README.md

---
license: cc-by-4.0
language:
  - am
task_categories:
  - automatic-speech-recognition
pretty_name: Amharic ASR Benchmark
size_categories:
  - 1K<n<10K
tags:
  - amharic
  - ethiopia
  - benchmark
  - evaluation
  - low-resource
---

# Amharic ASR Benchmark

An evaluation of open speech recognition models for Amharic, on a test set that
**none of them could have trained on**, with certain labels and honest
statistics.

Published by [Dataset.ET](https://dataset.et).

## Results

1,548 clips, 4.72 hours. Greedy decoding.
Character error rate, lower is better.

| model | CER | 95% CI | WER | |
|---|---|---|---|---|
| [`badrex/Ethio-ASR-amharic`](https://huggingface.co/badrex/Ethio-ASR-amharic) | **0.0946** | [0.0896, 0.0999] | 0.2845 | CTC, monolingual Amharic |
| [`badrex/Ethio-ASR-multilingual-600M`](https://huggingface.co/badrex/Ethio-ASR-multilingual-600M) | **0.0991** | [0.0939, 0.1042] | 0.2984 | CTC, w2v-BERT 2.0, five Ethiopian languages |
| [`b1n1yam/shook-medium-amharic-2k`](https://huggingface.co/b1n1yam/shook-medium-amharic-2k) | **0.1147** | [0.1087, 0.1202] | 0.2943 | Seq2seq, Whisper medium |
| [`badrex/Ethio-ASR-multilingual-1B`](https://huggingface.co/badrex/Ethio-ASR-multilingual-1B) | **0.1308** | [0.1249, 0.1366] | 0.3894 | CTC, MMS-based |
| [`b1n1yam/shook-tiny-amharic-stage2-polish`](https://huggingface.co/b1n1yam/shook-tiny-amharic-stage2-polish) | **0.2735** | [0.2648, 0.2820] | 0.6200 | Seq2seq, 37.8M parameters |

### Are the differences real?

Every model is scored on the **same 1,548 clips**, so comparing
marginal confidence intervals is the wrong test — they can overlap while the
difference is real. A paired bootstrap over 2,000
resamples of clips:

| comparison | mean CER difference | 95% CI | distinguishable |
|---|---|---|---|
| `Ethio-ASR-amharic` vs `Ethio-ASR-multilingual-600M` | -0.0045 | [-0.0067, -0.0022] | **yes** |
| `Ethio-ASR-multilingual-600M` vs `shook-medium-amharic-2k` | -0.0155 | [-0.0194, -0.0120] | **yes** |
| `shook-medium-amharic-2k` vs `Ethio-ASR-multilingual-1B` | -0.0161 | [-0.0202, -0.0120] | **yes** |
| `Ethio-ASR-multilingual-1B` vs `shook-tiny-amharic-stage2-polish` | -0.1427 | [-0.1498, -0.1361] | **yes** |

The top two intervals overlap and the difference is still real at p = 0.001.
Every adjacent pair here is separable.

## What makes this worth trusting

**The labels are certain.** A contributor was shown a sentence and read it
aloud, so the reference existed before the audio. Most speech benchmarks score
against transcripts someone typed while listening, which carries an unmeasured
error rate of its own.

**No model could have seen it.** Every model predates the test set by five to
nine months, verified against publication timestamps:

| asset | published |
|---|---|
| **this test set** | **2026-08-25** |
| `badrex/Ethio-ASR-*` | 2026-03-24 |
| `b1n1yam/shook-medium-amharic-2k` | 2025-12-08 |
| `b1n1yam/shook-tiny-*` | 2025-11-21 |

**Speaker-disjoint splits.** No voice appears in more than one split.

**The outputs are published.** `hypotheses/` holds every model's raw transcript
for all 1,548 clips. Do not trust these numbers — recompute them.

## Three findings worth stating

**The 600M model beats the 1B by 24% relative, at 40% of the size.** Larger is
not better within this family. We had been building on the 1B.

**A monolingual model narrowly beats the multilingual one** for Amharic
specifically: 0.0946 against 0.0991, a real difference. Multilingual training
costs a little here rather than helping.

**CER and WER disagree about ranking.** `shook-medium-amharic-2k` has worse CER
than `Ethio-ASR-multilingual-600M` (0.1147 vs 0.0991) but better WER
(0.2943 vs 0.2984). The sequence-to-sequence model produces fluent whole words
that match exactly; the CTC model gets characters closer but whole words
slightly off. For an agglutinative language written in Ge'ez script, which
metric you choose changes who wins.

## Read this before quoting anything

This is **one domain**: people reading prompts into a phone. On spontaneous
speech the same models are far worse — we measured roughly three times the
disagreement between them on podcast audio. These are not general Amharic ASR
figures. See [`docs/LIMITATIONS.md`](docs/LIMITATIONS.md).

Decoding is greedy, so every number is a lower bound, and that penalty is **not
evenly distributed** between CTC and sequence-to-sequence models.

## What went wrong

[`docs/FAILURES.md`](docs/FAILURES.md) is the honest engineering log: ten
failures including three that produced results which looked correct, a health
check that reported a working system as broken, and a shutdown timer we
triggered by trying to disable it.

It is the most useful document here. Benchmarks are usually published as though
they fell out of the sky.

## Contents

```
README.md                    this
docs/METHOD.md               test set, scoring, statistics
docs/FAILURES.md             what went wrong and what it cost
docs/LIMITATIONS.md          read before quoting
results/phase1_greedy.json   the numbers
results/bootstrap.json       intervals and paired tests
hypotheses/                  every model's raw output, per clip
scripts/                     the evaluation code
```

## Reproducing

```bash
python scripts/bench_asr.py --push-to <your-repo>
python scripts/bootstrap_ci.py build/am-v0.2.0 hypotheses/
```

The test set is [`snapwre/amharic-speech`](https://huggingface.co/datasets/snapwre/amharic-speech),
open under CC BY 4.0.

## Citing

```bibtex
@misc{datasetet2026amharicasr,
  title  = {Amharic ASR Benchmark: an uncontaminated evaluation of open speech models},
  author = {Dataset.ET},
  year   = {2026},
  url    = {https://huggingface.co/datasets/snapwre/amharic-asr-benchmark}
}
```

## Contact

Corrections and additional models are welcome — open a discussion. If you built
one of these models and think a decoding choice disadvantaged it, say so and we
will rerun it.

Chapi, Dataset.ET