lane D (M7): audio moment detection + pluggable AI classification
Fills events_ai.py against the frozen M7 contract:
- detect_candidates: RMS x spectral-flux, local-max over a 10s neighborhood,
>=P90, with an AC-RMS (std) silence/DC floor so flat audio yields no events.
Matches the fixture ground-truth pulses [3,10,17] within ~0.02s (<0.5s).
- GeminiClassifier (native video), ClaudeClassifier (claude-opus-4-8,
messages.parse, adaptive thinking, no temperature), LocalClassifier
(OpenAI-compatible, 6 frames, 1 retry). SDKs imported lazily.
- get_classifier: FESTIVAL4D_CLASSIFIER selection; unconfigured -> None
(candidates-only, logged).
- prepare_inputs: ffmpeg 3s clip (<=720p, audio kept) + 6 JPEGs (<=768px).
- run_events: reference-audio detect -> classify (insert-once in final state,
since db.py has no update-event helper); per-candidate exception isolation;
whole-track replaces machine events (keeps user edits), windowed is additive.
test_events_ai.py: candidate-vs-ground-truth (no ffmpeg/API), stub-classifier
orchestration, exception isolation, degradation, provider selection, silence.
Also updates the now-obsolete api stub test (test_detect_events_endpoint) to the
real M3 {result, events} shape; frozen api.py unchanged (see CR-1). pytest: 90 passed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
m3ultra
parent
4399bc90e2
commit
5c2d7c6ea3
@ -4,9 +4,9 @@ Two stages: cheap audio candidates first, then a vision LLM on candidates only.
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This module defines the **FROZEN classifier contract** — :class:`MomentClassification`
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(Pydantic model) and :class:`MomentClassifier` (Protocol) — exactly as spec M7. Lanes and
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integration depend on these shapes. Everything else here is a STUB that lane D fills:
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candidate detection, the provider classes, provider selection, and the ``run_events``
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entrypoint (called by both ``cli.py`` and ``api.py``'s ``POST /api/events/detect``).
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integration depend on these shapes. Lane D fills the rest: candidate detection, the provider
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classes, provider selection, and the ``run_events`` entrypoint (called by both ``cli.py`` and
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``api.py``'s ``POST /api/events/detect``).
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Provider selection (lane D): ``FESTIVAL4D_CLASSIFIER=gemini|claude|local`` (default
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``gemini``). Inputs prepared once per candidate: a ~3 s MP4 clip around ``t_global`` from
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@ -15,19 +15,31 @@ behavior: catch exceptions per candidate (one failure never aborts the batch); i
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selected provider is unconfigured, run candidate detection only (``event_type='candidate'``)
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and log that classification was skipped.
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The module imports with only ``pydantic`` present — provider SDKs (``google-genai``,
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``anthropic``, ``openai``) are imported lazily inside the provider bodies, never at load.
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The module imports with only ``pydantic`` present — heavy numeric libs (``numpy``,
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``librosa``, ``scipy``, ``soundfile``) and provider SDKs (``google-genai``, ``anthropic``,
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``openai``) are imported lazily inside the functions that need them, never at load.
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DB note (lane D): ``db.py`` (frozen) exposes ``add_event`` and ``clear_events`` but *no*
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update-event helper, so instead of "insert audio_auto candidate, later update to ai" this
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module inserts each event **once in its final state** — ``source='ai'`` with the classified
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type when a provider succeeds, else ``source='audio_auto'`` with ``event_type='candidate'``.
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The end state in the DB is identical to the spec's two-step description.
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"""
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from __future__ import annotations
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import base64
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import logging
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import os
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import shutil
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import subprocess
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from pathlib import Path
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from typing import Literal, Protocol, runtime_checkable
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from pydantic import BaseModel, Field
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from festival4d import config, db
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log = logging.getLogger("festival4d.events_ai")
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# The closed set of moment classes (spec M7). Lane C colors timeline markers by these.
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@ -40,6 +52,25 @@ EVENT_TYPES: tuple[str, ...] = (
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"artist_moment", "light_show", "quiet_moment", "other",
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)
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# ---------------------------------------------------------------------------
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# Tuning constants (lane D). Detection is verified against the synthetic fixture's
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# ground-truth pulse times (spec M0/M7); see backend/tests/test_events_ai.py.
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# ---------------------------------------------------------------------------
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STFT_HOP = 512 # librosa frame hop (samples)
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STFT_FRAME = 2048 # RMS window length (samples)
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NEIGHBORHOOD_S = 10.0 # a candidate must be the max over this centered window
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PEAK_PERCENTILE = 90.0 # ...and above this percentile of the detection function
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MERGE_S = 1.0 # merge peaks closer than this, keeping the strongest
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MIN_SIGNAL_STD = 1e-3 # AC-RMS floor (~−60 dBFS): silence AND constant/DC have ~0 std
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CANDIDATE_DURATION_S = 1.0 # stored event duration (a moment, not an interval)
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DEFAULT_WINDOW_S = 6.0 # window width when run_events is given only t_global_s
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CLIP_SECONDS = 3.0 # length of the classifier clip (spec M7: ~3 s)
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NUM_FRAMES = 6 # sampled JPEGs for providers without video input
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FRAME_LONG_EDGE = 768 # max long-edge px for sampled frames (spec M7: ≤768)
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CLIP_MAX_HEIGHT = 720 # clip capped at ≤720p (spec M7)
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AUDIO_EXTRACT_SR = 48_000 # sample rate when extracting reference audio via ffmpeg
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class MomentClassification(BaseModel):
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"""Structured classifier output (FROZEN CONTRACT, spec M7)."""
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@ -58,8 +89,17 @@ class MomentClassifier(Protocol):
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...
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_CLASSIFY_PROMPT = (
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"This is a ~3-second clip from a concert, captured around a detected audio spike. "
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"Classify the single most salient moment in it. Choose event_type from: "
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"bass_drop, pyro, confetti, crowd_wave, artist_moment, light_show, quiet_moment, other. "
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"Give a confidence in [0, 1] and one short human-readable sentence describing what happens."
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)
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# ---------------------------------------------------------------------------
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# Provider stubs (lane D fills bodies; classifiers import their SDK lazily).
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# Provider classes (lane D). Each imports its SDK lazily so the module loads with
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# only pydantic present; construction reads the key/endpoint from the environment.
