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Its RTF here is an upper bound and its latency (100 ms) is far above the ~40 ms a native (Rust/tract) runtime achieves. Read the DFN3 rows as a lower bound on quality and an upper bound on cost.", "ai-coustics rows are a **proprietary baseline** run through the licensed aic-sdk. They are a reference point, never a shipping code path.", "WER is measured with faster-whisper:base.en (CTranslate2 int8 CPU, greedy, model=base.en). A different recogniser will give different absolute numbers; the harness supports Deepgram and AssemblyAI so the *ordering* can be checked on another engine.", "DeepFilterNet3 runs at 48 kHz on 16 kHz source material upsampled to 48 kHz \u2014 there is no real content above 8 kHz for it to work with. That is the honest telephony/VoIP situation, but it is not the condition DFN3 was designed for.", "The test set is 10 speakers / ~190 reference words per condition. That is enough to separate large effects and not enough to resolve differences of a point or two of WER.", "**Run-to-run repeatability of the recogniser was measured, not assumed \u2014 and the standing warning did not reproduce here.** Elsewhere in this project faster-whisper on CPU has been seen to return different transcripts for byte-identical input (three runs of one aggregate gave 121.65 / 122.15 / 121.40%), and the standing guidance from that is to treat differences below roughly 6 pp on a small aggregate as decoder noise. On this host and this harness it did not happen: 25/25 scopes were bit-identical across repeated decodes (raw on 18 condition(s), 180 clips x 3 decodes; clearline on 5 condition(s), 50 clips x 3 decodes), including the arms where the recogniser is already hallucinating past 100% WER. Independently, re-measuring `raw` and `gtcrn` in a fresh process two days after the published run reproduced all 54 rows exactly (max \u0394WER 0.0000 pp). Three repeats cannot prove determinism and a different host, venv or thread count may well behave differently, so the conservative 6 pp guidance still governs anything quoted from another machine \u2014 but on **this** table the resolution limit is set by the size of the test set, not by the decoder: one reference word is 0.53 pp in a single condition (190 words), and about 0.03 pp pooled across all 18. Differences of a point or two between adjacent rows are still not results; differences of tens of points are.", "onnxruntime is pinned to 1 intra-op thread with sequential execution: the measured cost is one stream on one core, not a fan-out across the whole machine.", "The `telephony*` conditions in this table are stored at 16 kHz after a full 8 kHz carrier leg (band-limit, codec, packet loss), so every backend sees them at its own native rate. The genuinely narrowband experiment \u2014 where nothing is resampled \u2014 is a separate artefact (`bench/out/telephony.json`).", "Packet loss is modelled as bursty (2-state Gilbert-Elliott) erasure of 20 ms frames with attenuated-repeat concealment, not i.i.d. loss with silence fill. i.i.d. loss flatters every decoder and silence fill overstates the damage." ], "skipped": {}, "telephony": { "schema": 1, "generatedAt": "2026-08-14T01:28:47Z", "narrowSampleRate": 8000, "sttIngestResampler": "soxr_vhq", "conditions": [ "nb_clean", "nb_snr10", "nb_snr0", "nb_alaw_snr10", "nb_loss3_snr10", "nb_loss10_snr10" ], "resamplers": { "soxr_vhq": "polyphase/sinc, very high quality \u2014 the correct implementation", "linear": "linear interpolation, no anti-alias filter", "naive": "sample-repeat up / sample-drop down, no filtering at all" }, "clips": 60, "rows": [ { "path": "raw8k", "backend": null, "resampler": null, "wer": 0.11140350877192982, "insertions": 12, "refWords": 1140, "siSdrDb": 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down, no filtering at all", "si_sdr_vs_input_db": 150.0, "clips": 60 }, { "via_hz": 48000, "resampler": "soxr_vhq", "note": "polyphase/sinc, very high quality \u2014 the correct implementation", "si_sdr_vs_input_db": 38.31835918379795, "clips": 60 }, { "via_hz": 48000, "resampler": "soxr_hq", "note": "polyphase/sinc at the library default quality", "si_sdr_vs_input_db": 38.51518775445072, "clips": 60 }, { "via_hz": 