billingsmoore/tibetan-asr-nict-tib1-whisper-tiny

Whisper Tiny — Tibetan ASR (full fine-tune)

Fine-tuned openai/whisper-tiny for automatic speech recognition (ASR) on Lhasa Tibetan, released alongside:

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 full fine-tuning (all parameters updated, no quantization or adapters).

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)	0.156
SER (micro)	0.2351
BoTok-SWER (micro)	0.2975
BERT-SWER (micro, full 1,547-utt. test set)	0.118
Gem-SWER (micro, 500-utt. subset)	0.6759

See the full architecture comparison table below for how this configuration ranks against the other models in the study.

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

How to use

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="billingsmoore/tibetan-asr-nict-tib1-whisper-tiny")
result = pipe("path/to/audio.wav")
print(result["text"])

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.

Citation

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

@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.