Results
WER / CER (%), full test sets, lowercase + punctuation stripped:
Table with columns: Test set, whisper-tiny, whisper-tiny-id, whisper-tiny-id, short context| Test set | whisper-tiny | whisper-tiny-id | whisper-tiny-id, short context |
|---|
| FLEURS id (684 utts) | 61.6 / 27.6 | 19.1 / 6.3 | 19.2 / 6.4 |
| Common Voice 17 id (3629 utts) | 59.1 / 30.1 | 18.0 / 6.7 | 18.3 / 6.8 |
Latency, whisper.cpp q8_0, 4 s utterance:
Table with columns: CPU, Settings, ms / utterance, RTF| CPU | Settings | ms / utterance | RTF |
|---|
| Cortex-A53 x4 1.4 GHz (Orange Pi Zero3) | -t 4 -ac 256 | 898 | 0.22 |
| Cortex-A53 x4 | -t 3 -ac 256 | 1095 | 0.27 |
Output is lowercase without punctuation.
Files
model.safetensors (fp16) + tokenizer/config: transformers
ggml-tiny-id-q8_0.bin (43 MB): whisper.cpp. On ARM, q8_0 beats q5_0 on speed.
Usage
# -ac = encoder positions: 50 per second of audio, round up to a multiple of 64 (4 s -> 256, 8 s -> 448)whisper-cli -m ggml-tiny-id-q8_0.bin -l id -t 4 -ac 256 -bs 1 -bo 1 -nt -f audio16k.wav
from transformers import pipelineasr = pipeline("automatic-speech-recognition", model="maleo-ai/whisper-tiny-id")print(asr("audio.wav", generate_kwargs={"language": "indonesian", "task": "transcribe"})["text"])
Limitations
- About 6 WER points worse on telephone-band audio than on clean audio (training-time eval).
- Test sets are read speech; conversational audio will be harder.
- Orange Pi Zero3 has no thermal throttling (only a 110 C shutdown); sustained 4-thread decoding runs near 80 C. Use a heatsink, or
-t 3.
Citation
@misc{irawan2026whispertinyid, author = {Eka Tresna Irawan}, title = {whisper-tiny-id: Whisper tiny fine-tuned for Indonesian speech recognition}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/maleo-ai/whisper-tiny-id}}}