b1n1yam/qwen3-asr-0.6b-amharic-mixed

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.