mirror of
https://github.com/kuhyx/testsAndMisc.git
synced 2026-11-13 18:10:47 +01:00
Krzysztof kuhy Rudnicki
4f2b0ccdf7
Two levels of pure taxonomy carrying no information, and a 2-directory
cap leaves no room for them. Categories now sit directly under
linux_configuration/: features, fixes, utils, misc, fresh-install,
installers, diagnostics, gaming, boot_recovery, periodic_background, lib,
meta, tests. A script's own lib/ and tests/ are exempt from the cap, so
those stay put.
Depth violations 248 -> 139; roughly 40 of the remainder are inside
digital_wellbeing and hosts, deferred until the guard-lib repoint.
The moves are the easy half. What breaks is every path that counted
levels, and three classes of it had to be found by resolving paths rather
than reading diffs:
- 52 relative `source` lines for lib/common.sh and lib/android.sh. The
correct prefix differs per file: 40 were ../../ and 12 were ../../../,
so a blanket replace would have fixed one set and broken the other.
- REPO_ROOT in 7 fixes/lib/tests harnesses and setup_syncyomi_guard.sh,
each walking up four levels from a directory now two levels shallower.
This caused a real regression -- the fixes suite went red with
"log_warn: command not found" -- caught by running it and comparing
against the pre-move baseline worktree.
- conftest.py's sys.path entries, which took the pytest gate down with
14 collection errors, and the `# shellcheck source=` directives, which
took shellcheck down with SC1091.
16 symlinks in $HOME broke, including the entire i3blocks bar. All
repointed, and cpu_monitor/memory/disk/time/volume were run to confirm
they emit real output rather than merely resolving.
Verified: 1798 tests pass at 100% coverage, 8 of 9 lib/tests suites pass
(the ninth needs the coverage jail, identically at baseline), pre-commit
is clean, and the two untracked secret configs are still ignored after
their gitignore paths changed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01PJrqKERAo5QUeEHWS5Asgp
105 lines
3.1 KiB
Python
Executable File
105 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Resampling and cosine k-means, the maths behind speaker labelling.
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numpy is fetched through ``_try_import`` rather than imported at module
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scope, so this file remains importable on a machine without it - which is
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what lets the transcribe tools be unit-tested in CI.
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"""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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from _transcribe_media import _try_import
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if TYPE_CHECKING:
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import types
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import numpy as np
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import numpy.typing as npt
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_NDIM_2D = 2
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def _resample_linear(
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x: npt.NDArray[np.float32],
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src_sr: int,
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tgt_sr: int,
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) -> npt.NDArray[np.float32]:
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"""Linearly resample 1-D audio array."""
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np_mod = _try_import("numpy")
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if np_mod is None:
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msg = "numpy is required for resampling"
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raise RuntimeError(msg)
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if src_sr == tgt_sr:
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return x
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ratio = float(tgt_sr) / float(src_sr)
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n_out = max(1, round(x.shape[-1] * ratio))
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xp = np_mod.linspace(0.0, 1.0, num=x.shape[-1], endpoint=False)
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xq = np_mod.linspace(0.0, 1.0, num=n_out, endpoint=False)
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y = np_mod.interp(xq, xp, x.astype(np_mod.float32))
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return y.astype(np_mod.float32)
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def _kmeans_cosine(
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embs: list[Any],
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k: int,
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iters: int = 50,
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seed: int = 0,
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) -> npt.NDArray[np.int64]:
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"""Cluster embeddings with cosine-similarity k-means."""
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np_mod = _try_import("numpy")
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if np_mod is None:
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msg = "numpy is required for clustering"
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raise RuntimeError(msg)
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rng = np_mod.random.default_rng(seed)
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features = np_mod.asarray(embs, dtype=np_mod.float32)
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if features.ndim != _NDIM_2D or features.shape[0] == 0:
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return np_mod.zeros((0,), dtype=np_mod.int64)
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features = features / (np_mod.linalg.norm(features, axis=1, keepdims=True) + 1e-8)
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idxs = rng.choice(
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features.shape[0],
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size=min(k, features.shape[0]),
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replace=False,
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)
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centroids = features[idxs]
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if centroids.shape[0] < k:
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pad = rng.standard_normal(
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size=(
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k - centroids.shape[0],
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features.shape[1],
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)
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).astype(np_mod.float32)
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pad /= np_mod.linalg.norm(pad, axis=1, keepdims=True) + 1e-8
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centroids = np_mod.concatenate([centroids, pad], axis=0)
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return _run_kmeans_iterations(np_mod, features, centroids, k, iters)
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def _run_kmeans_iterations(
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np_mod: types.ModuleType,
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features: npt.NDArray[np.float32],
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centroids: npt.NDArray[np.float32],
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k: int,
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iters: int,
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) -> npt.NDArray[np.intp]:
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"""Run k-means iteration loop and return labels."""
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labels: Any = None
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for _ in range(iters):
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sims = features @ centroids.T
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labels = sims.argmax(axis=1)
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new_c = np_mod.zeros_like(centroids)
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for j in range(k):
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sel = features[labels == j]
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if sel.shape[0] == 0:
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new_c[j] = centroids[j]
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else:
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v = sel.mean(axis=0)
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v /= np_mod.linalg.norm(v) + 1e-8
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new_c[j] = v
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if np_mod.allclose(new_c, centroids, atol=1e-4):
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break
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centroids = new_c
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return labels
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