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# ---------------------------------------------------------------------------
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class GeminiClassifier:
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"""Default provider — Gemini 2.5 Flash, native video input (``GEMINI_API_KEY``)."""
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@ -67,78 +107,439 @@ class GeminiClassifier:
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model = "gemini-2.5-flash"
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def __init__(self) -> None:
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raise NotImplementedError("lane D (M7): implement GeminiClassifier")
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from google import genai
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self._genai = genai
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self._client = genai.Client() # reads GEMINI_API_KEY from the environment
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def classify(self, clip_path: Path, frames: list[Path]) -> MomentClassification:
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raise NotImplementedError("lane D (M7): implement GeminiClassifier.classify")
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genai = self._genai
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clip_bytes = Path(clip_path).read_bytes()
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resp = self._client.models.generate_content(
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model=self.model,
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contents=[
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genai.types.Part.from_bytes(data=clip_bytes, mime_type="video/mp4"),
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_CLASSIFY_PROMPT,
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],
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config={
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"response_mime_type": "application/json",
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"response_schema": MomentClassification,
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},
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)
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return MomentClassification.model_validate_json(resp.text)
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class ClaudeClassifier:
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"""Optional provider — Claude Opus 4.8, 6 frames as images (``ANTHROPIC_API_KEY``).
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Uses ``client.messages.parse(..., thinking={'type': 'adaptive'},
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output_format=MomentClassification)``; do **not** pass ``temperature`` (rejected on
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Opus 4.8). Model id ``claude-opus-4-8`` (never date-suffixed).
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output_format=MomentClassification)`` -> ``resp.parsed_output``; does **not** pass
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``temperature`` (rejected on Opus 4.8). Model id ``claude-opus-4-8`` (never date-suffixed).
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"""
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model = "claude-opus-4-8"
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def __init__(self) -> None:
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raise NotImplementedError("lane D (M7): implement ClaudeClassifier")
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import anthropic
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self._client = anthropic.Anthropic() # reads ANTHROPIC_API_KEY from the environment
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def classify(self, clip_path: Path, frames: list[Path]) -> MomentClassification:
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raise NotImplementedError("lane D (M7): implement ClaudeClassifier.classify")
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content: list[dict] = []
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for fp in frames[:NUM_FRAMES]:
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data = base64.standard_b64encode(Path(fp).read_bytes()).decode("ascii")
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content.append({
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"type": "image",
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"source": {"type": "base64", "media_type": "image/jpeg", "data": data},
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})
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content.append({"type": "text", "text": _CLASSIFY_PROMPT})
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resp = self._client.messages.parse(
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model=self.model,
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max_tokens=1024,
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thinking={"type": "adaptive"},
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output_format=MomentClassification,
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messages=[{"role": "user", "content": content}],
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)
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return resp.parsed_output
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class LocalClassifier:
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"""Offline/free provider — any OpenAI-compatible vision endpoint (Ollama/LM Studio/
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OpenRouter). Config: ``FESTIVAL4D_OPENAI_BASE_URL``, ``FESTIVAL4D_OPENAI_KEY``,
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``FESTIVAL4D_OPENAI_MODEL``. Sends the 6 frames as image parts; validates JSON with one
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retry on failure.
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retry on failure (local models are sloppier about JSON).
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"""
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def __init__(self) -> None:
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raise NotImplementedError("lane D (M7): implement LocalClassifier")
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from openai import OpenAI
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base_url = os.environ["FESTIVAL4D_OPENAI_BASE_URL"]
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# Local servers (Ollama/LM Studio) accept any non-empty key; OpenRouter needs a real one.
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api_key = os.environ.get("FESTIVAL4D_OPENAI_KEY") or "not-needed"
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self._model = os.environ["FESTIVAL4D_OPENAI_MODEL"]
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self._client = OpenAI(base_url=base_url, api_key=api_key)
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def classify(self, clip_path: Path, frames: list[Path]) -> MomentClassification:
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raise NotImplementedError("lane D (M7): implement LocalClassifier.classify")
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from pydantic import ValidationError
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schema = MomentClassification.model_json_schema()
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content: list[dict] = [{
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"type": "text",
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"text": (
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_CLASSIFY_PROMPT
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+ " Respond with ONLY a JSON object matching this schema: "
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+ repr(schema)
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),
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}]
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for fp in frames[:NUM_FRAMES]:
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b64 = base64.standard_b64encode(Path(fp).read_bytes()).decode("ascii")
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content.append({
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{b64}"},
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})
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messages = [{"role": "user", "content": content}]
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last_exc: Exception | None = None
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for attempt in range(2): # one retry on validation failure
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resp = self._client.chat.completions.create(
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model=self._model,
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messages=messages,
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response_format={"type": "json_object"},
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)
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text = resp.choices[0].message.content or ""
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try:
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return MomentClassification.model_validate_json(text)
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except ValidationError as exc:
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last_exc = exc
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log.warning("events: local classifier returned invalid JSON (attempt %d)",
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attempt + 1)
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assert last_exc is not None
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raise last_exc
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_PROVIDERS: dict[str, tuple[type, tuple[str, ...]]] = {
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# name -> (class, required environment variables)
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"gemini": (GeminiClassifier, ("GEMINI_API_KEY",)),
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"claude": (ClaudeClassifier, ("ANTHROPIC_API_KEY",)),
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"local": (LocalClassifier, ("FESTIVAL4D_OPENAI_BASE_URL", "FESTIVAL4D_OPENAI_MODEL")),
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}
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def get_classifier(name: str | None = None) -> MomentClassifier | None:
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"""Select a provider by name or ``FESTIVAL4D_CLASSIFIER`` (default ``gemini``).
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Returns a ready classifier, or ``None`` if the selected provider is unconfigured (no
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key/endpoint) — callers then run candidates-only. STUB — lane D implements selection.
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key/endpoint) or fails to initialize — callers then run candidates-only.
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"""
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_ = name or os.environ.get("FESTIVAL4D_CLASSIFIER", "gemini")
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raise NotImplementedError("lane D (M7): implement get_classifier")
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name = (name or os.environ.get("FESTIVAL4D_CLASSIFIER") or "gemini").lower()
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entry = _PROVIDERS.get(name)
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if entry is None:
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log.warning("events: unknown classifier %r; running candidates-only", name)
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return None
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cls, required = entry
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missing = [var for var in required if not os.environ.get(var)]
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if missing:
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log.info("events: provider %r unconfigured (missing %s); classification skipped, "
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"storing candidates only", name, ", ".join(missing))
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return None
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try:
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return cls()
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except Exception: # SDK import/construction failure -> degrade, don't crash the batch
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log.exception("events: failed to initialize %r classifier; running candidates-only", name)
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return None
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# ---------------------------------------------------------------------------
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# Candidate detection + orchestration (lane D fills bodies).
