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JacobLinCool
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Model Details
Model Provider
JacobLinCool
Model Tree
Qwen/Qwen3-ASR-0.6B
Input Modalities
Output Modalities
Supported Functionality
Same interface as TEA-ASR-1.1-fmt: force the
decoder prefix language Chinese format digits<asr_text> / … format zh-num<asr_text>.
Measured control strength (multi-digit panels, scripts/probe_numeral_flip.py):
| Panel (audio) | pair flip | digits compliance | zh-num compliance |
|---|---|---|---|
| CommonVoice zh-TW | 0.73 | 0.77 | 0.93 |
| NTUML2021 | 0.27 | 0.43 | 0.70 |
(Measured on this released checkpoint.)
At 780M the dial is a moderate bias: strong on read Taiwan-Mandarin speech, weaker on spontaneous lecture audio; single digits and decimals follow the domain's natural convention.
MER% (lower is better), same protocol as the family cards (content fold, full test splits).
| Benchmark | TEA-ASR-1.1-mini-fmt | TEA-ASR-1.1-mini | Qwen3-ASR-0.6B | Breeze-ASR-25 |
|---|---|---|---|---|
| CommonVoice 19 (zh-TW) | 5.27 | 5.12 | 5.79 | 8.03 |
| ASCEND (zh-en) | 11.25 | 11.20 | 12.54 | 17.53 |
| CSZS (zh-en) | 14.83 | 12.51 | 16.03 | 12.18 |
How to read this. The fmt variant slightly leads on lectures and ties ASCEND, but pays a real dense-code-switch premium (CSZS +2.3) for the numeral dial — at 780M the conditioned output space is small enough that convention training measurably competes with code-switch robustness (the 2B fmt variant pays only +0.35 there). Use it where the numeral convention matters and the audio is Mandarin-dominant; otherwise prefer the main mini.
Identical protocol and packaging to
TEA-ASR-1.1-mini: leak-free train/test
splits, < 10 hours of public training audio, rank-16 decoder LoRA + low-LR encoder LoRA merged
into one drop-in checkpoint, Traditional output from the model's own tokenizer (no runtime
post-processing; decode verified bit-exact on 152k+ sequences). The fmt recipe adds
numeral-convention counterfactual pairs (same audio, both conventions, opposite tags) mined from
the same corpora at zero extra audio budget.
Adapted from Qwen3-ASR (Apache-2.0); TaiMECS (CC-BY-4.0); benchmarks: Common Voice, ASCEND, CSZS, NTU ML2021. Released under the MIT License.
bibtex
@misc{teaasr2026,title = {Tokenizer-First Adaptation of Mandarin ASR to Taiwan Mandarin},author = {TEA-ASR contributors},year = {2026},note = {TEA-ASR (Taiwan Everyday Audio); adapted from Qwen3-ASR}}
| NTUML2021 | 7.44 | 7.53 | 11.03 | 7.50 |