FunASR vLLM Inference Engine Guide

---

Benchmark

Test set: 184 files, 11,541 seconds total. Models: Fun-ASR-Nano / GLM-ASR-Nano.

Model	Engine	VAD	RTFx	CER	Notes
Fun-ASR-Nano	PyTorch	dynamic	21	8.06%	Baseline
Fun-ASR-Nano	vLLM batch	dynamic	340	8.20%	16x speedup
Fun-ASR-Nano	Offline service (no SPK)	dynamic	102	8.14%
Fun-ASR-Nano	Offline service (+SPK)	dynamic	46	8.19%	SPK off by default
GLM-ASR-Nano	vLLM batch	fixed	265	12.93%	No long-audio support

vLLM matches PyTorch CER exactly (delta < 0.2%) while achieving 16–340x speedup.

---

Table of Contents

1. Installation & Environment
2. vLLM Engine Architecture
3. Offline SDK Inference
4. Streaming SDK Inference
5. Offline Speech Recognition Service
6. Streaming Speech Recognition Service
7. Dynamic VAD
8. API Reference
9. FAQ

---

1. Installation & Environment

Install vLLM first, choosing a version compatible with your NVIDIA driver's CUDA. vLLM pins and installs a matching torch / torchaudio / torchvision trio automatically, so do not install torch/torchaudio yourself — the three are ABI-locked, i.e. they must be the matching set built against each other (e.g. torch 2.10.0 ↔ torchaudio 2.10.0 ↔ torchvision 0.25.0).

# 1) Install vLLM first. Pick the version by the CUDA version shown in `nvidia-smi`
#    (the driver's max CUDA), NOT the runtime CUDA. vLLM brings a matching torch/torchaudio/torchvision.
#    driver CUDA 12.x  -> pip install vllm==0.19.1   (ships torch 2.10 / cu128)
#    driver CUDA >= 13 -> pip install vllm           (latest; ships torch 2.11 / cu130)
pip install "vllm==0.19.1"   # adjust to your driver CUDA; see note below

# 2) Then FunASR and the rest.
pip install funasr>=1.3.0

cd /path/to/FunASR && pip install -e .

Hardware: GPU ≥ 8 GB VRAM, CUDA ≥ 11.8. 16 GB+ recommended.

Why not pip install torch torchaudio? The torch/torchaudio/torchvision versions are determined by the vLLM release — each major vLLM version bumps them together (see vLLM's requirements/cuda.txt). Installing them by hand pulls the newest wheel, which may be built for a newer CUDA runtime than your driver supports; PyTorch then fails during CUDA initialization with The NVIDIA driver on your system is too old before FunASR even starts. Letting vLLM own the trio avoids this. If you still hit a driver-too-old error, install a vLLM version whose CUDA build matches the CUDA reported by nvidia-smi (e.g. vllm==0.19.1 for CUDA 12.x), or update the NVIDIA driver first.

---

2. vLLM Engine Architecture

Overall Architecture

FunASR's vLLM integration splits the ASR model into two independently running components:

┌──────────────────────────────────────────────────────────────┐
│                  FunASR + vLLM Inference Architecture        │
├──────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌─────────────── PyTorch (single GPU) ───────────┐          │
│  │                                                │          │
│  │  Audio ──→ Frontend ──→ Audio Encoder ──→ Adaptor         │
│  │            (fbank)      (SenseVoice/     (Transformer/    │
│  │                          Whisper)         MLP)            │
│  │                              │                            │
│  │                              ▼                            │
│  │                     Audio Embeddings                      │
│  │                              │                            │
│  │  Text Prompt ──→ Tokenize ──→ Embed                       │
│  │  (system/user/                  │                         │
│  │   hotwords/language)            │                         │
│  │                                 ▼                         │
│  │                          [Concat Embeddings]              │
│  └─────────────────────────────────┼─────────────┘           │
│                                    │                         │
│                                    ▼ EmbedsPrompt            │
│  ┌─────────────── vLLM Engine ────────────────────┐          │
│  │                                                │          │
│  │   PagedAttention + Continuous Batching         │          │
│  │   KV Cache management + CUDA Graph             │          │
│  │   Tensor Parallel (multi-GPU)                  │          │
│  │                                                │          │
│  │   Qwen3-0.6B / Llama-2B (LLM decoding)         │          │
│  │                                                │          │
│  └────────────────────┬───────────────────────────┘          │
│                       │                                      │
│                       ▼                                      │
│                Generated Text                                │
│                       │                                      │
│  ┌────────────────────┼──────────────────────────┐           │
│  │  (Optional) CTC Decoder ──→ Forced Alignment  │           │
│  │           ──→ Character-level timestamps      │           │
│  └───────────────────────────────────────────────┘           │
└──────────────────────────────────────────────────────────────┘

Why vLLM?