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# Candidate detection (spec M7). Verified against the fixture ground-truth pulses.
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# ---------------------------------------------------------------------------
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def detect_candidates(audio: "object", sr: int) -> list[float]:
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"""Audio candidate moments on the reference track (spec M7, lane D).
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Compute RMS energy + spectral-flux onset strength (librosa); return the times
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(reference-local seconds) of peaks that are local maxima over a 10 s neighborhood and
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above the 90th percentile. Verified against the fixture's ground-truth pulse times
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(±0.5 s).
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Compute RMS energy and spectral-flux onset strength (librosa), combine into a single
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detection function (product — bangs dominate in both, so the product isolates them
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cleanly from the regular beat floor), and return the times (reference-local seconds) of
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peaks that are local maxima over a ~10 s neighborhood and above the 90th percentile.
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Verified against the fixture's ground-truth pulse times (±0.5 s).
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"""
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raise NotImplementedError("lane D (M7): implement detect_candidates")
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import librosa
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import numpy as np
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from scipy.ndimage import maximum_filter1d
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y = np.asarray(audio, dtype=np.float32).reshape(-1)
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if y.size < STFT_FRAME:
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return []
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# A reference track with no real moments (silence, constant/DC, or a bare noise floor)
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# has ~0 AC energy. The percentile threshold below is purely *relative*, so on such a
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# flat signal it would flag ~1 spurious peak per second; gate on AC-RMS (std) first —
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# this catches silence and DC alike (peak amplitude does not: a DC offset has a large
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# peak but no transients, only STFT-boundary artifacts).
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if float(np.std(y)) < MIN_SIGNAL_STD:
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return []
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rms = librosa.feature.rms(y=y, hop_length=STFT_HOP, frame_length=STFT_FRAME)[0]
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flux = librosa.onset.onset_strength(y=y, sr=sr, hop_length=STFT_HOP)
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n = min(len(rms), len(flux))
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if n == 0:
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return []
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rms, flux = rms[:n], flux[:n]
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eps = 1e-12
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strength = (rms / (rms.max() + eps)) * (flux / (flux.max() + eps))
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# No transient structure at all (e.g. constant/DC audio -> flat detection function).
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if float(strength.max()) <= eps:
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return []
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times = librosa.frames_to_time(np.arange(n), sr=sr, hop_length=STFT_HOP)
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win = max(1, int(round(NEIGHBORHOOD_S * sr / STFT_HOP)))
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if win % 2 == 0:
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win += 1
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# A frame is a local maximum if it equals the max over its centered ~10 s window. Because
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# the fixture's bangs are >5 s apart, each is the unique max in its own window — a
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# min-distance peak picker would instead merge the 7 s-spaced bangs and drop one.
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is_local_max = strength >= (maximum_filter1d(strength, size=win, mode="nearest") - eps)
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threshold = float(np.percentile(strength, PEAK_PERCENTILE))
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idx = np.where(is_local_max & (strength >= threshold))[0]
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# Greedy merge of near-duplicate peaks (plateaus), keeping the strongest.
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peaks: list[float] = []
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for i in sorted(idx.tolist(), key=lambda j: -strength[j]):
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t = float(times[i])
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if all(abs(t - p) >= MERGE_S for p in peaks):
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peaks.append(t)
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return sorted(peaks)
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# ---------------------------------------------------------------------------
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# Reference-audio loading + classifier-input preparation (ffmpeg).
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# ---------------------------------------------------------------------------
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def _select_reference(videos: list) -> "object | None":
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"""The reference video (spec §2: offset 0). Fall back to the smallest |offset|."""
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if not videos:
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return None
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exact = [v for v in videos if v.offset_ms == 0]
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if exact:
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return exact[0]
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with_offset = [v for v in videos if v.offset_ms is not None]
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if with_offset:
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return min(with_offset, key=lambda v: abs(v.offset_ms))
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return videos[0]
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def _load_reference_audio(video) -> "tuple[object, int] | None":
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"""Load the reference video's audio as mono float samples.
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Independent of lane A: extract the audio straight from the raw video with ffmpeg (a
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reused ingest WAV would couple us to lane A's naming). Returns ``(samples, sr)`` or
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``None`` if the raw video or ffmpeg is unavailable.
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"""
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import tempfile
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import numpy as np
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import soundfile as sf
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raw = config.RAW_DIR / video.filename
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if not raw.exists():
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log.warning("events: raw video %s not found under %s", video.filename, config.RAW_DIR)
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return None
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if shutil.which("ffmpeg") is None:
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log.warning("events: ffmpeg not on PATH; cannot extract reference audio")
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return None
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with tempfile.TemporaryDirectory() as tmp:
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wav = Path(tmp) / "ref.wav"
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cmd = [
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"ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
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"-i", str(raw), "-vn", "-ac", "1", "-ar", str(AUDIO_EXTRACT_SR),
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"-f", "wav", str(wav),
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]
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try:
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subprocess.run(cmd, check=True, capture_output=True)
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except subprocess.CalledProcessError as exc:
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log.warning("events: ffmpeg audio extraction failed: %s", exc.stderr.decode("utf-8", "ignore"))
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return None
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samples, sr = sf.read(str(wav), dtype="float32", always_2d=False)
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if getattr(samples, "ndim", 1) > 1:
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samples = samples.mean(axis=1)
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return np.asarray(samples, dtype=np.float32), int(sr)
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def _select_best_video(videos: list, t_global_s: float) -> "tuple[object, float] | None":
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"""The video whose 3 s window around ``t_global`` is best-covered (most centered)."""
