Revert README to 7f2c1bd

README.md

@@ -39,11 +39,10 @@ Given an audio or video file, the model generates a compact speaker-aware transc
 - [Model Architecture](#model-architecture)
 - [Evaluation](#evaluation)
 - [Quickstart](#quickstart)
-  - [Serve with SGLang Omni](#serve-with-sglang-omni)
-  - [Serving with Hugging Face](#serving-with-native-hugging-face-transformers)
+  - [Environment Setup](#environment-setup)
   - [Python Usage](#python-usage)
   - [Custom Prompt and Hotwords](#custom-prompt-and-hotwords)
-  - [Serve with vLLM](#serve-with-vllm)
+  - [Serve with vLLM and SGLang](#serve-with-vllm-and-sglang)
   - [Subtitle Web App](#subtitle-web-app)
 - [Output Format](#output-format)
 - [More Information](#more-information)
@@ -163,102 +162,7 @@ We evaluate MOSS-Transcribe-Diarize using three objective metrics: Character Err
 
 ## Quickstart
 
-### Serve with SGLang Omni
-
-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 |
-
-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), here we list the performance of short/long-sequence mutli-speaker ASR tasks on single H100:
-
-
-1. `movies800times` for 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 |
-
-2. `aishell4_long` for 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 |
-
-
-### Serving with Native Hugging Face Transformers
+### Environment Setup
 
 Use a clean Python environment. The model uses custom Transformers code, so load the model and processor with `trust_remote_code=True`.
 
@@ -345,10 +249,9 @@ 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 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`.
+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`.
 
 ```bash
 uv pip install -U vllm \
@@ -376,6 +279,25 @@ 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: