dkhokhlov/whisper-tiny-hqq-4bit

HQQ 4-bit Whisper-Tiny

Model card source for dkhokhlov/whisper-tiny-hqq-4bit.

Related models

• dkhokhlov/whisper-base-hqq-4bit — HQQ 4-bit, whisper-base (CPU eval)
• dkhokhlov/whisper-small-hqq-4bit — HQQ 4-bit, whisper-small (A10 GPU eval)
• Source model: openai/whisper-tiny (fp32)
• Benchmark + code: dkhokhlov/whisper-cascade

Summary

openai/whisper-tiny quantized with HQQ 4-bit grouped quantization for CPU inference. Resident weight RAM (fp16 compute, the deployment mode) is 57.53 MB, 23.8% smaller than the unquantized fp16 model (75.52 MB). fp16 compute is WER-neutral; the published WER benchmark uses fp32 compute for cross-model comparability. The key setting is mixed precision: the whole encoder stack and fc1 are kept at 8-bit, the remaining decoder linears are 4-bit (see the repo README for the full config and the config-sweep ablation).

English (fleurs en_us, n=100) WER is 0.1367 vs 0.1381 fp32 (-1.0%), within n=100 noise. HQQ is within 5% relative of fp32 on every tested config (5 fleurs + 4 talkbank).

Results

English (fleurs en_us, n=100, fp32 compute):

Metric	unquantized fp32	HQQ 4-bit	Delta %
WER	0.1381	0.1367	-1.0%
Resident RAM (fp16)	75.52 MB	57.53 MB	-23.8%
Samples succeeded	100 / 100	100 / 100	-

HQQ is within 5% relative of fp32 on every tested config. The full multilingual and telephone WER tables, the cross-reference against whisper-base/whisper-small, and the size-by-component breakdown are in the repo README.

Load and use

The model auto-detects the spoken language and transcribes (multilingual Whisper behavior). Pass language to force a language when it is known.

import hqq_asr
pipe = hqq_asr.build_pipeline("dkhokhlov/whisper-tiny-hqq-4bit", quant="hqq")
text = pipe({"array": audio, "sampling_rate": 16000})["text"]                       # auto-detect
text = pipe({"array": audio, "sampling_rate": 16000},
             generate_kwargs={"language": "spanish", "task": "transcribe"})["text"]   # force

Command line (this repository):

make asr MODEL_ASR=dkhokhlov/whisper-tiny-hqq-4bit QUANT=hqq AUDIO=clip.wav

Reproduce

# 1. Quantize locally (writes whisper-tiny-hqq-4bit/).
python quantize.py

# 2. Measure baseline WER (fp32).
EVAL_LIMIT=100 MODEL_ASR=openai/whisper-tiny EVAL_CONFIG=en_us \
  EVAL_OUT=eval_baseline.json python eval_wer.py

# 3. Measure HQQ WER.
EVAL_LIMIT=100 QUANT=hqq MODEL_ASR=./whisper-tiny-hqq-4bit EVAL_CONFIG=en_us \
  EVAL_OUT=eval_hqq.json python eval_wer.py

# 4. Telephone benchmark (talkbank segment split).
EVAL_DATASET=diabolocom/talkbank_4_stt EVAL_CONFIG=en EVAL_SPLIT=segment EVAL_LIMIT=100 \
  MODEL_ASR=openai/whisper-tiny EVAL_OUT=talkbank_en_fp32.json python eval_wer.py

# 5. Publish (needs a Hugging Face write token).
PUSH=1 HQQ_REPO=dkhokhlov/whisper-tiny-hqq-4bit python quantize.py

License

MIT. Derived from openai/whisper-tiny (Apache-2.0) and HQQ. The quantized weights inherit the openai/whisper license terms.

Citation

See the repo README for the BibTeX entry.

Full details

Quantization config, config-sweep ablation, safetensors format, the full WER tables (multilingual fleurs, talkbank telephone, cross-reference), and the resident-RAM-by-component breakdown are in the repo README. Per-config WER evidence JSONs are committed under eval_multilingual/ and eval_telephone/ in dkhokhlov/whisper-cascade.