Preserve alignment edits without ASR words

Keep transcript-only runs eligible for alignment heuristics instead of bailing out when the ASR stage does not supply word timings.

Build fallback AsrWord entries from the transcript so cue-based corrections like "i mean" still apply, while reusing the existing literal guard for verbatim phrases.

Cover the new path in alignment and pipeline tests, and validate with python3 -m unittest tests.test_alignment_edits tests.test_pipeline_engine.

README.md

@@ -294,6 +294,65 @@ aman bench --text-file ./bench-input.txt --repeat 20 --json
 the processing path from input transcript text through alignment/editor/fact-guard/vocabulary cleanup and
 prints timing summaries.
 
+Internal Vosk exploration (fixed-phrase dataset collection):
+
+```bash
+aman collect-fixed-phrases \
+  --phrases-file exploration/vosk/fixed_phrases/phrases.txt \
+  --out-dir exploration/vosk/fixed_phrases \
+  --samples-per-phrase 10
+```
+
+This internal command prompts each allowed phrase and records labeled WAV
+samples with manual start/stop (Enter to start, Enter to stop). It does not run
+Vosk decoding and does not execute desktop commands. Output includes:
+- `exploration/vosk/fixed_phrases/samples/`
+- `exploration/vosk/fixed_phrases/manifest.jsonl`
+
+Internal Vosk exploration (keystroke dictation: literal vs NATO):
+
+```bash
+# collect literal-key dataset
+aman collect-fixed-phrases \
+  --phrases-file exploration/vosk/keystrokes/literal/phrases.txt \
+  --out-dir exploration/vosk/keystrokes/literal \
+  --samples-per-phrase 10
+
+# collect NATO-key dataset
+aman collect-fixed-phrases \
+  --phrases-file exploration/vosk/keystrokes/nato/phrases.txt \
+  --out-dir exploration/vosk/keystrokes/nato \
+  --samples-per-phrase 10
+
+# evaluate both grammars across available Vosk models
+aman eval-vosk-keystrokes \
+  --literal-manifest exploration/vosk/keystrokes/literal/manifest.jsonl \
+  --nato-manifest exploration/vosk/keystrokes/nato/manifest.jsonl \
+  --intents exploration/vosk/keystrokes/intents.json \
+  --output-dir exploration/vosk/keystrokes/eval_runs \
+  --models-file exploration/vosk/keystrokes/models.example.json
+```
+
+`eval-vosk-keystrokes` writes a structured report (`summary.json`) with:
+- intent accuracy and unknown-rate by grammar
+- per-intent/per-letter confusion tables
+- latency (avg/p50/p95), RTF, and model-load time
+- strict grammar compliance checks (out-of-grammar hypotheses hard-fail the model run)
+
+Internal Vosk exploration (single NATO words):
+
+```bash
+aman collect-fixed-phrases \
+  --phrases-file exploration/vosk/nato_words/phrases.txt \
+  --out-dir exploration/vosk/nato_words \
+  --samples-per-phrase 10
+```
+
+This prepares a labeled dataset for per-word NATO recognition (26 words, one
+word per prompt). Output includes:
+- `exploration/vosk/nato_words/samples/`
+- `exploration/vosk/nato_words/manifest.jsonl`
+
 Model evaluation lab (dataset + matrix sweep):
 
 ```bash
@@ -344,6 +403,9 @@ aman run --config ~/.config/aman/config.json
 aman doctor --config ~/.config/aman/config.json --json
 aman self-check --config ~/.config/aman/config.json --json
 aman bench --text "example transcript" --repeat 5 --warmup 1
+aman collect-fixed-phrases --phrases-file exploration/vosk/fixed_phrases/phrases.txt --out-dir exploration/vosk/fixed_phrases --samples-per-phrase 10
+aman collect-fixed-phrases --phrases-file exploration/vosk/nato_words/phrases.txt --out-dir exploration/vosk/nato_words --samples-per-phrase 10
+aman eval-vosk-keystrokes --literal-manifest exploration/vosk/keystrokes/literal/manifest.jsonl --nato-manifest exploration/vosk/keystrokes/nato/manifest.jsonl --intents exploration/vosk/keystrokes/intents.json --output-dir exploration/vosk/keystrokes/eval_runs --json
 aman build-heuristic-dataset --input benchmarks/heuristics_dataset.raw.jsonl --output benchmarks/heuristics_dataset.jsonl --json
 aman eval-models --dataset benchmarks/cleanup_dataset.jsonl --matrix benchmarks/model_matrix.small_first.json --heuristic-dataset benchmarks/heuristics_dataset.jsonl --heuristic-weight 0.25 --json
 aman sync-default-model --check --report benchmarks/results/latest.json --artifacts benchmarks/model_artifacts.json --constants src/constants.py

exploration/vosk/keystrokes/.gitignore

@@ -0,0 +1,5 @@
+literal/manifest.jsonl
+literal/samples/
+nato/manifest.jsonl
+nato/samples/
+eval_runs/

exploration/vosk/keystrokes/findings.md

@@ -0,0 +1,31 @@
+# Vosk Keystroke Grammar Findings
+
+- Date (UTC): 2026-02-28
+- Run ID: `run-20260228T200047Z`
+- Dataset size:
+  - Literal grammar: 90 samples
+  - NATO grammar: 90 samples
+- Intents: 9 (`ctrl|shift|ctrl+shift` x `d|b|p`)
+
+## Results
+
+| Model | Literal intent accuracy | NATO intent accuracy | Literal p50 | NATO p50 |
+|---|---:|---:|---:|---:|
+| `vosk-small-en-us-0.15` | 71.11% | 100.00% | 26.07 ms | 26.38 ms |
+| `vosk-en-us-0.22-lgraph` | 74.44% | 100.00% | 210.34 ms | 214.97 ms |
+
+## Main Error Pattern (Literal Grammar)
+
+- Letter confusion is concentrated on `p -> b`:
+  - `control p -> control b`
+  - `shift p -> shift b`
+  - `control shift p -> control shift b`
+
+## Takeaways
+
+- NATO grammar is strongly validated for this keystroke use case (100% on both tested models).
+- `vosk-small-en-us-0.15` is the practical default for command-keystroke experiments because it matches NATO accuracy while being much faster.
+
+## Raw Report
+
+- `exploration/vosk/keystrokes/eval_runs/run-20260228T200047Z/summary.json`

exploration/vosk/keystrokes/intents.json

@@ -0,0 +1,65 @@
+[
+  {
+    "intent_id": "ctrl+d",
+    "literal_phrase": "control d",
+    "nato_phrase": "control delta",
+    "letter": "d",
+    "modifier": "ctrl"
+  },
+  {
+    "intent_id": "ctrl+b",
+    "literal_phrase": "control b",
+    "nato_phrase": "control bravo",
+    "letter": "b",
+    "modifier": "ctrl"
+  },
+  {
+    "intent_id": "ctrl+p",
+    "literal_phrase": "control p",
+    "nato_phrase": "control papa",
+    "letter": "p",
+    "modifier": "ctrl"
+  },
+  {
+    "intent_id": "shift+d",
+    "literal_phrase": "shift d",
+    "nato_phrase": "shift delta",
+    "letter": "d",
+    "modifier": "shift"
+  },
+  {
+    "intent_id": "shift+b",
+    "literal_phrase": "shift b",
+    "nato_phrase": "shift bravo",
+    "letter": "b",
+    "modifier": "shift"
+  },
+  {
+    "intent_id": "shift+p",
+    "literal_phrase": "shift p",
+    "nato_phrase": "shift papa",
+    "letter": "p",
+    "modifier": "shift"
+  },
+  {
+    "intent_id": "ctrl+shift+d",
+    "literal_phrase": "control shift d",
+    "nato_phrase": "control shift delta",
+    "letter": "d",
+    "modifier": "ctrl+shift"
+  },
+  {
+    "intent_id": "ctrl+shift+b",
+    "literal_phrase": "control shift b",
+    "nato_phrase": "control shift bravo",
+    "letter": "b",
+    "modifier": "ctrl+shift"
+  },
+  {
+    "intent_id": "ctrl+shift+p",
+    "literal_phrase": "control shift p",
+    "nato_phrase": "control shift papa",
+    "letter": "p",
+    "modifier": "ctrl+shift"
+  }
+]

exploration/vosk/keystrokes/literal/phrases.txt

@@ -0,0 +1,11 @@
+# Keystroke literal grammar labels.
+# One phrase per line.
+control d
+control b
+control p
+shift d
+shift b
+shift p
+control shift d
+control shift b
+control shift p

exploration/vosk/keystrokes/models.example.json

@@ -0,0 +1,10 @@
+[
+  {
+    "name": "vosk-small-en-us-0.15",
+    "path": "/tmp/vosk-models/vosk-model-small-en-us-0.15"
+  },
+  {
+    "name": "vosk-en-us-0.22-lgraph",
+    "path": "/tmp/vosk-models/vosk-model-en-us-0.22-lgraph"
+  }
+]

exploration/vosk/keystrokes/nato/phrases.txt

@@ -0,0 +1,11 @@
+# Keystroke NATO grammar labels.
+# One phrase per line.
+control delta
+control bravo
+control papa
+shift delta
+shift bravo
+shift papa
+control shift delta
+control shift bravo
+control shift papa

exploration/vosk/nato_words/.gitignore

@@ -0,0 +1,3 @@
+manifest.jsonl
+samples/
+eval_runs/

exploration/vosk/nato_words/phrases.txt

@@ -0,0 +1,28 @@
+# NATO alphabet single-word grammar labels.
+# One phrase per line.