Feature	PyTorch generate()	vLLM
KV Cache management	Fixed allocation, wastes memory	PagedAttention, on-demand allocation
Batching	Manual padding required	Continuous Batching, automatic scheduling
CUDA optimization	None	CUDA Graph + operator fusion
Multi-GPU parallelism	Manual implementation	Tensor Parallel with one-line config
Throughput	RTFx ~20	RTFx 340+

Supported Models

Model	LLM component	Audio encoder	vLLM speedup
Fun-ASR-Nano	Qwen3-0.6B	SenseVoice	✓ 21.7x
GLM-ASR-Nano	Llama-2B	Whisper-like	✓ 7.6x
LLMASR	Qwen/Vicuna	Whisper	✓
Paraformer	No LLM	—	✗ Non-autoregressive
SenseVoice	No LLM	—	✗ Encoder-decoder

Key Implementation Details

1. Weight separation: LLM weights are extracted from model.pt and converted to HuggingFace format for vLLM loading
2. EmbedsPrompt: a vLLM input mode that feeds precomputed embedding vectors (rather than the usual token IDs) directly as the prompt (enabled via enable_prompt_embeds=True). Fun-ASR-Nano requires it because the audio, after the adaptor, is a sequence of continuous vectors — not tokens — so the audio embeddings and text embeddings are concatenated along the sequence dimension and submitted to vLLM as a whole
3. use_low_frame_rate: Fun-ASR-Nano's adaptor output must be truncated to the correct token count via a formula (critical for consistency)
4. Batch encode: Multiple audio files pass through extract_fbank → audio_encoder → audio_adaptor in a single forward pass
5. CTC timestamps: Encoder output is retained; after text generation, forced alignment yields character-level timing

---

3. Offline SDK Inference

Best suited for large-scale audio transcription and offline batch processing. vLLM's batching capability provides the greatest advantage in this scenario.

Design Principles

Offline SDK inference splits the ASR pipeline into two stages executed independently:

$ ┌─────────────────────────────────────────────────────────────────────┐ │ \text{Stage} 1: \text{Audio} \text{Encoding} (\text{PyTorch}, \text{single} \text{GPU}) │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ \text{Audio} \text{file} \text{list} ──→ \text{Group} (\text{batch} \text{of} 8) ──→ \text{Frontend} (\text{Fbank}) │ │ │ │ │ │ │ ▼ │ │ │ \text{SenseVoice} \text{Encoder} │ │ │ │ │ │ │ ▼ │ │ │ \text{Audio} \text{Adaptor} │ │ │ (\text{dim} \text{transform} + \text{LFR} \text{trunc}) │ │ │ │ │ │ └─── \text{Shared} \text{text} \text{prompt} \text{encoding} ────┐ ▼ │ │ (\text{system}/\text{hotwords}/\text{language}) │ \text{audio\_embeds} │ │ │ │ │ │ │ ▼ │ ▼ │ │ \text{prefix\_emb} ──→ [\text{concat}: \text{prefix} | \text{audio} | \text{suffix}] │ │ │ │ │ ▼ │ │ \text{EmbedsPrompt} (\text{N} \text{samples}) │ └──────────────────────────────────────────────────┼─────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────────┐ │ \text{Stage} 2: \text{LLM} \text{Decoding} (\text{vLLM}, \text{multi}-\text{GPU} \text{Tensor} \text{Parallel}) │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ \text{EmbedsPrompt} \times \text{N} ──→ \text{vLLM} \text{Continuous} \text{Batching} │ │ (\text{PagedAttention} + \text{CUDA} \text{Graph}) │ │ │ │ │ ▼ │ │ \text{Generated} \text{token\_ids} \times \text{N} │ │ │ │ │ ▼ │ │ \text{Decode} + \text{post}-\text{processing} (\text{strip} \text{special} \text{tokens}) │ │ │ │ │ ▼ │ │ (\text{Optional}) \text{CTC} \text{Forced} \text{Alignment} → \text{char} \text{timestamps}│ └─────────────────────────────────────────────────────────────────────┘ $