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best = None
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best_score = None
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best_t = 0.0
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for v in videos:
|
||||
t_video = config.t_video_from_global(t_global_s, v.offset_ms or 0.0, v.drift_ppm or 0.0)
|
||||
margin = min(t_video, (v.duration_s or 0.0) - t_video) # ≥ CLIP_SECONDS/2 => fully covers
|
||||
if best_score is None or margin > best_score:
|
||||
best_score, best, best_t = margin, v, t_video
|
||||
if best is None:
|
||||
return None
|
||||
return best, best_t
|
||||
|
||||
|
||||
def _events_workdir() -> Path:
|
||||
d = config.WORK_DIR / "events"
|
||||
d.mkdir(parents=True, exist_ok=True)
|
||||
return d
|
||||
|
||||
|
||||
def prepare_inputs(t_global_s: float) -> tuple[Path, list[Path]]:
|
||||
"""Prepare classifier inputs for a candidate: a ~3 s MP4 clip + 6 sampled JPEGs (M7)."""
|
||||
raise NotImplementedError("lane D (M7): implement prepare_inputs")
|
||||
"""Prepare classifier inputs for a candidate: a ~3 s MP4 clip + up to 6 sampled JPEGs (M7).
|
||||
|
||||
Picks the best-covering video, then uses ffmpeg to cut a ≤720p clip (audio kept, for
|
||||
Gemini's native-video path) and sample 6 frames (≤768 px long edge). Raises if ffmpeg
|
||||
or a usable source video is missing — ``run_events`` isolates that per candidate.
|
||||
"""
|
||||
if shutil.which("ffmpeg") is None:
|
||||
raise RuntimeError("ffmpeg not found on PATH — required to prepare classifier inputs")
|
||||
|
||||
videos = db.get_videos()
|
||||
picked = _select_best_video(videos, t_global_s)
|
||||
if picked is None:
|
||||
raise RuntimeError("no videos available to prepare classifier inputs")
|
||||
video, t_video = picked
|
||||
raw = config.RAW_DIR / video.filename
|
||||
if not raw.exists():
|
||||
raise RuntimeError(f"source video {raw} not found")
|
||||
|
||||
duration = video.duration_s or (t_video + CLIP_SECONDS)
|
||||
start = max(0.0, min(t_video - CLIP_SECONDS / 2.0, max(0.0, duration - CLIP_SECONDS)))
|
||||
|
||||
workdir = _events_workdir()
|
||||
tag = f"cand_{t_global_s:0.3f}".replace("-", "m")
|
||||
clip_path = workdir / f"{tag}.mp4"
|
||||
frames_dir = workdir / tag
|
||||
if frames_dir.exists():
|
||||
for old in frames_dir.glob("frame_*.jpg"):
|
||||
old.unlink()
|
||||
frames_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 3 s clip, downscaled only if taller than 720p, audio preserved, faststart for inline bytes.
|
||||
subprocess.run(
|
||||
["ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
|
||||
"-ss", f"{start:.3f}", "-i", str(raw), "-t", f"{CLIP_SECONDS:.3f}",
|
||||
"-vf", f"scale=-2:'min({CLIP_MAX_HEIGHT},ih)'",
|
||||
"-c:v", "libx264", "-preset", "veryfast", "-crf", "28", "-pix_fmt", "yuv420p",
|
||||
"-c:a", "aac", "-b:a", "96k", "-movflags", "+faststart", str(clip_path)],
|
||||
check=True, capture_output=True,
|
||||
)
|
||||
# 6 frames evenly across the 3 s window, long edge ≤ 768 px.
|
||||
scale = (f"scale='if(gt(iw,ih),min({FRAME_LONG_EDGE},iw),-2)':"
|
||||
f"'if(gt(iw,ih),-2,min({FRAME_LONG_EDGE},ih))'")
|
||||
fps = NUM_FRAMES / CLIP_SECONDS
|
||||
subprocess.run(
|
||||
["ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
|
||||
"-ss", f"{start:.3f}", "-i", str(raw), "-t", f"{CLIP_SECONDS:.3f}",
|
||||
"-vf", f"fps={fps},{scale}", "-frames:v", str(NUM_FRAMES),
|
||||
str(frames_dir / "frame_%02d.jpg")],
|
||||
check=True, capture_output=True,
|
||||
)
|
||||
frames = sorted(frames_dir.glob("frame_*.jpg"))
|
||||
return clip_path, frames
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Orchestration (spec M7). Entrypoint for the CLI + POST /api/events/detect.
|
||||
# ---------------------------------------------------------------------------
|
||||
def _classify_one(t_global_s: float, classifier: "MomentClassifier | None") -> tuple[str, str, float | None, str | None]:
|
||||
"""Resolve one candidate to (event_type, source, confidence, description).
|
||||
|
||||
No classifier -> a bare ``audio_auto`` candidate. Otherwise classify, isolating any
|
||||
failure (SDK error, bad clip) by falling back to a candidate — one failure never aborts
|
||||
the batch (spec M7).
|
||||
"""
|
||||
if classifier is None:
|
||||
return "candidate", "audio_auto", None, None
|
||||
try:
|
||||
clip_path, frames = prepare_inputs(t_global_s)
|
||||
result = classifier.classify(clip_path, frames)
|
||||
return result.event_type, "ai", result.confidence, result.description
|
||||
except Exception:
|
||||
log.exception("events: classification failed at t_global=%.3f s; storing as candidate",
|
||||
t_global_s)
|
||||
return "candidate", "audio_auto", None, None
|
||||
|
||||
|
||||
def run_events(t_global_s: float | None = None, window_s: float | None = None) -> dict:
|
||||
"""Detect audio candidates and (if a provider is configured) classify them.
|
||||
|
||||
Entrypoint for ``python -m festival4d events`` and ``POST /api/events/detect``. Inserts
|
||||
events via ``db`` (``source='audio_auto'``; classified ones updated to ``source='ai'``).
|
||||
Optionally restrict to a single window around ``t_global_s`` of width ``window_s``.
|
||||
Returns a summary dict. Per-candidate exceptions are isolated (spec M7).
|
||||
Entrypoint for ``python -m festival4d events`` and ``POST /api/events/detect``. Detects
|
||||
on the reference track, maps candidate times to ``t_global``, then inserts events via
|
||||
``db`` (``source='audio_auto'`` for bare candidates, ``source='ai'`` for classified ones;
|
||||
see the module docstring on why this is a single insert per event). Per-candidate
|
||||
exceptions are isolated. Returns a summary dict.
|
||||
|
||||
Whole-track detection (``t_global_s is None``) is authoritative: it first clears prior
|
||||
machine-generated events (``audio_auto`` + ``ai``) while preserving user edits. A windowed
|
||||
call (``t_global_s`` given, optional ``window_s``) only *adds* candidates inside the
|
||||
window and clears nothing, so it never disturbs the rest of the timeline.
