Ayase
Modular media quality metrics for video, image, and audio datasets.
Work in progress - APIs and module interfaces may change before 1.0.
What It Does
Ayase runs quality assessment modules over a dataset and writes structured per-sample metrics. 361 modules produce 478 metrics across 21 categories (NR-IQA, FR-IQA, NR-VQA, temporal, motion, audio, face, safety, aesthetics, text-video alignment, and more). Modules are independent - pick only what you need.
Full metric catalog: METRICS.md. Pretrained model catalog: MODELS.md.
Install
pip install ayase
Ayase is distributed as a single install. Runtime dependencies are managed by the project itself, and model weights are downloaded and cached on first use.
CLI
ayase scan ./dataset # default balanced pipeline
ayase scan ./dataset --deep # run every discovered module
ayase scan ./dataset --modules metadata,basic_quality # selected modules
ayase help # list every metric and provider
ayase help rqvqa_score # metric/module models, config, and usage
ayase modules list # show all 361 modules
ayase modules check # import/dependency readiness
ayase filter ./dataset --min-score 70 --output ./good # filter by quality
ayase stats ./dataset # dataset statistics for images/video
ayase tui # terminal UI
Python API
from ayase import AyasePipeline
pipeline = AyasePipeline(modules=["basic", "metadata", "motion"])
results = pipeline.run("./my_dataset")
for path, sample in results.items():
qm = sample.quality_metrics
if qm:
print(f"{sample.path.name}: technical={qm.technical_score} blur={qm.blur_score}")
pipeline.export("report.json") # also: report.csv, report.html
Configuration
ayase.toml in project root:
[general]
parallel_jobs = 8 # concurrency hint passed to capable modules/backends
[pipeline]
modules = ["metadata", "basic_quality", "motion"]
[output]
default_format = "json"
artifacts_dir = "reports"
Custom Modules
from ayase.models import QualityMetrics, Sample, ValidationIssue, ValidationSeverity
from ayase.pipeline import PipelineModule
import cv2
class BlurCheck(PipelineModule):
name = "blur_check"
description = "Flag blurry frames via Laplacian variance"
default_config = {"threshold": 100.0}
def process(self, sample: Sample) -> Sample:
img = cv2.imread(str(sample.path), cv2.IMREAD_GRAYSCALE)
if img is None:
return sample
score = float(cv2.Laplacian(img, cv2.CV_64F).var())
if sample.quality_metrics is None:
sample.quality_metrics = QualityMetrics()
sample.quality_metrics.blur_score = score
if score < self.config.get("threshold", 100.0):
sample.validation_issues.append(
ValidationIssue(
severity=ValidationSeverity.WARNING,
message=f"Blurry ({score:.0f})",
)
)
return sample
Modules auto-register via __init_subclass__. Config is available as self.config.
Development
git clone <repo-url> && cd ayase
pip install -e ".[dev]"
pytest # 8000+ tests, ~4 min
pytest tests/ --full # with ML model loading
License
MIT. Model weights downloaded at runtime carry their own licenses - see MODELS.md.
1 maintainer
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