Fun-ASR-Nano#

Fun-ASR-Nano is a multilingual audio transcription model served through the OpenAI-compatible /v1/audio/transcriptions endpoint. It accepts one uploaded audio file per request and returns text.

Prerequisites#

Install sglang-omni by following Installation, then download the model:

# Use the -hf variant
hf download FunAudioLLM/Fun-ASR-Nano-2512-hf

Server Configuration#

Fun-ASR-Nano runs a single ASR stage on one GPU.

sgl-omni serve \
  --model-path FunAudioLLM/Fun-ASR-Nano-2512-hf \
  --port 8000

Transcribe Audio#

curl -X POST http://localhost:8000/v1/audio/transcriptions \
  -F model=FunAudioLLM/Fun-ASR-Nano-2512-hf \
  -F file=@tests/data/query_to_cars.wav \
  -F language=en \
  -F response_format=json

import requests

with open("tests/data/query_to_cars.wav", "rb") as f:
    resp = requests.post(
        "http://localhost:8000/v1/audio/transcriptions",
        data={
            "model": "FunAudioLLM/Fun-ASR-Nano-2512-hf",
            "language": "en",
            "response_format": "json",
        },
        files={"file": ("query_to_cars.wav", f, "audio/wav")},
        timeout=300,
    )

resp.raise_for_status()
print(resp.json()["text"])

Request Parameters#

Parameter	Type	Default	Description
`file`	file	required	Audio file uploaded as multipart form data
`model`	string	server default	Model identifier
`language`	string	unset	Language hint. `en`/`english`/`英文` transcribe to English; `zh`/`cn`/`chinese`/`中文` (or unset) transcribes to Chinese; other values pass through as the target language
`response_format`	string	`json`	`json`, `verbose_json`, or `text`
`temperature`	float	`0.0`	Sampling temperature; `0.0` (greedy) is the correct decoding mode for Fun-ASR-Nano and the default
`max_new_tokens`	integer	duration-based	Generation budget scaled to the audio duration. Explicit values must be between 1 and 200

Benchmarking#

SeedTTS EN/ZH concurrency/WER benchmarking for Fun-ASR-Nano lives in benchmarks/eval/benchmark_asr_seedtts.py. Pass the Fun-ASR-Nano model path with --model-path.

# Download the test set once:
python -m benchmarks.dataset.prepare --dataset seedtts

# Launch Fun-ASR-Nano:
sgl-omni serve --model-path FunAudioLLM/Fun-ASR-Nano-2512-hf --port 8000

# Sweep the full SeedTTS EN set (1088 clips) at 1..64 concurrency, 3 repeats:
python -m benchmarks.eval.benchmark_asr_seedtts \
  --model-path FunAudioLLM/Fun-ASR-Nano-2512-hf --port 8000 \
  --concurrencies 1,2,4,8,16,32,64 --repeats 3

# Quick smoke on a 20-sample subset:
python -m benchmarks.eval.benchmark_asr_seedtts \
  --model-path FunAudioLLM/Fun-ASR-Nano-2512-hf --port 8000 \
  --max-samples 20 --concurrencies 2 --repeats 1

Benchmark Results#

Measured on a single H100 80 GB (bf16, DP=1, default server settings) against the full SeedTTS sets. Each row is the mean of 3 runs with one discarded warmup pass per level. RTF is processing time divided by audio duration (lower is better). audio_s/s is seconds of audio transcribed per wall-clock second.

SeedTTS EN (1088 clips, mean clip length 4.69 s). Corpus WER was 0.0171 at every level through concurrency 32:

Concurrency	Throughput (samples/s)	Mean latency (s)	p95 latency (s)	RTF mean	audio_s/s
1	26.44	0.038	0.047	0.0082	124
2	42.55	0.047	0.058	0.0102	200
4	62.35	0.064	0.088	0.0139	293
8	90.24	0.088	0.121	0.0192	423
16	127.46	0.125	0.167	0.0270	598
32	127.44	0.249	0.334	0.0539	598
64	137.98	0.453	0.542	0.0988	647

SeedTTS ZH (2020 clips, mean clip length 4.68 s). Corpus WER, effectively character level after normalization, was 0.0135 at every level through concurrency 32:

Concurrency	Throughput (samples/s)	Mean latency (s)	p95 latency (s)	RTF mean	audio_s/s
1	26.96	0.037	0.048	0.0080	126
2	45.97	0.043	0.056	0.0094	215
4	58.28	0.069	0.093	0.0148	273
8	79.76	0.100	0.138	0.0216	373
16	138.23	0.116	0.160	0.0249	647
32	167.42	0.190	0.264	0.0410	784
64	165.75	0.381	0.475	0.0825	776

At concurrency 64 a single worker rejects roughly 2 to 5 percent of requests with HTTP 500 by design, because the request-build backlog admits at most 16 pending builds per worker. Qwen3-ASR shows the same shedding behavior at this level. For higher client concurrency, serve behind the DP=2 managed router, matching the ASR CI topology.

Known Limitations#

The endpoint accepts one uploaded file per request.
Each uploaded audio segment must be 30 seconds or shorter, matching the official Fun-ASR VAD segment limit. Split longer recordings before upload.
itn and hotwords are supported by the model request builder but not exposed as form fields on the public transcription endpoint.
prompt is accepted by the HTTP endpoint for OpenAI compatibility, but Fun-ASR-Nano currently ignores it (use hotwords inside the builder for context biasing instead).
Audio is resampled to 16 kHz before transcription.
bf16 is strongly recommended; fp16 can overflow to NaN in the adaptor path.

Fun-ASR-Nano

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