J. Moore, S. Li and P. Lauren, "Evaluating Tibetan ASR With Segmented Word Error Rate: Beyond Character-Level Metrics," in IEEE Access, vol. 14, pp. 101790-101805, 2026, doi: 10.1109/ACCESS.2026.3709206.

This repo is one of 14 model checkpoints released with the paper, benchmarking 5 ASR architectures (Whisper Tiny/Base/Small, Wav2Vec 2.0 Base, HuBERT Base) under full fine-tuning, LoRA, and QLoRA (8-bit/4-bit) adaptation. See billingsmoore/tibetan-asr-nict-tib1-* for the full set.

Model description

Whisper is an encoder–decoder transformer, originally pre-trained by OpenAI on 680,000 hours of weakly-labeled multilingual speech-text data, fine-tuned here for sequence-to-sequence Tibetan transcription. This checkpoint uses LoRA fine-tuning (rank-decomposition adapters, base weights frozen and unquantized).

LoRA configuration: r=8, alpha=16, dropout=0.1, targeting the attention projections (k_proj, q_proj, v_proj, out_proj) and both feed-forward layers (fc1, fc2) of every Whisper transformer block.

Training data

Fine-tuned on NICT-Tib1, a corpus of transcribed Lhasa Tibetan speech from 20 speakers (Soky, Gong & Li, 2022). The data was split by speaker (85/15, not by utterance) so that no speaker appears in both splits: 15,099 training utterances from 17 speakers, 1,547 test utterances from the remaining 3 speakers.

Training procedure

Base model: openai/whisper-tiny
Learning rate: 3.75e-5
Max steps: 4000 (warmup 500)
Effective batch size: 16 (per-device batch size 2, gradient accumulation 8)
Mixed precision: fp16, with gradient checkpointing
Generation max length: 225

Evaluation results

Evaluated on the 1,547-utterance NICT-Tib1 test set under five metrics: Character Error Rate (CER), Syllable Error Rate (SER), and three Segmented Word Error Rate (SWER) variants using different automatic word segmenters (BoTok-SWER, BERT-SWER, and Gem-SWER, the last computed on a 500-utterance subset for API cost reasons). All values are micro-averaged. See the paper for full bootstrap confidence intervals.

Metric	Value
CER (micro)	0.7587
SER (micro)	0.9824
BoTok-SWER (micro)	1.0849
BERT-SWER (micro, full 1,547-utt. test set)	0.544
Gem-SWER (micro, 500-utt. subset)	1.1739

Full model family comparison (standard fine-tuning)

Model	Repo	CER	SER	BoTok-SWER	BERT-SWER	Gem-SWER
HuBERT Base	tibetan-asr-nict-tib1-hubert-base	0.1352	0.3690	0.4477	0.161	0.9653
Wav2Vec 2.0 Base	tibetan-asr-nict-tib1-wav2vec2-base	0.0745	0.2152	0.2747	0.097	0.7447
Whisper Tiny	tibetan-asr-nict-tib1-whisper-tiny	0.1560	0.2351	0.2975	0.118	0.6759
Whisper Base	tibetan-asr-nict-tib1-whisper-base	0.1417	0.2083	0.2600	0.105	0.6314
Whisper Small	tibetan-asr-nict-tib1-whisper-small	0.1185	0.1692	0.2042	0.086	0.5337

[!WARNING] This is a LoRA/QLoRA fine-tuned checkpoint. In the paper's benchmark, all LoRA and QLoRA configurations showed catastrophic word-level degradation relative to full fine-tuning of the same base model, despite retaining partial character-level accuracy. This repo is published for reproducibility of that (negative) result, not as a recommended deployment artifact. If you need a usable Tibetan ASR model, use the standard fine-tuned checkpoint instead.

How to use

python

from transformers import WhisperForConditionalGeneration, WhisperProcessor
from peft import PeftModel

base_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", device_map="auto")
model = PeftModel.from_pretrained(base_model, "billingsmoore/tibetan-asr-nict-tib1-whisper-tiny-lora")
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny", language="bo", task="transcribe")

# generate as usual, e.g.:
# inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
# predicted_ids = model.generate(**inputs)
# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

Limitations

Trained and evaluated only on read-speech, modern Lhasa (Central) Tibetan news recordings; performance on Amdo or Kham dialects, Classical Tibetan, or spontaneous/conversational speech is unknown.
The test set contains only 3 speakers (by design, to prevent speaker leakage), which limits how confidently results generalize across speaker, age, and prosodic variation.
CER alone understates errors that matter at the word level for Tibetan; see the paper for why SER and SWER are necessary complements when judging output quality.
LoRA/QLoRA adapters showed severe sequence-level collapse in this study (SWER often exceeding 1.0, i.e. worse than a random-length hypothesis) despite retaining moderate CER — treat this checkpoint as a research artifact documenting that failure mode, not as a usable transcription model.

Citation

If you use this model, please cite the paper and the source NICT-Tib1 corpus:

bibtex

@ARTICLE{11592371,
  author={Moore, Jacob and Li, Sheng and Lauren, Paula},
  journal={IEEE Access},
  title={Evaluating Tibetan ASR With Segmented Word Error Rate: Beyond Character-Level Metrics},
  year={2026},
  volume={14},
  number={},
  pages={101790-101805},
  keywords={Modeling;Automatic speech recognition;Error analysis;LoRa;Measurement;Ranking (statistics);Quantization (signal);Bit error rate;Standards;Training;Tibetan;automatic speech recognition;word error rate;low-resource language},
  doi={10.1109/ACCESS.2026.3709206}
}

@inproceedings{soky2022nict,
  title={Nict-tib1: A public speech corpus of lhasa dialect for benchmarking tibetan language speech recognition systems},
  author={Soky, Kak and Gong, Zhuo and Li, Sheng},
  booktitle={2022 25th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}

License

Released under Apache License 2.0, inherited from the base model openai/whisper-tiny.