Datasets:

ytdata
/

vitw-context-select

# Selection rules v1 (provisional_machine_screened). Frozen once subsets are built;
# any change must bump selection_version and keep the old manifests.
selection_version: v1
annotation_status_default: provisional_machine_screened   # no human listening yet
normalizer: vitw_en_norm_v1
candidate_generator: cmudict_homophone_nearphone_v2       # alt zipf >= 2.5, max 6 alts
exclude_function_words_from_buckets: true

selector:
  model: openai/whisper-large-v3@06f233fe06e710322aca913c1bc4249a0d71fce1
  decode: {greedy: true, language: en, prompt: null, fp16: true, word_timestamps: true}
  note: >
    Whisper has an internal decoder LM; it is NOT a pure acoustic oracle.
    HF beam N-best was dropped (transformers 5.4.0 under python -I returned identical beams and OOMed).
acoustic:
  # crop = approximate target window (greedy word timestamps; interpolated from
  # neighbours if the target was deleted) +/- 0.3 s; decoder sees only the task
  # prefix + candidate word (no sentence context).
  crop_margin: "min over NON-homophone alts a of [lp_whisper(target|crop) - lp_whisper(alt|crop)]"
  crop_margin_all: "same over all alts incl. homophones (diagnostic only; homophone gap = spelling prior)"
  homophone_only: "target whose alternatives are all homophones -> acoustic_label homophone_only (crop cannot separate them)"
  ambiguous_if: "selector top-1 target wrong OR crop_margin < 1.0"
  clear_if: "selector top-1 target correct AND crop_margin >= 3.0"
  unknown_if: "otherwise, or window_source == interpolated_too_short"
  thresholds: {ambiguous_margin: 1.0, clear_margin: 3.0, max_selector_utt_wer: 0.5}
  unreliable_if: "selector utterance WER > 0.5 (hallucination / unintelligible) -> acoustic unknown"

context:
  lm: Qwen/Qwen2.5-7B-Instruct (raw text, no chat template) -- nomination only, not ground truth
  # margins are natural-log probabilities; min over all alternatives (hardest alternative)
  prefix_margin: "min_a [lp(target|prefix) - lp(alt|prefix)]"
  prior_margin: "min_a [lp(target|BOS) - lp(alt|BOS)] (context-free word prior)"
  prefix_shift: "min_a [(lp(t|prefix)-lp(a|prefix)) - (lp(t|BOS)-lp(a|BOS))]"
  full_margin: "min_a [lp(sentence_t) - lp(sentence_a)]   # oracle bidirectional diagnostic"
  full_shift: "min_a [(lp(sent_t)-lp(sent_a)) - (lp(t|BOS)-lp(a|BOS))]"
  high_margin: 2.3      # ~10x odds for the reference over the hardest alternative
  high_shift: 1.0       # context must add >= e^1 odds beyond the word prior
  low_margin: 0.7       # below this for BOTH prefix and full -> low / undeterminable
  availability:
    both: prefix high and full high
    prefix_available: prefix high, full not high
    suffix_only: prefix not high, full high
    none: neither high

buckets:
  main_challenge: acoustic ambiguous AND context high (any availability)
  ambiguous_negative: acoustic ambiguous AND context low
  clear_control: acoustic clear AND context high
  general_control: acoustic clear AND context low
  homophone_semantic: acoustic_label homophone_only AND context high (semantic word choice; NOT evidence that acoustic degradation drives routing)
  unassigned: everything else (medium context, alignment unknown, function word) -- kept with reason

baseline_error_enriched: "selector top-1 aligned target word != reference, ignoring surface variants (numbers 7/seven, ordinals, British/American spelling, cannot/can't; surface_equiv_v1)"
inclusion_never_uses: [MoE outputs, MoE routing, MoE accuracy]
partition: {seed: 20261108, dev_frac: 0.2, unit: template group}

decision_log:
  - date: 2026-11-09
    change: homophone alternatives removed from acoustic crop margin; homophone-only targets moved to separate bucket homophone_semantic
    reason: 720 dev homophone targets were labelled acoustically clear only through Whisper spelling prior (plan sec 2.3 requires homophones as a separate class)
    data_used: dev distributions only; no MoE outputs
  - date: 2026-11-09
    change: low_margin kept at 0.7 after dev sweep (0.7/1.0/1.5/2.3 -> low 33/40/55/71 of 3115 dev targets)
    reason: low-context targets are rare in this corpus under any threshold; keep conservative definition, report low-context buckets as exploratory
  - date: 2026-11-11
    change: baseline_error_enriched ignores surface-form variants (surface_equiv_v1)
    reason: root review of strict subset found 44 number-format and spelling variants (seven->7, center->centre) counted as selector errors
    data_used: strict subset audit only; no MoE outputs
  - date: 2026-11-11
    change: surface_equiv_v2 (toward/towards, compounds like break room/breakroom); selector utterance WER > 0.5 -> acoustic unknown
    reason: root spot-check of 8 random strict-test rows found 2 whole-utterance hallucinations ("Thank you for watching.") and 2 surface variants
    data_used: strict subset audit only; no MoE outputs

