b1n1yam/qwen3-asr-0.6b-amharic-gold-silver

Qwen3-ASR-0.6B Amharic - Curated

An Amharic automatic speech recognition checkpoint derived from Qwen3-ASR-0.6B.

This variant is tuned on a curated Amharic speech-text corpus and is the recommended release when performance on the curated-source evaluation 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%

The curated variant is stronger on the curated-source heldout. The broader mixed variant generalizes better to WAXAL in this evaluation.

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-gold-silver",
    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

• 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.
• This release is not designed for speaker identification, translation, or timestamp alignment.

Training Data Disclosure

The model was adapted with a privately maintained, curated Amharic speech-text corpus. 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.