|
||||
"""
|
||||
raise NotImplementedError("lane D (M7): implement run_events")
|
||||
db.init_engine()
|
||||
db.init_db()
|
||||
|
||||
selected = (os.environ.get("FESTIVAL4D_CLASSIFIER") or "gemini").lower()
|
||||
videos = db.get_videos()
|
||||
reference = _select_reference(videos)
|
||||
if reference is None:
|
||||
log.warning("events: no videos registered; run `synthetic` or `ingest` first")
|
||||
return _summary(selected, False, 0, 0, 0, None, note="no videos registered")
|
||||
|
||||
loaded = _load_reference_audio(reference)
|
||||
if loaded is None:
|
||||
return _summary(selected, False, 0, 0, 0, None,
|
||||
note="no reference audio (missing raw video or ffmpeg)")
|
||||
audio, sr = loaded
|
||||
|
||||
local_times = detect_candidates(audio, sr)
|
||||
off = reference.offset_ms or 0.0
|
||||
drift = reference.drift_ppm or 0.0
|
||||
cand_globals = [config.t_global_from_video(t, off, drift) for t in local_times]
|
||||
|
||||
window = None
|
||||
if t_global_s is not None:
|
||||
w = window_s if window_s is not None else DEFAULT_WINDOW_S
|
||||
lo, hi = t_global_s - w / 2.0, t_global_s + w / 2.0
|
||||
cand_globals = [t for t in cand_globals if lo <= t <= hi]
|
||||
window = {"t_global_s": t_global_s, "window_s": w}
|
||||
else:
|
||||
# Whole-track detection replaces the machine timeline; user events are kept.
|
||||
db.clear_events("audio_auto")
|
||||
db.clear_events("ai")
|
||||
|
||||
classifier = get_classifier(selected)
|
||||
n_ai = n_auto = 0
|
||||
for t_global in cand_globals:
|
||||
event_type, source, confidence, description = _classify_one(t_global, classifier)
|
||||
db.add_event(
|
||||
t_global_s=t_global,
|
||||
event_type=event_type,
|
||||
source=source,
|
||||
duration_s=CANDIDATE_DURATION_S,
|
||||
confidence=confidence,
|
||||
description=description,
|
||||
)
|
||||
if source == "ai":
|
||||
n_ai += 1
|
||||
else:
|
||||
n_auto += 1
|
||||
|
||||
summary = _summary(selected, classifier is not None, len(cand_globals), n_ai, n_auto, window)
|
||||
log.info("events: reference=%s candidates=%d classified=%d candidates_only=%d provider=%s",
|
||||
reference.filename, len(cand_globals), n_ai, n_auto,
|
||||
selected if classifier is not None else f"{selected} (unconfigured)")
|
||||
return summary
|
||||
|
||||
|
||||
def _summary(provider: str, configured: bool, candidates: int, classified: int,
|
||||
candidates_only: int, window: dict | None, note: str | None = None) -> dict:
|
||||
out = {
|
||||
"provider": provider,
|
||||
"classifier_configured": configured,
|
||||
"candidates": candidates,
|
||||
"classified": classified,
|
||||
"candidates_only": candidates_only,
|
||||
"window": window,
|
||||
}
|
||||
if note is not None:
|
||||
out["note"] = note
|
||||
return out
|
||||
|
||||
@ -91,10 +91,16 @@ def test_cors_allows_vite_origin(client):
|
||||
assert resp.headers.get("access-control-allow-origin") == "http://localhost:5173"
|
||||
|
||||
|
||||
def test_detect_degrades_gracefully(client):
|
||||
data = client.post("/api/events/detect", json={}).json()
|
||||
assert "note" in data and "events" in data # stubbed lane D -> 200 with note
|
||||
assert client.post("/api/events/detect", json={}).status_code == 200
|
||||
def test_detect_events_endpoint(client):
|
||||
# Lane D (M7) has landed: POST /api/events/detect runs real detection and returns
|
||||
# {result, events} (no more stub "note"). result is the run_events summary.
|
||||
resp = client.post("/api/events/detect", json={})
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert "result" in data and "events" in data
|
||||
assert {"candidates", "classified", "candidates_only", "classifier_configured"} <= set(data["result"])
|
||||
assert isinstance(data["events"], list)
|
||||
assert client.post("/api/events/detect", json={}).status_code == 200 # idempotent
|
||||
|
||||
|
||||
def test_annotation_stored(client):
|
||||
|
||||
288
backend/tests/test_events_ai.py
Normal file
288
backend/tests/test_events_ai.py
Normal file
@ -0,0 +1,288 @@
|
||||
"""Tests for events_ai.py (lane D / spec M7).
|
||||
|
||||
No test hits a real API or ffmpeg: candidate detection runs on the synthetic fixture's
|
||||
in-memory audio, and the classifier orchestration is exercised with stub classifiers and a
|
||||
monkeypatched input-preparer. This mirrors spec M7's acceptance:
|
||||
- candidates land within ±0.5 s of the ground-truth pulse times, and
|
||||
- the two-stage loop (detect -> classify) works independent of any provider.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
from pydantic import ValidationError
|
||||
|
||||
from festival4d import config, db, events_ai, synthetic
|
||||
from festival4d.events_ai import (
|
||||
ClaudeClassifier,
|
||||
GeminiClassifier,
|
||||
LocalClassifier,
|
||||
MomentClassification,
|
||||
MomentClassifier,
|
||||
detect_candidates,
|
||||
get_classifier,
|
||||
run_events,
|
||||
)
|
||||
|
||||
SR = config.SYNTH_AUDIO_SR
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers / stub classifiers (no network)
|
||||
# ---------------------------------------------------------------------------
|
||||
def _reference_audio():
|
||||
"""cam0's audio: the master signal sliced at offset 0 (local == global time)."""
|
||||
master, gt = synthetic.synth_master_audio(SR)
|
||||
audio = synthetic.slice_for_offset(master, SR, 0.0, config.SYNTH_DURATION_S)
|
||||
return audio, gt["pulse_times_global_s"]
|
||||
|
||||
|
||||
class StubClassifier:
|
||||
"""Always returns the same valid classification; counts calls."""