	# Selection rules v1 (provisional_machine_screened). Frozen once subsets are built;
	# any change must bump selection_version and keep the old manifests.
	selection_version: v1
	annotation_status_default: provisional_machine_screened # no human listening yet
	normalizer: vitw_en_norm_v1
	candidate_generator: cmudict_homophone_nearphone_v2 # alt zipf >= 2.5, max 6 alts
	exclude_function_words_from_buckets: true

	selector:
	model: openai/whisper-large-v3@06f233fe06e710322aca913c1bc4249a0d71fce1
	decode: {greedy: true, language: en, prompt: null, fp16: true, word_timestamps: true}
	note: >
	Whisper has an internal decoder LM; it is NOT a pure acoustic oracle.
	HF beam N-best was dropped (transformers 5.4.0 under python -I returned identical beams and OOMed).

	acoustic:
	# crop = approximate target window (greedy word timestamps; interpolated from
	# neighbours if the target was deleted) +/- 0.3 s; decoder sees only the task
	# prefix + candidate word (no sentence context).
	crop_margin: "min over NON-homophone alts a of [lp_whisper(target\|crop) - lp_whisper(alt\|crop)]"
	crop_margin_all: "same over all alts incl. homophones (diagnostic only; homophone gap = spelling prior)"
	homophone_only: "target whose alternatives are all homophones -> acoustic_label homophone_only (crop cannot separate them)"
	ambiguous_if: "selector top-1 target wrong OR crop_margin < 1.0"
	clear_if: "selector top-1 target correct AND crop_margin >= 3.0"
	unknown_if: "otherwise, or window_source == interpolated_too_short"
	thresholds: {ambiguous_margin: 1.0, clear_margin: 3.0, max_selector_utt_wer: 0.5}
	unreliable_if: "selector utterance WER > 0.5 (hallucination / unintelligible) -> acoustic unknown"

	context:
	lm: Qwen/Qwen2.5-7B-Instruct (raw text, no chat template) -- nomination only, not ground truth
	# margins are natural-log probabilities; min over all alternatives (hardest alternative)
	prefix_margin: "min_a [lp(target\|prefix) - lp(alt\|prefix)]"
	prior_margin: "min_a [lp(target\|BOS) - lp(alt\|BOS)] (context-free word prior)"
	prefix_shift: "min_a [(lp(t\|prefix)-lp(a\|prefix)) - (lp(t\|BOS)-lp(a\|BOS))]"
	full_margin: "min_a [lp(sentence_t) - lp(sentence_a)] # oracle bidirectional diagnostic"
	full_shift: "min_a [(lp(sent_t)-lp(sent_a)) - (lp(t\|BOS)-lp(a\|BOS))]"
	high_margin: 2.3 # ~10x odds for the reference over the hardest alternative
	high_shift: 1.0 # context must add >= e^1 odds beyond the word prior
	low_margin: 0.7 # below this for BOTH prefix and full -> low / undeterminable
	availability:
	both: prefix high and full high
	prefix_available: prefix high, full not high
	suffix_only: prefix not high, full high
	none: neither high

	buckets:
	main_challenge: acoustic ambiguous AND context high (any availability)
	ambiguous_negative: acoustic ambiguous AND context low
	clear_control: acoustic clear AND context high
	general_control: acoustic clear AND context low
	homophone_semantic: acoustic_label homophone_only AND context high (semantic word choice; NOT evidence that acoustic degradation drives routing)
	unassigned: everything else (medium context, alignment unknown, function word) -- kept with reason

	baseline_error_enriched: "selector top-1 aligned target word != reference, ignoring surface variants (numbers 7/seven, ordinals, British/American spelling, cannot/can't; surface_equiv_v1)"
	inclusion_never_uses: [MoE outputs, MoE routing, MoE accuracy]
	partition: {seed: 20261108, dev_frac: 0.2, unit: template group}

	decision_log:
	- date: 2026-11-09
	change: homophone alternatives removed from acoustic crop margin; homophone-only targets moved to separate bucket homophone_semantic
	reason: 720 dev homophone targets were labelled acoustically clear only through Whisper spelling prior (plan sec 2.3 requires homophones as a separate class)
	data_used: dev distributions only; no MoE outputs
	- date: 2026-11-09
	change: low_margin kept at 0.7 after dev sweep (0.7/1.0/1.5/2.3 -> low 33/40/55/71 of 3115 dev targets)
	reason: low-context targets are rare in this corpus under any threshold; keep conservative definition, report low-context buckets as exploratory
	- date: 2026-11-11
	change: baseline_error_enriched ignores surface-form variants (surface_equiv_v1)
	reason: root review of strict subset found 44 number-format and spelling variants (seven->7, center->centre) counted as selector errors
	data_used: strict subset audit only; no MoE outputs
	- date: 2026-11-11
	change: surface_equiv_v2 (toward/towards, compounds like break room/breakroom); selector utterance WER > 0.5 -> acoustic unknown
	reason: root spot-check of 8 random strict-test rows found 2 whole-utterance hallucinations ("Thank you for watching.") and 2 surface variants
	data_used: strict subset audit only; no MoE outputs