()
             pieces.append(edge_fade(waveform, self.sample_rate))
-        return np.clip(np.concatenate(pieces), -1.0, 1.0)
+        waveform = np.clip(np.concatenate(pieces), -1.0, 1.0)
+        return self.sample_rate, waveform
 
     def save(self, text: str, output: str | Path, **kwargs: object) -> Path:
         destination = Path(output)
         destination.parent.mkdir(parents=True, exist_ok=True)
-        sf.write(destination, self.synthesize(text, **kwargs), self.sample_rate)
+        sample_rate, waveform = self.synthesize(text, **kwargs)
+        sf.write(destination, waveform, sample_rate)
         return destination
 
 

release_manifest.json

@@ -7,6 +7,31 @@
   "smoke_audio_seconds": 3.968,
   "standalone_cpu_smoke_audio_seconds": 3.616,
   "files": [
+    {
+      "path": ".gitattributes",
+      "bytes": 1519,
+      "sha256": "11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361"
+    },
+    {
+      "path": "assets/evidence/asr-consensus.svg",
+      "bytes": 3225,
+      "sha256": "95c304a8c1c31a70bc09c72b5211de65277a19b11459c11cb19e20d70fe594d2"
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