|
||||
|
||||
def __init__(self, event_type: str = "bass_drop") -> None:
|
||||
self.calls = 0
|
||||
self.event_type = event_type
|
||||
|
||||
def classify(self, clip_path: Path, frames: list[Path]) -> MomentClassification:
|
||||
self.calls += 1
|
||||
return MomentClassification(
|
||||
event_type=self.event_type, confidence=0.9, description="stub classification"
|
||||
)
|
||||
|
||||
|
||||
class FlakyClassifier:
|
||||
"""Raises on a chosen call to exercise per-candidate exception isolation."""
|
||||
|
||||
def __init__(self, fail_on_call: int = 2) -> None:
|
||||
self.calls = 0
|
||||
self.fail_on_call = fail_on_call
|
||||
|
||||
def classify(self, clip_path: Path, frames: list[Path]) -> MomentClassification:
|
||||
self.calls += 1
|
||||
if self.calls == self.fail_on_call:
|
||||
raise RuntimeError("simulated provider failure")
|
||||
return MomentClassification(event_type="pyro", confidence=0.6, description="ok")
|
||||
|
||||
|
||||
def _fresh_reference_db(offset_ms: float = 0.0) -> None:
|
||||
"""A clean DB with a single reference video (matching cam0)."""
|
||||
db.init_engine()
|
||||
db.reset_db()
|
||||
db.add_video(
|
||||
filename="cam0.mp4", duration_s=config.SYNTH_DURATION_S, fps=config.SYNTH_FPS,
|
||||
width=config.SYNTH_VIDEO_W, height=config.SYNTH_VIDEO_H,
|
||||
offset_ms=offset_ms, drift_ppm=0.0, sync_confidence=1.0,
|
||||
)
|
||||
|
||||
|
||||
def _stub_run(monkeypatch, classifier):
|
||||
"""Wire run_events to run real detection on fixture audio + a stub classifier."""
|
||||
audio, _ = _reference_audio()
|
||||
monkeypatch.setattr(events_ai, "_load_reference_audio", lambda video: (audio, SR))
|
||||
monkeypatch.setattr(events_ai, "get_classifier", lambda name=None: classifier)
|
||||
monkeypatch.setattr(
|
||||
events_ai, "prepare_inputs",
|
||||
lambda t: (Path("/nonexistent/clip.mp4"), [Path("/nonexistent/frame_00.jpg")]),
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Candidate detection (the acceptance-critical numeric test)
|
||||
# ---------------------------------------------------------------------------
|
||||
def test_detect_candidates_matches_ground_truth():
|
||||
audio, pulses = _reference_audio()
|
||||
cands = detect_candidates(audio, SR)
|
||||
|
||||
# Recall: every ground-truth pulse has a candidate within ±0.5 s.
|
||||
for pulse in pulses:
|
||||
nearest = min(abs(c - pulse) for c in cands)
|
||||
assert nearest < 0.5, f"pulse {pulse}s unmatched; nearest candidate {nearest:.3f}s away"
|
||||
|
||||
# Precision: no spurious candidates — each maps to a distinct ground-truth pulse.
|
||||
for c in cands:
|
||||
nearest = min(abs(c - pulse) for pulse in pulses)
|
||||
assert nearest < 0.5, f"spurious candidate at {c:.3f}s ({nearest:.3f}s from any pulse)"
|
||||
assert len(cands) == len(pulses), (cands, pulses)
|
||||
|
||||
|
||||
def test_detect_candidates_short_audio_is_empty():
|
||||
import numpy as np
|
||||
|
||||
assert detect_candidates(np.zeros(16, dtype=np.float32), SR) == []
|
||||
|
||||
|
||||
def test_detect_candidates_silent_or_flat_audio_is_empty():
|
||||
"""Silence / DC / a bare noise floor must yield NO candidates, never fabricate events."""
|
||||
import numpy as np
|
||||
|
||||
n = SR * 6
|
||||
assert detect_candidates(np.zeros(n, dtype=np.float32), SR) == [] # digital silence
|
||||
assert detect_candidates(np.full(n, 0.5, dtype=np.float32), SR) == [] # constant / DC
|
||||
rng = np.random.default_rng(0)
|
||||
assert detect_candidates((1e-4 * rng.standard_normal(n)).astype(np.float32), SR) == [] # noise floor
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Frozen contract sanity (MomentClassification / MomentClassifier)
|
||||
# ---------------------------------------------------------------------------
|
||||
def test_moment_classification_validates_bounds_and_enum():
|
||||
ok = MomentClassification(event_type="pyro", confidence=0.5, description="x")
|
||||
assert ok.event_type == "pyro"
|
||||
with pytest.raises(ValidationError):
|
||||
MomentClassification(event_type="pyro", confidence=1.5, description="x") # >1
|
||||
with pytest.raises(ValidationError):
|
||||
MomentClassification(event_type="not_a_type", confidence=0.5, description="x")
|
||||
|
||||
|
||||
def test_stub_satisfies_classifier_protocol():
|
||||
assert isinstance(StubClassifier(), MomentClassifier)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Provider selection + degradation (no network — constructors don't call out)
|
||||
# ---------------------------------------------------------------------------
|
||||
def test_get_classifier_unconfigured_returns_none(monkeypatch):
|
||||
for var in ("GEMINI_API_KEY", "ANTHROPIC_API_KEY",
|
||||
"FESTIVAL4D_OPENAI_BASE_URL", "FESTIVAL4D_OPENAI_MODEL", "FESTIVAL4D_OPENAI_KEY"):
|
||||
monkeypatch.delenv(var, raising=False)
|
||||
assert get_classifier("gemini") is None
|
||||
assert get_classifier("claude") is None
|
||||
assert get_classifier("local") is None
|
||||
|
||||
|
||||
def test_get_classifier_unknown_provider_returns_none():
|
||||
assert get_classifier("does-not-exist") is None
|
||||
|
||||
|
||||
def test_get_classifier_defaults_to_gemini(monkeypatch):
|
||||
monkeypatch.delenv("FESTIVAL4D_CLASSIFIER", raising=False)
|
||||
monkeypatch.setenv("GEMINI_API_KEY", "test-key")
|
||||
assert isinstance(get_classifier(), GeminiClassifier)
|
||||
|
||||
|
||||
def test_get_classifier_local_when_configured(monkeypatch):
|
||||
monkeypatch.setenv("FESTIVAL4D_OPENAI_BASE_URL", "http://localhost:11434/v1")
|
||||
monkeypatch.setenv("FESTIVAL4D_OPENAI_MODEL", "qwen2.5-vl")
|
||||
monkeypatch.delenv("FESTIVAL4D_OPENAI_KEY", raising=False)
|
||||
clf = get_classifier("local")
|
||||
assert isinstance(clf, LocalClassifier)
|
||||
assert isinstance(clf, MomentClassifier)
|
||||
|
||||
|
||||
def test_get_classifier_claude_when_configured(monkeypatch):
|
||||
monkeypatch.setenv("ANTHROPIC_API_KEY", "test-key")
|
||||
assert isinstance(get_classifier("claude"), ClaudeClassifier)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Orchestration loop (stub classifier — no API, no ffmpeg)
|
||||
# ---------------------------------------------------------------------------
|
||||
def test_run_events_classifies_all_candidates(monkeypatch):
|
||||
_fresh_reference_db()
|
||||
stub = StubClassifier(event_type="bass_drop")
|
||||
_stub_run(monkeypatch, stub)
|
||||
|
||||
result = run_events()
|
||||
|
||||
assert result["candidates"] == 3
|
||||
assert result["classified"] == 3
|
||||
assert result["candidates_only"] == 0
|
||||
assert result["classifier_configured"] is True
|
||||
assert stub.calls == 3
|
||||
|
||||
events = db.get_events()
|
||||
assert len(events) == 3
|
||||
assert all(e.source == "ai" for e in events)
|
||||
assert all(e.event_type == "bass_drop" for e in events)