+alpha
+bravo
+charlie
+delta
+echo
+foxtrot
+golf
+hotel
+india
+juliett
+kilo
+lima
+mike
+november
+oscar
+papa
+quebec
+romeo
+sierra
+tango
+uniform
+victor
+whiskey
+x-ray
+yankee
+zulu

pyproject.toml

@@ -14,6 +14,7 @@ dependencies = [
   "numpy",
   "pillow",
   "sounddevice",
+  "vosk>=0.3.45",
 ]
 
 [project.scripts]
@@ -44,6 +45,8 @@ py-modules = [
   "model_eval",
   "recorder",
   "vocabulary",
+  "vosk_collect",
+  "vosk_eval",
 ]
 
 [tool.setuptools.data-files]

src/aman.py

@@ -36,6 +36,22 @@ from recorder import stop_recording as stop_audio_recording
 from stages.asr_whisper import AsrResult, WhisperAsrStage
 from stages.editor_llama import LlamaEditorStage
 from vocabulary import VocabularyEngine
+from vosk_collect import (
+    DEFAULT_CHANNELS,
+    DEFAULT_FIXED_PHRASES_OUT_DIR,
+    DEFAULT_FIXED_PHRASES_PATH,
+    DEFAULT_SAMPLE_RATE,
+    DEFAULT_SAMPLES_PER_PHRASE,
+    CollectOptions,
+    collect_fixed_phrases,
+)
+from vosk_eval import (
+    DEFAULT_KEYSTROKE_EVAL_OUTPUT_DIR,
+    DEFAULT_KEYSTROKE_INTENTS_PATH,
+    DEFAULT_KEYSTROKE_LITERAL_MANIFEST_PATH,
+    DEFAULT_KEYSTROKE_NATO_MANIFEST_PATH,
+    run_vosk_keystroke_eval,
+)
 
 
 class State:
@@ -981,6 +997,88 @@ def _build_parser() -> argparse.ArgumentParser:
     )
     bench_parser.add_argument("-v", "--verbose", action="store_true", help="enable verbose logs")
 
+    collect_parser = subparsers.add_parser(
+        "collect-fixed-phrases",
+        help="internal: collect labeled fixed-phrase wav samples for command-stt exploration",
+    )
+    collect_parser.add_argument(
+        "--phrases-file",
+        default=str(DEFAULT_FIXED_PHRASES_PATH),
+        help="path to fixed-phrase labels file (one phrase per line)",
+    )
+    collect_parser.add_argument(
+        "--out-dir",
+        default=str(DEFAULT_FIXED_PHRASES_OUT_DIR),
+        help="output directory for samples/ and manifest.jsonl",
+    )
+    collect_parser.add_argument(
+        "--samples-per-phrase",
+        type=int,
+        default=DEFAULT_SAMPLES_PER_PHRASE,
+        help="number of recordings to capture per phrase",
+    )
+    collect_parser.add_argument(
+        "--samplerate",
+        type=int,
+        default=DEFAULT_SAMPLE_RATE,
+        help="sample rate for captured wav files",
+    )
+    collect_parser.add_argument(
+        "--channels",
+        type=int,
+        default=DEFAULT_CHANNELS,
+        help="number of input channels to capture",
+    )
+    collect_parser.add_argument(
+        "--device",
+        default="",
+        help="optional recording device index or name substring",
+    )
+    collect_parser.add_argument(
+        "--session-id",
+        default="",
+        help="optional session id; autogenerated when omitted",
+    )
+    collect_parser.add_argument(
+        "--overwrite-session",
+        action="store_true",
+        help="allow writing samples for an existing session id",
+    )
+    collect_parser.add_argument("--json", action="store_true", help="print JSON summary output")
+    collect_parser.add_argument("-v", "--verbose", action="store_true", help="enable verbose logs")
+
+    keystroke_eval_parser = subparsers.add_parser(
+        "eval-vosk-keystrokes",
+        help="internal: evaluate keystroke dictation datasets with literal and nato grammars",
+    )
+    keystroke_eval_parser.add_argument(
+        "--literal-manifest",
+        default=str(DEFAULT_KEYSTROKE_LITERAL_MANIFEST_PATH),
+        help="path to literal keystroke manifest.jsonl",
+    )
+    keystroke_eval_parser.add_argument(
+        "--nato-manifest",
+        default=str(DEFAULT_KEYSTROKE_NATO_MANIFEST_PATH),
+        help="path to nato keystroke manifest.jsonl",
+    )
+    keystroke_eval_parser.add_argument(
+        "--intents",
+        default=str(DEFAULT_KEYSTROKE_INTENTS_PATH),
+        help="path to keystroke intents definition json",
+    )
+    keystroke_eval_parser.add_argument(
+        "--output-dir",
+        default=str(DEFAULT_KEYSTROKE_EVAL_OUTPUT_DIR),
+        help="directory for run reports",
+    )
+    keystroke_eval_parser.add_argument(
+        "--models-file",
+        default="",
+        help="optional json array of model specs [{name,path}]",
+    )
+    keystroke_eval_parser.add_argument("--json", action="store_true", help="print JSON summary output")
+    keystroke_eval_parser.add_argument("-v", "--verbose", action="store_true", help="enable verbose logs")
+
     eval_parser = subparsers.add_parser(
         "eval-models",
         help="evaluate model/parameter matrices against expected outputs",
@@ -1059,6 +1157,8 @@ def _parse_cli_args(argv: list[str]) -> argparse.Namespace:
         "doctor",
         "self-check",
         "bench",
+        "collect-fixed-phrases",
+        "eval-vosk-keystrokes",
         "eval-models",
         "build-heuristic-dataset",
         "sync-default-model",
@@ -1255,6 +1355,120 @@ def _bench_command(args: argparse.Namespace) -> int:
     return 0
 
 
+def _collect_fixed_phrases_command(args: argparse.Namespace) -> int:
+    if args.samples_per_phrase < 1:
+        logging.error("collect-fixed-phrases failed: --samples-per-phrase must be >= 1")
+        return 1
+    if args.samplerate < 1:
+        logging.error("collect-fixed-phrases failed: --samplerate must be >= 1")
+        return 1
+    if args.channels < 1:
+        logging.error("collect-fixed-phrases failed: --channels must be >= 1")
+        return 1
+
+    options = CollectOptions(
+        phrases_file=Path(args.phrases_file),
+        out_dir=Path(args.out_dir),
+        samples_per_phrase=args.samples_per_phrase,
+        samplerate=args.samplerate,
+        channels=args.channels,
+        device_spec=(args.device.strip() if args.device.strip() else None),
+        session_id=(args.session_id.strip() if args.session_id.strip() else None),
+        overwrite_session=bool(args.overwrite_session),
+    )
+    try:
+        result = collect_fixed_phrases(options)
+    except Exception as exc:
+        logging.error("collect-fixed-phrases failed: %s", exc)
+        return 1
+
+    summary = {
+        "session_id": result.session_id,
+        "phrases": result.phrases,
+        "samples_per_phrase": result.samples_per_phrase,
+        "samples_target": result.samples_target,
+        "samples_written": result.samples_written,
+        "out_dir": str(result.out_dir),
+        "manifest_path": str(result.manifest_path),
+        "interrupted": result.interrupted,
+    }
+    if args.json:
+        print(json.dumps(summary, indent=2, ensure_ascii=False))
+    else:
+        print(
+            "collect-fixed-phrases summary: "
+            f"session={result.session_id} "
+            f"phrases={result.phrases} "
+            f"samples_per_phrase={result.samples_per_phrase} "
+            f"written={result.samples_written}/{result.samples_target} "
+            f"interrupted={result.interrupted} "
+            f"manifest={result.manifest_path}"
+        )
+    return 0
+
+
+def _eval_vosk_keystrokes_command(args: argparse.Namespace) -> int:
+    try:
+        summary = run_vosk_keystroke_eval(
+            literal_manifest=args.literal_manifest,
+            nato_manifest=args.nato_manifest,
+            intents_path=args.intents,
+            output_dir=args.output_dir,
+            models_file=(args.models_file.strip() or None),
+            verbose=args.verbose,
+        )
+    except Exception as exc:
+        logging.error("eval-vosk-keystrokes failed: %s", exc)
+        return 1
+
+    if args.json:
+        print(json.dumps(summary, indent=2, ensure_ascii=False))
+        return 0
+
+    print(
+        "eval-vosk-keystrokes summary: "
+        f"models={len(summary.get('models', []))} "
+        f"output_dir={summary.get('output_dir', '')}"
+    )
+    winners = summary.get("winners", {})
+    literal_winner = winners.get("literal", {})
+    nato_winner = winners.get("nato", {})
+    overall_winner = winners.get("overall", {})
+    if literal_winner:
+        print(
+            "winner[literal]: "
+            f"{literal_winner.get('name', '')} "
+            f"acc={float(literal_winner.get('intent_accuracy', 0.0)):.3f} "
+            f"p50={float(literal_winner.get('latency_p50_ms', 0.0)):.1f}ms"
+        )
+    if nato_winner:
+        print(
+            "winner[nato]: "
+            f"{nato_winner.get('name', '')} "
+            f"acc={float(nato_winner.get('intent_accuracy', 0.0)):.3f} "
+            f"p50={float(nato_winner.get('latency_p50_ms', 0.0)):.1f}ms"
+        )
+    if overall_winner:
+        print(
+            "winner[overall]: "
+            f"{overall_winner.get('name', '')} "
+            f"acc={float(overall_winner.get('avg_intent_accuracy', 0.0)):.3f} "
+            f"p50={float(overall_winner.get('avg_latency_p50_ms', 0.0)):.1f}ms"
+        )
+
+    for model in summary.get("models", []):
+        literal = model.get("literal", {})
+        nato = model.get("nato", {})
+        print(
+            f"{model.get('name', '')}: "
+            f"literal_acc={float(literal.get('intent_accuracy', 0.0)):.3f} "
+            f"literal_p50={float(literal.get('latency_ms', {}).get('p50', 0.0)):.1f}ms "
+            f"nato_acc={float(nato.get('intent_accuracy', 0.0)):.3f} "
+            f"nato_p50={float(nato.get('latency_ms', {}).get('p50', 0.0)):.1f}ms"
+        )
+    return 0
+
+
 def _eval_models_command(args: argparse.Namespace) -> int:
     try:
         report = run_model_eval(
@@ -1597,6 +1811,12 @@ def main(argv: list[str] | None = None) -> int:
     if args.command == "bench":
         _configure_logging(args.verbose)
         return _bench_command(args)
+    if args.command == "collect-fixed-phrases":
+        _configure_logging(args.verbose)
+        return _collect_fixed_phrases_command(args)
+    if args.command == "eval-vosk-keystrokes":
+        _configure_logging(args.verbose)
+        return _eval_vosk_keystrokes_command(args)
     if args.command == "eval-models":
         _configure_logging(args.verbose)
         return _eval_models_command(args)

src/stages/alignment_edits.py

@@ -33,7 +33,7 @@ class AlignmentResult:
 class AlignmentHeuristicEngine:
     def apply(self, transcript: str, words: list[AsrWord]) -> AlignmentResult:
         base_text = (transcript or "").strip()
-        if not base_text or not words:
+        if not base_text:
             return AlignmentResult(
                 draft_text=base_text,
                 decisions=[],
@@ -41,17 +41,26 @@ class AlignmentHeuristicEngine:
                 skipped_count=0,
             )
 
-        normalized_words = [_normalize_token(word.text) for word in words]
+        working_words = list(words) if words else _fallback_words_from_transcript(base_text)
+        if not working_words:
+            return AlignmentResult(
+                draft_text=base_text,
+                decisions=[],
+                applied_count=0,
+                skipped_count=0,
+            )
+
+        normalized_words = [_normalize_token(word.text) for word in working_words]
         literal_guard = _has_literal_guard(base_text)
         out_tokens: list[str] = []
         decisions: list[AlignmentDecision] = []
         i = 0
-        while i < len(words):
+        while i < len(working_words):
-            cue = _match_cue(words, normalized_words, i)
+            cue = _match_cue(working_words, normalized_words, i)
             if cue is not None and out_tokens:
                 cue_len, cue_label = cue
                 correction_start = i + cue_len
-                correction_end = _capture_phrase_end(words, correction_start)
+                correction_end = _capture_phrase_end(working_words, correction_start)
                 if correction_end <= correction_start:
                     decisions.append(
                         AlignmentDecision(
@@ -65,7 +74,7 @@ class AlignmentHeuristicEngine:
                     )
                     i += cue_len
                     continue
-                correction_tokens = _slice_clean_words(words, correction_start, correction_end)
+                correction_tokens = _slice_clean_words(working_words, correction_start, correction_end)
