Formosan ASR — Amis
This is FormosanBank's current general-purpose automatic speech recognition checkpoint for Amis. It was selected by the lowest normalized micro word error rate among the completed language-level TACL systems evaluated on the same frozen all-corpora test partition.
Model
- Repository:
FormosanBank/formosan-asr-amis - Architecture family: XLS-R 53
- Training path: train-only pan-Formosan continued pretraining, pan-Formosan supervised fine-tuning, and language adaptation
- Base model:
facebook/wav2vec2-large-xlsr-53 - Parameters are stored in
model.safetensors
Training lineage
- Base checkpoint:
facebook/wav2vec2-large-xlsr-53. - Continued self-supervised pretraining on the frozen pan-Formosan training partition only; development and test audio were excluded.
- Supervised pan-Formosan ASR training, then adaptation to the target language's pooled training corpora.
The target-language supervised stage used Bible, ILRDF, NTU, ePark1, ePark2, ePark3. The experiment used
dataset_v1, frozen split_v2, normalization formosan_safe_v1, and seed
13. Full machine-readable provenance is included in
training_config.json.
Evaluation
Evaluation uses the frozen, leakage-controlled split_v2 test manifests and
normalized text. Metrics are computed by summing edit counts across all
available Amis test corpora, so larger corpora contribute in
proportion to their reference tokens.
- Normalized micro WER: 7.67%
- Normalized micro CER: 1.89%
- Test utterances: 3,601
- Test corpora: 6
- Automated readiness: review
The automated readiness label is review, with zero hard failures. The retained warning is recorded in readiness_summary.json and should be considered when reproducing or promoting the checkpoint.
| Corpus | Utterances | WER | CER |
|---|---|---|---|
| Bible | 1029 | 3.04% | 0.94% |
| ILRDF | 568 | 13.18% | 4.10% |
| NTU | 66 | 33.33% | 7.84% |
| ePark1 | 439 | 19.28% | 3.60% |
| ePark2 | 275 | 7.35% | 1.20% |
| ePark3 | 1224 | 11.11% | 2.39% |
Detailed edit counts are provided in evaluation_results.json and
metrics_by_corpus.tsv.
These figures are specific to the frozen TACL evaluation protocol. They should not be compared directly with the April 2026 stage-one releases, which used a different experiment snapshot.
Usage
from transformers import pipeline
repo_id = "FormosanBank/formosan-asr-amis"
transcribe = pipeline("automatic-speech-recognition", model=repo_id)
result = transcribe("path/to/16khz_audio.wav")
print(result["text"])
For long recordings, segment the audio before inference. The training recipe used utterances between 2 and 20 seconds.
Intended use
This checkpoint supports research, education, language documentation, and revitalization work involving Amis. Human review is recommended before using transcriptions in archives, publications, teaching materials, or other consequential settings.
Limitations
Performance varies substantially across corpora, speakers, recording conditions, dialects, speaking styles, and orthographic conventions. The aggregate score can hide weak performance on a particular corpus. The model may omit, substitute, or hallucinate words and should not be treated as an authoritative transcription source.
Data provenance
Training and evaluation artifacts come from the frozen Hunter Formosan TACL pipeline. Source corpus IDs can include Bible, ILRDF, NTU, ePark1, ePark2, ePark3, Xuan, YeddaPalemeqBlog, Youtube, and YutasWilang, depending on language availability. Audio is not redistributed in this repository. Underlying recordings retain their original rights and access conditions.
License and attribution
This model release and FormosanBank annotations/metadata are provided under
CC BY 4.0. The upstream facebook/wav2vec2-large-xlsr-53 checkpoint is distributed under
Apache 2.0. Users remain responsible for respecting the terms associated with
underlying source recordings.
Please cite FormosanBank:
@misc{mohamed2024formosanbank,
author = {Mohamed, W. and Le Ferrand, É. and Sung, L.-M. and Prud'hommeaux, E. and Hartshorne, J. K.},
title = {FormosanBank},
year = {2024},
note = {Electronic Resource},
url = {https://ai4commsci.gitbook.io/formosanbank}
}