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bijaykumarsingh
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Model Details
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bijaykumarsingh
Model Tree
Qwen/Qwen3-ASR-1.7B-hf
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-ASR-1.7B-hfas)Evaluated on a strictly speaker-disjoint held-out test split (2,266 utterances, 31 unique speakers, 3.18 hours, 22,596 total words) with standardized Unicode NFC and punctuation stripping:
| Metric | Score | 95% Bootstrap CI |
|---|---|---|
| Word Error Rate (WER) | 25.05% | [23.87%, 26.14%] |
| Character Error Rate (CER) | 9.95% | — |
| Match Error Rate (MER) | 24.36% | — |
| Word Information Lost (WIL) | 39.51% | — |
| Training Wall-Clock Time | 5.07 hours | (NVIDIA A40 48GB) |
python
from transformers import AutoProcessor, AutoModelForCausalLMfrom peft import PeftModel base_model_id = "Qwen/Qwen3-ASR-1.7B-hf"adapter_id = "bijaykumarsingh/qwen3-asr-1.7b-assamese-lora" processor = AutoProcessor.from_pretrained(base_model_id)base_model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()bibtex
@article{singh2026assamese_lalm, title={Architectural Inductive Bias Trumps Parameter Scale: Adapting Large Audio-Language Models to Low-Resource Assamese ASR}, author={Singh, Bijay Kumar}, journal={arXiv preprint}, year={2026}}from transformers import AutoProcessor, AutoModelForCausalLMfrom peft import PeftModel base_model_id = "Qwen/Qwen3-ASR-1.7B-hf"adapter_id = "bijaykumarsingh/qwen3-asr-1.7b-assamese-lora" processor = AutoProcessor.from_pretrained(base_model_id)base_model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()@article{singh2026assamese_lalm, title={Architectural Inductive Bias Trumps Parameter Scale: Adapting Large Audio-Language Models to Low-Resource Assamese ASR}, author={Singh, Bijay Kumar}, journal={arXiv preprint}, year={2026}}