AudioBox Aesthetics (CE, CU, PC, PQ).

Thin wrapper around Meta's audiobox_aesthetics predictor. Returns four per-clip dimensions (CE — Content Enjoyment, CU — Content Usefulness, PC — Production Complexity, PQ — Production Quality); score exposes PQ, the dimension V2A papers typically report on. The remaining three are surfaced under details.

The earlier Verse-Bench combined score (CE + CU + PQ + (11 − PC)) / 4 is non-standard and is deliberately not used.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric`

BaseMetric()

Abstract base class for all eval metrics.

Two execution shapes:

Per-sample (is_set_metric=False, default) — implement :meth:compute. The Evaluator calls it once per input sample and returns one :class:MetricResult per sample.
Set-vs-set (is_set_metric=True) — implement :meth:accumulate (called once per sample to buffer features) and :meth:finalize (called once after all samples to compute the corpus-level result). Use :meth:reset to clear buffers and :meth:merge_from to fold multi-GPU per-worker state together.

Optionally override :meth:setup to eagerly load models. Metrics that chunk along the time dim for memory hardcode their own chunk size in __init__ (see optical_flow for the canonical example). Eval always processes one video per :meth:Evaluator.evaluate call; compute / accumulate receive a single sample, not a batch.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.to`

to(device: str | torch.device) -> BaseMetric

Move metric (and its internal models) to device.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.setup`

setup() -> None

Eagerly load models. Called once by :class:EvalWorker.

Default is a no-op; metrics with no eager state (pixel math, closed-form ops) inherit this. Override only if your metric needs to load weights.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.compute`

compute(sample: dict) -> MetricResult

Per-sample metrics: compute the score for one sample.

sample["video"] is (T, C, H, W) float in [0, 1]. sample["reference"] (if used) has the same shape. Return self._skip(sample, reason) for missing inputs.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.reset`

reset() -> None

Clear accumulator state at the start of each evaluate() call.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.accumulate`

accumulate(sample: dict) -> None

Buffer per-sample features for a corpus-level metric.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.finalize`

finalize() -> MetricResult

Compute the corpus-level result from buffered state.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.BaseMetric.merge_from`

merge_from(other: BaseMetric) -> None

Multi-GPU: fold another worker's accumulator state into this one.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.register`

register(name: str)

Decorator to register a metric class.

Usage::

@register("ssim")
class SSIMMetric(BaseMetric):
    ...

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.MetricResult`

MetricResult(name: str, score: float | None, details: dict[str, Any] = dict())

Standard result container returned by all metrics.

score is None when the metric was skipped (e.g. missing required input). Check details["skipped"] for the reason.

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.AudioBoxAestheticsMetric`

AudioBoxAestheticsMetric()

AudioBox Aesthetics: PQ as the primary score, CE/CU/PC/PQ in details.

Source

@register("audio.audiobox_aesthetics")
class AudioBoxAestheticsMetric(BaseMetric):
    """AudioBox Aesthetics: PQ as the primary score, CE/CU/PC/PQ in details."""

    name = "audio.audiobox_aesthetics"
    requires_reference = False
    higher_is_better = True
    needs_gpu = True
    is_set_metric = False
    dependencies = ["audiobox_aesthetics"]

    def __init__(self) -> None:
        super().__init__()
        self._predictor: Any = None

    def to(self, device):
        super().to(device)
        if self._predictor is not None:
            self._predictor.model.to(self.device)
            self._predictor.device = self.device
        return self

    def setup(self) -> None:
        if self._predictor is not None:
            return
        from audiobox_aesthetics.infer import initialize_predictor
        predictor = initialize_predictor()
        # Upstream's setup_model() always lands on default CUDA (cuda:0).
        # Re-pin onto this worker's device so multi-GPU eval actually parallelizes.
        predictor.model.to(self.device)
        predictor.device = self.device
        self._predictor = predictor

    @torch.no_grad()
    def compute(self, sample: dict) -> MetricResult:
        from pathlib import Path

        if self._predictor is None:
            self.setup()

        audio_path = sample.get("audio")
        if audio_path is None:
            return self._skip(sample, "missing 'audio'")
        if not Path(audio_path).exists():
            return self._skip(sample, f"audio file not found: {audio_path}")

        try:
            score = self._predictor.forward([{"path": audio_path}])[0]
        except Exception as exc:  # pragma: no cover — upstream raises vary
            return self._skip(sample, f"audiobox predictor failed: {type(exc).__name__}: {exc}")
        try:
            return MetricResult(
                name=self.name,
                score=float(score["PQ"]),
                details={
                    "CE": float(score["CE"]),
                    "CU": float(score["CU"]),
                    "PC": float(score["PC"]),
                    "PQ": float(score["PQ"]),
                },
            )
        except (KeyError, TypeError) as exc:
            return self._skip(sample, f"audiobox returned unexpected shape: {exc}")

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.AudioBoxAestheticsMetric.to`

to(device)

Source

def to(self, device):
    super().to(device)
    if self._predictor is not None:
        self._predictor.model.to(self.device)
        self._predictor.device = self.device
    return self

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.AudioBoxAestheticsMetric.setup`

setup() -> None

Source

def setup(self) -> None:
    if self._predictor is not None:
        return
    from audiobox_aesthetics.infer import initialize_predictor
    predictor = initialize_predictor()
    # Upstream's setup_model() always lands on default CUDA (cuda:0).
    # Re-pin onto this worker's device so multi-GPU eval actually parallelizes.
    predictor.model.to(self.device)
    predictor.device = self.device
    self._predictor = predictor

`fastvideo.eval.metrics.audio.audiobox_aesthetics.metric.AudioBoxAestheticsMetric.compute`

compute(sample: dict) -> MetricResult

Source

@torch.no_grad()
def compute(self, sample: dict) -> MetricResult:
    from pathlib import Path

    if self._predictor is None:
        self.setup()

    audio_path = sample.get("audio")
    if audio_path is None:
        return self._skip(sample, "missing 'audio'")
    if not Path(audio_path).exists():
        return self._skip(sample, f"audio file not found: {audio_path}")

    try:
        score = self._predictor.forward([{"path": audio_path}])[0]
    except Exception as exc:  # pragma: no cover — upstream raises vary
        return self._skip(sample, f"audiobox predictor failed: {type(exc).__name__}: {exc}")
    try:
        return MetricResult(
            name=self.name,
            score=float(score["PQ"]),
            details={
                "CE": float(score["CE"]),
                "CU": float(score["CU"]),
                "PC": float(score["PC"]),
                "PQ": float(score["PQ"]),
            },
        )
    except (KeyError, TypeError) as exc:
        return self._skip(sample, f"audiobox returned unexpected shape: {exc}")