Update SGLang Omni usage in README

README.md

@@ -279,25 +279,95 @@ curl http://localhost:8000/v1/audio/transcriptions \
   -F temperature="0"
 ```
 
-The same request format can be used with an SGLang OpenAI-compatible server:
+The recommended way to serve MOSS-Transcribe-Diarize is [SGLang Omni](https://github.com/sgl-project/sglang-omni) through the OpenAI-compatible `/v1/audio/transcriptions` endpoint. Install `sglang-omni` by following the [installation guide](https://github.com/sgl-project/sglang-omni/blob/main/docs/get_started/installation.md), then download the model:
 
 ```bash
-python -m sglang.launch_server \
+hf download OpenMOSS-Team/MOSS-Transcribe-Diarize
+```
+
+Serve the model:
+
+```bash
+sgl-omni serve \
   --model-path OpenMOSS-Team/MOSS-Transcribe-Diarize \
-  --served-model-name OpenMOSS-Team/MOSS-Transcribe-Diarize \
-  --trust-remote-code \
-  --host 0.0.0.0 \
-  --port 30000
+  --port 8000 \
+  --max-running-requests 16 \
+  --cuda-graph-max-bs 16 \
+  --mem-fraction-static 0.80
 ```
 
+Use `response_format=verbose_json` when you need parsed speaker segments. `json` returns the raw transcript text only.
+
 ```bash
-curl http://localhost:30000/v1/audio/transcriptions \
-  -F model="OpenMOSS-Team/MOSS-Transcribe-Diarize" \
-  -F file=@"audio.wav" \
-  -F response_format="json" \
-  -F temperature="0"
+curl -X POST http://localhost:8000/v1/audio/transcriptions \
+  -F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
+  -F file=@audio.wav \
+  -F response_format=verbose_json
 ```
 
+```python
+import requests
+
+with open("audio.wav", "rb") as f:
+    resp = requests.post(
+        "http://localhost:8000/v1/audio/transcriptions",
+        data={
+            "model": "OpenMOSS-Team/MOSS-Transcribe-Diarize",
+            "response_format": "verbose_json",
+        },
+        files={"file": ("audio.wav", f, "audio/wav")},
+        timeout=300,
+    )
+
+resp.raise_for_status()
+payload = resp.json()
+print(payload["text"])
+for segment in payload.get("segments", []):
+    print(f"[{segment['start']:.2f}-{segment['end']:.2f}] {segment['text']}")
+```
+
+For longer multi-speaker audio, raise `max_new_tokens` so the decoder can finish the full diarized transcript:
+
+```bash
+curl -X POST http://localhost:8000/v1/audio/transcriptions \
+  -F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
+  -F file=@audio.wav \
+  -F response_format=verbose_json \
+  -F max_new_tokens=65536
+```
+
+| Parameter | Type | Default | Description |
+|---|---|---|---|
+| `file` | file | required | Audio file uploaded as multipart form data |
+| `model` | string | server default | Model identifier |
+| `language` | string | unset | Optional language hint |
+| `response_format` | string | `json` | `json`, `verbose_json`, or `text` |
+| `temperature` | float | model default (`0.0`) | Sampling temperature |
+| `max_new_tokens` | int | `5120` | Max generated tokens; raise for long audio, for example `65536` |
+| `prompt` | string | unset | Optional instruction override; omit to use the built-in transcribe+diarize prompt |
+
+For benchmarking, performance numbers, and implementation details, see the [SGLang Omni cookbook](https://github.com/sgl-project/sglang-omni/blob/main/docs/cookbook/moss_transcribe_diarize.md). The following single-H100 results are reported for short- and long-sequence multi-speaker ASR tasks.
+
+`movies` short-sequence ASR:
+
+| Concurrency | Throughput (req/s) | Mean latency (s) | RTF mean | audio_s/s |
+|---:|---:|---:|---:|---:|
+| 1 | 2.57 | 0.388 | 0.0612 | 29.76 |
+| 2 | 4.89 | 0.409 | 0.0659 | 56.55 |
+| 4 | 6.62 | 0.513 | 0.0790 | 76.64 |
+| 8 | 6.80 | 0.533 | 0.0810 | 78.70 |
+| 16 | 7.08 | 0.659 | 0.0922 | 81.98 |
+
+`aishell4_long` long-sequence ASR:
+
+| Concurrency | Throughput (req/s) | Mean latency (s) | RTF mean | audio_s/s |
+|---:|---:|---:|---:|---:|
+| 1 | 0.022 | 45.2 | 0.0197 | 50.64 |
+| 2 | 0.032 | 60.7 | 0.0265 | 74.25 |
+| 4 | 0.036 | 105.6 | 0.0461 | 81.64 |
+| 8 | 0.040 | 172.6 | 0.0754 | 90.62 |
+| 16 | 0.043 | 282.8 | 0.1237 | 98.83 |
+
 ### Subtitle Web App
 
 The source package includes a local subtitle workflow for upload, review, subtitle export, and optional FFmpeg burn-in: