Update README model capabilities

README.md

@@ -28,7 +28,7 @@ pipeline_tag: audio-text-to-text
 
 MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness.
 
-It supports transcription and diarization across 50+ languages.
+It supports transcription and diarization across 50+ languages, single-pass inference on audio recordings up to 90 minutes long, and custom hotword prompting for domain-specific terms.
 
 Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as `[S01]`, `[S02]`, and beyond.
 
@@ -44,7 +44,6 @@ Given an audio or video file, the model generates a compact speaker-aware transc
 - [Quickstart](#quickstart)
   - [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)
   - [Subtitle Web App](#subtitle-web-app)
 - [Output Format](#output-format)
@@ -70,14 +69,6 @@ Core capabilities:
   <img src="Model_Architecture.png" alt="MOSS-Transcribe-Diarize 0.9B model architecture" width="900">
 </p>
 
-| Component | Specification |
-|---|---|
-| Text backbone | Qwen3-0.6B style causal decoder |
-| Audio encoder | Whisper-Medium encoder configuration |
-| Audio frontend | `WhisperFeatureExtractor`, 16 kHz, 80 mel bins, 30 s chunks |
-| Audio-text bridge | 4x temporal merge + MLP adaptor |
-| Fusion | Audio features replace <code>&lt;&#124;audio_pad&#124;&gt;</code> embeddings via `masked_scatter` |
-| Output format | Compact `[start][Sxx]text[end]` transcript with speaker tags such as `[S01]` |
 
 This Hugging Face repository includes the custom Transformers remote code required to load the model with `trust_remote_code=True`.
 
@@ -308,27 +299,6 @@ curl -X POST http://localhost:8000/v1/audio/transcriptions \
   -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
@@ -339,37 +309,7 @@ curl -X POST http://localhost:8000/v1/audio/transcriptions \
   -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