JacobLinCool/TEA-ASR-1-mini

TEA-ASR-1-mini · 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-mini is the 780M compact model (best accuracy-per-parameter). A companion TEA-ASR-1 shares the identical recipe — see `JacobLinCool/TEA-ASR-1`.

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-mini")
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 models, the original (unadapted) Qwen3-ASR bases, and two references — Breeze-ASR-25 (a Taiwan-specialist ASR) and Whisper-large-v3. Bold = this model.

How to read this. TEA-ASR-1-mini is the efficient model on this page. Across the suite, TEA-ASR-1 posts the best (or tied-best) error rate on every benchmark, ahead of the Taiwan-specialist Breeze-ASR-25 and far ahead of Whisper-large-v3; TEA-ASR-1-mini delivers most of that quality at well under half the parameters (780M vs 2B). Against the unadapted Qwen3-ASR base, the gain in this content-folded recognition metric is largest on in-domain lectures (NTUML2021); on the other sets recognition is on par or better — and, importantly, 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.

Speed & memory

Measured on NVIDIA RTX 5090 (32 GB) (bf16, batch 1, 50 utterances, greedy decode). xRT = audio seconds processed per wall-clock second (higher is faster); RTF = wall-clock / audio (lower is faster); peak VRAM is the maximum allocated during inference.

Figures

Accuracy across the four public benchmarks (content-fold MER%, lower is better):

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Speed and memory (single GPU, bf16, batch 1):

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Ablation — tokenizer × finetune. Content MER isolates the finetune gain (the script fold hides tokenizer effects); raw MER isolates the tokenizer-first localization that makes the output Traditional + Taiwan-lexicon:

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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 (e.g. the base) 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.

• —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-0.6B (frozen AuT audio encoder + Qwen3 decoder).
• —Adaptation: a rank-16 decoder-only LoRA trained on a few hours of public audio (CommonVoice zh-TW, ASCEND, NTUML2021), with general + code-switch replay to preserve the base model's broad and bilingual ability. The audio encoder is left frozen.
• —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.
• —Decoding tip: pass language="Chinese" for Taiwan speech; this also prevents translation-style outputs on dense code-switch.

Limitations

• —Dense synthetic code-switch (CSZS): the smaller TEA-ASR-1-mini trails the Taiwan specialist on this set; the flagship TEA-ASR-1 leads it. For heavy code-switch, prefer TEA-ASR-1.
• —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.

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}
}

Built on Qwen3-ASR (Apache-2.0). The TEA-ASR adaptation and this checkpoint are released under the MIT License; the underlying Qwen3-ASR weights remain subject to the Apache-2.0 license and its attribution/NOTICE terms.