Betterflow — IndicConformer CTC, ONNX int8 (8 Indic languages)
ONNX int8 exports of AI4Bharat's per-language IndicConformer hybrid CTC/RNNT checkpoints,
prepared for on-device offline ASR through sherpa-onnx's OfflineRecognizer.from_nemo_ctc.
These are derived artefacts. We are not the authors of the weights.
Provenance and licence
| Upstream | ai4bharat/indicconformer_stt_<lang>_hybrid_ctc_rnnt_large |
| Upstream licence | MIT (verified on each source model card, not inferred) |
| This repo's licence | MIT, inherited |
| What changed | CTC head exported to ONNX; dynamic int8 quantization; sherpa metadata stamped |
| What did NOT change | the weights themselves — no fine-tuning, no distillation |
Each language directory carries export-meta.json with the sha256 of the source .nemo, so
provenance is checkable rather than asserted.
Please cite AI4Bharat for the underlying models.
Contents
Eight languages: gu pa bn mr ml te ta kn.
<lang>/model.int8.onnx 140,337,395 B (identical size across languages, DISTINCT weights)
<lang>/tokens.txt 67,605 B 5,633 entries
<lang>/export-meta.json source sha256, checksums, export settings
int8 sha256 — all eight distinct
| lang | sha256 (first 16) | lang | sha256 (first 16) |
|---|---|---|---|
gu |
9f8031996be1a4d7 |
ml |
81c172f6ee3796f5 |
pa |
1ad447f477040bbb |
te |
b0b3d335f2505ecd |
bn |
8b1bd509bb36ba86 |
ta |
08b85596996429e1 |
mr |
af22de1897679e4a |
kn |
16d950f36cfdb01e |
The eight files are byte-identical in size because they share an architecture. They are different models — the checksums above are the check that matters.
⚠️ Two things that will silently break this
1. normalize_type must be per_feature. It is stamped on every graph. If it is stripped or
set to "", the model loads, runs, and returns EMPTY with no error. Verify by decoding, never
by a successful load.
Required ONNX metadata: vocab_size=5633, subsampling_factor=4,
normalize_type=per_feature, feature_dim=80.
2. tokens.txt opens with Bengali tokens in every language directory — this is correct. The
vocabulary is AI4Bharat's shared 22-language tokenizer (5,633 = 22 × 256 + 1) and the CTC head is
multi-softmax. It looks like a packaging error and is not. The file is byte-identical across all
eight (ee60967630213f31…); it is duplicated per directory so each bundle installs independently.
Unlike the 600m multilingual checkpoint, these per-language models did learn to suppress the
other 21 blocks — measured 100% own-script output on all eight, 0/400 wrong-alphabet characters —
so no language_id masking is required.
Measured quality
Scored on held-out ARTPARK-IISc/Vaani, 50 clips × 50 speakers per language, against the
general-purpose multilingual Whisper bundle these replace.
| lang | incumbent pooled WER | this export | script purity | catastrophic |
|---|---|---|---|---|
pa |
106.2% | 20.2% | 0% → 100% | 13 → 0 |
gu |
106.6% | 22.6% | 0% → 100% | 16 → 0 |
bn |
135.1% | 30.6% | 0% → 100% | 21 → 0 |
mr |
100.5% | 36.6% | 83% → 100% | 14 → 0 |
ml |
111.9% | 37.2% | 0% → 100% | 16 → 1 |
te |
115.5% | 45.0% | 6% → 100% | 8 → 1 |
ta |
87.0% | 52.1% | 100% → 100% | 3 → 1 |
kn |
100.3% | 59.5% | 100% → 100% | 2 → 1 |
Script purity is the headline, not WER. A 0% score means the incumbent was not producing the language's alphabet at all. Catastrophic failures fell from 93/400 to 5/400.
te and kn remain insertion-bound and are not recommended without further evaluation.
Runtime cost
⚠️ Peak RSS scales with utterance length — it is not a constant
Conformer self-attention is O(T²) in sequence length, so memory is a function of how long the speaker talks. Measured in-process (Android arm64, sherpa-onnx 1.13.4, int8, 2 threads, one process per utterance, ±1 MB reproducible):
| utterance | peak RSS | utterance | peak RSS | |
|---|---|---|---|---|
| 5 s | 285 MB | 45 s | 868 MB | |
| 10 s | 350 MB | 60 s | 986 MB | |
| 20 s | 482 MB | 90 s | 1,628 MB | |
| 30 s | 606 MB | 134 s | 2,758 MB |
Budget accordingly: a typical 3–10 s dictation utterance costs under 350 MB, but a two-minute monologue costs 2.7 GB. If you have a memory ceiling, cap utterance length — that is the cheapest control and it belongs in your app, not in the model.
| RTF | 0.062–0.064 (~16× real time on an M4 laptop) |
| RTF, 2× ARM Cortex-A55 @1.25 GHz | 1.33 (~13 s for a 10 s utterance) |
| RTF, 4× Cortex-A78 | 0.255 |
The int8 build quantizes all 54 Conv nodes to ConvInteger. Leaving them in fp32 — as some
other re-exports do — makes ONNX Runtime allocate an im2col workspace that scales with clip length,
pushing peak RSS to 2,099–2,635 MB from the same nominal precision. If you are getting 4× the
memory you expect from an int8 Conformer, check your Conv node dtypes.
Cost of that choice: ConvInteger is ~3.6× slower than fp32 Conv on CPU.
Not evaluated here
Device-side accuracy, languages outside the eight, streaming/partial decoding (these are offline models), and the RNNT head (not exported — it needs numba/CUDA).