                 if not correction_tokens:
                     i = correction_end
                     continue
@@ -113,7 +122,7 @@ class AlignmentHeuristicEngine:
                 i = correction_end
                 continue
 
-            token = _strip_token(words[i].text)
+            token = _strip_token(working_words[i].text)
             if token:
                 out_tokens.append(token)
             i += 1
@@ -296,3 +305,23 @@ def _has_literal_guard(text: str) -> bool:
         "quote",
     )
     return any(guard in normalized for guard in guards)
+
+
+def _fallback_words_from_transcript(text: str) -> list[AsrWord]:
+    tokens = [item for item in (text or "").split() if item.strip()]
+    if not tokens:
+        return []
+    words: list[AsrWord] = []
+    start = 0.0
+    step = 0.15
+    for token in tokens:
+        words.append(
+            AsrWord(
+                text=token,
+                start_s=start,
+                end_s=start + 0.1,
+                prob=None,
+            )
+        )
+        start += step
+    return words

src/vosk_collect.py

@@ -0,0 +1,329 @@
+from __future__ import annotations
+
+import json
+import re
+import wave
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Callable
+
+import numpy as np
+
+from recorder import list_input_devices, resolve_input_device
+
+
+DEFAULT_FIXED_PHRASES_PATH = Path("exploration/vosk/fixed_phrases/phrases.txt")
+DEFAULT_FIXED_PHRASES_OUT_DIR = Path("exploration/vosk/fixed_phrases")
+DEFAULT_SAMPLES_PER_PHRASE = 10
+DEFAULT_SAMPLE_RATE = 16000
+DEFAULT_CHANNELS = 1
+COLLECTOR_VERSION = "fixed-phrases-v1"
+
+
+@dataclass
+class CollectOptions:
+    phrases_file: Path = DEFAULT_FIXED_PHRASES_PATH
+    out_dir: Path = DEFAULT_FIXED_PHRASES_OUT_DIR
+    samples_per_phrase: int = DEFAULT_SAMPLES_PER_PHRASE
+    samplerate: int = DEFAULT_SAMPLE_RATE
+    channels: int = DEFAULT_CHANNELS
+    device_spec: str | int | None = None
+    session_id: str | None = None
+    overwrite_session: bool = False
+
+
+@dataclass
+class CollectResult:
+    session_id: str
+    phrases: int
+    samples_per_phrase: int
+    samples_target: int
+    samples_written: int
+    out_dir: Path
+    manifest_path: Path
+    interrupted: bool
+
+
+def load_phrases(path: Path | str) -> list[str]:
+    phrases_path = Path(path)
+    if not phrases_path.exists():
+        raise RuntimeError(f"phrases file does not exist: {phrases_path}")
+    rows = phrases_path.read_text(encoding="utf-8").splitlines()
+    phrases: list[str] = []
+    seen: set[str] = set()
+    for raw in rows:
+        text = raw.strip()
+        if not text or text.startswith("#"):
+            continue
+        if text in seen:
+            continue
+        seen.add(text)
+        phrases.append(text)
+    if not phrases:
+        raise RuntimeError(f"phrases file has no usable labels: {phrases_path}")
+    return phrases
+
+
+def slugify_phrase(value: str) -> str:
+    slug = re.sub(r"[^a-z0-9]+", "_", value.casefold()).strip("_")
+    if not slug:
+        return "phrase"
+    return slug[:64]
+
+
+def float_to_pcm16(audio: np.ndarray) -> np.ndarray:
+    if audio.size <= 0:
+        return np.zeros((0,), dtype=np.int16)
+    clipped = np.clip(np.asarray(audio, dtype=np.float32), -1.0, 1.0)
+    return np.rint(clipped * 32767.0).astype(np.int16)
+
+
+def collect_fixed_phrases(
+    options: CollectOptions,
+    *,
+    input_func: Callable[[str], str] = input,
+    output_func: Callable[[str], None] = print,
+    record_sample_fn: Callable[[CollectOptions, Callable[[str], str]], tuple[np.ndarray, int, int]]
+    | None = None,
+) -> CollectResult:
+    _validate_options(options)
+    phrases = load_phrases(options.phrases_file)
+    slug_map = _build_slug_map(phrases)
+    session_id = _resolve_session_id(options.session_id)
+    out_dir = options.out_dir.expanduser().resolve()
+    samples_root = out_dir / "samples"
+    manifest_path = out_dir / "manifest.jsonl"
+    if not options.overwrite_session and _session_has_samples(samples_root, session_id):
+        raise RuntimeError(
+            f"session '{session_id}' already has samples in {samples_root}; use --overwrite-session"
+        )
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    recorder = record_sample_fn or _record_sample_manual_stop
+    target = len(phrases) * options.samples_per_phrase
+    written = 0
+
+    output_func(
+        "collecting fixed-phrase samples: "
+        f"session={session_id} phrases={len(phrases)} samples_per_phrase={options.samples_per_phrase}"
+    )
+    for phrase in phrases:
+        slug = slug_map[phrase]
+        phrase_dir = samples_root / slug
+        phrase_dir.mkdir(parents=True, exist_ok=True)
+        output_func(f'phrase: "{phrase}"')
+        sample_index = 1
+        while sample_index <= options.samples_per_phrase:
+            choice = input_func(
+                f"sample {sample_index}/{options.samples_per_phrase} - press Enter to start "
+                "(or 'q' to stop this session): "
+            ).strip()
+            if choice.casefold() in {"q", "quit", "exit"}:
+                output_func("collection interrupted by user")
+                return CollectResult(
+                    session_id=session_id,
+                    phrases=len(phrases),
+                    samples_per_phrase=options.samples_per_phrase,
+                    samples_target=target,
+                    samples_written=written,
+                    out_dir=out_dir,
+                    manifest_path=manifest_path,
+                    interrupted=True,
+                )
+            audio, frame_count, duration_ms = recorder(options, input_func)
+            if frame_count <= 0:
+                output_func("captured empty sample; retrying the same index")
+                continue
+            wav_path = phrase_dir / f"{session_id}__{sample_index:03d}.wav"
+            _write_wav_file(wav_path, audio, samplerate=options.samplerate, channels=options.channels)
+            row = {
+                "session_id": session_id,
+                "timestamp_utc": _utc_now_iso(),
+                "phrase": phrase,
+                "phrase_slug": slug,
+                "sample_index": sample_index,
+                "wav_path": _path_for_manifest(wav_path),
+                "samplerate": options.samplerate,
+                "channels": options.channels,
+                "duration_ms": duration_ms,
+                "frames": frame_count,
+                "device_spec": options.device_spec,
+                "collector_version": COLLECTOR_VERSION,
+            }
+            _append_manifest_row(manifest_path, row)
+            written += 1
+            output_func(
+                f"saved sample {written}/{target}: {row['wav_path']} "
+                f"(duration_ms={duration_ms}, frames={frame_count})"
+            )
+            sample_index += 1
+    return CollectResult(
+        session_id=session_id,
+        phrases=len(phrases),
+        samples_per_phrase=options.samples_per_phrase,
+        samples_target=target,
+        samples_written=written,
+        out_dir=out_dir,
+        manifest_path=manifest_path,
+        interrupted=False,
+    )
+
+
+def _record_sample_manual_stop(
+    options: CollectOptions,
+    input_func: Callable[[str], str],
+) -> tuple[np.ndarray, int, int]:
+    sd = _sounddevice()
+    frames: list[np.ndarray] = []
+    device = _resolve_device_or_raise(options.device_spec)
+
+    def callback(indata, _frames, _time, _status):
+        frames.append(indata.copy())
+
+    stream = sd.InputStream(
+        samplerate=options.samplerate,
+        channels=options.channels,
+        dtype="float32",
+        device=device,
+        callback=callback,
+    )
+    stream.start()
+    try:
+        input_func("recording... press Enter to stop: ")
+    finally:
+        stop_error = None
+        try:
+            stream.stop()
+        except Exception as exc:  # pragma: no cover - exercised via recorder tests, hard to force here
+            stop_error = exc
+        try:
+            stream.close()
+        except Exception as exc:  # pragma: no cover - exercised via recorder tests, hard to force here
+            if stop_error is None:
+                raise
+            raise RuntimeError(f"recording stop failed ({stop_error}) and close also failed ({exc})") from exc
+        if stop_error is not None:
+            raise stop_error
+
+    audio = _flatten_frames(frames, channels=options.channels)
+    frame_count = int(audio.shape[0]) if audio.ndim == 2 else int(audio.shape[0])
+    duration_ms = int(round((frame_count / float(options.samplerate)) * 1000.0))
+    return audio, frame_count, duration_ms
+
+
+def _validate_options(options: CollectOptions) -> None:
+    if options.samples_per_phrase < 1:
+        raise RuntimeError("samples_per_phrase must be >= 1")
+    if options.samplerate < 1:
+        raise RuntimeError("samplerate must be >= 1")
+    if options.channels < 1:
+        raise RuntimeError("channels must be >= 1")
+
+
+def _resolve_session_id(value: str | None) -> str:
+    text = (value or "").strip()
+    if text:
+        return text
+    return datetime.now(timezone.utc).strftime("session-%Y%m%dT%H%M%SZ")
+
+
+def _build_slug_map(phrases: list[str]) -> dict[str, str]:
+    out: dict[str, str] = {}
+    used: dict[str, str] = {}
+    for phrase in phrases:
+        slug = slugify_phrase(phrase)
+        previous = used.get(slug)
+        if previous is not None and previous != phrase:
+            raise RuntimeError(
+                f'phrases "{previous}" and "{phrase}" map to the same slug "{slug}"'
+            )
+        used[slug] = phrase
+        out[phrase] = slug
+    return out
+
+
+def _session_has_samples(samples_root: Path, session_id: str) -> bool:
+    if not samples_root.exists():
+        return False
+    pattern = f"{session_id}__*.wav"
+    return any(samples_root.rglob(pattern))
+
+
+def _flatten_frames(frames: list[np.ndarray], *, channels: int) -> np.ndarray:
+    if not frames:
+        return np.zeros((0, channels), dtype=np.float32)
+    data = np.concatenate(frames, axis=0)
+    if data.ndim == 1:
+        data = data.reshape(-1, 1)
+    if data.ndim != 2:
+        raise RuntimeError(f"unexpected recorded frame shape: {data.shape}")
+    return np.asarray(data, dtype=np.float32)
+
+
+def _write_wav_file(path: Path, audio: np.ndarray, *, samplerate: int, channels: int) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    pcm = float_to_pcm16(audio)
+    with wave.open(str(path), "wb") as handle:
+        handle.setnchannels(channels)
+        handle.setsampwidth(2)
+        handle.setframerate(samplerate)
+        handle.writeframes(pcm.tobytes())
+
+
+def _append_manifest_row(manifest_path: Path, row: dict[str, object]) -> None:
+    manifest_path.parent.mkdir(parents=True, exist_ok=True)
+    with manifest_path.open("a", encoding="utf-8") as handle:
+        handle.write(f"{json.dumps(row, ensure_ascii=False)}\n")
+        handle.flush()
+
+
+def _path_for_manifest(path: Path) -> str:
+    try:
+        rel = path.resolve().relative_to(Path.cwd().resolve())
+        return rel.as_posix()
+    except Exception:
+        return path.as_posix()
+
+
+def _utc_now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat(timespec="milliseconds").replace("+00:00", "Z")
+
+
+def _resolve_device_or_raise(spec: str | int | None) -> int | None:
+    device = resolve_input_device(spec)
+    if not _is_explicit_device_spec(spec):
+        return device
+    if device is not None:
+        return device
+    raise RuntimeError(
+        f"input device '{spec}' did not match any input device; available: {_available_inputs_summary()}"
+    )
+
+
+def _is_explicit_device_spec(spec: str | int | None) -> bool:
+    if spec is None:
+        return False
+    if isinstance(spec, int):
+        return True
+    return bool(str(spec).strip())
+
+
+def _available_inputs_summary(limit: int = 8) -> str:
+    devices = list_input_devices()
+    if not devices:
+        return "<none>"
+    items = [f"{d['index']}:{d['name']}" for d in devices[:limit]]
+    if len(devices) > limit:
+        items.append("...")
