Qwen3-ASR causal

Standalone runtime for the causal streaming audio tower published at qfuxa/qwen3-asr-0.6b-streaming.

The weights are not stored in this repository. The runtime downloads the causal tower from Hugging Face and loads the unchanged decoder, adapter and feature extractor from Qwen/Qwen3-ASR-0.6B.

Install

pip install -e ".[streaming]"

For CUDA/vLLM serving, including the causal audio tower path:

pip install -e ".[vllm]"

For Apple Silicon vLLM Metal serving, install vLLM with the official vllm-metal instructions first, then install the Metal extra:

pip install -e ".[metal]"

Usage

HF Transformers causal runtime:

qwen3-asr-causal transcribe audio.wav --backend hf --language en

vLLM CUDA runtime with the live prompt-embedding append path:

WLK_QWEN3_VLLM_LIVE_MULTIPROCESSING=1 \
qwen3-asr-causal transcribe audio.wav \
  --backend vllm \
  --decoder-backend vllm-live \
  --language en

The conservative vLLM fallback uses text-decoder prefix caching:

qwen3-asr-causal transcribe audio.wav \
  --backend vllm \
  --decoder-backend vllm-text \
  --language en

The Apple Silicon backend exposes the same runtime through qwen3_asr_causal.metal for WhisperLiveKit and local benchmark integration.

How it works

The original Qwen3-ASR audio tower is offline and fully bidirectional. This runtime uses a fine-tuned causal tower that runs append-only:

• fixed 1.92 s audio blocks, with full attention inside each block;
• causal per-layer KV across blocks;
• bounded 15 s left attention window;
• monotonically increasing sinusoidal positions;
• no re-encoding of already finalized audio blocks.

The live text contract is append-only. At each non-final update the runtime cuts the hypothesis portion aligned to the last 250 ms of audio and only publishes text that extends the previously published prefix. End of stream flushes the held-back tail, but still cannot revise words already published.

Results

Long-form evaluation: 21 MCIF/ACL conference talks, about 2 h total, accented scientific English, human references, Whisper text normalization.

system	scoring contract	WER	compute per second of audio
Offline, VAD-segmented	full file available, no latency	7.6	offline
Streaming Qwen3-ASR (windowed)	settled transcript	8.4	126 GFLOPs avg, growing to 172 within each segment
Streaming Qwen3-ASR (windowed)	live, no rewrite, 250 ms tail cut + EOS flush	12.6	126 GFLOPs avg, growing to 172
Streaming Qwen3-ASR causal (v0.2 tower)	settled transcript	17.2	42 GFLOPs, constant
Streaming Qwen3-ASR causal (v0.2 tower)	live, no rewrite (shipped default policy)	17.6	42 GFLOPs, constant

Offline-done-right is the quality ceiling; the windowed backend is that strategy made streaming; the causal tower trades ~1.4x the live windowed WER for 3.0x (avg) to 4.1x (peak) less compute, constant in stream age (benchmarks/suite/run_flops.py, FlopCounterMode on real weights).

Language support: the causal tower is English-only: its LibriSpeech distillation degraded other languages severely (FLEURS: fr 7.8→37.9, de 12.6→49.8 WER, zh 11.4→85.7 CER vs windowed; see benchmarks/suite/results/mps_20260707_fleurs/). Use the windowed backend for non-English sessions.

Short-form through the same streaming stack at the shipped default: LibriSpeech test-clean 3.73 / test-other 7.34 (for reference, Voxtral Mini Realtime 3B @480 ms publishes 2.1 / 5.5 with ~5x the parameters). The v0.1 tower measured 3.67 / 7.22: the diverse-audio phase trades ~0.1 pt of short-form for ~1.2 pt of long-form.

Commit latency

Per-committed-word latency (word spoken → word committed, feeding at 1.0x), measured by replaying the 21-talk events under the live contract (benchmarks/suite/results/mps_20260707_latency/):

policy	p50	p95	live WER (whisper-norm)
hold 6 / stable 2 (old default)	5.9 s	13.6 s	17.2
hold 6 / stable 1 (default)	4.1 s	7.9 s	17.6
hold 2 / stable 1 (--qwen3-streaming-hold-back-words 2)	2.2 s	7.9 s	17.8

At the old default, 93% of words were committed by the ~12-16 s punctuation rollover rather than the stability policy: one stability iteration is enough on the causal hypothesis stream. The 2.2 s point sits at the 1.92 s block cadence floor. The unstable tail is additionally visible immediately through get_buffer() (~2 s behind the audio head).

Streaming RTF is ASR compute divided by audio duration, excluding model load. The H100 bars are pure HF Transformers/CUDA. The A100 bars are vLLM CUDA. The Metal bars use vLLM Metal on Apple Silicon.

Key numbers:

• H100 HF Transformers: normal windowed 0.293 RTF, causal 0.160 RTF.
• Apple M5 vLLM Metal: normal 0.262 RTF, causal 0.156 RTF.
• Apple M-series HF/MPS fp16: causal 0.116 RTF over the full 21-talk set, and quality parity with the CUDA bf16 runs.
• A100 vLLM CUDA: normal 0.146 RTF, causal vllm-live 0.099 RTF.
• The A100 vllm-text fallback measured 0.113 RTF on the same 22 s smoke.
• CPU fp32 (Apple M-series cores): causal 0.82 RTF on a 277 s talk, real-time capable, single session.

These speed smokes are not WER claims; use the long-form WER table above for quality comparisons.

Hardware support

target	backend	dtype	status
CUDA (Linux)	hf or vllm (vllm-live / vllm-text)	bf16	primary; vLLM is the multi-session path
Apple Silicon	hf (pure torch/MPS)	fp16	measured: quality parity with CUDA bf16, RTF 0.116
Apple Silicon	qwen3_asr_causal.metal (vLLM Metal / MLX)	fp16	Python 3.12, darwin arm64 only
CPU	hf	fp32	works, RTF ~0.8; no quantized path yet

The HF backend serializes concurrent sessions behind one decode lock: treat it as the single-session/edge path and use the vLLM CUDA backend when serving many sessions.

Python

from qwen3_asr_causal import Qwen3StreamingASR, Qwen3StreamingOnlineProcessor

asr = Qwen3StreamingASR(
    lan="en",
    model_size="Qwen/Qwen3-ASR-0.6B",
    qwen3_streaming_audio_backend="causal",
    qwen3_streaming_tower_checkpoint="qfuxa/qwen3-asr-0.6b-streaming",
)
processor = Qwen3StreamingOnlineProcessor(asr)

Development

Default tests are lightweight and do not download Qwen weights:

python -m py_compile $(find src/qwen3_asr_causal -name "*.py")
pytest -q

Real inference is opt-in:

QWEN3_ASR_CAUSAL_E2E=1 pytest tests/test_e2e.py

The ongoing research notebooks, training scripts, run logs, and WER/RTF benchmarks live in experiments/qwen3-causal/ and benchmarks/. WhisperLiveKit imports this package instead of carrying a second copy of the Qwen runtime.

License

Apache-2.0.