HuBERT Base — Tibetan ASR (full fine-tune)
Fine-tuned facebook/hubert-base-ls960 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
HuBERT is a self-supervised speech encoder pre-trained by iteratively clustering acoustic units to learn hidden structure, fine-tuned here with a CTC decoder for 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:
facebook/hubert-base-ls960 - Learning rate: 1.0e-4
- Max steps: 4000 (warmup 500)
- Effective batch size: 16 (per-device batch size 2, gradient accumulation 8)
- Decoder: CTC
- Mixed precision: fp16, with gradient checkpointing
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) | 0.1352 |
| SER (micro) | 0.369 |
| BoTok-SWER (micro) | 0.4477 |
| BERT-SWER (micro, full 1,547-utt. test set) | 0.161 |
| Gem-SWER (micro, 500-utt. subset) | 0.9653 |
This model's CER was comparable to Whisper Tiny's, but it recorded the worst word-level scores (SER, BoTok-SWER, BERT-SWER, Gem-SWER) of any architecture benchmarked — CTC's conditional-independence assumption appears to interact poorly with Tibetan's agglutinative morphology.
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-hubert-base")
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 facebook/hubert-base-ls960.