breeze-asr-25
Converted faster-whisper / CTranslate2 model for OpenAI Whisper.
- Source (PyTorch):
MediaTek-Research/Breeze-ASR-25 - Converted artifact:
rhap-llm-audio/faster-Breeze-ASR-25 - Quantization:
float32(see CTranslate2 quantization) - Device tested:
cpu - Converted at: 2026-08-22
- Pipeline:
whisper-to-faster-whisper
Usage
faster-whisper (Python)
from faster_whisper import WhisperModel
model = WhisperModel(
"rhap-llm-audio/faster-Breeze-ASR-25",
device="cpu",
compute_type="float32",
)
segments, info = model.transcribe("audio.mp3")
for seg in segments:
print(f"[{seg.start:.2f} → {seg.end:.2f}] {seg.text}")
speaches (OpenAI-compatible STT server)
This model card includes the metadata block speaches filters on
(library_name: ctranslate2, pipeline_tag: automatic-speech-recognition,
language:). After a regular POST /v1/models download, speaches will
list this in GET /v1/models.
curl -X POST http://localhost:8000/v1/models \
-H 'Content-Type: application/json' \
-d '{"model": "rhap-llm-audio/faster-Breeze-ASR-25"}'
curl http://localhost:8000/v1/audio/transcriptions \
-F "file=@audio.mp3" \
-F "model=rhap-llm-audio/faster-Breeze-ASR-25"
Files
| File | Purpose |
|---|---|
model.bin |
CTranslate2 weights (float32) |
config.json |
faster-whisper model config |
vocabulary.json |
tokenizer vocab |
tokenizer.json |
HF tokenizer |
preprocessor_config.json |
whisper feature-extractor config |
README.md |
this card (speaches filter requires it) |
License
Inherited from upstream MediaTek-Research/Breeze-ASR-25. See source repo for the original model
license. The conversion process itself adds no additional licensing terms.
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