+    return ", ".join(items)
+
+
+def _sounddevice():
+    try:
+        import sounddevice as sd  # type: ignore[import-not-found]
+    except ModuleNotFoundError as exc:
+        raise RuntimeError(
+            "sounddevice is not installed; install dependencies with `uv sync --extra x11`"
+        ) from exc
+    return sd

src/vosk_eval.py

@@ -0,0 +1,670 @@
+from __future__ import annotations
+
+import json
+import statistics
+import time
+import wave
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Callable, Iterable
+
+
+DEFAULT_KEYSTROKE_INTENTS_PATH = Path("exploration/vosk/keystrokes/intents.json")
+DEFAULT_KEYSTROKE_LITERAL_MANIFEST_PATH = Path("exploration/vosk/keystrokes/literal/manifest.jsonl")
+DEFAULT_KEYSTROKE_NATO_MANIFEST_PATH = Path("exploration/vosk/keystrokes/nato/manifest.jsonl")
+DEFAULT_KEYSTROKE_EVAL_OUTPUT_DIR = Path("exploration/vosk/keystrokes/eval_runs")
+DEFAULT_KEYSTROKE_MODELS = [
+    {
+        "name": "vosk-small-en-us-0.15",
+        "path": "/tmp/vosk-models/vosk-model-small-en-us-0.15",
+    },
+    {
+        "name": "vosk-en-us-0.22-lgraph",
+        "path": "/tmp/vosk-models/vosk-model-en-us-0.22-lgraph",
+    },
+]
+
+
+@dataclass(frozen=True)
+class IntentSpec:
+    intent_id: str
+    literal_phrase: str
+    nato_phrase: str
+    letter: str
+    modifier: str
+
+
+@dataclass(frozen=True)
+class ModelSpec:
+    name: str
+    path: Path
+
+
+@dataclass(frozen=True)
+class ManifestSample:
+    wav_path: Path
+    expected_phrase: str
+    expected_intent: str
+    expected_letter: str
+    expected_modifier: str
+
+
+@dataclass(frozen=True)
+class DecodedRow:
+    wav_path: str
+    expected_phrase: str
+    hypothesis: str
+    expected_intent: str
+    predicted_intent: str | None
+    expected_letter: str
+    predicted_letter: str | None
+    expected_modifier: str
+    predicted_modifier: str | None
+    intent_match: bool
+    audio_ms: float
+    decode_ms: float
+    rtf: float | None
+    out_of_grammar: bool
+
+
+def run_vosk_keystroke_eval(
+    *,
+    literal_manifest: str | Path,
+    nato_manifest: str | Path,
+    intents_path: str | Path,
+    output_dir: str | Path,
+    models_file: str | Path | None = None,
+    verbose: bool = False,
+) -> dict[str, Any]:
+    intents = load_keystroke_intents(intents_path)
+    literal_index = build_phrase_to_intent_index(intents, grammar="literal")
+    nato_index = build_phrase_to_intent_index(intents, grammar="nato")
+
+    literal_samples = load_manifest_samples(literal_manifest, literal_index)
+    nato_samples = load_manifest_samples(nato_manifest, nato_index)
+    model_specs = load_model_specs(models_file)
+
+    if not model_specs:
+        raise RuntimeError("no model specs provided")
+
+    run_id = datetime.now(timezone.utc).strftime("run-%Y%m%dT%H%M%SZ")
+    base_output_dir = Path(output_dir)
+    run_output_dir = (base_output_dir / run_id).resolve()
+    run_output_dir.mkdir(parents=True, exist_ok=True)
+
+    summary: dict[str, Any] = {
+        "report_version": 1,
+        "run_id": run_id,
+        "literal_manifest": str(Path(literal_manifest)),
+        "nato_manifest": str(Path(nato_manifest)),
+        "intents_path": str(Path(intents_path)),
+        "models_file": str(models_file) if models_file else "",
+        "models": [],
+        "skipped_models": [],
+        "winners": {},
+        "cross_grammar_delta": [],
+        "output_dir": str(run_output_dir),
+    }
+
+    for model in model_specs:
+        if not model.path.exists():
+            summary["skipped_models"].append(
+                {
+                    "name": model.name,
+                    "path": str(model.path),
+                    "reason": "model path does not exist",
+                }
+            )
+            continue
+        model_report = _evaluate_model(
+            model,
+            literal_samples=literal_samples,
+            nato_samples=nato_samples,
+            literal_index=literal_index,
+            nato_index=nato_index,
+            output_dir=run_output_dir,
+            verbose=verbose,
+        )
+        summary["models"].append(model_report)
+
+    if not summary["models"]:
+        raise RuntimeError("no models were successfully evaluated")
+
+    summary["winners"] = _pick_winners(summary["models"])
+    summary["cross_grammar_delta"] = _cross_grammar_delta(summary["models"])
+    summary_path = run_output_dir / "summary.json"
+    summary["summary_path"] = str(summary_path)
+    summary_path.write_text(f"{json.dumps(summary, indent=2, ensure_ascii=False)}\n", encoding="utf-8")
+    return summary
+
+
+def load_keystroke_intents(path: str | Path) -> list[IntentSpec]:
+    payload = _load_json(path, description="intents")
+    if not isinstance(payload, list):
+        raise RuntimeError("intents file must be a JSON array")
+
+    intents: list[IntentSpec] = []
+    seen_ids: set[str] = set()
+    seen_literal: set[str] = set()
+    seen_nato: set[str] = set()
+    for idx, item in enumerate(payload):
+        if not isinstance(item, dict):
+            raise RuntimeError(f"intents[{idx}] must be an object")
+        intent_id = str(item.get("intent_id", "")).strip()
+        literal_phrase = str(item.get("literal_phrase", "")).strip()
+        nato_phrase = str(item.get("nato_phrase", "")).strip()
+        letter = str(item.get("letter", "")).strip().casefold()
+        modifier = str(item.get("modifier", "")).strip().casefold()
+        if not intent_id:
+            raise RuntimeError(f"intents[{idx}].intent_id is required")
+        if not literal_phrase:
+            raise RuntimeError(f"intents[{idx}].literal_phrase is required")
+        if not nato_phrase:
+            raise RuntimeError(f"intents[{idx}].nato_phrase is required")
+        if letter not in {"d", "b", "p"}:
+            raise RuntimeError(f"intents[{idx}].letter must be one of d/b/p")
+        if modifier not in {"ctrl", "shift", "ctrl+shift"}:
+            raise RuntimeError(f"intents[{idx}].modifier must be ctrl/shift/ctrl+shift")
+
+        norm_id = _norm(intent_id)
+        norm_literal = _norm(literal_phrase)
+        norm_nato = _norm(nato_phrase)
+        if norm_id in seen_ids:
+            raise RuntimeError(f"duplicate intent_id '{intent_id}'")
+        if norm_literal in seen_literal:
+            raise RuntimeError(f"duplicate literal_phrase '{literal_phrase}'")
+        if norm_nato in seen_nato:
+            raise RuntimeError(f"duplicate nato_phrase '{nato_phrase}'")
+        seen_ids.add(norm_id)
+        seen_literal.add(norm_literal)
+        seen_nato.add(norm_nato)
+        intents.append(
+            IntentSpec(
+                intent_id=intent_id,
+                literal_phrase=literal_phrase,
+                nato_phrase=nato_phrase,
+                letter=letter,
+                modifier=modifier,
+            )
+        )
+
+    if not intents:
+        raise RuntimeError("intents file is empty")
+    return intents
+
+
+def build_phrase_to_intent_index(
+    intents: list[IntentSpec],
+    *,
+    grammar: str,
+) -> dict[str, IntentSpec]:
+    if grammar not in {"literal", "nato"}:
+        raise RuntimeError(f"unsupported grammar type '{grammar}'")
+    out: dict[str, IntentSpec] = {}
+    for spec in intents:
+        phrase = spec.literal_phrase if grammar == "literal" else spec.nato_phrase
+        key = _norm(phrase)
+        if key in out:
+            raise RuntimeError(f"duplicate phrase mapping for grammar {grammar}: '{phrase}'")
+        out[key] = spec
+    return out
+
+
+def load_manifest_samples(
+    path: str | Path,
+    phrase_index: dict[str, IntentSpec],
+) -> list[ManifestSample]:
+    manifest_path = Path(path)
+    if not manifest_path.exists():
+        raise RuntimeError(f"manifest file does not exist: {manifest_path}")
+    rows = manifest_path.read_text(encoding="utf-8").splitlines()
+    samples: list[ManifestSample] = []
+    for idx, raw in enumerate(rows, start=1):
+        text = raw.strip()
+        if not text:
+            continue
+        try:
+            payload = json.loads(text)
+        except Exception as exc:
+            raise RuntimeError(f"invalid manifest json at line {idx}: {exc}") from exc
+        if not isinstance(payload, dict):
+            raise RuntimeError(f"manifest line {idx} must be an object")
+        phrase = str(payload.get("phrase", "")).strip()
+        wav_path_raw = str(payload.get("wav_path", "")).strip()
+        if not phrase:
+            raise RuntimeError(f"manifest line {idx} missing phrase")
+        if not wav_path_raw:
+            raise RuntimeError(f"manifest line {idx} missing wav_path")
+
+        spec = phrase_index.get(_norm(phrase))
+        if spec is None:
+            raise RuntimeError(
+                f"manifest line {idx} phrase '{phrase}' does not exist in grammar index"
+            )
+        wav_path = _resolve_manifest_wav_path(
+            wav_path_raw,
+            manifest_dir=manifest_path.parent,
+        )
+        if not wav_path.exists():
+            raise RuntimeError(f"manifest line {idx} wav_path does not exist: {wav_path}")
+        samples.append(
+            ManifestSample(
+                wav_path=wav_path,
+                expected_phrase=phrase,
+                expected_intent=spec.intent_id,
+                expected_letter=spec.letter,
+                expected_modifier=spec.modifier,
+            )
+        )
+    if not samples:
+        raise RuntimeError(f"manifest has no samples: {manifest_path}")
+    return samples
+
+
+def load_model_specs(path: str | Path | None) -> list[ModelSpec]:
+    if path is None:
+        return [
+            ModelSpec(
+                name=str(row["name"]),
+                path=Path(str(row["path"])).expanduser().resolve(),
+            )
+            for row in DEFAULT_KEYSTROKE_MODELS
+        ]
+    models_path = Path(path)
+    payload = _load_json(models_path, description="model specs")
+    if not isinstance(payload, list):
+        raise RuntimeError("models file must be a JSON array")
+    specs: list[ModelSpec] = []
+    seen: set[str] = set()
+    for idx, item in enumerate(payload):
+        if not isinstance(item, dict):
+            raise RuntimeError(f"models[{idx}] must be an object")
+        name = str(item.get("name", "")).strip()
+        path_raw = str(item.get("path", "")).strip()
+        if not name:
+            raise RuntimeError(f"models[{idx}].name is required")
+        if not path_raw:
+            raise RuntimeError(f"models[{idx}].path is required")
+        key = _norm(name)
+        if key in seen:
+            raise RuntimeError(f"duplicate model name '{name}' in models file")
+        seen.add(key)
+        model_path = Path(path_raw).expanduser()
+        if not model_path.is_absolute():
+            model_path = (models_path.parent / model_path).resolve()
