README.md

---
base_model: Qwen/Qwen3-ASR-0.6B
license: mit
library_name: transformers
pipeline_tag: automatic-speech-recognition
language:
  - zh
  - en
tags:
  - automatic-speech-recognition
  - taiwan-mandarin
  - traditional-chinese
  - code-switching
  - qwen3-asr
  - speech
---

# TEA-ASR-1.1-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.

`TEA-ASR-1.1-mini` is the **780M compact model (best accuracy-per-parameter)** of the second generation. For the
2B flagship, see [`JacobLinCool/TEA-ASR-1.1`](https://huggingface.co/JacobLinCool/TEA-ASR-1.1). Compared with the
first-generation `TEA-ASR-1-mini`, this release **substantially improves code-switching** — ASCEND and CSZS drop by
1.29 and 0.70 points absolute — with CommonVoice roughly level.

> **Need a controllable numeral style?** The
> [`TEA-ASR-1.1-mini-fmt`](https://huggingface.co/JacobLinCool/TEA-ASR-1.1-mini-fmt) variant adds a
> numeral-convention dial for formatting-sensitive, Mandarin-dominant audio (it trades dense
> code-switch robustness — see its card). This model stays the best compact pick for recognition.

## What's new in 1.1-mini

- 🔀 **Code-switch leap** — ASCEND 12.49 → **11.20**, CSZS 13.21 → **12.51**; embedded English is *transcribed*,
  not translated.
- 🏷️ **Format tags** — trained with output-convention tags: an optional decoder-prefix control that biases toward
  verbatim English and a chosen numeral style (see [Format tags](#format-tags)).
- 🪶 Still a **single drop-in checkpoint**, `< 10 hours` of public training audio, no runtime post-processing.

## Quick start

```bash
pip install qwen-asr
```

```python
from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.from_pretrained("JacobLinCool/TEA-ASR-1.1-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](#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-mini vs TEA-ASR-1-mini** (780M, same protocol, lower is better):

| Benchmark | TEA-ASR-1.1-mini | TEA-ASR-1-mini | Δ |
|---|---|---|---|
| CommonVoice 19 (zh-TW) | **5.12** | 5.14 | −0.02 |
| ASCEND (zh-en) | **11.20** | 12.49 | −1.29 |
| CSZS (zh-en) | **12.51** | 13.21 | −0.70 |
| NTUML2021 | 7.53 | **7.37** | +0.16 |

**How to read this.** 1.1-mini delivers most of the 2B flagship's quality at well under half the parameters
(780M vs 2B) and leads every 0.6B-class system in the table. Against the first-generation mini it is a clear
code-switch upgrade (ASCEND −1.29, CSZS −0.70), trading a small step back on the in-domain lecture set (NTUML2021
+0.16). 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.

## Format tags

`TEA-ASR-1.1-mini` was trained with **output-convention format tags** — an optional prefix, in the same channel as
the language hint, that steers *formatting* without changing the recognition:

- **`keep-en`** — transcribe embedded English verbatim (do not translate dense code-switch).
- **`digits` / `zh-num`** — force Arabic (`123`) or Chinese (`一二三`) numerals.

Plain decoding (no tag) works well by default; the tags are for callers who need a specific convention. Try them
interactively in the [Space](https://huggingface.co/spaces/JacobLinCool/TEA-ASR-Demo).

## 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`. Evaluation runs on the **full, untouched** test splits; CSZS is not used in training at
  all. Every number above is leak-free.

## How it was built

- **Base** `Qwen/Qwen3-ASR-0.6B` (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](https://huggingface.co/datasets/JacobLinCool/TaiMECS)),
  with general + code-switch **replay** and English-preservation training so dense code-switch is *transcribed*, not
  translated, plus error-analysis-driven targeted supplements and the format-tag conditioning above.
- **Localization**: Traditional-script + Taiwan-lexicon output is rendered through the model's **own tokenizer**
  (baked once at build time); there is **no post-processing at inference**.
- **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

- **Compact-model trade-off**: on the hardest code-switch sets and on in-domain lectures the 780M mini trails the
  2B `TEA-ASR-1.1`; for the best accuracy prefer the flagship.
- **Scope**: validated on the Qwen3-ASR family; loads via the `qwen-asr` package exactly like the base.

## Acknowledgements

- Base model: [Qwen3-ASR](https://huggingface.co/Qwen/Qwen3-ASR-0.6B) by Alibaba Cloud (Apache-2.0; underlying
  weights remain subject to the Apache-2.0 license and its attribution/NOTICE terms).
- [TaiMECS](https://huggingface.co/datasets/JacobLinCool/TaiMECS) (CC-BY-4.0).
- Benchmarks: Common Voice (Mozilla), ASCEND (CAiRE), CSZS, NTU ML2021.

## Citation

```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}
}
```

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