48000, "resampler": "linear", "note": "linear interpolation, no anti-alias filter", "si_sdr_vs_input_db": 150.0, "clips": 60 }, { "via_hz": 48000, "resampler": "naive", "note": "sample-repeat up / sample-drop down, no filtering at all", "si_sdr_vs_input_db": 150.0, "clips": 60 } ], "nativeVsUpsampled": { "native_backend": "aic-quail-l-8khz", "upsampled_backend": "aic-quail", "native_wer": 0.11052631578947368, "upsampled_wer": 0.11754385964912281, "delta_pp": -0.7017543859649131, "note": "same vendor, same family, 8 kHz weights vs 16 kHz weights on a resampled path" }, "findings": [ "**Nobody ships speaker isolation at 8 kHz, and almost nobody ships 8 kHz at all.** Every ai-coustics Voice Focus and VAD-VF model in their catalogue is 16 kHz. ai-coustics is the *only* vendor here with any genuine narrowband enhancement weights (`aic-quail-l-8khz`, `aic-quail-s-8khz`, `aic-rook-l-8khz`); GTCRN, FastEnhancer and DeepFilterNet3 have no 8 kHz checkpoint at all, so on a phone call they can only be run through a resample wrapper. That gap is structural, and it is the wedge \u2014 the opportunity is scarcity, not damage.", "**Native 8 kHz beats the resampled wideband path by 0.7 pp of WER** for the same vendor (`aic-quail-l-8khz` 11.1% vs `aic-quail` 11.8%). Real, but modest \u2014 within a small multiple of the 0.09 pp resolution limit, so it is a reason to build native, not a headline number to market.", "**Nothing beat the untouched caller audio.** The best path (aic-quail-l-8khz) ties the raw8k control \u2014 11.05% vs 11.14%, a 0.09 pp difference, which is 1 word out of 1,140. Every other path is worse than doing nothing. On narrowband telephony the null hypothesis is still undefeated, which is precisely the space Clearline has to win.", "**The resample round trip on its own does nothing \u2014 the 'resample tax' premise did not survive its control.** 8k->16k->8k with a naive sample-repeat/sample-drop resampler scores 150 dB SI-SDR against its input, because at an exact 2:1 ratio it is algebraically the identity. A correct polyphase resampler scores 38 dB \u2014 *lower*, because it actually filters. Any claim that resampling per se destroys a fixed fraction of caller audio is not supported here, and we should not make one.", "**With no model in the path, resampler quality is worth 0.18 pp of WER** \u2014 `soxr_vhq` 10.96% against `linear` 11.14%, versus a raw8k control of 11.14%. That is 2 words; it is a tie.", "**With a wideband model in the path, resampler choice moved WER by -3.0 pp on average \u2014 and it moved the wrong way.** Sign convention: positive = the naive/linear resampler produced more errors than `soxr_vhq`. Across 6 (model, resampler) pairs the mean is -3.0 pp, so on balance the *naive* resampler transcribed better. Every single pair is negative \u2014 the naive resampler helped *every* model, not just on average. Least affected: `aic-quail`/`naive` at -0.4 pp. Most affected: `fastenhancer-b`/`linear` at -5.6 pp. The plausible mechanism is that a polyphase resampler is an extra filtering stage the model was not trained on, while sample-repeat/sample-drop at 2:1 is an identity \u2014 so the naive path hands the model a *less* altered signal. Either way the data does not support a resample-quality claim." ], "skipped": {} }, "regimes": { "raw": { "label": "Raw (no processing)", "native_sample_rate": 16000, "resampled": false, "telephony": { "conditions": [ "telephony", "telephony_snr10", "telephony_alaw", "telephony_g722", "telephony_opus12k", "telephony_loss3", "telephony_loss10" ], "conditions_scored": [ "telephony", "telephony_snr10", "telephony_alaw", "telephony_g722", "telephony_opus12k", "telephony_loss3", "telephony_loss10" ], "wer": 0.081203007518797, "ref_words": 1330, "substitutions": 90, "insertions": 12, "deletions": 6, "si_sdr_db": 8.655543798473788, "pesq_wb": 1.8996521898678371, "stoi": 0.9051823174004429 }, "wideband": { "conditions": [ "clean", "noise_snr-5", "noise_snr0", "noise_snr5", "noise_snr10", "noise_snr20", "babble_snr5", "competing_snr0", "competing_snr5", "reverb", "reverb_snr10", "competing_snr10" ], "conditions_scored": [ "clean", "noise_snr-5", "noise_snr0", "noise_snr5", "noise_snr10", 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