mirror of
https://github.com/kuhyx/testsAndMisc.git
synced 2026-11-10 20:07:04 +01:00
Krzysztof kuhy Rudnicki
fb51e991fe
The largest untested Python file left in the repo, at 356 lines against the cap. Tests land first so the split that follows is verified against tests that predate the seam. 63 tests: duration probing through both ffmpeg paths, the transcode fallback and temp-file cleanup, audio loading's three outcomes, linear resampling, cosine k-means, the ECAPA classifier loader, per-segment embedding extraction, and diarize_segments end to end. numpy is real here, not a double. It is a declared dependency, and the core of this file is clustering maths - assertions against a mocked numpy would prove nothing about whether two speakers actually get two labels. The _try_import seam passes numpy through while stubbing torch and speechbrain, so the tests still run in CI with neither installed. The clustering tests assert real properties: two opposed groups separate, magnitude is ignored because the metric is cosine, and a fixed seed reproduces. The last three coverage gaps were _load_audio's ffmpeg fallback in both outcomes, the minimum-slice widening, and the zero-iteration k-means branch. Split across six test modules after two intermediate arrangements went over the cap, and one move silently broke a test by separating it from its doubles - caught by the test count holding at 63 through every rearrangement. 338 tests pass in the directory, up from 275 by exactly the 63 added. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011w2Pj4Ek3ZNMQVYcFHgXDY
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
"""Tests for loading the ECAPA speaker-embedding classifier.
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speechbrain is never installed in CI and is reached only through
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``_try_import``, so the encoder is doubled here. A model that fails to load
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must clean up any temporary audio the caller had already transcoded.
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"""
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from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING
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import _transcribe_diarize as diar
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import pytest
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if TYPE_CHECKING:
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from pathlib import Path
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def _importer(available: dict[str, object]) -> object:
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"""Return a _try_import double resolving only the named modules."""
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def _try(name: str) -> object | None:
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return available.get(name)
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return _try
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# --------------------------------------------------------------------------- #
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# _load_speaker_classifier
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# --------------------------------------------------------------------------- #
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def test_load_speaker_classifier_returns_none_without_speechbrain(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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"""Without speechbrain there is no embedding model to load."""
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monkeypatch.setattr(diar, "_try_import", lambda _n: None)
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assert diar._load_speaker_classifier(None) is None
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def test_load_speaker_classifier_loads_the_ecapa_model(
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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"""The ECAPA model is loaded on CPU into a cache directory."""
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recorded: dict[str, object] = {}
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class _Encoder:
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@staticmethod
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def from_hparams(**kwargs: object) -> str:
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recorded.update(kwargs)
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return "classifier"
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module = type("M", (), {"EncoderClassifier": _Encoder})()
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monkeypatch.setattr(
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diar, "_try_import", _importer({"speechbrain.inference": module})
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)
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assert diar._load_speaker_classifier(None) == "classifier"
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assert recorded["source"] == "speechbrain/spkrec-ecapa-voxceleb"
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assert recorded["run_opts"] == {"device": "cpu"}
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@pytest.mark.parametrize("error", [OSError("no net"), RuntimeError("bad weights")])
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def test_load_speaker_classifier_cleans_up_after_a_failure(
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monkeypatch: pytest.MonkeyPatch,
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caplog: pytest.LogCaptureFixture,
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tmp_path: Path,
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error: Exception,
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) -> None:
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"""A model that will not load warns and removes the temp audio."""
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temp = tmp_path / "scratch.wav"
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temp.write_bytes(b"RIFF")
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class _Encoder:
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@staticmethod
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def from_hparams(**_kwargs: object) -> None:
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raise error
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module = type("M", (), {"EncoderClassifier": _Encoder})()
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monkeypatch.setattr(
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diar, "_try_import", _importer({"speechbrain.inference": module})
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)
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with caplog.at_level(logging.WARNING):
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assert diar._load_speaker_classifier(str(temp)) is None
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assert "Could not load speaker embedding model" in caplog.text
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assert not temp.exists()
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