Key design decisions:

1. Weight separation: On first run, weights with the llm.* prefix are extracted from model.pt and saved in HuggingFace safetensors format for vLLM (cached in the Qwen3-0.6B-vllm/ directory)
2. Embedding concatenation: The text prompt is encoded through the LLM's embed_tokens layer into embeddings, then concatenated with the audio adaptor output along the sequence dimension: [prefix_emb | audio_emb | suffix_emb], and submitted to vLLM as an EmbedsPrompt
3. Low Frame Rate truncation: Adaptor output must be truncated to the correct length using: fake_token_len = ((((fbank_len - 3 + 2) // 2 - 3 + 2) // 2) - 1) // 2 + 1, ensuring consistency with the PyTorch training pipeline
4. Batch audio encoding: Multiple audio files are grouped in batches of 8 through the encoder + adaptor forward pass, reducing GPU kernel launch overhead
5. Shared text prompt: When hotwords and language are identical within a batch, prefix_emb and suffix_emb are computed only once
6. CTC timestamps: Encoder output is preserved; after LLM text generation, forced alignment produces character-level timestamps

Why faster than PyTorch generate()?

Dimension	PyTorch	vLLM
KV Cache	Fixed pre-allocation (wastes memory)	PagedAttention on-demand allocation
Batching	Manual padding alignment	Continuous Batching auto-scheduling
CUDA	Sequential per-sample execution	CUDA Graph + operator fusion
Multi-GPU	Manual implementation	Tensor Parallel one-line config
Result	RTFx ~20	RTFx 340+ (16x speedup)

Universal Interface (Recommended)

from funasr.auto.auto_model_vllm import AutoModelVLLM

model = AutoModelVLLM(
    model="FunAudioLLM/Fun-ASR-Nano-2512",
    hub="ms",                    # or "hf"
    tensor_parallel_size=2,      # multi-GPU parallel
    gpu_memory_utilization=0.8,
)

results = model.generate(
    ["audio1.wav", "audio2.wav"],
    language="中文",
    hotwords=["张三", "北京"],
)
for r in results:
    print(f"[{r['key']}] {r['text']}")

Direct Interface

from funasr.models.fun_asr_nano.inference_vllm import FunASRNanoVLLM

engine = FunASRNanoVLLM.from_pretrained(
    model="FunAudioLLM/Fun-ASR-Nano-2512",
    tensor_parallel_size=4,
)

results = engine.generate(
    inputs="wav.scp",  # supports scp/jsonl/file lists
    hotwords=["开放时间"],
    language="中文",
    max_new_tokens=512,
)

Command Line

cd examples/industrial_data_pretraining/fun_asr_nano

# Single file
python demo_vllm.py --input audio.wav --language 中文

# Batch + multi-GPU
python demo_vllm.py --input wav.scp --tensor-parallel-size 4 --batch-size 32

# With hotwords + save results
python demo_vllm.py --input audio.wav --hotwords 张三 北京 --output results.jsonl

---

4. Streaming SDK Inference

Processes audio in 720 ms chunks incrementally, outputting progressively stable recognition results. Suited for SDK-integrated real-time subtitle scenarios.

Design Principles

Audio stream (720 ms chunks)
    │ Cumulative re-encoding (each chunk covers all audio from the start)
    ▼
┌──────────────────────────┐
│ Stage 1: First 10 chunks │  ← No prev_text; batch generation
│ Identify stable output   │
└──────────┬───────────────┘
           ▼
┌──────────────────────────┐
│ Stage 2: Subsequent      │  ← Use stable output as prev_text
└──────────┬───────────────┘
           ▼
Each chunk: [fixed region (confirmed)] + [8-char unfixed (may change)]

