[Docs] Update SGLang Omni Serving Commands

README.md

@@ -42,7 +42,8 @@ Given an audio or video file, the model generates a compact speaker-aware transc
   - [Environment Setup](#environment-setup)
   - [Python Usage](#python-usage)
   - [Custom Prompt and Hotwords](#custom-prompt-and-hotwords)
-  - [Serve with vLLM and SGLang](#serve-with-vllm-and-sglang)
+  - [Serve with SGLang Omni](#serve-with-sglang-omni)
+  - [Serve with vLLM](#serve-with-vllm)
   - [Subtitle Web App](#subtitle-web-app)
 - [Output Format](#output-format)
 - [More Information](#more-information)
@@ -249,9 +250,84 @@ To add hotwords, append a short hint to the default prompt:
 
 More prompt recipes are available in the GitHub repository: <https://github.com/OpenMOSS/MOSS-Transcribe-Diarize/blob/main/examples/prompts.md>
 
-### Serve with vLLM and SGLang
+### Serve with SGLang Omni
 
-MOSS-Transcribe-Diarize supports vLLM serving through the OpenAI-compatible transcription API. Use a pinned vLLM nightly build that includes the MOSS-Transcribe-Diarize model registration. Choose one of the following commands: for CUDA 12 environments, use `cu129`; for CUDA 13 environments, use `cu130`.
+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
+hf download OpenMOSS-Team/MOSS-Transcribe-Diarize
+```
+
+Serve the model:
+
+```bash
+sgl-omni serve \
+  --model-path OpenMOSS-Team/MOSS-Transcribe-Diarize \
+  --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 -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 (e.g. `65536`) |
+| `prompt` | string | unset | Optional instruction override; omit to use the built-in transcribe+diarize prompt |
+
+`verbose_json` parses the model markup into OpenAI-style `segments` with `start`, `end`, and speaker-prefixed `text` (for example `[S01]...`). `json` / `text` return the full transcript string without segment parsing.
+
+For benchmarking, performance numbers, and more details, see the [SGLang Omni cookbook](https://github.com/sgl-project/sglang-omni/blob/main/docs/cookbook/moss_transcribe_diarize.md).
+
+### Serve with vLLM
+
+MOSS-Transcribe-Diarize also supports vLLM serving through the OpenAI-compatible transcription API. Use a pinned vLLM nightly build that includes the MOSS-Transcribe-Diarize model registration. Choose one of the following commands: for CUDA 12 environments, use `cu129`; for CUDA 13 environments, use `cu130`.
 
 ```bash
 uv pip install -U vllm \
@@ -279,25 +355,6 @@ curl http://localhost:8000/v1/audio/transcriptions \
   -F temperature="0"
 ```
 
-The same request format can be used with an SGLang OpenAI-compatible server:
-
-```bash
-python -m sglang.launch_server \
-  --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
-```
-
-```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"
-```
-
 ### Subtitle Web App
 
 The source package includes a local subtitle workflow for upload, review, subtitle export, and optional FFmpeg burn-in: