Instructions to use Kushtrim/Qwen3-ASR-0.6B-Norwegian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kushtrim/Qwen3-ASR-0.6B-Norwegian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kushtrim/Qwen3-ASR-0.6B-Norwegian")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Kushtrim/Qwen3-ASR-0.6B-Norwegian") model = AutoModelForMultimodalLM.from_pretrained("Kushtrim/Qwen3-ASR-0.6B-Norwegian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qwen3-ASR-0.6B Norwegian
This is a fine-tuned variant of Qwen/Qwen3-ASR-0.6B for Norwegian automatic speech recognition.
It is trained on a couple thousand well-curated hours of Norwegian audio and transcripts.
Intended use
- ASR / transcription for Norwegian speech (general-purpose).
- Works best on clean speech audio (mono, 16 kHz) with transcripts matching the dataset conventions.
Limitations
- Performance depends heavily on domain/accent/noise conditions; evaluate on your target audio before deployment.
- Streaming / timestamps support is provided by the upstream toolkit; quality of timestamps depends on the forced-aligner setup and audio conditions.
Base model (Qwen3-ASR)
From the upstream model card, Qwen3-ASR provides:
- Language identification + ASR in one model (supports many languages/dialects).
- Offline + streaming inference modes via the
qwen-asrtoolkit. - Optional timestamps via the Qwen forced-aligner.
See the full upstream documentation here:
How to use (Transformers backend via qwen-asr)
Install:
pip install -U qwen-asr
Load this model from a local folder (this directory) and transcribe:
import torch
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained(
"./",
dtype=torch.bfloat16,
device_map="cuda:0",
max_inference_batch_size=32,
max_new_tokens=256,
)
results = model.transcribe(
audio="path/to/audio.wav",
language="Norwegian", # or None for auto language ID
)
print(results[0].language)
print(results[0].text)
Training data
- Language: Norwegian
- Duration: A couple thousand well-curated hours
- Type: supervised ASR fine-tuning (audio โ transcript)
Training recipe (high level)
Extracted from saved transformers training artifacts in this folder:
- Learning rate:
2e-5 - Batch size (per device): 8
- Grad accumulation: 1
- Precision: BF16
- Optimizer:
adamw_torch_fused - Seed: 42
Notes
- Qwen3-ASR fine-tuning data typically uses transcripts with a language prefix like
language Norwegian<asr_text>...(see upstream docs); this fine-tune follows that convention.
Development and attribution
- Original architecture and base weights: Qwen Team, through
Qwen/Qwen3-ASR-0.6B - Norwegian adaptation, fine-tuning, training-data curation, packaging, and release: Kushtrim Visoka
- Model card and ongoing maintenance: Kushtrim Visoka
Acknowledgements
This work builds on Qwen3-ASR by the Qwen team. The original architecture and base weights remain the work of their respective authors. This repository documents the Norwegian fine-tuning and release work carried out by Kushtrim Visoka and does not claim authorship of the original Qwen3-ASR model.
Citation
If you use this fine-tuned model, please cite this repository and the original Qwen3-ASR model.
@misc{visoka_qwen3_asr_06b_norwegian_2026,
author = {Visoka, Kushtrim},
title = {Qwen3-ASR-0.6B Norwegian},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/Kushtrim/Qwen3-ASR-0.6B-Norwegian},
note = {Fine-tuned from Qwen/Qwen3-ASR-0.6B}
}
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