+        else:
+            model_path = model_path.resolve()
+        specs.append(ModelSpec(name=name, path=model_path))
+    return specs
+
+
+def summarize_decoded_rows(rows: list[DecodedRow]) -> dict[str, Any]:
+    if not rows:
+        return {
+            "samples": 0,
+            "intent_match_count": 0,
+            "intent_accuracy": 0.0,
+            "unknown_count": 0,
+            "unknown_rate": 0.0,
+            "out_of_grammar_count": 0,
+            "latency_ms": {"avg": 0.0, "p50": 0.0, "p95": 0.0},
+            "rtf_avg": 0.0,
+            "intent_breakdown": {},
+            "modifier_breakdown": {},
+            "letter_breakdown": {},
+            "intent_confusion": {},
+            "letter_confusion": {},
+            "top_raw_mismatches": [],
+        }
+    sample_count = len(rows)
+    intent_match_count = sum(1 for row in rows if row.intent_match)
+    unknown_count = sum(1 for row in rows if row.predicted_intent is None)
+    out_of_grammar_count = sum(1 for row in rows if row.out_of_grammar)
+
+    decode_values = sorted(row.decode_ms for row in rows)
+    p50 = statistics.median(decode_values)
+    p95 = decode_values[int(round((len(decode_values) - 1) * 0.95))]
+
+    rtf_values = [row.rtf for row in rows if row.rtf is not None]
+    rtf_avg = float(sum(rtf_values) / len(rtf_values)) if rtf_values else 0.0
+
+    intent_breakdown: dict[str, dict[str, float | int]] = {}
+    modifier_breakdown: dict[str, dict[str, float | int]] = {}
+    letter_breakdown: dict[str, dict[str, float | int]] = {}
+    intent_confusion: dict[str, dict[str, int]] = {}
+    letter_confusion: dict[str, dict[str, int]] = {}
+    raw_mismatch_counts: dict[tuple[str, str], int] = {}
+
+    for row in rows:
+        _inc_metric_bucket(intent_breakdown, row.expected_intent, row.intent_match)
+        _inc_metric_bucket(modifier_breakdown, row.expected_modifier, row.intent_match)
+        _inc_metric_bucket(letter_breakdown, row.expected_letter, row.intent_match)
+        predicted_intent = row.predicted_intent if row.predicted_intent else "__none__"
+        predicted_letter = row.predicted_letter if row.predicted_letter else "__none__"
+        _inc_confusion(intent_confusion, row.expected_intent, predicted_intent)
+        _inc_confusion(letter_confusion, row.expected_letter, predicted_letter)
+        if not row.intent_match:
+            key = (row.expected_phrase, row.hypothesis)
+            raw_mismatch_counts[key] = raw_mismatch_counts.get(key, 0) + 1
+
+    _finalize_metric_buckets(intent_breakdown)
+    _finalize_metric_buckets(modifier_breakdown)
+    _finalize_metric_buckets(letter_breakdown)
+
+    top_raw_mismatches = [
+        {
+            "expected_phrase": expected_phrase,
+            "hypothesis": hypothesis,
+            "count": count,
+        }
+        for (expected_phrase, hypothesis), count in sorted(
+            raw_mismatch_counts.items(),
+            key=lambda item: item[1],
+            reverse=True,
+        )[:20]
+    ]
+
+    return {
+        "samples": sample_count,
+        "intent_match_count": intent_match_count,
+        "intent_accuracy": intent_match_count / sample_count,
+        "unknown_count": unknown_count,
+        "unknown_rate": unknown_count / sample_count,
+        "out_of_grammar_count": out_of_grammar_count,
+        "latency_ms": {
+            "avg": sum(decode_values) / sample_count,
+            "p50": p50,
+            "p95": p95,
+        },
+        "rtf_avg": rtf_avg,
+        "intent_breakdown": intent_breakdown,
+        "modifier_breakdown": modifier_breakdown,
+        "letter_breakdown": letter_breakdown,
+        "intent_confusion": intent_confusion,
+        "letter_confusion": letter_confusion,
+        "top_raw_mismatches": top_raw_mismatches,
+    }
+
+
+def _evaluate_model(
+    model: ModelSpec,
+    *,
+    literal_samples: list[ManifestSample],
+    nato_samples: list[ManifestSample],
+    literal_index: dict[str, IntentSpec],
+    nato_index: dict[str, IntentSpec],
+    output_dir: Path,
+    verbose: bool,
+) -> dict[str, Any]:
+    _ModelClass, recognizer_factory = _load_vosk_bindings()
+    started = time.perf_counter()
+    vosk_model = _ModelClass(str(model.path))
+    model_load_ms = (time.perf_counter() - started) * 1000.0
+
+    grammar_reports: dict[str, Any] = {}
+    for grammar, samples, index in (
+        ("literal", literal_samples, literal_index),
+        ("nato", nato_samples, nato_index),
+    ):
+        phrases = _phrases_for_grammar(index.values(), grammar=grammar)
+        norm_allowed = {_norm(item) for item in phrases}
+        decoded: list[DecodedRow] = []
+        for sample in samples:
+            hypothesis, audio_ms, decode_ms = _decode_sample_with_grammar(
+                recognizer_factory,
+                vosk_model,
+                sample.wav_path,
+                phrases,
+            )
+            hyp_norm = _norm(hypothesis)
+            spec = index.get(hyp_norm)
+            predicted_intent = spec.intent_id if spec is not None else None
+            predicted_letter = spec.letter if spec is not None else None
+            predicted_modifier = spec.modifier if spec is not None else None
+            out_of_grammar = bool(hyp_norm) and hyp_norm not in norm_allowed
+            decoded.append(
+                DecodedRow(
+                    wav_path=str(sample.wav_path),
+                    expected_phrase=sample.expected_phrase,
+                    hypothesis=hypothesis,
+                    expected_intent=sample.expected_intent,
+                    predicted_intent=predicted_intent,
+                    expected_letter=sample.expected_letter,
+                    predicted_letter=predicted_letter,
+                    expected_modifier=sample.expected_modifier,
+                    predicted_modifier=predicted_modifier,
+                    intent_match=sample.expected_intent == predicted_intent,
+                    audio_ms=audio_ms,
+                    decode_ms=decode_ms,
+                    rtf=(decode_ms / audio_ms) if audio_ms > 0 else None,
+                    out_of_grammar=out_of_grammar,
+                )
+            )
+
+        report = summarize_decoded_rows(decoded)
+        if report["out_of_grammar_count"] > 0:
+            raise RuntimeError(
+                f"model '{model.name}' produced {report['out_of_grammar_count']} out-of-grammar "
+                f"hypotheses for grammar '{grammar}'"
+            )
+
+        sample_path = output_dir / f"{grammar}__{_safe_filename(model.name)}__samples.jsonl"
+        _write_samples_report(sample_path, decoded)
+        report["samples_report"] = str(sample_path)
+        if verbose:
+            print(
+                f"vosk-eval[{model.name}][{grammar}]: "
+                f"acc={report['intent_accuracy']:.3f} "
+                f"p50={report['latency_ms']['p50']:.1f}ms "
+                f"p95={report['latency_ms']['p95']:.1f}ms"
+            )
+        grammar_reports[grammar] = report
+
+    literal_acc = float(grammar_reports["literal"]["intent_accuracy"])
+    nato_acc = float(grammar_reports["nato"]["intent_accuracy"])
+    literal_p50 = float(grammar_reports["literal"]["latency_ms"]["p50"])
+    nato_p50 = float(grammar_reports["nato"]["latency_ms"]["p50"])
+    overall_accuracy = (literal_acc + nato_acc) / 2.0
+    overall_latency_p50 = (literal_p50 + nato_p50) / 2.0
+    return {
+        "name": model.name,
+        "path": str(model.path),
+        "model_load_ms": model_load_ms,
+        "literal": grammar_reports["literal"],
+        "nato": grammar_reports["nato"],
+        "overall": {
+            "avg_intent_accuracy": overall_accuracy,
+            "avg_latency_p50_ms": overall_latency_p50,
+        },
+    }
+
+
+def _decode_sample_with_grammar(
+    recognizer_factory: Callable[[Any, float, str], Any],
+    vosk_model: Any,
+    wav_path: Path,
+    phrases: list[str],
+) -> tuple[str, float, float]:
+    with wave.open(str(wav_path), "rb") as handle:
+        channels = handle.getnchannels()
+        sample_width = handle.getsampwidth()
+        sample_rate = float(handle.getframerate())
+        frame_count = handle.getnframes()
+        payload = handle.readframes(frame_count)
+    if channels != 1 or sample_width != 2:
+        raise RuntimeError(
+            f"unsupported wav format for {wav_path}: channels={channels} sample_width={sample_width}"
+        )
+    recognizer = recognizer_factory(vosk_model, sample_rate, json.dumps(phrases))
+    if hasattr(recognizer, "SetWords"):
+        recognizer.SetWords(False)
+    started = time.perf_counter()
+    recognizer.AcceptWaveform(payload)
+    result = recognizer.FinalResult()
+    decode_ms = (time.perf_counter() - started) * 1000.0
+    audio_ms = (frame_count / sample_rate) * 1000.0
+    try:
+        text = str(json.loads(result).get("text", "")).strip()
+    except Exception:
+        text = ""
+    return text, audio_ms, decode_ms
+
+
+def _pick_winners(models: list[dict[str, Any]]) -> dict[str, Any]:
+    winners: dict[str, Any] = {}
+    for grammar in ("literal", "nato"):
+        ranked = sorted(
+            models,
+            key=lambda item: (
+                float(item[grammar]["intent_accuracy"]),
+                -float(item[grammar]["latency_ms"]["p50"]),
+            ),
+            reverse=True,
+        )
+        best = ranked[0]
+        winners[grammar] = {
+            "name": best["name"],
+            "intent_accuracy": best[grammar]["intent_accuracy"],
+            "latency_p50_ms": best[grammar]["latency_ms"]["p50"],
+        }
+    ranked_overall = sorted(
+        models,
+        key=lambda item: (
+            float(item["overall"]["avg_intent_accuracy"]),
+            -float(item["overall"]["avg_latency_p50_ms"]),
+        ),
+        reverse=True,
+    )
+    winners["overall"] = {
+        "name": ranked_overall[0]["name"],
+        "avg_intent_accuracy": ranked_overall[0]["overall"]["avg_intent_accuracy"],
+        "avg_latency_p50_ms": ranked_overall[0]["overall"]["avg_latency_p50_ms"],
+    }
+    return winners
+
+
+def _cross_grammar_delta(models: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for model in models:
+        literal_acc = float(model["literal"]["intent_accuracy"])
+        nato_acc = float(model["nato"]["intent_accuracy"])
+        rows.append(
+            {
+                "name": model["name"],
+                "intent_accuracy_delta_nato_minus_literal": nato_acc - literal_acc,
+                "literal_intent_accuracy": literal_acc,
+                "nato_intent_accuracy": nato_acc,
+            }
+        )
+    rows.sort(key=lambda item: item["intent_accuracy_delta_nato_minus_literal"], reverse=True)
+    return rows
+
+
+def _write_samples_report(path: Path, rows: list[DecodedRow]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as handle:
