twmht/Breeze-ASR-26-ct2-float32

Breeze-ASR-26 (CTranslate2, float32)

This is an unofficial CTranslate2 conversion of MediaTek-Research/Breeze-ASR-26, a Whisper-large-v2 model fine-tuned for Taiwanese Hokkien (Taigi). It is made for use with faster-whisper and other CTranslate2 runtimes. It is not published or endorsed by MediaTek Research. The weights are unchanged and keep the original Apache-2.0 license.

The model transcribes Taigi speech into Mandarin Chinese characters, not Tâi-lô or Taigi orthography. See the original model card for the training data, benchmark and limitations.

Conversion

ct2-transformers-converter --model MediaTek-Research/Breeze-ASR-26 \
    --output_dir Breeze-ASR-26-ct2-float32 --quantization float32 \
    --copy_files preprocessor_config.json

• ctranslate2 4.8.5, transformers 5.20.2.
• The original repository has no tokenizer.json. It was generated from the original tokenizer (AutoTokenizer.from_pretrained(...).backend_tokenizer.save("tokenizer.json")). The vocabulary has 51,865 entries, the same as Whisper large-v2.
• model.bin is about 6.2 GB. Use compute_type="float16" or "int8_float16" at load time to run it with less memory. These types are converted when the model loads, and they were not tested here.

Usage

from faster_whisper import WhisperModel

model = WhisperModel("twmht/Breeze-ASR-26-ct2-float32", device="cuda", compute_type="float16")
segments, info = model.transcribe(
    "audio.wav",
    language="zh",
    beam_size=5,
    no_repeat_ngram_size=8,
)
print("".join(s.text for s in segments))

Set language="zh". The model has no Taigi language token, and the original model card uses Chinese as well.

Repetition loops

On short clips, the original model sometimes repeats one phrase many times. In one test, a short single-word clip produced the same phrase about 50 times. Setting no_repeat_ngram_size removed every loop in the tests below. The value counts tokens, not characters, and a Chinese character often takes 1 to 2 tokens.

Tests with the Hugging Face transformers pipeline (float16, beam 5), not with this CTranslate2 model:

• 100 short Common Voice Taigi clips: loops dropped from 4 clips to 0 with no_repeat_ngram_size of 3, 4, 5, 6 or 8.
• 18 broadcast ads of about 30 seconds each (Executive Yuan public service announcements, Taigi audio against the official Mandarin script): mean CER was 0.392 with no setting, 0.377 with 4 and 0.316 with 8. With 4, the Mandarin versions of the same ads lost words in the middle (mean CER 0.117 to 0.192). With 8 they did not (0.097).

I recommend 8. Larger values were not tested. The value was not tuned for this CTranslate2 model.

Verification

• With the same prompt (<|startoftranscript|><|zh|><|transcribe|><|notimestamps|>), float32 and greedy decoding, this model produced token-for-token identical output to the original Hugging Face model on 8 of 8 test clips (30-second ads).
• Through faster-whisper with default settings, the output is not identical to the Hugging Face pipeline. On the 18 ads (Taigi audio), faster-whisper float32 with no_repeat_ngram_size=8 had a mean CER of 0.377, against 0.316 for the Hugging Face pipeline with the same setting. Greedy decoding on 36 clips gave a mean character similarity of 0.92 between the two, and a few clips ended earlier in faster-whisper. I did not find the cause. It may come from the faster-whisper pipeline (windowing, feature extraction or decoding options) rather than from the converted weights. Compare on your own data before you depend on exact output.

Credits

Original model: MediaTek-Research/Breeze-ASR-26 (Apache-2.0). Please cite the original work:

@misc{lan2026breezetaigibenchmarksmodels,
      title={Breeze Taigi: Benchmarks and Models for Taiwanese Hokkien Speech Recognition and Synthesis},
      author={Yu-Siang Lan and Chia-Sheng Liu and Yi-Chang Chen and Po-Chun Hsu and Allyson Chiu and Shun-Wen Lin and Da-shan Shiu and Yuan-Fu Liao},
      year={2026},
      eprint={2603.19259},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.19259}
}