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.

Fun-ASR does not support /v1/audio/translations; that endpoint returns HTTP 400. Use /v1/audio/transcriptions.

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"])

Stream Transcription#

Set the multipart stream field to true and keep response_format as json or text to receive Server-Sent Events (SSE):

curl -N -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 \
  -F stream=true

The response contains zero or more transcript.text.delta events, followed by one transcript.text.done event with the complete post-processed transcript, then data: [DONE]. Streaming primarily reduces time to first text; it does not change the final transcript.

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`
`stream`	boolean	`false`	Emit SSE text deltas. Streaming accepts only `json` or `text` response formats
`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

# Measure text TTFT and inter-chunk latency through the SSE endpoint:
python -m benchmarks.eval.benchmark_asr_seedtts \
  --model-path FunAudioLLM/Fun-ASR-Nano-2512-hf --port 8000 \
  --max-samples 20 --concurrencies 2 --repeats 1 --stream

Benchmark Results#

Measured on a single H100 80 GB (bf16, DP=1) against the full SeedTTS sets, with the pre-coalescing stage defaults (max_running_requests=32, request_build_max_pending=16, prefill coalescing off). 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). RTFx is successful input-audio seconds divided by wall-clock seconds (higher is better).

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	RTFx
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	RTFx
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

A single worker sheds requests with HTTP 500 by design once the request-build backlog is full; the current default admits at most 32 pending builds per worker. Under the pre-coalescing defaults above (16 pending builds) that cost roughly 2 to 5 percent of requests at concurrency 64. 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.
prompt carries context biasing: a comma-separated list of terms likely to appear in the audio (names, jargon). Each term becomes a hotword in the model’s prompt. Biasing raises the model’s preference for the supplied terms; it does not force them, and an irrelevant list can hurt accuracy.
itn and explicit hotwords remain available to in-process callers of the request builder; explicit hotwords take precedence over prompt.
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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