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docs(ecosystem): add MOSS diarization deployment path
Signed-off-by: LauraGPT <[email protected]>
This commit is contained in:
LauraGPT
@@ -15,6 +15,7 @@ Use this page to choose the shortest deployment path for a product, demo, benchm
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| Runtime WebSocket service | Live captions, meetings, call-center streams | [Runtime service docs](../runtime/readme.md) | Use when partial results, endpointing, or long-lived audio streams matter. |
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| ONNX/C++ runtime | High-concurrency CPU services or embedded realtime ASR | [ONNX runtime docs](../runtime/onnxruntime/readme.md) | Keep this path when latency/concurrency is already proven; add text post-processing for fixed business terms before moving to GPU LLMs. |
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| vLLM acceleration | Higher-throughput LLM-based ASR with Fun-ASR-Nano | [vLLM guide](./vllm_guide.md) | Use for LLM decoder throughput; does not apply to non-autoregressive Paraformer. |
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| MOSS-Transcribe-Diarize | Long-form multi-speaker transcription with timestamps and speaker labels | [Third-party MOSS deployment guide](./moss_transcribe_diarize.md) | OpenMOSS Apache-2.0 model served with vLLM or SGLang Omni; not a FunASR-owned model or `AutoModel` backend. |
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| MCP server | Claude/Cursor/desktop agent speech tools | [MCP example](../examples/mcp_server/) | Good when the ASR result should be exposed as a local tool. |
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| Subtitle generator | SRT/VTT from long audio or video | [Subtitle example](../examples/subtitle/) | Use verbose segments and speaker labels when readability matters. |
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| Batch ASR script | Archives, meetings, datasets, repeated offline runs | [Batch example](../examples/batch_asr_improved.py) | Add queueing, manifests, and retry logs for production use. |
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@@ -13,6 +13,7 @@
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| Kubernetes API | Cluster service 向け internal speech API | [Kubernetes template](../examples/openai_api/kubernetes/) | private `ClusterIP` から開始。公開範囲を広げる前に auth、TLS、network policy、GPU scheduling を追加します。 |
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| Runtime WebSocket service | Live captions、meeting、call-center stream | [Runtime service docs](../runtime/readme.md) | partial result、endpointing、long-lived audio stream が重要な場合に使います。 |
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| vLLM acceleration | Fun-ASR-Nano の LLM-based ASR throughput 向上 | [vLLM guide](./vllm_guide.md) | LLM decoder throughput 向け。non-autoregressive Paraformer には適用しません。 |
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| MOSS-Transcribe-Diarize | 長時間の複数話者 transcription、timestamp、speaker label | [Third-party MOSS guide](./moss_transcribe_diarize.md) | OpenMOSS の Apache-2.0 model を vLLM / SGLang Omni で提供します。FunASR 所有 model や `AutoModel` backend ではありません。 |
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| MCP server | Claude/Cursor/desktop agent の speech tool | [MCP example](../examples/mcp_server/) | ASR 結果を local tool として Agent に渡したい場合に便利です。 |
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| Subtitle generator | 長時間 audio/video から SRT/VTT 作成 | [Subtitle example](../examples/subtitle/) | readability が重要な場合は verbose segment と speaker label を使います。 |
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| Batch ASR script | Archive、meeting、dataset、繰り返し offline run | [Batch example](../examples/batch_asr_improved.py) | production では queue、manifest、retry log を追加してください。 |
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@@ -13,6 +13,7 @@
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| Kubernetes API | Cluster service용 internal speech API | [Kubernetes template](../examples/openai_api/kubernetes/) | private `ClusterIP`부터 시작합니다. 범위를 넓히기 전에 auth, TLS, network policy, GPU scheduling을 추가하세요. |
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| Runtime WebSocket service | Live captions, meeting, call-center stream | [Runtime service docs](../runtime/readme.md) | partial result, endpointing, long-lived audio stream이 중요할 때 사용합니다. |
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| vLLM acceleration | Fun-ASR-Nano의 LLM-based ASR throughput 향상 | [vLLM guide](./vllm_guide.md) | LLM decoder throughput용입니다. non-autoregressive Paraformer에는 적용되지 않습니다. |
