{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.21145",
  "title": "Scaling Forced Alignment to End-User Devices",
  "summary": "The method replaces quadratic-time-and-space Viterbi forced alignment (as in torchaudio) with a Hirschberg-based in-place variant plus constrained-random-walk search-space pruning, enabling alignment of multi-hour audio on CPU-class hardware. The cost is implementation complexity beyond a standard Viterbi decoder and reliance on an existing acoustic model to supply frame-level emissions; the alignment quality itself is unchanged without pruning. With pruning enabled, alignment accuracy can degrade in roughly 2% of tested cases, particularly where transcriptions contain errors that the random-walk model does not capture.",
  "rationale": "For an engineer mining speech training data on constrained infrastructure, this directly removes the memory wall that blocks long-audio alignment and runs on a CPU, so it can be adopted immediately at no GPU or cloud cost.",
  "technique": "Linear-memory pruned Viterbi forced alignment",
  "family": "outro",
  "recommendation": "adotar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.21145",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
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      "statement": "Hirschberg optimization reduces memory from 140 GB to 5 MB for three-hour inputs with identical alignments in one-third the CPU time of torchaudio; pruning adds a further 2x speedup on inputs longer than 20 minutes while preserving accuracy in over 98% of tested cases.",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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