taiwan-asr-toolkit 0.5.4

Production-grade Traditional Chinese / Taiwan Mandarin ASR toolkit — Qwen3-ASR + Breeze-ASR-25, LLM polish, speaker diarization, RTX 5090 / Blackwell tuned, RTF up to 1554x.

Taiwan ASR Toolkit

Production-grade Traditional Chinese (Taiwan Mandarin) speech-to-text — RTF up to 1554x on a single RTX 5090

Qwen3-ASR-1.7B + MediaTek Breeze-ASR-25 · Hot-word injection · LLM context polish · Speaker diarization · OpenCC s2twp · 72 TDD tests

Why this exists · Quick start · Benchmarks · Usage · Architecture · Benchmark deep dive

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Why this exists

If you've tried openai/whisper-large-v3 or whisperX on Taiwan Mandarin recordings, you've hit:

• Output is Simplified Chinese by default (you keep getting 软件 instead of 軟體)
• Whisper's built-in VAD silently fails on long sparse audio → 一個 48-min 失控段
• Proper nouns die: 延三舍 / 研三舍 (NTU dorms) become 圓三 / 圓山
• Generic Whisper is not tuned for Taiwan vocabulary — homophone errors everywhere
• Variable-length VAD chunks waste 5-10x compute through padding

This toolkit fixes all of those. Two production-grade Mandarin ASR models, identical pipeline for fair comparison, glossary-driven hot-word injection at the source, LLM polish with proper-noun protection, OpenCC s2twp baked in. Tested on real lecture/interview/standard recordings.

Star this repo if you've been burned by condition_on_previous_text=True on Mandarin. We feel you.

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Quick start

Zero-effort try (Colab)

Click the Open in Colab badge at the top — opens a file picker so you can upload any Taiwan-Mandarin clip from your machine and run the full pipeline on a Colab GPU. No local install required.

Install from PyPI

pip install --index-url https://download.pytorch.org/whl/cu128 torch torchaudio
pip install taiwan-asr-toolkit

# transcribe with the bundled NTU glossary (packaged in the wheel)
asr-breeze your_audio.mp3 --glossary-file builtin

Run on your own audio (clone-and-go)

git clone https://github.com/thc1006/taiwan-asr-toolkit.git && cd taiwan-asr-toolkit
pip install --index-url https://download.pytorch.org/whl/cu128 torch torchaudio
pip install -e ".[all]"

# Drop any Taiwan-Mandarin audio file (m4a / mp3 / wav / mp4 / flac) into the repo:
asr-breeze your_audio.m4a --glossary-file builtin
cat "transcripts/breeze/$(basename your_audio .m4a)_breeze.txt"

That's it — Taiwan Mandarin in, Traditional Chinese transcript out.

No fixture is bundled in this repo. The toolkit deliberately ships zero real-voice audio to avoid any chance of leaking identifiable speakers. Bring your own clip; the Colab quickstart notebook opens a file picker so users can upload their own.

On your own audio

asr-breeze path/to/your_audio.mp3 --glossary-file builtin
# or use your own glossary file:
asr-breeze path/to/your_audio.mp3 --glossary-file my_terms.txt
# Output: transcripts/breeze/{filename}_breeze.{txt,srt,json}

Output is Traditional Chinese (Taiwan), with timestamps, segment-level + word-level (Breeze) timing, and proper SRT subtitles.

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Benchmarks

Real numbers on a single RTX 5090 (Blackwell sm_120, 32 GB GDDR7) + i9-14900 (24 threads). Test corpus: 11 audio files, 712.6 minutes (≈12 hours) of Taiwan-Mandarin lectures + interviews.

Speed (Real-Time Factor — higher = faster)

Audio file	Length	Breeze RTF	Qwen3 RTF
4-min standard recording	4 min	189x	136x
24-min standard recording	24 min	239x	199x
65-min interview	65 min	341x	297x
140-min lecture (TASA)	140 min	546x	448x
189-min sparse audio	189 min	1554x	1497x
All 11 files combined	712 min	382x	354x
Total ASR time		111.9 s	126.2 s

Quality vs hallucination (proxy metrics, no GT)

Metric	Qwen3	Breeze
Avg quality score (35% coverage + 20% c/s + 15% Trad + 15% vocab + 15% no-halluc)	0.815	0.808
Per-file wins (out of 11)	8	3
Catastrophic >60s segments	0	0
Coverage % (transcribed vs audio time)	83.3%	79.8%
OpenCC s2twp Traditional ratio	0.97	0.97

Real CER on a 55-second hand-corrected sample

Model	CER	Notes
Breeze-ASR-25 + glossary	2.34%	hot-word injection fixes 圓三 → 研三 at source
Qwen3-ASR-1.7B	68.42%	over-transcribes (97 extra chars not in fixed-time GT)

Numbers are from one author-held internal recording; the audio itself is not redistributed. Bring your own ground-truth and run asr-bench --gt-dir path/to/your_gts/ to reproduce on your data. The hot-word effect is locked separately as a regression test in tests/test_glossary_effect.py, which pytest.skip()s cleanly when the audio is not present locally.

