README.md

---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- ne
tags:
- nepali
- telephony
- benchmark
- asr
pretty_name: NepTel v0.1 — Nepali real-telephony ASR benchmark
size_categories:
- n<1K
---

# NepTel v0.1 — Nepali real-telephony ASR benchmark

75 scored segments / 2,375 reference words of **real Nepali call-center audio** (genuine
two-party customer-support calls), with human-reviewed reference transcripts. To our knowledge
this is the first public Nepali ASR benchmark on real call audio rather than read-aloud speech.

This repository exists so anyone can benchmark a Nepali ASR system **without any access
request**: the audio is cut and ready, no gate, no approval step.

**Evaluation only. Do not train on this data.**
Canary: `NEPTEL-CANARY-2026-8f3a1c92`

## Contents

- `audio/` — 77 segment wavs (22.05 kHz mono, ~31 minutes total)
- `references.json` — reference transcripts and per-segment provenance

`references.json` lists all 77 segments; the 2 carrying an `excluded` field (one caught by the
speaking-rate gate, one flagged unintelligible in native-speaker review) are skipped by the
scorer, leaving the 75 scored segments / 2,375 words quoted in published results. They are kept
rather than deleted so the exclusions stay auditable.

## Score a system

```bash
git clone https://github.com/Ampixa/nepaliconformer
cd nepaliconformer/benchmark
python fetch_audio.py neptel_audio        # pulls this dataset, no login

# then plug your system in — pick the adapter that fits:
python run_system.py --hf openai/whisper-large-v3 --name whisper --lang ne
python run_system.py --nemo your_model.nemo --name mine
python run_system.py --cmd "your-cli -f {audio}" --name mine
python run_system.py --py yourpkg.module:transcribe --name mine
```

Each writes `outputs/<name>.json` and prints the WER; add
`--compare outputs/nepali-conformer-offline.json` for a paired bootstrap against ours.

The scorer normalizes both sides identically (digits to spoken Nepali words, punctuation
stripped) and reports a paired bootstrap when given `--compare`.

## Current standings

| system | WER |
|---|---|
| nepaliconformer offline (121M) | 33.81 |
| Kriti — Naamche Labs (119M) | 40.59 |
| nepaliconformer streaming (520 ms) | 59.87 |
| MMS-1B-all — Meta, zero-shot (965M) | 81.01 |
| IndicWav2Vec-Nepali — community mirror (94M) | 86.57 |
| Whisper-large-v3 — zero-shot, tuned (809M) | 96.29 |

Every system's raw hypotheses are published in the
[repo](https://github.com/Ampixa/nepaliconformer/tree/master/benchmark/outputs), so all of these
are re-derivable. **We would love to be beaten** — run the scorer and open a PR.

## Reference protocol

1. Audio cut into fixed 25-second windows from the vendor's dual-channel per-speaker files.
2. Reference drafts by Google Chirp 2 (`ne-NP`).
3. A native Nepali speaker reviewed every segment against the audio: accept / correct / flag.
   Flagged segments are excluded. 95.9% of usable segments in the most recent batch were
   accepted verbatim.
4. A speaking-rate gate (>6 words/sec) guards against transcription-engine hallucinations
   becoming ground truth.

**Known limitations:** 3 calls from one vendor; vendor-side PII muting leaves digital-zero gaps
in some segments; references are reviewed drafts rather than from-scratch transcriptions;
systems trained on Chirp 2 pseudo-labels (including ours) share label lineage with the reference
drafts. Sized for ±2-point deltas between systems, not for absolute-truth WERs. Full provenance,
including declared conflicts of interest, is in
[PROVENANCE.md](https://github.com/Ampixa/nepaliconformer/blob/master/benchmark/PROVENANCE.md).

## Source audio and license

Segments are cut from
[InfoBayAI/Nepali_Call_Center_Audio_Dataset_Dual_Channel](https://huggingface.co/datasets/InfoBayAI/Nepali_Call_Center_Audio_Dataset_Dual_Channel),
published by InfoBayAI under CC-BY-4.0, which permits redistribution with attribution. The audio
here is that dataset, cut into segments; the reference transcripts are ours, also CC-BY-4.0.
Please credit both InfoBayAI (source audio) and Ampixa Labs (segmentation and references).