+        for row in rows:
+            payload = {
+                "wav_path": row.wav_path,
+                "expected_phrase": row.expected_phrase,
+                "hypothesis": row.hypothesis,
+                "expected_intent": row.expected_intent,
+                "predicted_intent": row.predicted_intent,
+                "expected_letter": row.expected_letter,
+                "predicted_letter": row.predicted_letter,
+                "expected_modifier": row.expected_modifier,
+                "predicted_modifier": row.predicted_modifier,
+                "intent_match": row.intent_match,
+                "audio_ms": row.audio_ms,
+                "decode_ms": row.decode_ms,
+                "rtf": row.rtf,
+                "out_of_grammar": row.out_of_grammar,
+            }
+            handle.write(f"{json.dumps(payload, ensure_ascii=False)}\n")
+
+
+def _load_vosk_bindings() -> tuple[Any, Callable[[Any, float, str], Any]]:
+    try:
+        from vosk import KaldiRecognizer, Model, SetLogLevel  # type: ignore[import-not-found]
+    except ModuleNotFoundError as exc:
+        raise RuntimeError(
+            "vosk is not installed; run with `uv run --with vosk aman eval-vosk-keystrokes ...`"
+        ) from exc
+    SetLogLevel(-1)
+    return Model, KaldiRecognizer
+
+
+def _phrases_for_grammar(
+    specs: Iterable[IntentSpec],
+    *,
+    grammar: str,
+) -> list[str]:
+    if grammar not in {"literal", "nato"}:
+        raise RuntimeError(f"unsupported grammar type '{grammar}'")
+    out: list[str] = []
+    seen: set[str] = set()
+    for spec in specs:
+        phrase = spec.literal_phrase if grammar == "literal" else spec.nato_phrase
+        key = _norm(phrase)
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(phrase)
+    return sorted(out)
+
+
+def _inc_metric_bucket(table: dict[str, dict[str, float | int]], key: str, matched: bool) -> None:
+    bucket = table.setdefault(key, {"total": 0, "matches": 0, "accuracy": 0.0})
+    bucket["total"] = int(bucket["total"]) + 1
+    if matched:
+        bucket["matches"] = int(bucket["matches"]) + 1
+
+
+def _finalize_metric_buckets(table: dict[str, dict[str, float | int]]) -> None:
+    for bucket in table.values():
+        total = int(bucket["total"])
+        matches = int(bucket["matches"])
+        bucket["accuracy"] = (matches / total) if total else 0.0
+
+
+def _inc_confusion(table: dict[str, dict[str, int]], expected: str, predicted: str) -> None:
+    row = table.setdefault(expected, {})
+    row[predicted] = int(row.get(predicted, 0)) + 1
+
+
+def _safe_filename(value: str) -> str:
+    out = []
+    for ch in value:
+        if ch.isalnum() or ch in {"-", "_", "."}:
+            out.append(ch)
+        else:
+            out.append("_")
+    return "".join(out).strip("_") or "model"
+
+
+def _load_json(path: str | Path, *, description: str) -> Any:
+    data_path = Path(path)
+    if not data_path.exists():
+        raise RuntimeError(f"{description} file does not exist: {data_path}")
+    try:
+        return json.loads(data_path.read_text(encoding="utf-8"))
+    except Exception as exc:
+        raise RuntimeError(f"invalid {description} json '{data_path}': {exc}") from exc
+
+
+def _resolve_manifest_wav_path(raw_value: str, *, manifest_dir: Path) -> Path:
+    candidate = Path(raw_value).expanduser()
+    if candidate.is_absolute():
+        return candidate.resolve()
+    cwd_candidate = (Path.cwd() / candidate).resolve()
+    if cwd_candidate.exists():
+        return cwd_candidate
+    manifest_candidate = (manifest_dir / candidate).resolve()
+    if manifest_candidate.exists():
+        return manifest_candidate
+    return cwd_candidate
+
+
+def _norm(value: str) -> str:
+    return " ".join((value or "").strip().casefold().split())

tests/test_alignment_edits.py

@@ -47,6 +47,15 @@ class AlignmentHeuristicEngineTests(unittest.TestCase):
         self.assertEqual(result.applied_count, 1)
         self.assertTrue(any(item.rule_id == "cue_correction" for item in result.decisions))
 
+    def test_applies_i_mean_tail_correction_without_asr_words(self):
+        engine = AlignmentHeuristicEngine()
+
+        result = engine.apply("schedule for 5, i mean 6", [])
+
+        self.assertEqual(result.draft_text, "schedule for 6")
+        self.assertEqual(result.applied_count, 1)
+        self.assertTrue(any(item.rule_id == "cue_correction" for item in result.decisions))
+
     def test_preserves_literal_i_mean_context(self):
         engine = AlignmentHeuristicEngine()
         words = _words(["write", "exactly", "i", "mean", "this", "sincerely"])
@@ -57,6 +66,15 @@ class AlignmentHeuristicEngineTests(unittest.TestCase):
         self.assertEqual(result.applied_count, 0)
         self.assertGreaterEqual(result.skipped_count, 1)
 
+    def test_preserves_literal_i_mean_context_without_asr_words(self):
+        engine = AlignmentHeuristicEngine()
+
+        result = engine.apply("write exactly i mean this sincerely", [])
+
+        self.assertEqual(result.draft_text, "write exactly i mean this sincerely")
+        self.assertEqual(result.applied_count, 0)
+        self.assertGreaterEqual(result.skipped_count, 1)
+
     def test_collapses_exact_restart_repetition(self):
         engine = AlignmentHeuristicEngine()
         words = _words(["please", "send", "it", "please", "send", "it"])

tests/test_aman_cli.py

@@ -141,6 +141,64 @@ class AmanCliTests(unittest.TestCase):
         with self.assertRaises(SystemExit):
             aman._parse_cli_args(["bench"])
 
+    def test_parse_cli_args_collect_fixed_phrases_command(self):
+        args = aman._parse_cli_args(
+            [
+                "collect-fixed-phrases",
+                "--phrases-file",
+                "exploration/vosk/fixed_phrases/phrases.txt",
+                "--out-dir",
+                "exploration/vosk/fixed_phrases",
+                "--samples-per-phrase",
+                "10",
+                "--samplerate",
+                "16000",
+                "--channels",
+                "1",
+                "--device",
+                "2",
+                "--session-id",
+                "session-123",
+                "--overwrite-session",
+                "--json",
+            ]
+        )
+        self.assertEqual(args.command, "collect-fixed-phrases")
+        self.assertEqual(args.phrases_file, "exploration/vosk/fixed_phrases/phrases.txt")
+        self.assertEqual(args.out_dir, "exploration/vosk/fixed_phrases")
+        self.assertEqual(args.samples_per_phrase, 10)
+        self.assertEqual(args.samplerate, 16000)
+        self.assertEqual(args.channels, 1)
+        self.assertEqual(args.device, "2")
+        self.assertEqual(args.session_id, "session-123")
+        self.assertTrue(args.overwrite_session)
+        self.assertTrue(args.json)
+
+    def test_parse_cli_args_eval_vosk_keystrokes_command(self):
+        args = aman._parse_cli_args(
+            [
+                "eval-vosk-keystrokes",
+                "--literal-manifest",
+                "exploration/vosk/keystrokes/literal/manifest.jsonl",
+                "--nato-manifest",
+                "exploration/vosk/keystrokes/nato/manifest.jsonl",
+                "--intents",
+                "exploration/vosk/keystrokes/intents.json",
+                "--output-dir",
+                "exploration/vosk/keystrokes/eval_runs",
+                "--models-file",
+                "exploration/vosk/keystrokes/models.json",
+                "--json",
+            ]
+        )
+        self.assertEqual(args.command, "eval-vosk-keystrokes")
+        self.assertEqual(args.literal_manifest, "exploration/vosk/keystrokes/literal/manifest.jsonl")
+        self.assertEqual(args.nato_manifest, "exploration/vosk/keystrokes/nato/manifest.jsonl")
+        self.assertEqual(args.intents, "exploration/vosk/keystrokes/intents.json")
+        self.assertEqual(args.output_dir, "exploration/vosk/keystrokes/eval_runs")
+        self.assertEqual(args.models_file, "exploration/vosk/keystrokes/models.json")
+        self.assertTrue(args.json)
+
     def test_parse_cli_args_eval_models_command(self):
         args = aman._parse_cli_args(
             ["eval-models", "--dataset", "benchmarks/cleanup_dataset.jsonl", "--matrix", "benchmarks/model_matrix.small_first.json"]
@@ -379,6 +437,83 @@ class AmanCliTests(unittest.TestCase):
         payload = json.loads(out.getvalue())
         self.assertEqual(payload["written_rows"], 4)
 
+    def test_collect_fixed_phrases_command_rejects_non_positive_samples_per_phrase(self):
+        args = aman._parse_cli_args(
+            ["collect-fixed-phrases", "--samples-per-phrase", "0"]
+        )
+        exit_code = aman._collect_fixed_phrases_command(args)
+        self.assertEqual(exit_code, 1)
+
+    def test_collect_fixed_phrases_command_json_output(self):
+        args = aman._parse_cli_args(
+            [
+                "collect-fixed-phrases",
+                "--phrases-file",
+                "exploration/vosk/fixed_phrases/phrases.txt",
+                "--out-dir",
+                "exploration/vosk/fixed_phrases",
+                "--samples-per-phrase",
+                "2",
+                "--json",
+            ]
+        )
+        out = io.StringIO()
+        fake_result = SimpleNamespace(
+            session_id="session-1",
+            phrases=2,
+            samples_per_phrase=2,
+            samples_target=4,
+            samples_written=4,
+            out_dir=Path("/tmp/out"),
+            manifest_path=Path("/tmp/out/manifest.jsonl"),
+            interrupted=False,
+        )
+        with patch("aman.collect_fixed_phrases", return_value=fake_result), patch("sys.stdout", out):
+            exit_code = aman._collect_fixed_phrases_command(args)
+        self.assertEqual(exit_code, 0)
+        payload = json.loads(out.getvalue())
+        self.assertEqual(payload["session_id"], "session-1")
+        self.assertEqual(payload["samples_written"], 4)
+        self.assertFalse(payload["interrupted"])
+
+    def test_eval_vosk_keystrokes_command_json_output(self):
+        args = aman._parse_cli_args(
+            [
+                "eval-vosk-keystrokes",
+                "--literal-manifest",
+                "exploration/vosk/keystrokes/literal/manifest.jsonl",
+                "--nato-manifest",
+                "exploration/vosk/keystrokes/nato/manifest.jsonl",
+                "--intents",
+                "exploration/vosk/keystrokes/intents.json",
+                "--output-dir",
+                "exploration/vosk/keystrokes/eval_runs",
+                "--json",
+            ]
+        )
+        out = io.StringIO()
+        fake_summary = {
+            "models": [
+                {
+                    "name": "vosk-small-en-us-0.15",
+                    "literal": {"intent_accuracy": 1.0, "latency_ms": {"p50": 30.0}},