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| MOSS-Transcribe-Diarize | 긴 다중 화자 transcription, timestamp, speaker label | [Third-party MOSS guide](./moss_transcribe_diarize.md) | OpenMOSS의 Apache-2.0 model을 vLLM / SGLang Omni로 제공합니다. FunASR 소유 model 또는 `AutoModel` backend가 아닙니다. |
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| MCP server | Claude/Cursor/desktop agent speech tool | [MCP example](../examples/mcp_server/) | ASR 결과를 local tool로 Agent에 전달할 때 유용합니다. |
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| Subtitle generator | 긴 audio/video에서 SRT/VTT 생성 | [Subtitle example](../examples/subtitle/) | readability가 중요하면 verbose segment와 speaker label을 사용합니다. |
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| Batch ASR script | Archive, meeting, dataset, 반복 offline run | [Batch example](../examples/batch_asr_improved.py) | production에서는 queue, manifest, retry log를 추가하세요. |
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@@ -15,6 +15,7 @@
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| Runtime WebSocket 服务 | 实时字幕、会议、客服流式音频 | [Runtime 服务文档](../runtime/readme_cn.md) | 需要中间结果、断句或长连接音频流时选择。 |
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| ONNX/C++ Runtime | 高并发 CPU 服务或嵌入式实时 ASR | [ONNX Runtime 文档](../runtime/onnxruntime/readme.md) | 如果延迟和并发已经验证，不要轻易替换；固定业务词优先做文本后处理。 |
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| vLLM 加速 | Fun-ASR-Nano 等 LLM-based ASR 高吞吐 | [vLLM 指南](./vllm_guide.md) | 适合 LLM 解码吞吐；不适用于非自回归 Paraformer。 |
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| MOSS-Transcribe-Diarize | 长音频多人转写、时间戳和说话人标签 | [第三方 MOSS 部署指南](./moss_transcribe_diarize.md) | OpenMOSS 以 Apache-2.0 发布，通过 vLLM 或 SGLang Omni 服务；不是 FunASR 自有模型或 `AutoModel` 后端。 |
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| MCP 服务 | Claude/Cursor/桌面 Agent 语音工具 | [MCP 示例](../examples/mcp_server/) | 适合把 ASR 结果暴露成一个本地工具。 |
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| 字幕生成 | 从长音频或视频生成 SRT/VTT | [字幕示例](../examples/subtitle/) | 需要可读性时使用 verbose segments 和说话人标签。 |
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| 批处理脚本 | 录音归档、会议纪要、数据集处理 | [批处理示例](../examples/batch_asr_improved.py) | 生产使用时建议增加队列、manifest 和重试日志。 |
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@@ -0,0 +1,153 @@
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# MOSS-Transcribe-Diarize in the FunASR ecosystem
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[中文](./moss_transcribe_diarize_zh.md)
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This guide connects the third-party
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[OpenMOSS/MOSS-Transcribe-Diarize](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize)
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model to the FunASR deployment ecosystem. The model is published by OpenMOSS
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under Apache-2.0; it is not a FunASR model and is not registered in FunASR
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`AutoModel`.
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MOSS-Transcribe-Diarize jointly generates transcription, timestamps, and
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speaker labels such as `[S01]`. An application therefore does not need to
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assemble an external VAD, ASR, and diarization pipeline. This is a deployment
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property, not a claim that the model has no internal segmentation or chunking.
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## Pinned sources
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This guide was verified against:
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- source: `OpenMOSS/MOSS-Transcribe-Diarize@cb765f2b0fe6f7a298aa2002e2281ae693d1f3c3`
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- model: `OpenMOSS-Team/MOSS-Transcribe-Diarize@e8681d68e7042738ffca8ac8212bc8fcb1131ab8`
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- license: Apache-2.0 in the pinned upstream source and model metadata
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- vLLM nightly index: `68b4a1d582818e67adc903bf1b8fc5a5447da2fa`
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Pin all three revisions. The model uses `trust_remote_code`; do not execute a
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floating model revision in a production service.
