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. 393 modules produce 587 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.

Isolated installation

Applications that already have their own PyTorch, NumPy, or media dependency constraints can keep Ayase completely outside their environment:

pip install ayase --no-deps

from ayase_client import AyasePipeline

pipeline = AyasePipeline(modules=["basic", "metadata", "motion"])
results = pipeline.run("./my_dataset")
pipeline.export("report.json")
pipeline.close()

This installs the same Ayase wheel without ML dependencies. On first use the client creates a private full-runtime venv and starts a loopback worker. Media is passed by local path. Regular pip install ayase remains unchanged.

Optional check: ayase-client doctor. To use an existing runtime, set AYASE_RUNTIME_PYTHON.

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 393 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

Ayase's own code is MIT. Model weights downloaded at runtime carry their own licenses - see MODELS.md.

Release files for ayase 0.1.79

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ayase 0.1.79
File	Size	Uploaded
ayase-0.1.79.tar.gz	13.5 MB	Sep 24, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for ayase 0.1.79
File	Interpreter	ABI	Platform	Reset
ayase-0.1.79-py3-none-any.whl 15.8 MB Sep 24, 2026	Python 3	none	any	Details

Total release size: 29.3 MB

Release files / ayase-0.1.79.tar.gz

Download URL	ayase-0.1.79.tar.gz
Size	13.5 MB
Tags	Source
SHA-256 checksum How to use checksums	`1e8794351de95d1500faac968b1ba0755922520f8d0a07e65d5d6d4fcca11dc3`
BLAKE2b-256 checksum How to use checksums	`b708208505491030d2e6b8e4873e45851f8185376c153cac461870aa756139cf`
Upload date	Sep 24, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.10.11`

Release files / ayase-0.1.79-py3-none-any.whl

Download URL	ayase-0.1.79-py3-none-any.whl
Size	15.8 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`c28383b35a07a8096a4f89c3110355f3f2ea01d90149ca0d3239452323208f7e`
BLAKE2b-256 checksum How to use checksums	`132be44aecc1972b717fa021e6b141332a9bdd27ab3077dfef3cbd4733f06863`
Upload date	Sep 24, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.10.11`