Skip room-tone tail chunks; collapse punctuated repetition loops

[alexkroman]

Two inference-time fixes that came out of auditing the 2026-10-09 RunPod served eval (12 datasets × n=1000, plus full AMI / AMI-SDM / Loquacious / Earnings22 / GigaSpeech).

1. Quiet tail chunks hallucinate (audible_chunks)

Clips over 18 s are chunked (chunk_bounds). The tail chunk after the talker stops is room tone, and room tone passes is_silent's absolute 1e-5 RMS check. Decoded, it comes back as a memorized sentence or a stray word:

Clip	Before	After
CV 450	Her hair flowed down her back. The film was directed by the director of the same name.	Her hair flowed down her back.
CV 533	It's usually customary to bring the money with you. The first of the series was "The Man Who Would Be King".	…with you.
Loquacious 1311	…the old sculptor, Euphranor. Okay.	…the old sculptor, Euphranor.
Earnings22 477	…the operational performance. The	…the operational performance.

This costs about 0.7 WER on CommonVoice, which is the whole served-vs-local gap there (7.3 vs 6.6).

audible_chunks now empties a chunk when its loudest 100 ms frame is more than 30 dB below the clip's 95th-percentile frame. The empty chunk then transcribes as "" and never reaches the model. A one-chunk clip is never emptied.

Threshold, measured on the cached eval clips (datasets_cache/eval_samples, every clip over 18 s in the first 3,000 rows of 9 datasets, 957 chunks):

• Noise-only chunks: ≤ −36 dB
• Speech chunks: ≥ −17.5 dB

−30 dB leaves the wider margin on the speech side.

Real model run (MPS) on every clip where the gate fires:

• 9 clips: 8 lose the hallucinated tail, 1 is unchanged.
• No real word is dropped.

Not fixed: tails that contain real background speech (e.g. Loquacious 2209, CV 66). A level gate can't tell that apart from the target speaker.

2. Punctuated repetition loops (_truncate_repetitions)

The old rule matched repeats exactly, so loops the model punctuates got through. Example: "Yeah Yeah Yeah … Yeah. Yeah. Yeah," in GigaSpeech.

My first version ignored punctuation at 3 or more repeats. That hurt: 3–5 punctuated repeats are real speech ("No, no, no,", "Practice, practice, practice."), and collapsing them cost 40 errors over 47 rows. So the original rule is unchanged, and a second, punctuation-insensitive pass collapses only runs of 6 or more.

Replayed over all 53,755 served eval predictions:

• 7 change, all of them loops.
• Errors on those rows go from 124 to 102.

Refactor

The chunking helpers move to asr_processing.py, and stream_chunks moves to diarization.py next to pack_spans. This keeps asr_pipeline.py under the 600-line cap. Both files are already in the Hub bundle and in save_pretrained's copy list, and I checked that the flat-layout import works.

Not in this PR

• Eval-client retries: already merged in Retry transient errors in the ta serve eval client #132.
• Stale serving pod: the pod that ran this eval started before Batched RunPod inference server (ta serve) replacing the HF endpoint handler #129 merged, which explains why an Earnings22 "The The The…" loop that current code collapses still got through. Needs ta runpod deploy plus a serve --force restart.
• Transient !! DỊCH decode corruption: 11 consecutive AMI rows in one batch, ~0.03 WER. Not reproduced, so no fix here.
• Eval normalizer gaps (acronyms, compounds, ellipsis, "'cause"): worth 0.35–1.1 WER per dataset, to be handled separately.

Test plan

• New unit tests: room-tone tail emptied, quiet word in the tail kept, single chunk never gated; punctuated loops collapse, short punctuated repeats are kept
• Full suite: 1252 passed
• Pre-commit gate (ruff, black, pyright, file-length, vulture, bandit)
• Real-model A/B on the eval clips where the gate fires
• Redeploy pod and re-run CV / Loquacious served eval

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