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## Choose a serving path
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| Path | Environment | Response contract | Use when |
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|---|---|---|---|
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| vLLM | CUDA 12 (`cu129`) or CUDA 13 (`cu130`) | `response_format=json` returns raw `[start][Sxx]text[end]` text | You already operate vLLM or need its scheduler |
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| SGLang Omni | CUDA 13 in the current upstream guide | `response_format=verbose_json` returns parsed segments | You need structured speaker segments directly from the API |
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| Transformers | PyTorch process | Python objects and raw tagged text | Evaluation, debugging, or custom preprocessing |
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The two HTTP backends share `/v1/audio/transcriptions`, but their documented
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response formats are not interchangeable. Do not promise vLLM `segments`
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without testing the exact vLLM revision.
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## vLLM
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Create an isolated environment and install the upstream-pinned nightly build:
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```bash
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uv venv --python 3.12 .venv-moss
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uv pip install --python .venv-moss/bin/python -U 'vllm[audio]' \
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--torch-backend=auto \
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--extra-index-url https://wheels.vllm.ai/68b4a1d582818e67adc903bf1b8fc5a5447da2fa/cu129
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```
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The `audio` extra is required. A plain `vllm` install can start the server but
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returns HTTP 400 (`Invalid or unsupported audio file`) because no audio decoder
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is installed.
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Start the service with the immutable model revision:
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```bash
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CUDA_VISIBLE_DEVICES=0 .venv-moss/bin/vllm serve \
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OpenMOSS-Team/MOSS-Transcribe-Diarize \
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--revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 \
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--served-model-name moss-transcribe-diarize \
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--trust-remote-code \
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--host 127.0.0.1 \
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--port 8898
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```
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Submit audio and validate the speaker-tagged text:
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```bash
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curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions \
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-F file=@audio.wav \
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-F model=moss-transcribe-diarize \
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-F response_format=json \
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-F temperature=0 \
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| tee moss-transcription.json
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python - <<'PY'
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import json
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with open("moss-transcription.json", encoding="utf-8") as stream:
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payload = json.load(stream)
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text = payload.get("text", "")
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assert text.strip(), payload
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assert "[S01]" in text, text
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print(text)
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PY
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```
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For long recordings, test `max_completion_tokens` against the longest expected
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meeting. A larger limit can increase memory use and tail latency.
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### Reproduced vLLM contract
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The FunASR ecosystem validation used vLLM
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`0.23.1rc1.dev949+g68b4a1d58`, Torch `2.11.0+cu129`, and one H100 80GB:
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- the bundled 6.000-second sample
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(`ea03e1f473ad1618a03da3327a545369cb8f6f06cb0f4115535e5a866167d47e`)
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returned HTTP 200 and `[0.96][S01]... [5.94]`;
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- an A + 0.8-second silence + B + 0.8-second silence + A probe
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(`dbb32bcfed2e8226bedf64248a9f4a44685b293a4696d18fb4cfa701b04db912`)
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returned HTTP 200 with `S01 -> S02 -> S01` and timestamps through 19.08 seconds.
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This proves the pinned API and speaker-return contract on those inputs. It is
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not a diarization accuracy result, overlap test, throughput benchmark, or
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production capacity claim.
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## SGLang Omni
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Follow the pinned upstream SGLang Omni installation guide for CUDA 13, then
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download the immutable model snapshot and serve the local directory:
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```bash
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hf download OpenMOSS-Team/MOSS-Transcribe-Diarize \
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--revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 \
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--local-dir .models/moss-transcribe-diarize
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sgl-omni serve \
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--model-path .models/moss-transcribe-diarize \
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--port 8898 \
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--max-running-requests 16 \
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--cuda-graph-max-bs 16 \
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--mem-fraction-static 0.80
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```
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Request parsed segments:
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```bash
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curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions \
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-F file=@audio.wav \
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-F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
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-F response_format=verbose_json
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```
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Verify every segment has start/end timing, text, and the expected speaker
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field before wiring the response into subtitles, meeting notes, or analytics.
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## Production validation
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Use real multi-speaker audio, not only a short single-speaker smoke sample:
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- measure speaker consistency across long turns and speaker returns;
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- test overlap, crosstalk, music, noise, and long silence;
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- verify timestamp monotonicity and full-audio coverage;
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- record GPU, CUDA, Torch, backend commit, model revision, audio duration,
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generation limit, wall latency, and peak memory;
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- keep authentication, TLS, request limits, and retention policy at the API
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gateway; bind the model worker to a private address.
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When reporting a problem to FunASR, state that this is the OpenMOSS third-party
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path and include the exact backend and upstream revisions. Model architecture or
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weight issues belong upstream; deployment-center contract and documentation
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issues can be filed in FunASR.
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@@ -0,0 +1,141 @@
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# 在 FunASR 生态中部署 MOSS-Transcribe-Diarize
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[English](./moss_transcribe_diarize.md)
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本文把第三方
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[OpenMOSS/MOSS-Transcribe-Diarize](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize)
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模型接入 FunASR 部署生态。模型由 OpenMOSS 以 Apache-2.0 发布，不是 FunASR
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自有模型，也没有注册到 FunASR `AutoModel`。
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MOSS-Transcribe-Diarize 会联合生成转写、时间戳和 `[S01]` 等说话人标签，应用侧
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不必再拼接外部 VAD、ASR 和 diarization 管线。这里描述的是部署形态，不表示模型
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内部没有分块或分段。
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## 固定上游版本
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本文按以下不可变版本验证：
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- 源码：`OpenMOSS/MOSS-Transcribe-Diarize@cb765f2b0fe6f7a298aa2002e2281ae693d1f3c3`
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- 模型：`OpenMOSS-Team/MOSS-Transcribe-Diarize@e8681d68e7042738ffca8ac8212bc8fcb1131ab8`
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- 许可证：固定上游源码和模型元数据中的 Apache-2.0
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- vLLM nightly 索引：`68b4a1d582818e67adc903bf1b8fc5a5447da2fa`
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三者都要固定。模型依赖 `trust_remote_code`，生产服务不要执行浮动的模型 revision。
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## 选择服务后端
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| 路径 | 环境 | 响应契约 | 适用场景 |
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|---|---|---|---|
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| vLLM | CUDA 12（`cu129`）或 CUDA 13（`cu130`） | `response_format=json` 返回原始 `[start][Sxx]text[end]` 文本 | 已运行 vLLM，或需要其调度能力 |
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| SGLang Omni | 当前上游指南为 CUDA 13 | `response_format=verbose_json` 返回解析后的 segments | API 需要直接返回结构化说话人分段 |
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| Transformers | PyTorch 进程 | Python 对象和原始标签文本 | 评测、排障或自定义预处理 |
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两个 HTTP 后端都使用 `/v1/audio/transcriptions`，但官方文档中的响应格式不能混为
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一谈。没有对 exact vLLM revision 做验证时，不要承诺 vLLM 返回 `segments`。
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## vLLM
