asr-468m-apache-v2 — Apache-2.0 multilingual ASR that beats Audio8-ASR-0.1B
A 7-language (Chinese, English, French, German, Japanese, Korean, Cantonese) speech-to-text model distilled from Qwen3-ASR-0.6B (Apache-2.0), at 467.81M parameters (1.44x Audio8-ASR-0.1B's size) — outperforming Audio8-ASR-0.1B outright while remaining fully Apache-2.0 and commercially usable (Audio8 is CC-BY-NC and cannot be used commercially).
Architecture: Qwen3-ASR-0.6B's frozen 18×896 audio tower (186.38M) + an 8-layer × 1024 decoder
with the full 151,936-token vocabulary (no vocab pruning — no vocab_remap.json needed, unlike
the size-matched Luigi/asr-324m-apache).
Method: the best independently-trained 6-layer/42,000-vocab specialist had its 6 trained layers
re-inserted at their original depth indices into the 8-layer parent
(Luigi/asr-468m-apache-base), plus a
full-vocabulary graft (the specialist's 42k rows kept, the other ~110k rows filled from the parent's
own embedding) — no further training. Full derivation:
docs/findings.md
("Stage 4 postscript").
Results (200-clip FLEURS test gate, all-refs; macro mixes CER for zh/ja/ko/yue and WER for en/fr/de)
| language | Audio8-ASR-0.1B | this model | delta |
|---|---|---|---|
| French | 20.88 | 16.40 | −4.48 ✅ |
| Cantonese | 16.16 | 14.78 | −1.38 ✅ |
| Japanese | 17.97 | 16.63 | −1.34 ✅ |
| Korean | 13.97 | 11.89 | −2.08 ✅ |
| Chinese | 11.94 | 12.08 | +0.14 |
| English | 8.51 | 10.71 | +2.20 |
| German | 17.73 | 19.35 | +1.62 |
| macro | 15.31 | 14.55 | −0.76 ✅ beats Audio8 |
Usage
Full vocabulary — no id remapping needed.
import torch
from qwen_asr.core.transformers_backend.modeling_qwen3_asr import Qwen3ASRForConditionalGeneration
from qwen_asr.core.transformers_backend.processing_qwen3_asr import Qwen3ASRProcessor
proc = Qwen3ASRProcessor.from_pretrained("Luigi/asr-468m-apache-v2")
model = Qwen3ASRForConditionalGeneration.from_pretrained(
"Luigi/asr-468m-apache-v2", dtype=torch.bfloat16).cuda().eval()
Training data (all commercially usable)
Common Voice 17 (CC0), WenetSpeech4TTS (CC-BY-4.0), Multilingual LibriSpeech (CC-BY-4.0), LibriSpeech (CC-BY-4.0), FLEURS (CC-BY-4.0), AISHELL-1 (Apache-2.0), YouTube-Cantonese / OrcinusOrca (MIT). Attribution for CC-BY sources: trained in part on WenetSpeech4TTS, Multilingual LibriSpeech, LibriSpeech, and FLEURS, each licensed CC-BY-4.0 by their creators.
Audio8-ASR-0.1B is used only as a measurement reference — its weights are never loaded, merged, or distilled from.