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Features

	What it does
Two SOTA Mandarin ASR models	Qwen/Qwen3-ASR-1.7B + MediaTek-Research/Breeze-ASR-25. Both run, both compared.
Traditional Chinese always	OpenCC s2twp post-processing converts any leftover 簡體 → 繁體 (Taiwan idioms): 軟件→軟體, 激光→雷射, 視頻→影片.
Hot-word injection	Pass --glossary-file builtin for the packaged NTU glossary (or your own .txt); proper nouns get fed to Whisper's initial_prompt + hotwords. Fixes 圓三 → 研三, 祝福二族 → 住輔二組, etc. at the source.
Symmetric pipeline	Same Silero VAD ONNX, same chunking, same dtype on both models. The benchmark measures the model, not the plumbing.
Multi-file pool batching	Cross-file length-sorted batching keeps batch=48 fully utilized when transcribing folders of mixed-length files.
LLM context polish	Optional Qwen3-8B post-correction with NTU glossary protection (won't accidentally "fix" 研三舍 to 延長).
Speaker diarization	Optional pyannote 3.x integration with open-mirror fallback (no gated-license blocker).
Real CER measurement	jiwer-based CER with s2twp normalization. Bring your own ground-truth or use the included approximate fixture.
72 TDD tests	Including 5 invariant tests that lock the Breeze model ID so optimizations can't accidentally swap to a different Whisper variant.
Blackwell-native	bf16 + cuDNN-SDPA + torch.compile for RTX 5090. Auto-falls back gracefully on Hopper/Ada/Ampere/CPU.

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Usage

Single-file transcription

# Breeze (Whisper-Large-v2 fine-tune, fastest)
asr-breeze "music/lecture.mp3" --glossary-file builtin

# Qwen3-ASR (more comprehensive coverage)
asr-qwen3 "music/interview.m4a"

# Both at once (same audio, two transcripts to compare)
./run.sh both "music/standard_recording.mp3"

Batch transcription (auto pool batching)

# Transcribes everything in music/ via Qwen3 with pool batching
asr-qwen3 music/*.mp3 music/*.m4a

# Same with Breeze (now also gets cross-file batched if multiple files)
asr-breeze music/*.{mp3,m4a,wav} --glossary-file builtin

Power-user flags

Flag	Effect
--glossary-file PATH	(Breeze) Inject domain terms via Whisper's prompt + hotwords. Use --glossary-file builtin for the packaged NTU dorm/dept glossary, or pass your own .txt file.
--fast	(Breeze) int8_bfloat16 quantization, ~1.5x speedup, +0.3-0.5% CER on Mandarin
--beam N	(Breeze) Beam size; default 5. --beam 1 = greedy (fastest), --beam 10 = max accuracy
--no-aligner	(Qwen3) Skip ForcedAligner-0.6B; ~25% faster but loses word-level timestamps
--no-pool	(Qwen3) Disable cross-file chunk pooling for multi-file runs
--internal-vad	(Breeze) Use faster-whisper's built-in VAD instead of our ONNX Silero (not recommended)
--no-s2tw	Disable OpenCC s2twp post-processing

LLM context polish (post-process)

# Polish a Breeze output with Qwen3-8B + glossary protection
asr-polish transcripts/breeze/lecture_breeze.json --glossary-file builtin
# → transcripts/breeze-polished/lecture_breeze-polished.{txt,srt,json}

Speaker diarization

# Adds [SPEAKER_00] / [SPEAKER_01] labels to each segment
asr-diarize transcripts/breeze/interview_breeze.json music/interview.m4a
# Requires HF license accept on:
# - https://hf.co/pyannote/speaker-diarization-3.1
# - https://hf.co/pyannote/speaker-diarization-community-1
# - https://hf.co/pyannote/segmentation-3.0

Benchmark + CER report

# Generates docs/BENCHMARK.md with speed + quality metrics.
# --gt-dir points at a folder of {audio_stem}_first_{N}s_gt.txt files
# you provide yourself; the repo no longer ships any voice fixtures.
asr-bench --gt-dir path/to/your_gt_dir

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Architecture

audio (.mp3/.m4a/.wav/...)
       ↓ ffmpeg pipe → numpy float32 16kHz mono
       ↓
Silero VAD ONNX (CPU SIMD, ~3-5x faster than PyTorch backend)
       ↓ ≤28s chunks
       ├──→ Qwen3-ASR-1.7B + ForcedAligner (HF transformers, bf16, batch=48)
       └──→ Breeze-ASR-25 (CTranslate2, bf16, batch=32, beam=5, hotwords)
                                ↓
                  OpenCC s2twp 簡→繁(台灣慣用詞)
                                ↓
              ┌───────────┬─────────────┐
              ↓           ↓             ↓
            TXT          SRT          JSON