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创建隔离环境并安装上游固定的 nightly build：
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```bash
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uv venv --python 3.12 .venv-moss
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uv pip install --python .venv-moss/bin/python -U 'vllm[audio]' \
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--torch-backend=auto \
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--extra-index-url https://wheels.vllm.ai/68b4a1d582818e67adc903bf1b8fc5a5447da2fa/cu129
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```
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必须安装 `audio` extra。只安装 plain `vllm` 时服务可以启动，但因为没有音频解码器，
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请求会返回 HTTP 400（`Invalid or unsupported audio file`）。
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使用不可变模型 revision 启动：
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```bash
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CUDA_VISIBLE_DEVICES=0 .venv-moss/bin/vllm serve \
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OpenMOSS-Team/MOSS-Transcribe-Diarize \
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--revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 \
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--served-model-name moss-transcribe-diarize \
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--trust-remote-code \
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--host 127.0.0.1 \
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--port 8898
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```
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提交音频并验证说话人标签文本：
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|
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```bash
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curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions \
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-F file=@audio.wav \
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-F model=moss-transcribe-diarize \
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-F response_format=json \
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-F temperature=0 \
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| tee moss-transcription.json
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python - <<'PY'
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import json
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with open("moss-transcription.json", encoding="utf-8") as stream:
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payload = json.load(stream)
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text = payload.get("text", "")
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assert text.strip(), payload
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assert "[S01]" in text, text
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print(text)
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PY
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```
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长音频需要按业务最长会议验证 `max_completion_tokens`。增大上限也会影响显存和尾延迟。
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### 已复现的 vLLM 契约
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FunASR 生态验证使用 vLLM `0.23.1rc1.dev949+g68b4a1d58`、Torch
|
||||
`2.11.0+cu129` 和一张 H100 80GB：
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||||
|
||||
- 仓库内 6.000 秒样例
|
||||
（`ea03e1f473ad1618a03da3327a545369cb8f6f06cb0f4115535e5a866167d47e`）
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||||
返回 HTTP 200 和 `[0.96][S01]... [5.94]`；
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- A + 0.8 秒静音 + B + 0.8 秒静音 + A 的拼接样例
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（`dbb32bcfed2e8226bedf64248a9f4a44685b293a4696d18fb4cfa701b04db912`）
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返回 HTTP 200、`S01 -> S02 -> S01`，时间戳覆盖到 19.08 秒。
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这证明固定版本对这些输入满足 API 和说话人返回契约，不是 diarization 精度、重叠语音、
