Qwen3-ASR-0.6B Amharic - Broader Mixed
An Amharic automatic speech recognition checkpoint derived from Qwen3-ASR-0.6B.
This variant uses a broader Amharic speech-text mixture and is the recommended release when generalization beyond the curated training domain is the priority.
Benchmark
Results use fixed held-out evaluation sets and identical deterministic
generation settings (language=None, max_new_tokens=512, batch size 1).
Lower WER and CER are better.
| Evaluation | Curated variant | Broader mixed variant |
|---|---|---|
| Curated-source heldout WER | 36.99% | 37.46% |
| Curated-source heldout CER | 15.71% | 16.18% |
| WAXAL WER | 37.56% | 36.57% |
| WAXAL CER | 16.45% | 15.57% |
| Supplemental-source heldout WER | Not evaluated | 31.47% |
| Supplemental-source heldout CER | Not evaluated | 13.87% |
The broader mixed variant improves WAXAL WER by 0.99 percentage points over the curated variant, while the curated variant remains 0.47 points better on its source-matched heldout.
Live Colab Demo
Open the Gradio demo notebook, then open it in Google Colab and run the cells in order. The final cell creates a temporary public Gradio link while the notebook remains connected.
Usage
pip install -U qwen-asr
import torch
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained(
"b1n1yam/qwen3-asr-0.6b-amharic-mixed",
dtype=torch.bfloat16,
device_map="cuda:0",
max_inference_batch_size=8,
max_new_tokens=512,
)
result = model.transcribe(
audio="path/to/audio.wav",
language=None,
)
print(result[0].text)
Audio should contain clear Amharic speech. Keep language=None; this is the
setting used for the reported benchmarks.
Intended Use
- General-purpose Amharic speech transcription
- Research and evaluation of Amharic ASR
- Further domain adaptation with appropriately licensed data
Limitations
- Performance varies by recording conditions, speaker, dialect, domain, and audio duration.
- The model can omit words or generate incorrect text. Human review is required for consequential uses.
- The benchmark covers a limited set of held-out corpora and should not be interpreted as universal Amharic ASR performance.
- Supplemental training material received structural validation and exact audio deduplication, but was not assigned the same row-level quality tiers as the curated corpus.
- This release is not designed for speaker identification, translation, or timestamp alignment.
Training Data Disclosure
The model was adapted with a privately maintained mixture of curated and supplemental Amharic speech-text data. Source manifests and row-level metadata are not included in this repository.
License and Attribution
This model is released under the Apache-2.0 license, following the upstream Qwen3-ASR-0.6B license. Please also review the upstream model card.