fasr-asr-qwen3asr 0.5.2

Qwen3 ASR model for fasr

fasr-asr-qwen3asr

Chinese documentation

Qwen3-ASR speech recognition for fasr. The plugin uses the bundled Qwen3-ASR vLLM backend and supports both batch transcription and cumulative streaming transcription on the same loaded engine.

Install

pip install fasr-asr-qwen3asr

Registered Model

Registry name	Class selected by size	Best for
qwen3asr	Qwen3ASRSmall or Qwen3ASRLarge	GPU ASR without word timestamps

Use size="small" for Qwen/Qwen3-ASR-0.6B and size="large" for Qwen/Qwen3-ASR-1.7B.

Pipeline Usage

from fasr import AudioPipeline

pipeline = (
    AudioPipeline()
    .add_pipe("detector", model="fsmn")
    .add_pipe(
        "recognizer",
        model="qwen3asr",
        size="small",
        gpu_memory_utilization=0.7,
        max_new_tokens=2048,
    )
    .add_pipe("sentencizer", model="ct_transformer")
)

Quick choices:

Goal	Use	Result
Lower VRAM	size="small"	Uses the 0.6B checkpoint
Better accuracy	size="large"	Uses the 1.7B checkpoint, needs more GPU memory
Leave GPU headroom	gpu_memory_utilization=0.6	vLLM reserves less memory
Long-form output	max_new_tokens=4096 or higher	Allows longer generation
Bias vocabulary	context="..."	Adds prompt context for names or rare terms
Force language	language="zh"	Uses a fixed language hint

Confection Config

[asr_model]
@asr_models = "qwen3asr"
size = "small"
gpu_memory_utilization = 0.7
max_new_tokens = 2048
language = "zh"
context = "product names: fasr, Qwen3-ASR"

Inside a pipeline:

[pipeline]
@pipelines = "AudioPipeline.v1"
pipe_order = ["recognizer"]

[pipeline.pipes]

[pipeline.pipes.recognizer]
@pipes = "thread_pipe"
batch_size = 1

[pipeline.pipes.recognizer.component]
@components = "recognizer"

[pipeline.pipes.recognizer.component.model]
@asr_models = "qwen3asr"
size = "small"
gpu_memory_utilization = 0.7
max_new_tokens = 2048
language = "zh"
context = "product names: fasr, Qwen3-ASR"

Direct Model Usage

from fasr.config import registry

model = registry.asr_models.get("qwen3asr")(
    size="small",
    gpu_memory_utilization=0.7,
)

spans = model.transcribe(audio_spans)
for span in spans:
    print(span.text)

Streaming uses the same model instance and returns cumulative text. Consumers should overwrite the displayed partial text instead of appending deltas:

model = registry.asr_models.get("qwen3asr")(
    size="small",
    chunk_size_ms=2000,
    language="zh",
)

for chunk in audio_chunks:
    span = model.push_chunk(chunk)
    if span is not None:
        print(span.text)

Parameters

Parameter	Type / range	Default	Higher / true	Lower / false	Change when
size	"small" or "large"	"small"	"large" improves capacity, needs more VRAM	"small" is cheaper	Accuracy or resource budget changes
gpu_memory_utilization	float, (0, 1]	0.8	More KV-cache headroom, more VRAM reserved	Leaves more VRAM for other processes	vLLM OOMs or underuses GPU
max_new_tokens	int >= 1	4096	Longer outputs, more decode work	Shorter cap, less compute	Text is truncated or memory is tight
max_inference_batch_size	int, -1 or positive	-1	-1 lets backend choose	Smaller cap reduces peak memory	Batch inference OOMs
max_model_len	int or None	None	Longer prompt+generation context	Shorter context, less memory	Long prompts or OOMs
language	str or None	None	Fixed language hint	Auto language behavior	Language is known
context	str	""	More biasing context	Less prompt bias	Rare terms or names are missed
chunk_size_ms	int or None	None	Less frequent streaming decode	More responsive streaming	Streaming latency/throughput tuning
unfixed_chunk_num	int or None	None	More initial chunks without prefix fixing	Earlier prefix use	Early streaming text is unstable
unfixed_token_num	int or None	None	More rollback, more correction room	Less rollback, more stable prefix	Streaming revisions are too aggressive or too sticky

Generic checkpoint fields such as checkpoint, cache_dir, endpoint, revision, and force_download are inherited from the base model.

Output

• Batch mode writes full text to span.raw_text.
• Streaming mode returns cumulative AudioSpan(raw_text=...).
• Word or character timestamps are not returned by this plugin.

Dependencies

• fasr
• transformers == 4.57.6
• vllm == 0.14.0
• accelerate == 1.12.0
• librosa
• soundfile
• Python 3.10-3.12