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吞吐或生产容量结论。
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## SGLang Omni
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|
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按固定上游 SGLang Omni 安装指南准备 CUDA 13 环境，然后下载不可变模型快照并从本地目录启动：
|
||||
|
||||
```bash
|
||||
hf download OpenMOSS-Team/MOSS-Transcribe-Diarize \
|
||||
--revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 \
|
||||
--local-dir .models/moss-transcribe-diarize
|
||||
|
||||
sgl-omni serve \
|
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--model-path .models/moss-transcribe-diarize \
|
||||
--port 8898 \
|
||||
--max-running-requests 16 \
|
||||
--cuda-graph-max-bs 16 \
|
||||
--mem-fraction-static 0.80
|
||||
```
|
||||
|
||||
请求结构化分段：
|
||||
|
||||
```bash
|
||||
curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions \
|
||||
-F file=@audio.wav \
|
||||
-F model=OpenMOSS-Team/MOSS-Transcribe-Diarize \
|
||||
-F response_format=verbose_json
|
||||
```
|
||||
|
||||
接入字幕、会议纪要或分析系统前，验证每个 segment 都包含起止时间、文本和预期的 speaker 字段。
|
||||
|
||||
## 上线前验证
|
||||
|
||||
不要只用短单人音频，应使用真实多人长音频：
|
||||
|
||||
- 检查长轮次及说话人再次出现时的身份一致性；
|
||||
- 覆盖重叠语音、串音、音乐、噪声和长静音；
|
||||
- 验证时间戳单调递增并覆盖完整音频；
|
||||
- 记录 GPU、CUDA、Torch、后端 commit、模型 revision、音频时长、生成上限、
|
||||
墙钟延迟和峰值显存；
|
||||
- 在 API 网关完成认证、TLS、请求限制和数据留存策略，模型 worker 只绑定内网。
|
||||
|
||||
向 FunASR 报告问题时，请明确这是 OpenMOSS 第三方路径，并附上后端与上游 exact
|
||||
revision。模型架构或权重问题应反馈给上游；部署中心的契约或文档问题可以在 FunASR 提交。
|
||||
@@ -0,0 +1,50 @@
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
GUIDES = (
|
||||
ROOT / "docs" / "moss_transcribe_diarize.md",
|
||||
ROOT / "docs" / "moss_transcribe_diarize_zh.md",
|
||||
)
|
||||
MATRICES = (
|
||||
ROOT / "docs" / "deployment_matrix.md",
|
||||
ROOT / "docs" / "deployment_matrix_zh.md",
|
||||
ROOT / "docs" / "deployment_matrix_ja.md",
|
||||
ROOT / "docs" / "deployment_matrix_ko.md",
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("guide", GUIDES)
|
||||
def test_moss_guides_pin_upstream_and_separate_serving_contracts(guide: Path) -> None:
|
||||
text = guide.read_text(encoding="utf-8")
|
||||
|
||||
for marker in (
|
||||
"OpenMOSS/MOSS-Transcribe-Diarize",
|
||||
"cb765f2b0fe6f7a298aa2002e2281ae693d1f3c3",
|
||||
"OpenMOSS-Team/MOSS-Transcribe-Diarize",
|
||||
"e8681d68e7042738ffca8ac8212bc8fcb1131ab8",
|
||||
"68b4a1d582818e67adc903bf1b8fc5a5447da2fa",
|
||||
"vllm[audio]",
|
||||
"0.23.1rc1.dev949+g68b4a1d58",
|
||||
"dbb32bcfed2e8226bedf64248a9f4a44685b293a4696d18fb4cfa701b04db912",
|
||||
"S01 -> S02 -> S01",
|
||||
"/v1/audio/transcriptions",
|
||||
"response_format=json",
|
||||
"response_format=verbose_json",
|
||||
"[S01]",
|
||||
"Apache-2.0",
|
||||
):
|
||||
assert marker in text, f"{guide.name} is missing {marker}"
|
||||
|
||||
assert "AutoModel" in text
|
||||
assert "third-party" in text.lower() or "第三方" in text
|
||||
|
||||
|
||||
@pytest.mark.parametrize("matrix", MATRICES)
|
||||
def test_all_deployment_matrices_link_moss_guide(matrix: Path) -> None:
|
||||
text = matrix.read_text(encoding="utf-8")
|
||||
|
||||
assert "MOSS-Transcribe-Diarize" in text
|
||||
assert "moss_transcribe_diarize.md" in text
|
||||
@@ -1,7 +1,85 @@
|
||||
{
|
||||
"schema_version": 1,
|
||||
"verified": "2026-08-13",
|
||||
"verified": "2026-08-29",
|
||||
"deployments": [
|
||||
{
|
||||
"id": "moss-transcribe-diarize",
|
||||
"routes": {"zh": "/deploy/moss-transcribe-diarize.html", "en": "/en/deploy/moss-transcribe-diarize.html"},
|
||||
"maturity": "community-verified",
|
||||
"selector_rank": 12,
|
||||
"workloads": ["batch", "private-api"],
|
||||
"hardware": ["nvidia-gpu", "kubernetes"],
|
||||
"priorities": ["throughput"],
|
||||
"models": ["OpenMOSS-Team/MOSS-Transcribe-Diarize (third-party Apache-2.0 model)"],
|
||||
"operating_systems": ["Linux"],
|
||||
"interfaces": ["OpenAI-compatible HTTP", "vLLM", "SGLang Omni", "Transformers"],
|
||||
"tested": {"funasr": "ecosystem contract; third-party model@e8681d68", "runtime": "vLLM 0.23.1rc1.dev949+g68b4a1d58 / Torch 2.11.0+cu129 / H100 80GB", "verified": "2026-08-29"},
|
||||
"commands": {
|
||||