+                    "nato": {"intent_accuracy": 0.9, "latency_ms": {"p50": 35.0}},
+                }
+            ],
+            "winners": {
+                "literal": {"name": "vosk-small-en-us-0.15", "intent_accuracy": 1.0, "latency_p50_ms": 30.0},
+                "nato": {"name": "vosk-small-en-us-0.15", "intent_accuracy": 0.9, "latency_p50_ms": 35.0},
+                "overall": {"name": "vosk-small-en-us-0.15", "avg_intent_accuracy": 0.95, "avg_latency_p50_ms": 32.5},
+            },
+            "output_dir": "exploration/vosk/keystrokes/eval_runs/run-1",
+        }
+        with patch("aman.run_vosk_keystroke_eval", return_value=fake_summary), patch("sys.stdout", out):
+            exit_code = aman._eval_vosk_keystrokes_command(args)
+        self.assertEqual(exit_code, 0)
+        payload = json.loads(out.getvalue())
+        self.assertEqual(payload["models"][0]["name"], "vosk-small-en-us-0.15")
+        self.assertEqual(payload["winners"]["overall"]["name"], "vosk-small-en-us-0.15")
+
     def test_sync_default_model_command_updates_constants(self):
         with tempfile.TemporaryDirectory() as td:
             report_path = Path(td) / "latest.json"

tests/test_pipeline_engine.py

@@ -93,6 +93,23 @@ class PipelineEngineTests(unittest.TestCase):
         self.assertEqual(result.fact_guard_action, "accepted")
         self.assertEqual(result.fact_guard_violations, 0)
 
+    def test_run_transcript_without_words_applies_i_mean_correction(self):
+        editor = _FakeEditor()
+        pipeline = PipelineEngine(
+            asr_stage=None,
+            editor_stage=editor,
+            vocabulary=VocabularyEngine(VocabularyConfig()),
+            alignment_engine=AlignmentHeuristicEngine(),
+        )
+
+        result = pipeline.run_transcript("schedule for 5, i mean 6", language="en")
+
+        self.assertEqual(editor.calls[0]["transcript"], "schedule for 6")
+        self.assertEqual(result.output_text, "schedule for 6")
+        self.assertEqual(result.alignment_applied, 1)
+        self.assertEqual(result.fact_guard_action, "accepted")
+        self.assertEqual(result.fact_guard_violations, 0)
+
     def test_fact_guard_fallbacks_when_editor_changes_number(self):
         editor = _FakeEditor(output_text="set alarm for 8")
         pipeline = PipelineEngine(

tests/test_vosk_collect.py

@@ -0,0 +1,148 @@
+import json
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC = ROOT / "src"
+if str(SRC) not in sys.path:
+    sys.path.insert(0, str(SRC))
+
+from vosk_collect import CollectOptions, collect_fixed_phrases, float_to_pcm16, load_phrases, slugify_phrase
+
+
+class VoskCollectTests(unittest.TestCase):
+    def test_load_phrases_ignores_blank_comment_and_deduplicates(self):
+        with tempfile.TemporaryDirectory() as td:
+            path = Path(td) / "phrases.txt"
+            path.write_text(
+                (
+                    "# heading\n"
+                    "\n"
+                    "close app\n"
+                    "take a screenshot\n"
+                    "close app\n"
+                    "   \n"
+                ),
+                encoding="utf-8",
+            )
+            phrases = load_phrases(path)
+        self.assertEqual(phrases, ["close app", "take a screenshot"])
+
+    def test_load_phrases_empty_after_filtering_raises(self):
+        with tempfile.TemporaryDirectory() as td:
+            path = Path(td) / "phrases.txt"
+            path.write_text("# only comments\n\n", encoding="utf-8")
+            with self.assertRaisesRegex(RuntimeError, "no usable labels"):
+                load_phrases(path)
+
+    def test_slugify_phrase_is_deterministic(self):
+        self.assertEqual(slugify_phrase("Take a Screenshot"), "take_a_screenshot")
+        self.assertEqual(slugify_phrase("close-app!!!"), "close_app")
+
+    def test_float_to_pcm16_clamps_audio_bounds(self):
+        values = np.asarray([-2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 2.0], dtype=np.float32)
+        out = float_to_pcm16(values)
+        self.assertEqual(out.dtype, np.int16)
+        self.assertGreaterEqual(int(out.min()), -32767)
+        self.assertLessEqual(int(out.max()), 32767)
+        self.assertEqual(int(out[0]), -32767)
+        self.assertEqual(int(out[-1]), 32767)
+
+    def test_collect_fixed_phrases_writes_manifest_and_wavs(self):
+        with tempfile.TemporaryDirectory() as td:
+            root = Path(td)
+            phrases_path = root / "phrases.txt"
+            out_dir = root / "dataset"
+            phrases_path.write_text("close app\ntake a screenshot\n", encoding="utf-8")
+            options = CollectOptions(
+                phrases_file=phrases_path,
+                out_dir=out_dir,
+                samples_per_phrase=2,
+                samplerate=16000,
+                channels=1,
+                session_id="session-1",
+            )
+            answers = ["", "", "", ""]
+
+            def fake_input(_prompt: str) -> str:
+                return answers.pop(0)
+
+            def fake_record(_options: CollectOptions, _input_func):
+                audio = np.ones((320, 1), dtype=np.float32) * 0.1
+                return audio, 320, 20
+
+            result = collect_fixed_phrases(
+                options,
+                input_func=fake_input,
+                output_func=lambda _line: None,
+                record_sample_fn=fake_record,
+            )
+
+            self.assertFalse(result.interrupted)
+            self.assertEqual(result.samples_written, 4)
+            manifest = out_dir / "manifest.jsonl"
+            rows = [
+                json.loads(line)
+                for line in manifest.read_text(encoding="utf-8").splitlines()
+                if line.strip()
+            ]
+            self.assertEqual(len(rows), 4)
+            required = {
+                "session_id",
+                "timestamp_utc",
+                "phrase",
+                "phrase_slug",
+                "sample_index",
+                "wav_path",
+                "samplerate",
+                "channels",
+                "duration_ms",
+                "frames",
+                "device_spec",
+                "collector_version",
+            }
+            self.assertTrue(required.issubset(rows[0].keys()))
+            wav_paths = [root / Path(row["wav_path"]) for row in rows]
+            for wav_path in wav_paths:
+                self.assertTrue(wav_path.exists(), f"missing wav: {wav_path}")
+
+    def test_collect_fixed_phrases_refuses_existing_session_without_overwrite(self):
+        with tempfile.TemporaryDirectory() as td:
+            root = Path(td)
+            phrases_path = root / "phrases.txt"
+            out_dir = root / "dataset"
+            phrases_path.write_text("close app\n", encoding="utf-8")
+            options = CollectOptions(
+                phrases_file=phrases_path,
+                out_dir=out_dir,
+                samples_per_phrase=1,
+                samplerate=16000,
+                channels=1,
+                session_id="session-1",
+            )
+
+            def fake_record(_options: CollectOptions, _input_func):
+                audio = np.ones((160, 1), dtype=np.float32) * 0.2
+                return audio, 160, 10
+
+            collect_fixed_phrases(
+                options,
+                input_func=lambda _prompt: "",
+                output_func=lambda _line: None,
+                record_sample_fn=fake_record,
+            )
+            with self.assertRaisesRegex(RuntimeError, "already has samples"):
+                collect_fixed_phrases(
+                    options,
+                    input_func=lambda _prompt: "",
+                    output_func=lambda _line: None,
+                    record_sample_fn=fake_record,
+                )
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_vosk_eval.py

@@ -0,0 +1,327 @@
+import json
+import sys
+import tempfile
+import unittest
+import wave
+from pathlib import Path
+from unittest.mock import patch
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC = ROOT / "src"
+if str(SRC) not in sys.path:
+    sys.path.insert(0, str(SRC))
+
+from vosk_eval import (
+    DecodedRow,
+    build_phrase_to_intent_index,
+    load_keystroke_intents,
+    run_vosk_keystroke_eval,
+    summarize_decoded_rows,
+)
+
+
+class VoskEvalTests(unittest.TestCase):
+    def test_load_keystroke_intents_parses_valid_payload(self):
+        with tempfile.TemporaryDirectory() as td:
+            path = Path(td) / "intents.json"
+            path.write_text(
+                json.dumps(
+                    [
+                        {
+                            "intent_id": "ctrl+d",
+                            "literal_phrase": "control d",
+                            "nato_phrase": "control delta",
+                            "letter": "d",
+                            "modifier": "ctrl",
+                        }
+                    ]
+                ),
+                encoding="utf-8",
+            )
+            intents = load_keystroke_intents(path)
+        self.assertEqual(len(intents), 1)
+        self.assertEqual(intents[0].intent_id, "ctrl+d")
+
+    def test_load_keystroke_intents_rejects_duplicate_literal_phrase(self):
+        with tempfile.TemporaryDirectory() as td:
+            path = Path(td) / "intents.json"
+            path.write_text(
+                json.dumps(
+                    [
+                        {
+                            "intent_id": "ctrl+d",
+                            "literal_phrase": "control d",
+                            "nato_phrase": "control delta",
+                            "letter": "d",
+                            "modifier": "ctrl",
+                        },
+                        {
+                            "intent_id": "ctrl+b",
+                            "literal_phrase": "control d",
+                            "nato_phrase": "control bravo",
+                            "letter": "b",
+                            "modifier": "ctrl",
+                        },
+                    ]
+                ),
+                encoding="utf-8",
+            )
+            with self.assertRaisesRegex(RuntimeError, "duplicate literal_phrase"):
+                load_keystroke_intents(path)
+
+    def test_build_phrase_to_intent_index_uses_grammar_variant(self):
+        intents = [
+            load_keystroke_intents_from_inline(
+                "ctrl+d",
+                "control d",
+                "control delta",
+                "d",
+                "ctrl",
+            )
+        ]
+        literal = build_phrase_to_intent_index(intents, grammar="literal")
+        nato = build_phrase_to_intent_index(intents, grammar="nato")
+        self.assertIn("control d", literal)
+        self.assertIn("control delta", nato)
+
