mirror of
https://github.com/kuhyx/testsAndMisc.git
synced 2026-11-10 20:07:04 +01:00
Krzysztof kuhy Rudnicki
08aac8d7d1
i3 config was filed under periodic_background, which it never was. It is now linux_configuration/i3/ (config + installer) and linux_configuration/i3blocks/ (the status scripts), both at depth 2. misc/testsAndMisc-bash/ was a directory named after the repo it already lived in, so its contents moved up to misc/. Its stray .gitignore came along, keeping the media rules working from the new location; its .vscode/tasks.json did not, because it invoked Bash/transcribe.sh, a path this repo dropped long ago. Most of this commit is the references the previous flatten missed. Its regex skipped anything with a variable prefix, which left 16 files -- including check_and_enable_services.sh and install_core_system.sh, the orchestrators -- still naming $CONFIG_DIR/scripts/periodic_background/... It had also rewritten three i3 paths into scripts/i3blocks/, a directory that has never existed; the i3blocks suites caught that by failing. 13 more symlinks broke on the i3 move. Repointed, and cpu_monitor/volume/ disk were run again to confirm the bar still reports. Depth 139 -> 104. Roughly 47 of the remainder sit in digital_wellbeing and hosts, still deferred. The rest encode real structure -- a C project, Python package directories, artgate's routes/styles, systemd unit dirs -- so flattening them further would destroy information rather than taxonomy. Verified: 1798 tests at 100%, both i3blocks suites pass, the shell coverage gate is clean, and the relocated media rules still ignore what they should while test_fw.srt stays tracked. 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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