|
||||
# Candidate times map to t_global (reference offset 0) near the fixture pulses.
|
||||
times = sorted(e.t_global_s for e in events)
|
||||
for got, pulse in zip(times, (3.0, 10.0, 17.0)):
|
||||
assert abs(got - pulse) < 0.5
|
||||
|
||||
|
||||
def test_run_events_unconfigured_stores_candidates_only(monkeypatch):
|
||||
_fresh_reference_db()
|
||||
audio, _ = _reference_audio()
|
||||
monkeypatch.setattr(events_ai, "_load_reference_audio", lambda video: (audio, SR))
|
||||
monkeypatch.setattr(events_ai, "get_classifier", lambda name=None: None)
|
||||
# Degraded path must never prepare inputs. A *raised* tripwire would be swallowed by
|
||||
# _classify_one's `except Exception`; record calls with a side effect instead.
|
||||
prepared: list = []
|
||||
monkeypatch.setattr(events_ai, "prepare_inputs", lambda t: prepared.append(t))
|
||||
|
||||
result = run_events()
|
||||
|
||||
assert prepared == [] # prepare_inputs was never called when unconfigured
|
||||
|
||||
assert result["candidates"] == 3
|
||||
assert result["classified"] == 0
|
||||
assert result["candidates_only"] == 3
|
||||
assert result["classifier_configured"] is False
|
||||
|
||||
events = db.get_events()
|
||||
assert len(events) == 3
|
||||
assert all(e.source == "audio_auto" for e in events)
|
||||
assert all(e.event_type == "candidate" for e in events)
|
||||
|
||||
|
||||
def test_run_events_isolates_per_candidate_failure(monkeypatch):
|
||||
_fresh_reference_db()
|
||||
flaky = FlakyClassifier(fail_on_call=2) # 2nd candidate fails
|
||||
_stub_run(monkeypatch, flaky)
|
||||
|
||||
result = run_events()
|
||||
|
||||
assert result["candidates"] == 3
|
||||
assert result["classified"] == 2 # two succeeded
|
||||
assert result["candidates_only"] == 1 # the failed one fell back to a candidate
|
||||
assert flaky.calls == 3 # the batch was not aborted
|
||||
|
||||
events = db.get_events()
|
||||
assert sum(e.source == "ai" for e in events) == 2
|
||||
assert sum(e.source == "audio_auto" for e in events) == 1
|
||||
|
||||
|
||||
def test_run_events_whole_track_replaces_machine_events_keeps_user(monkeypatch):
|
||||
_fresh_reference_db()
|
||||
# Pre-existing events: a stale AI one and a user correction.
|
||||
db.add_event(t_global_s=99.0, event_type="other", source="ai", description="stale")
|
||||
db.add_event(t_global_s=5.0, event_type="crowd_wave", source="user", description="mine")
|
||||
_stub_run(monkeypatch, StubClassifier())
|
||||
|
||||
run_events()
|
||||
|
||||
events = db.get_events()
|
||||
user = [e for e in events if e.source == "user"]
|
||||
ai = [e for e in events if e.source == "ai"]
|
||||
assert len(user) == 1 and user[0].description == "mine" # user edit preserved
|
||||
assert len(ai) == 3 # fresh detection, stale AI gone
|
||||
assert not any(e.description == "stale" for e in events)
|
||||
|
||||
|
||||
def test_run_events_windowed_adds_in_window_and_clears_nothing(monkeypatch):
|
||||
_fresh_reference_db()
|
||||
# A machine event OUTSIDE the window must survive — windowed detection is purely additive.
|
||||
db.add_event(t_global_s=3.0, event_type="bass_drop", source="ai", description="pre-existing")
|
||||
_stub_run(monkeypatch, StubClassifier())
|
||||
|
||||
result = run_events(t_global_s=10.0, window_s=4.0) # window [8, 12] -> only the ~10 s pulse
|
||||
|
||||
assert result["candidates"] == 1
|
||||
assert result["window"] == {"t_global_s": 10.0, "window_s": 4.0}
|
||||
events = db.get_events()
|
||||
assert any(e.description == "pre-existing" for e in events) # out-of-window event preserved
|
||||
assert len(events) == 2
|
||||
new = [e for e in events if e.description != "pre-existing"]
|
||||
assert len(new) == 1 and abs(new[0].t_global_s - 10.0) < 0.5
|
||||
|
||||
|
||||
def test_run_events_no_videos_degrades(monkeypatch):
|
||||
db.init_engine()
|
||||
db.reset_db()
|
||||
result = run_events()
|
||||
assert result["candidates"] == 0
|
||||
assert "note" in result
|
||||
assert db.get_events() == []
|
||||
@ -18,3 +18,18 @@ Instead append an entry here and work around it locally; the integration agent a
|
||||
---
|
||||
|
||||
<!-- entries below -->
|
||||
|
||||
### CR-1 — lane D — 2026-07-16 — Update obsolete stub assertion in test_api.py
|
||||
- **File:** `backend/tests/test_api.py::test_detect_degrades_gracefully`
|
||||
- **Problem:** That foundation test asserted the *stub* contract of `POST /api/events/detect`
|
||||
(`"note" in data`), which was correct only while lane D was unimplemented. With M7 landed,
|
||||
`run_events` no longer raises `NotImplementedError`, so `api.py` returns the real
|
||||
`{result, events}` shape (spec M3) and the stub assertion fails.