Usage

from funasr.models.fun_asr_nano.inference_vllm_streaming import FunASRNanoStreamingVLLM

engine = FunASRNanoStreamingVLLM.from_pretrained(
    model="FunAudioLLM/Fun-ASR-Nano-2512",
    chunk_ms=720,
    rollback_chars=8,
)

for result in engine.streaming_generate("audio.wav", language="中文"):
    if result["is_final"]:
        print(f"Final: {result['text']}")
    else:
        print(f"[{result['audio_duration_ms']:.0f}ms] Confirmed: {result['fixed_text']}")

Output Characteristics

Accumulated audio	Output quality
< 1.5 s	Empty or noise
1.5–3.0 s	Partially correct
> 3.0 s	Accurate output

Note: repetition_penalty cannot be used with EmbedsPrompt. Here the prompt is a sequence of embedding vectors with no corresponding token IDs, whereas repetition_penalty needs the prompt's token IDs to down-weight already-seen tokens in the logits; applying it under EmbedsPrompt indexes out of bounds and triggers a CUDA device-side assert.

---

5. Offline Speech Recognition Service

5.1 Service Architecture

Client                                  serve_vllm.py
  │                                        │
  │── HTTP / OpenAI / WebSocket ─────────→│
  │                                        │
  │                                   ┌────┴────────────────────────┐
  │                                   │ 1. Receive complete audio   │
  │                                   │ 2. Dynamic VAD (≤60 s/seg)  │
  │                                   │ 3. vLLM batch all segments  │
  │                                   │ 4. CTC timestamps (per-char)│
  │                                   │ 5. Speaker diarization (opt)│
  │                                   └────┬────────────────────────┘
  │                                        │
  │←── JSON result ───────────────────────│

Characteristics:

• Processes audio only after it arrives in full — ideal for file transcription
• Dynamic VAD preserves long segments (≤60 s), reducing boundary-cut losses
• Batch inference over all VAD segments maximizes throughput
• Automatically outputs character-level timestamps
• Speaker diarization is off by default; clients can enable it

5.2 Starting the Service

CUDA_VISIBLE_DEVICES=0 python examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py \
    --port 8899 \
    --model FunAudioLLM/Fun-ASR-Nano-2512 \
    --gpu-memory-utilization 0.5

About CUDA_VISIBLE_DEVICES: the =0 in the examples is just an illustrative value ("use GPU 0"), not a fixed requirement. It selects which GPUs are visible to this process (indexed as in nvidia-smi), a single GPU machine does not need to set it.

• Single GPU: small models like 0.6B / 1.7B can run several instances on one card — point multiple processes at the same GPU (e.g. all =0) sharing it via MPS, or split across cards with process A =0, B =1 (see §6.7).

5.3 Protocol 1: HTTP REST — POST /asr

The most feature-complete interface, supporting speaker diarization, timestamps, and hotwords.

Request: multipart/form-data

Parameter	Type	Default	Description
file	file	required	Audio file (wav/mp3/flac)
language	string	None	Language ("中文" / "English" / ...), None for auto
hotwords	string	""	Hotwords, comma-separated
spk	bool	false	Enable speaker diarization
timestamp	bool	true	Output character-level timestamps

Response:

{
    "text": "Full transcription text",
    "segments": [
        {
            "text": "Segment text",
            "start": 1.7,
            "end": 14.8,
            "speaker": "SPK0",
            "words": [
                {"word": "砸", "start": 2.02, "end": 2.08},
                {"word": "了", "start": 2.26, "end": 2.32}
            ]
        }
    ],
    "duration": 227.4,
    "processing_time": 3.422,
    "rtf": 0.015
}

Client examples:

# cURL
curl -X POST http://localhost:8899/asr \
    -F "file=@meeting.wav" -F "language=中文" -F "spk=true"

# Python requests
import requests
resp = requests.post("http://localhost:8899/asr",
    files={"file": open("audio.wav", "rb")},
    data={"language": "中文", "spk": "true"})
result = resp.json()

// JavaScript fetch
const form = new FormData();
form.append("file", audioBlob, "audio.wav");
form.append("language", "中文");
form.append("spk", "true");
const resp = await fetch("http://localhost:8899/asr", { method: "POST", body: form });
const result = await resp.json();

5.4 Protocol 2: OpenAI Whisper Compatible — POST /v1/audio/transcriptions

Compatible with the OpenAI Whisper API standard; works directly with the OpenAI SDK.