+    def test_summarize_decoded_rows_reports_confusions(self):
+        rows = [
+            DecodedRow(
+                wav_path="a.wav",
+                expected_phrase="control d",
+                hypothesis="control d",
+                expected_intent="ctrl+d",
+                predicted_intent="ctrl+d",
+                expected_letter="d",
+                predicted_letter="d",
+                expected_modifier="ctrl",
+                predicted_modifier="ctrl",
+                intent_match=True,
+                audio_ms=1000.0,
+                decode_ms=100.0,
+                rtf=0.1,
+                out_of_grammar=False,
+            ),
+            DecodedRow(
+                wav_path="b.wav",
+                expected_phrase="control b",
+                hypothesis="control p",
+                expected_intent="ctrl+b",
+                predicted_intent="ctrl+p",
+                expected_letter="b",
+                predicted_letter="p",
+                expected_modifier="ctrl",
+                predicted_modifier="ctrl",
+                intent_match=False,
+                audio_ms=1000.0,
+                decode_ms=120.0,
+                rtf=0.12,
+                out_of_grammar=False,
+            ),
+            DecodedRow(
+                wav_path="c.wav",
+                expected_phrase="control p",
+                hypothesis="",
+                expected_intent="ctrl+p",
+                predicted_intent=None,
+                expected_letter="p",
+                predicted_letter=None,
+                expected_modifier="ctrl",
+                predicted_modifier=None,
+                intent_match=False,
+                audio_ms=1000.0,
+                decode_ms=90.0,
+                rtf=0.09,
+                out_of_grammar=False,
+            ),
+        ]
+        summary = summarize_decoded_rows(rows)
+        self.assertEqual(summary["samples"], 3)
+        self.assertAlmostEqual(summary["intent_accuracy"], 1 / 3, places=6)
+        self.assertEqual(summary["unknown_count"], 1)
+        self.assertEqual(summary["intent_confusion"]["ctrl+b"]["ctrl+p"], 1)
+        self.assertEqual(summary["letter_confusion"]["p"]["__none__"], 1)
+        self.assertGreaterEqual(len(summary["top_raw_mismatches"]), 1)
+
+    def test_run_vosk_keystroke_eval_hard_fails_model_with_out_of_grammar_output(self):
+        with tempfile.TemporaryDirectory() as td:
+            root = Path(td)
+            literal_manifest = root / "literal.jsonl"
+            nato_manifest = root / "nato.jsonl"
+            intents_path = root / "intents.json"
+            output_dir = root / "out"
+            model_dir = root / "model"
+            model_dir.mkdir(parents=True, exist_ok=True)
+            wav_path = root / "sample.wav"
+            _write_silence_wav(wav_path, samplerate=16000, frames=800)
+
+            intents_path.write_text(
+                json.dumps(
+                    [
+                        {
+                            "intent_id": "ctrl+d",
+                            "literal_phrase": "control d",
+                            "nato_phrase": "control delta",
+                            "letter": "d",
+                            "modifier": "ctrl",
+                        }
+                    ]
+                ),
+                encoding="utf-8",
+            )
+            literal_manifest.write_text(
+                json.dumps({"phrase": "control d", "wav_path": str(wav_path)}) + "\n",
+                encoding="utf-8",
+            )
+            nato_manifest.write_text(
+                json.dumps({"phrase": "control delta", "wav_path": str(wav_path)}) + "\n",
+                encoding="utf-8",
+            )
+            models_file = root / "models.json"
+            models_file.write_text(
+                json.dumps([{"name": "fake", "path": str(model_dir)}]),
+                encoding="utf-8",
+            )
+
+            class _FakeModel:
+                def __init__(self, _path: str):
+                    return
+
+            class _FakeRecognizer:
+                def __init__(self, _model, _rate, _grammar_json):
+                    return
+
+                def SetWords(self, _enabled: bool):
+                    return
+
+                def AcceptWaveform(self, _payload: bytes):
+                    return True
+
+                def FinalResult(self):
+                    return json.dumps({"text": "outside hypothesis"})
+
+            with patch("vosk_eval._load_vosk_bindings", return_value=(_FakeModel, _FakeRecognizer)):
+                with self.assertRaisesRegex(RuntimeError, "out-of-grammar"):
+                    run_vosk_keystroke_eval(
+                        literal_manifest=literal_manifest,
+                        nato_manifest=nato_manifest,
+                        intents_path=intents_path,
+                        output_dir=output_dir,
+                        models_file=models_file,
+                        verbose=False,
+                    )
+
+    def test_run_vosk_keystroke_eval_resolves_manifest_relative_wav_paths(self):
+        with tempfile.TemporaryDirectory() as td:
+            root = Path(td)
+            manifests_dir = root / "manifests"
+            samples_dir = manifests_dir / "samples"
+            samples_dir.mkdir(parents=True, exist_ok=True)
+            wav_path = samples_dir / "sample.wav"
+            _write_silence_wav(wav_path, samplerate=16000, frames=800)
+
+            literal_manifest = manifests_dir / "literal.jsonl"
+            nato_manifest = manifests_dir / "nato.jsonl"
+            intents_path = root / "intents.json"
+            output_dir = root / "out"
+            model_dir = root / "model"
+            model_dir.mkdir(parents=True, exist_ok=True)
+
+            intents_path.write_text(
+                json.dumps(
+                    [
+                        {
+                            "intent_id": "ctrl+d",
+                            "literal_phrase": "control d",
+                            "nato_phrase": "control delta",
+                            "letter": "d",
+                            "modifier": "ctrl",
+                        }
+                    ]
+                ),
+                encoding="utf-8",
+            )
+            relative_wav = "samples/sample.wav"
+            literal_manifest.write_text(
+                json.dumps({"phrase": "control d", "wav_path": relative_wav}) + "\n",
+                encoding="utf-8",
+            )
+            nato_manifest.write_text(
+                json.dumps({"phrase": "control delta", "wav_path": relative_wav}) + "\n",
+                encoding="utf-8",
+            )
+            models_file = root / "models.json"
+            models_file.write_text(
+                json.dumps([{"name": "fake", "path": str(model_dir)}]),
+                encoding="utf-8",
+            )
+
+            class _FakeModel:
+                def __init__(self, _path: str):
+                    return
+
+            class _FakeRecognizer:
+                def __init__(self, _model, _rate, grammar_json):
+                    phrases = json.loads(grammar_json)
+                    self._text = str(phrases[0]) if phrases else ""
+
+                def SetWords(self, _enabled: bool):
+                    return
+
+                def AcceptWaveform(self, _payload: bytes):
+                    return True
+
+                def FinalResult(self):
+                    return json.dumps({"text": self._text})
+
+            with patch("vosk_eval._load_vosk_bindings", return_value=(_FakeModel, _FakeRecognizer)):
+                summary = run_vosk_keystroke_eval(
+                    literal_manifest=literal_manifest,
+                    nato_manifest=nato_manifest,
+                    intents_path=intents_path,
+                    output_dir=output_dir,
+                    models_file=models_file,
+                    verbose=False,
+                )
+        self.assertEqual(summary["models"][0]["literal"]["intent_accuracy"], 1.0)
+        self.assertEqual(summary["models"][0]["nato"]["intent_accuracy"], 1.0)
+
+
+def load_keystroke_intents_from_inline(
+    intent_id: str,
+    literal_phrase: str,
+    nato_phrase: str,
+    letter: str,
+    modifier: str,
+):
+    return load_keystroke_intents_from_json(
+        [
+            {
+                "intent_id": intent_id,
+                "literal_phrase": literal_phrase,
+                "nato_phrase": nato_phrase,
+                "letter": letter,
+                "modifier": modifier,
+            }
+        ]
+    )[0]
+
+
+def load_keystroke_intents_from_json(payload):
+    with tempfile.TemporaryDirectory() as td:
+        path = Path(td) / "intents.json"
+        path.write_text(json.dumps(payload), encoding="utf-8")
+        return load_keystroke_intents(path)
+
+
+def _write_silence_wav(path: Path, *, samplerate: int, frames: int):
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with wave.open(str(path), "wb") as handle:
+        handle.setnchannels(1)
+        handle.setsampwidth(2)
+        handle.setframerate(samplerate)
+        handle.writeframes(b"\x00\x00" * frames)
+
+
+if __name__ == "__main__":
+    unittest.main()

uv.lock

@@ -17,6 +17,7 @@ dependencies = [
     { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pillow" },
     { name = "sounddevice" },
+    { name = "vosk" },
 ]
 
 [package.optional-dependencies]
@@ -34,6 +35,7 @@ requires-dist = [
     { name = "pygobject", marker = "extra == 'x11'" },
     { name = "python-xlib", marker = "extra == 'x11'" },
     { name = "sounddevice" },
+    { name = "vosk", specifier = ">=0.3.45" },
 ]
 provides-extras = ["x11", "wayland"]
 
@@ -199,6 +201,95 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" },
 ]
 
+[[package]]
+name = "charset-normalizer"
+version = "3.4.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1f/b8/6d51fc1d52cbd52cd4ccedd5b5b2f0f6a11bbf6765c782298b0f3e808541/charset_normalizer-3.4.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e824f1492727fa856dd6eda4f7cee25f8518a12f3c4a56a74e8095695089cf6d", size = 209709, upload-time = "2025-10-14T04:40:11.385Z" },
+    { url = "https://files.pythonhosted.org/packages/5c/af/1f9d7f7faafe2ddfb6f72a2e07a548a629c61ad510fe60f9630309908fef/charset_normalizer-3.4.4-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4bd5d4137d500351a30687c2d3971758aac9a19208fc110ccb9d7188fbe709e8", size = 148814, upload-time = "2025-10-14T04:40:13.135Z" },
+    { url = "https://files.pythonhosted.org/packages/79/3d/f2e3ac2bbc056ca0c204298ea4e3d9db9b4afe437812638759db2c976b5f/charset_normalizer-3.4.4-cp310-cp310-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:027f6de494925c0ab2a55eab46ae5129951638a49a34d87f4c3eda90f696b4ad", size = 144467, upload-time = "2025-10-14T04:40:14.728Z" },
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