|
||||
- **Proposed change:** rename to `test_detect_events_endpoint` and assert the real landed
|
||||
contract (200; `result` is the run_events summary with candidates/classified/… keys; `events`
|
||||
is a list; idempotent on repeat).
|
||||
- **Workaround in place:** applied the minimal edit directly (test file, not a frozen contract
|
||||
file). The frozen `api.py` app is **unchanged**. Only lane D's owned modules + this obsolete
|
||||
stub assertion were touched. Full suite green (89 passed).
|
||||
- **Decision:** (integration) — informational; the change is a direct, unavoidable consequence
|
||||
of lane D landing. Confirm the assertion matches the intended M3 detect-response shape.
|
||||
|
||||
59
plan/status/lane-D.md
Normal file
59
plan/status/lane-D.md
Normal file
@ -0,0 +1,59 @@
|
||||
# Status — lane-D
|
||||
|
||||
## Round 1 — 2026-07-16 — STATUS: ready_to_merge
|
||||
|
||||
**Adversarial review (self-run, 5 lenses → verify):** 3 findings, all confirmed & fixed —
|
||||
(1) `detect_candidates` fabricated ~1 candidate/s on silent/DC/noise audio → added an AC-RMS
|
||||
(std) floor `MIN_SIGNAL_STD`; silence/DC/noise now return `[]`, fixture unchanged [3,10,17];
|
||||
(2) degradation test's raised tripwire was swallowed by `except Exception` → switched to a
|
||||
call-recorder (`assert prepared == []`); (3) windowed test ran on a fresh DB → now seeds an
|
||||
out-of-window `ai` event and asserts it survives. `pytest` **90 passed** after fixes.
|
||||
|
||||
|
||||
**Directives acknowledged:** round 0 of plan/DIRECTIVES.md (foundation merged; lanes may start;
|
||||
lane D is small + fully independent; merge when acceptance passes — no waiting on siblings).
|
||||
|
||||
**Acceptance checklist** (plan/lane-D-events.md + spec M7) — implementation complete, verified:
|
||||
- [x] `detect_candidates` finds candidates within ±0.5 s of fixture ground-truth pulses
|
||||
[3.0, 10.0, 17.0]. Evidence: `test_events_ai.py::test_detect_candidates_matches_ground_truth`
|
||||
(recall+precision+count). RMS×spectral-flux, centered-10 s-window local-max, ≥ P90.
|
||||
- [x] `MomentClassification` + `MomentClassifier` frozen contract preserved verbatim; import
|
||||
of events_ai pulls in ZERO heavy libs/SDKs (all lazy). Evidence: import-cost probe → `[]`.
|
||||
- [x] Three providers implemented against the installed SDKs (verified surfaces): Gemini
|
||||
(google-genai 2.11.0, `Part.from_bytes(data,mime_type)` + `generate_content` structured
|
||||
output), Claude (anthropic 0.116.0, `messages.parse(output_format=…, thinking=adaptive,
|
||||
max_tokens)` → `parsed_output`, NO temperature, model `claude-opus-4-8`), Local
|
||||
(openai 2.45.0, `chat.completions.create` json_object, 6 image_url parts, 1 retry).
|
||||
- [x] Provider selection `FESTIVAL4D_CLASSIFIER=gemini|claude|local` (default gemini);
|
||||
unconfigured → candidates-only + log line. Evidence: `test_get_classifier_*` (5 tests).
|
||||
- [x] Per-candidate exception isolation. Evidence: `test_run_events_isolates_per_candidate_failure`
|
||||
(2 ai + 1 fallback candidate; batch of 3 completes despite one raising).
|
||||
- [x] `run_events` fills the frozen entrypoint (cli.py + api.py dispatch); writes via db helpers
|
||||
only (insert-once in final state — db.py has no update-event helper; end state matches spec).
|
||||
- [x] `pytest` green with NO network: **89 passed** across 5 consecutive full-suite runs.
|
||||
- [x] Real-path end-to-end (ffmpeg-decoded AAC audio, not in-memory): isolated 20 s fixture →
|
||||
`run_events()` degraded → candidates at 3.008/10.016/17.013 s (≤0.016 s error), exactly 3,
|
||||
no spurious. Evidence: scratchpad/e2e_verify.py → RESULT PASS.
|
||||
|
||||
**Done this round:** implemented `backend/festival4d/events_ai.py` (candidate detection + 3
|
||||
providers + get_classifier + prepare_inputs + run_events orchestration) and
|
||||
`backend/tests/test_events_ai.py` (15 tests). Detection algorithm locked via a scratch
|
||||
experiment before coding. Confirmed Claude call shape via claude-api skill and all three SDK
|
||||
surfaces via introspection. Currently running a 5-lens adversarial review workflow
|
||||
(spec-compliance / provider-SDK / detection-orchestration / contract-ownership / test-quality)
|
||||
before merge.
|
||||
|
||||
**Coordinator note (out-of-strict-ownership edit):** updated `backend/tests/test_api.py`
|
||||
`test_detect_degrades_gracefully` → `test_detect_events_endpoint`. That foundation test asserted
|
||||
the STUB behavior (`"note" in data`) of `POST /api/events/detect`; my landing replaces the stub
|
||||
with real detection returning `{result, events}`. The frozen `api.py` app is UNCHANGED — only the
|
||||
now-obsolete stub assertion was updated. Flagged here for transparency; see plan/CHANGE_REQUESTS.md CR-1.
|
||||
|
||||
**Note on shared tree:** this working directory holds all four lanes' uncommitted work
|
||||
concurrently (A/B/C/D). I will stage and commit ONLY my owned files. One transient
|
||||
`test_audio_sync.py` (lane A) failure was observed once mid-run (concurrent write) but the suite
|
||||
is deterministically green (89/89 ×5); not caused by lane D.
|
||||
|
||||
**Blockers / questions for coordinator:** none.
|
||||
|
||||
**Next:** address any confirmed review findings, then merge `lane/d-events` → main.
|
||||
Loading…
Reference in New Issue
Block a user