Request: multipart/form-data

Parameter	Type	Default	Description
file	file	required	Audio file
model	string	"fun-asr-nano"	Model name (compatibility field)
language	string	None	Language
response_format	string	"json"	"json" / "text" / "verbose_json"
timestamp_granularities	string	"word"	"word" / "segment"
spk	bool	false	Speaker diarization (FunASR extension)

Response (verbose_json):

{
    "task": "transcribe",
    "language": "zh",
    "duration": 5.17,
    "text": "我一直没有照顾孩子，但是我想要抚养权。",
    "segments": [
        {
            "id": 0, "start": 0.0, "end": 5.15,
            "text": "我一直没有照顾孩子，但是我想要抚养权。",
            "words": [{"word": "我", "start": 0.42, "end": 0.48}, ...]
        }
    ]
}

Client examples:

# OpenAI SDK (recommended)
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8899/v1", api_key="none")
result = client.audio.transcriptions.create(
    model="fun-asr-nano",
    file=open("audio.wav", "rb"),
    response_format="verbose_json",
)
print(result.text)

# cURL
curl -X POST http://localhost:8899/v1/audio/transcriptions \
    -F "file=@audio.wav" -F "model=fun-asr-nano" -F "response_format=verbose_json"

5.5 Protocol 3: WebSocket — ws://host:port/ws

WebSocket interface for the offline service. Send complete audio, then receive results. Speaker clustering is performed automatically on STOP, and results include the spk field.

Client → Server:

Message	Description
"START"	Begin session
"LANGUAGE:中文"	Set language (optional)
"HOTWORDS:word1,word2"	Set hotwords (optional)
[binary]	PCM16 16 kHz mono audio data
"STOP"	End session; request recognition result

Server → Client:

{"event": "started"}
{"event": "language_set", "language": "中文"}
{"sentences": [{"text":"...","start":..,"end":..}], "is_final": true, "duration_ms": 5170}
{"event": "stopped"}

Client example:

import asyncio, websockets, json, numpy as np, soundfile as sf

async def offline_ws(audio_path):
    audio, sr = sf.read(audio_path)
    pcm = (audio * 32768).astype(np.int16)

    async with websockets.connect("ws://localhost:8899/ws") as ws:
        await ws.send("START")
        await ws.recv()
        await ws.send("LANGUAGE:中文")
        await ws.recv()

        # Send complete audio
        await ws.send(pcm.tobytes())
        await ws.send("STOP")

        # Receive result
        async for msg in ws:
            data = json.loads(msg)
            if data.get("is_final"):
                for s in data["sentences"]:
                    print(f"[{s['start']/1000:.1f}s] {s['text']}")
                break

asyncio.run(offline_ws("audio.wav"))

---

6. Streaming Speech Recognition Service

6.1 Service Architecture

Client (microphone / audio stream)     serve_realtime_ws.py
  │                                      │
  │── WebSocket PCM16 16 kHz ───────────→│
  │   (~100 ms per frame, continuous)    │
  │                                      │
  │                                 ┌────┴──────────────────────────┐
  │                                 │ Real-time loop:               │
  │                                 │  ├─ Dynamic VAD (60 ms chunk) │
  │                                 │  ├─ Endpoint → vLLM decode    │
  │                                 │  ├─ No endpoint → partial     │
  │                                 │  └─ Streaming SPK assignment  │
  │                                 └────┬──────────────────────────┘
  │                                      │
  │←── JSON real-time push ──────────────│

Characteristics:

• Audio arrives frame by frame; processing starts immediately
• Natural sentence segmentation based on VAD endpoints
• Confirmed segment text is locked and never changes; partial text updates in real time
• Streaming speaker assignment + global re-clustering on STOP
• First-word latency ~480 ms

6.2 Starting the Service

CUDA_VISIBLE_DEVICES=0 python examples/industrial_data_pretraining/fun_asr_nano/serve_realtime_ws.py \
    --port 10095 --language 中文 --hotword-file hotword_list

6.3 WebSocket Protocol

Connection: ws://host:10095

Client → Server:

Message	Format	Description
Start	"START"	Initialize session
Hotwords	"HOTWORDS:word1,word2"	Optional
Language	"LANGUAGE:中文"	Optional
Audio	binary	PCM16 16 kHz mono
End	"STOP"	Final decode + SPK re-clustering

Server → Client:

{"event": "started"}
{"sentences": [{"text":"你好","start":300,"end":1200,"spk":0}], "partial": "世界", "is_final": false}
{"sentences": [...], "is_final": true}
{"event": "stopped"}

Fields: sentences[] = locked segments, partial = text being spoken (may change), is_final = true after STOP.

