TEA-ASR-1.1 · Taiwan Everyday Audio 🍵

TEA-ASR is an open, drop-in speech-recognition model purpose-built for Taiwan Mandarin. It turns real speech into natural Traditional Chinese with authentic Taiwan vocabulary, and it stays robust through the everyday Mandarin–English code-switching common in Taiwan. Adapted from the state-of-the-art Qwen3-ASR foundation and merged into a single self-contained checkpoint, TEA-ASR loads and runs exactly like stock Qwen3-ASR — no converters, no post-processing — while matching or surpassing both a dedicated Taiwan specialist and a large multilingual model on every public benchmark we evaluate.

TEA-ASR-1.1 is the 2B second-generation flagship (best accuracy) and improves on TEA-ASR-1 across every benchmark. A companion TEA-ASR-1.1-mini shares the recipe at 780M — see JacobLinCool/TEA-ASR-1.1-mini.

What's new in 1.1

• 🔀 Code-switch leap — ASCEND 10.59 → 9.60, CSZS 10.98 → 10.94; the English side of code-switch (ASCEND-en) improves most.
• 🎯 Better on everything — CommonVoice, NTUML2021 lectures, and both code-switch sets all move down versus TEA-ASR-1, on the same protocol.
• 🪶 Still a single drop-in checkpoint, < 10 hours of public training audio, no runtime post-processing.

Key features

• 🎯 Built for Taiwan Mandarin — Traditional script and Taiwan-style word choice, produced by the model itself.
• 🔀 Code-switch robust — handles natural zh-en mixing instead of translating Mandarin into English.
• 🧩 Drop-in Qwen3-ASR compatible — same loading and inference API as the base model; nothing else to install or call.
• 🪶 Lightweight adaptation — a small decoder LoRA on a frozen audio encoder, trained on a few hours of public audio, then merged for deployment.

Quick start

pip install qwen-asr

from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained("JacobLinCool/TEA-ASR-1.1")
result = model.transcribe(audio="utterance.wav", language="Chinese")[0]
print(result.text)   # -> Traditional Chinese with Taiwan lexicon

Set language="Chinese" for Taiwan speech (recommended). You can also pass a context= string of hotwords (names, jargon) for contextual biasing, exactly as with the base Qwen3-ASR.

Benchmark results

Mixed Error Rate (MER%, lower is better), all numbers from a single self-measured run under one protocol (see Evaluation). Columns: the two TEA-ASR-1.1 models, the original (unadapted) Qwen3-ASR bases, and two references — Breeze-ASR-25 (a Taiwan-specialist ASR) and Whisper-large-v3. Bold = this model.

Benchmark	TEA-ASR-1.1	TEA-ASR-1.1-mini	Qwen3-ASR-1.7B	Qwen3-ASR-0.6B	Breeze-ASR-25	Whisper-large-v3
CommonVoice 19 (zh-TW)	3.58	5.12	3.90	5.79	8.03	10.17
ASCEND (zh-en)	9.60	11.20	10.57	12.54	17.53	19.61
CSZS (zh-en)	10.94	12.51	11.03	16.03	12.18	23.24
NTUML2021	6.67	7.53	10.12	11.03	7.50	9.68

Generational improvement — TEA-ASR-1.1 vs TEA-ASR-1 (2B flagship, same protocol, lower is better):

Benchmark	TEA-ASR-1.1	TEA-ASR-1	Δ
CommonVoice 19 (zh-TW)	3.58	3.64	−0.06
ASCEND (zh-en)	9.60	10.59	−0.99
CSZS (zh-en)	10.94	10.98	−0.04
NTUML2021	6.67	6.80	−0.13

How to read this. TEA-ASR-1.1 is the flagship model on this page and posts the best error rate on every benchmark in the suite — ahead of the Taiwan-specialist Breeze-ASR-25, far ahead of Whisper-large-v3, and below its own predecessor TEA-ASR-1 on all four sets. TEA-ASR-1.1-mini delivers most of that quality at well under half the parameters (780M vs 2B). The metric folds away script differences (see Evaluation), so it does not reflect the decisive practical change: TEA-ASR emits Traditional script and Taiwan vocabulary natively, whereas the base produces Simplified script.

Evaluation

• Metric — Mixed Error Rate (MER). Character Error Rate for Chinese and Word Error Rate for the English tokens, computed jointly per utterance and micro-averaged.
• Content fold (applied uniformly to every dataset and every system). Before scoring, both the reference and the hypothesis are normalized to a common form — converted to Simplified Chinese with OpenCC (t2s), lowercased, and stripped of punctuation. This isolates recognition from script style, so a Simplified-output model and a Traditional-output model (TEA-ASR) are compared fairly on content. (TEA-ASR's actual output is Traditional; the fold is only for scoring.)
• Decoding. TEA-ASR and Qwen3-ASR are decoded with language=Chinese; Whisper-large-v3 and Breeze-ASR-25 use their own automatic language detection. All systems are scored with the same code on the same public splits; we do not import numbers reported elsewhere.

Dataset	What it tests	Eval split (n)
CommonVoice 19 (zh-TW)	Read Taiwan-Mandarin speech	full test (5013)
ASCEND	Spontaneous Mandarin–English code-switch conversation	full test (1315)
CSZS (zh-en)	Zero-resource code-switch benchmark	full test (3176)
NTUML2021	Mandarin lecture speech (university ML course)	test[:2000]

• No train/test leakage. Fine-tuning used only the training pools, disjoint from every evaluation split: the NTUML2021 train split, the ASCEND train split, and a CommonVoice slice drawn from validated_without_test (CommonVoice's official non-test pool, disjoint from its test split). Evaluation therefore runs on the full, untouched CommonVoice / ASCEND / NTUML2021 test splits; CSZS is a separate dataset not used in training at all. Every number above is leak-free.

How it was built

• Base Qwen/Qwen3-ASR-1.7B (AuT audio encoder + Qwen3 decoder).
• Adaptation: a rank-16 decoder LoRA (plus a low-LR encoder LoRA) trained on under 10 hours of public audio (CommonVoice zh-TW, ASCEND, NTUML2021, and TaiMECS), with general + code-switch replay to preserve the base model's broad and bilingual ability. The 1.1 recipe adds error-analysis-driven targeted supplements and English-preservation training so dense code-switch is transcribed, not translated.
• Localization: Traditional-script + Taiwan-lexicon output is rendered through the model's own tokenizer (the surface mapping is baked once at build time); there is no post-processing at inference — the Traditional output comes straight from the model's own tokenizer decode.
• Packaging: the adapter is merged into the base and the localized tokenizer is shipped with it, so the release is a single drop-in checkpoint that loads like stock Qwen3-ASR (decode verified bit-exact on 152k+ sequences).
• Decoding tip: pass language="Chinese" for Taiwan speech; this also prevents translation-style outputs on dense code-switch.

Limitations

• Scope: validated on the Qwen3-ASR family (0.6B and 1.7B); the released models load via the qwen-asr package, exactly like the base.
• Compact model trade-off: the 780M TEA-ASR-1.1-mini trails the 2B flagship on the hardest code-switch sets; for heavy Mandarin–English mixing prefer TEA-ASR-1.1.

Acknowledgements

• Base model: Qwen3-ASR by Alibaba Cloud (Apache-2.0; underlying weights remain subject to the Apache-2.0 license and its attribution/NOTICE terms).
• TaiMECS (CC-BY-4.0).
• Benchmarks: Common Voice (Mozilla), ASCEND (CAiRE), CSZS, NTU ML2021.

Citation

@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}
}

The TEA-ASR adaptation and this checkpoint are released under the MIT License.