   ┌──── Optional post-processing ────┐
   │ asr-polish  asr-diarize  asr-bench  │
   │ Qwen3-8B    pyannote     CER + RTF     │
   └──────────────────────────────────┘

Full architectural details: docs/ARCHITECTURE.md

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Project structure

taiwan-asr-toolkit/
├── src/taiwan_asr/
│   ├── __init__.py    ← package; minimal export to avoid heavy import cascades
│   ├── common.py      ← shared utils: ffmpeg pipe, OpenCC, Silero VAD, glossary, Segment
│   ├── qwen3.py       ← Qwen3-ASR + multi-file chunk pool
│   ├── breeze.py      ← Breeze-ASR-25 with manual VAD + hot-word injection
│   ├── polish.py      ← Qwen3-8B LLM context correction (glossary-protected)
│   ├── diarize.py     ← pyannote.audio speaker diarization
│   ├── cer_eval.py    ← jiwer-based CER with s2twp normalization
│   └── benchmark.py   ← speed + accuracy report
├── glossary.txt       ← default NTU glossary (dorm/dept names)
├── run.sh             ← convenience wrapper around asr-* CLI commands
├── pyproject.toml     ← project metadata, deps, CLI scripts (asr-qwen3, asr-breeze, …)
├── tests/             ← 72 TDD tests (including 5 Breeze invariants)
├── docs/              ← BENCHMARK.md / ARCHITECTURE.md / INSTALL.md
└── archive/           ← legacy Colab notebooks (kept for reference only)

After pip install -e . the following CLI commands are on PATH: asr-qwen3, asr-breeze, asr-polish, asr-diarize, asr-bench, asr-cer.

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Testing & contributing

# All 72 tests, no model load required for "fast" tier
pytest -m fast

# Breeze contract tests (NEVER allowed to fail)
pytest -m breeze_invariant

# Full suite (some need VAD load)
pytest

The toolkit follows strict TDD. Any contribution must:

1. Have a failing test that now passes
2. Keep all 56 existing tests green
3. Pass pytest -m breeze_invariant (which locks MediaTek-Research/Breeze-ASR-25 as Breeze's model)
4. Keep Traditional Chinese (Taiwan) output

See CONTRIBUTING.md for the full guide.

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vs alternatives

	This toolkit	whisperX	faster-whisper (raw)	openai/whisper
Taiwan Traditional Chinese by default	s2twp baked-in
Two SOTA Mandarin models compared	Qwen3 + Breeze	Whisper only	Whisper only	Whisper only
Fixes 圓三/延三 → 研三 proper-noun ASR errors	glossary hot-word		manual prompt
LLM context polish with proper-noun protection	Qwen3-8B + glossary
Speaker diarization (open-mirror fallback)	tensorlake mirror	pyannote (gated)
RTX 5090 / Blackwell native (bf16 + cuDNN-SDPA)
TDD with model-invariant lock	72 tests
Best RTF on long Mandarin audio	1554x	~70x	~250x	~30x

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Citation & credits

This toolkit is integration plumbing — credit goes to the model authors:

• MediaTek-Research/Breeze-ASR-25 (HuggingFace) — Whisper-Large-v2 fine-tune for Taiwan Mandarin
• Alibaba/Tongyi Qwen3-ASR-1.7B (HuggingFace) — multilingual ASR with ForcedAligner
• OpenAI Whisper — base architecture for Breeze-ASR-25
• CTranslate2 / faster-whisper — Whisper inference engine
• pyannote/audio — speaker diarization
• OpenCC — simplified-traditional Chinese conversion (s2twp recipe)
• Silero VAD — fast voice activity detection

If this toolkit helps your research, please cite the underlying models. A toolkit-level citation is fine but optional:

@software{taiwan_asr_toolkit,
  title = {Taiwan ASR Toolkit: Production-grade Traditional Chinese Speech-to-Text Pipeline},
  author = {Taiwan ASR Toolkit Contributors},
  year   = {2026},
  url    = {https://github.com/thc1006/taiwan-asr-toolkit},
  note   = {Qwen3-ASR-1.7B + MediaTek Breeze-ASR-25 with hot-word injection, LLM polish, and speaker diarization}
}

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License

MIT for this toolkit's code. See LICENSE.

Third-party model licenses (you must comply with each):

• Qwen models: Apache 2.0
• Breeze-ASR-25: Apache 2.0
• Silero VAD: MIT
• pyannote: gated, requires HF license accept

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** Made with bf16 tensor cores in Taiwan **

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