Sequence diagram:

Client              Server
  │── START ───────→│
  │←─ started ──────│
  │── [audio] ─────→│
  │←─ {partial} ────│  #refer to 6.5
  │── [audio] ─────→│
  │←─ {sentences+partial} ─│  (VAD cut a sentence)
  │── STOP ────────→│
  │←─ {is_final:true} ────│
  │←─ stopped ─────│

6.4 Client Usage

Python CLI:

python client_python.py --server ws://localhost:10095 --mic
python client_python.py --server ws://localhost:10095 --file audio.wav

Browser: Open client_mic.html

Custom Python:

import asyncio, websockets, numpy as np, json

async def stream(audio_path):
    import soundfile as sf
    audio, sr = sf.read(audio_path)
    pcm = (audio * 32768).astype(np.int16)

    async with websockets.connect("ws://localhost:10095") as ws:
        await ws.send("START")
        await ws.recv()

        for i in range(0, len(pcm), 1600):
            await ws.send(pcm[i:i+1600].tobytes())
            await asyncio.sleep(0.05)

        await ws.send("STOP")
        async for msg in ws:
            data = json.loads(msg)
            if data.get("is_final"):
                for s in data["sentences"]:
                    print(f"[{s['start']/1000:.1f}s] {s['text']}")
                break

asyncio.run(stream("audio.wav"))

6.5 Partial preview mechanism and long-sentence behavior

What partial is and how it's produced While the user is speaking, the streaming service periodically (default decode_interval≈0.48s in serve_realtime_ws.py) decodes "the current sentence from its start up to now," emitting provisional text (the partial field in the protocol, which may be overwritten by later refreshes), until VAD detects the sentence end and locks it into sentences. This lets the user see text as they speak.

Note: serve_vllm.py's /ws (§5) has no partial and only returns at sentence end; use serve_realtime_ws.py for live preview.

Principle: why each partial re-encodes the whole segment from the start Fun-ASR-Nano's acoustic encoder (SenseVoice) is a full-context, non-streaming encoder — each frame's representation depends on the context of the entire segment. When the sentence continues and the audio grows, the context of the earlier frames changes, so the previously computed encoding no longer holds. It therefore cannot cache history and encode only the new frames the way a streaming / causal encoder would; it must run the whole "start → now" segment through the encoder again.

Resulting behavior: partial gets slower on long sentences (O(L²)) Because each refresh re-encodes from the sentence start, the longer a sentence, the longer each partial's audio and the more refreshes occur — so total encoding work grows quadratically with sentence length. In practice a ~29 s continuous utterance is fully re-encoded a dozen-plus times, with single-pass encoder time climbing from tens to hundreds of milliseconds. (The §4 SDK streaming "each chunk contains all audio from the start to now" is the same mechanism; long files behave the same way.)

Usage guidance

• Normal conversational speech has natural pauses, so VAD splits it into relatively short utterances and each partial's cost is naturally bounded — usually nothing to worry about.
• Only very long, pauseless continuous speech (e.g. reading aloud) makes a single utterance keep growing and the partial preview progressively slower — this is an inherent property of the full-context encoder, not a configuration issue. If your scenario has such audio and is sensitive to preview latency, you can bound the partial encoding window in code (e.g. encode only the last few seconds); this is a code change, not a config option, and it does not affect locked sentences (which still run on the full audio, with identical final transcripts).

6.6 Cost of speaker diarization (SPK) and how to disable it

At startup, serve_realtime_ws.py loads the SPK model by default (hardcoded to iic/speech_eres2netv2_sv_zh-cn_16k-common) and runs speaker assignment for each VAD-completed sentence during streaming. Note:

• SPK is of limited effectiveness on Fun-ASR-Nano (see #2944); most real-time ASR scenarios do not need speaker separation.
• Streaming SPK is expensive and grows with the session: each sentence re-clusters all historical embeddings (O(N²), more expensive per sentence as the session grows) and synchronously blocks the event loop; the session also re-clusters everything again at the end, so the per-sentence clustering during streaming is overwritten by the final result — redundant as far as the final output is concerned. This is especially pronounced under long sessions + high concurrency.
• Disabling currently requires a code change: spk_model is loaded by default and hardcoded in serve_realtime_ws.py, with no flag.
• Recommendation: don't load spk_model when speaker separation isn't needed; if diarization is required, take labels from the final result, or use a Paraformer + cam++ pipeline.

