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Luigi/
asr-468m-apache-base

Name: asr-468m-apache-base
Availability: InStock

automatic-speech-recognition

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asr-468m-apache-base

A 7-language (Chinese, English, French, German, Japanese, Korean, Cantonese) speech-to-text model distilled from Qwen3-ASR-0.6B (Apache-2.0). This is the Stage-1 base checkpoint — the quality target reached before any parameter compression, at 467.81M parameters. It statistically ties Audio8-ASR-0.1B (macro 15.36 vs 15.31) at ~1.4x its size, fully Apache-2.0 where Audio8 is CC-BY-NC and unusable commercially.

If you want the smaller, size-matched release (323.77M, Audio8's exact parameter budget, at a quality cost — see its model card for the honest tradeoff), use Luigi/asr-324m-apache instead. This base checkpoint is also the required starting point for reproducing that model's compression pipeline.

Code, full training pipeline, and every finding: github.com/vieenrose/asr-324m-apache.

Results (200-clip FLEURS test gate, all-refs macro; CER for zh/ja/ko/yue, WER for en/fr/de)

15.36 vs Audio8-ASR-0.1B's 15.31 — a statistical tie.

Usage

Unlike the 324M release, this checkpoint's vocabulary is not pruned (full 151,936-id Qwen3 tokenizer), so no id remapping is needed.

python

import torch
from qwen_asr.core.transformers_backend.modeling_qwen3_asr import Qwen3ASRForConditionalGeneration
from qwen_asr.core.transformers_backend.processing_qwen3_asr import Qwen3ASRProcessor

path = "Luigi/asr-468m-apache-base"
proc = Qwen3ASRProcessor.from_pretrained(path)
model = Qwen3ASRForConditionalGeneration.from_pretrained(path, dtype=torch.bfloat16).cuda().eval()

NATIVE = ("<|im_start|>system\n<|im_end|>\n<|im_start|>user\n<|audio_pad|><|im_end|>\n"
          "<|im_start|>assistant\n")

def transcribe(wav_16k_float32, language="Chinese", max_new_tokens=128):
    e = proc(text=NATIVE + f"language {language}<asr_text>", audio=[wav_16k_float32],
             sampling_rate=16000, return_tensors="pt")
    e = {k: (v.cuda() if torch.is_tensor(v) else v) for k, v in e.items()}
    if "input_features" in e:
        e["input_features"] = e["input_features"].to(torch.bfloat16)
    with torch.no_grad(), torch.autocast("cuda", dtype=torch.bfloat16):
        out = model.generate(**e, max_new_tokens=max_new_tokens, do_sample=False)
    ids = out[0][e["input_ids"].shape[1]:].tolist()
    return proc.tokenizer.decode(ids, skip_special_tokens=True)

language accepts: Chinese, English, French, German, Japanese, Korean, Cantonese.

Training data and attribution

Trained on Common Voice 17 (CC0), WenetSpeech4TTS, Multilingual LibriSpeech, LibriSpeech, and FLEURS (all CC-BY-4.0). This model was trained in part on WenetSpeech4TTS, Multilingual LibriSpeech, LibriSpeech, and FLEURS, each licensed CC-BY-4.0 by their respective creators.

Audio8-ASR-0.1B is used only as a measurement reference throughout — its weights are never loaded, merged, or distilled from.