6.7 Production concurrency and multi-process deployment

serve_realtime_ws.py is a single-asyncio-event-loop service: both decode() (timed partial) and add_audio() (decode triggered at VAD sentence end) synchronously block the entire event loop — while any one connection is decoding, all others pause sending/receiving. Therefore:

• The single-process concurrency ceiling comes from event-loop serialization, not GPU compute. Under high concurrency GPU utilization stays low and the encoder runs at ~86× real time; mistaking this for insufficient GPU and adding cards or tensor parallelism yields little (TP only splits the LLM, not the standalone encoder).
• The right way to scale (currently) = multiple independent processes on one card + CUDA MPS + nginx round-robin: each process has its own GIL and CUDA context, sidestepping the single-loop serialization; MPS lets the processes truly share the GPU concurrently and fill the idle compute; nginx round-robins across the WebSocket backends. Beyond a single card's headroom, scale out horizontally (one instance per card + a load balancer).
• Sustainable concurrency has no universal "supports N connections" number. The ceiling is set not by the number of connections but by how many are speaking at the same moment — each speaking connection triggers a partial decode roughly once per second, all serialized on that single event loop. It mainly varies with: ① silence ratio — in real turn-taking users spend most of the time listening, so far fewer are decoding simultaneously than are connected, whereas a continuous monologue keeps nearly every connection decoding; ② sentence length — longer sentences make each partial encode more expensive (see 6.5's O(L²)), raising load at the same connection count. So the same "single L20 + multi-process + MPS" setup can sustain dozens of connections under turn-taking-like load but markedly fewer under long, pauseless speech. Any "supports X connections" figure holds only for the traffic profile it was measured under — benchmark with your own real traffic (sentence length, pauses, continuous or not) rather than treating someone else's number as your spec.

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7. Dynamic VAD

fsmn-vad enables dynamic silence thresholds by default. Offline and streaming modes use different configurations.

Accumulated duration	Offline (preserve long segs ≤60 s)	Streaming (balance latency)
≤ 5 s	2000 ms	2000 ms
5–10 s	2000 ms	1500 ms
10–15 s	1000 ms	1000 ms
15–20 s	1000 ms	800 ms
20–30 s	800 ms	800 ms
30–45 s	600 ms	400 ms
45–60 s	200–400 ms	100 ms
> 60 s	100 ms	100 ms

Offline mode favors longer segments to reduce boundary-cut losses; streaming mode tightens faster to reduce latency.

Customization

model.generate(input="audio.wav", silence_schedule=[(5000,1500), (20000,800), (float('inf'),300)])

GLM-ASR does not support long-segment inference; pass dynamic_silence=False when using it.

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8. API Reference

Parameter	AutoModelVLLM	serve_vllm.py	serve_realtime_ws.py
model	✓	--model	--model
gpu_memory_utilization	✓	--gpu-memory-utilization	--gpu-memory-utilization
tensor_parallel_size	✓	—	--tensor-parallel-size
max_model_len	✓	--max-model-len	--max-model-len
language	generate() param	API param	--language / LANGUAGE:
hotwords	generate() param	API param	--hotword-file / HOTWORDS:

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9. FAQ

Q: Offline or streaming? Complete files → offline (high throughput). Microphone / live stream → streaming (low latency).

Q: Can GLM-ASR use dynamic VAD? It does not support long-segment inference. Use dynamic_silence=False.

Q: Performance impact of SPK? RTFx drops from 102 to 46. CER is unchanged. Disabled by default.

Q: Entry points for custom development? Offline: serve_vllm.process_audio() / FunASRNanoVLLM.generate() Streaming: serve_realtime_ws.RealtimeASRSession

Q: Slow first startup? vLLM initialization takes 60–90 s (KV Cache + CUDA Graph warmup). Subsequent inferences are instant.