"install": [
|
||||
"uv venv --python 3.12 .venv-moss && uv pip install --python .venv-moss/bin/python -U 'vllm[audio]' --torch-backend=auto --extra-index-url https://wheels.vllm.ai/68b4a1d582818e67adc903bf1b8fc5a5447da2fa/cu129",
|
||||
"HF_HUB_ENABLE_HF_TRANSFER=1 hf download OpenMOSS-Team/MOSS-Transcribe-Diarize --revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 --local-dir .models/moss-transcribe-diarize"
|
||||
],
|
||||
"launch": [
|
||||
"CUDA_VISIBLE_DEVICES=0 .venv-moss/bin/vllm serve OpenMOSS-Team/MOSS-Transcribe-Diarize --revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 --served-model-name moss-transcribe-diarize --trust-remote-code --host 127.0.0.1 --port 8898"
|
||||
],
|
||||
"health": [
|
||||
"curl -fsS http://127.0.0.1:8898/health",
|
||||
"curl -fsS http://127.0.0.1:8898/v1/models"
|
||||
],
|
||||
"smoke": [
|
||||
"curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions -F file=@runtime/llama.cpp/tests/sample.wav -F model=moss-transcribe-diarize -F response_format=json -F temperature=0 | tee /tmp/moss-transcription.json",
|
||||
"python - <<'PY'\nimport json\n\nwith open('/tmp/moss-transcription.json', encoding='utf-8') as stream:\n payload = json.load(stream)\ntext = payload.get('text', '')\nassert text.strip(), payload\nassert '[S01]' in text, text\nprint(text)\nPY"
|
||||
]
|
||||
},
|
||||
"evidence": [
|
||||
{"label": "OpenMOSS upstream repository and Apache-2.0 source license", "url": "https://github.com/OpenMOSS/MOSS-Transcribe-Diarize"},
|
||||
{"label": "upstream source pinned at cb765f2", "url": "https://github.com/OpenMOSS/MOSS-Transcribe-Diarize/commit/cb765f2b0fe6f7a298aa2002e2281ae693d1f3c3"},
|
||||
{"label": "official model snapshot pinned at e8681d68", "url": "https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize/tree/e8681d68e7042738ffca8ac8212bc8fcb1131ab8"},
|
||||
{"label": "official vLLM and SGLang Omni serving guide", "url": "https://github.com/OpenMOSS/MOSS-Transcribe-Diarize/blob/cb765f2b0fe6f7a298aa2002e2281ae693d1f3c3/README.md"},
|
||||
{"label": "FunASR ecosystem integration guide", "url": "https://github.com/modelscope/FunASR/blob/main/docs/moss_transcribe_diarize.md"}
|
||||
],
|
||||
"benchmarks": [
|
||||
{
|
||||
"model": "OpenMOSS-Team/MOSS-Transcribe-Diarize@e8681d68",
|
||||
"runtime": "vLLM 0.23.1rc1.dev949+g68b4a1d58 / Torch 2.11.0+cu129",
|
||||
"hardware": "NVIDIA H100 80GB HBM3",
|
||||
"workload": "OpenAI-compatible transcription API smoke test plus synthetic A-to-B-to-A speaker-return probe",
|
||||
"audio": "Bundled 6.000 s Mandarin sample; synthetic 19.147 s file built as sample A + 0.8 s silence + sample B + 0.8 s silence + sample A",
|
||||
"settings": "Pinned model and vLLM revisions; vllm[audio]; response_format=json; temperature=0; one request at a time",
|
||||
"timing_scope": "Client wall time after /health became ready; local HTTP and decoding included; model download and engine startup excluded",
|
||||
"result": "Both requests returned HTTP 200. The 6.000 s sample produced S01 with timestamps 0.96-5.94. The 19.147 s probe produced S01 -> S02 -> S01 with timestamps covering 0.96-19.08. Observed client times were 1.034 s and 0.171 s respectively.",
|
||||
"qualification": "A functional API contract and speaker-return probe on synthetic concatenation, not a diarization accuracy study, overlap test, throughput benchmark, or production capacity promise.",
|
||||
"source": "https://github.com/modelscope/FunASR/blob/main/docs/moss_transcribe_diarize.md",
|
||||
"verified": "2026-08-29"
|
||||
}
|
||||
],
|
||||
"translations": {
|
||||
"zh": {
|
||||
"name": "MOSS 统一转写与说话人分离",
|
||||
"summary": "在 FunASR 部署中心接入 OpenMOSS 发布的第三方 Apache-2.0 模型，一次生成长音频转写、时间戳和说话人标签。",
|
||||
"fit": ["会议、访谈、播客和通话的多人长音频", "希望通过同一个模型输出文本、时间戳与说话人编号", "已有 NVIDIA GPU，并需要 OpenAI 兼容的音频转写接口"],
|
||||
"not_fit": ["实时流式字幕或低延迟端点检测", "CPU、桌面边缘设备或 Windows 原生部署", "要求 FunASR 自有模型权重或 FunASR AutoModel API 的项目"],
|
||||
"selection_reason": "模型端到端联合生成转写与说话人标签，用户不必再拼接外部 VAD、ASR 和 diarization 管线；vLLM 与 SGLang Omni 都提供 /v1/audio/transcriptions。",
|
||||
"primary_limitation": "这是 OpenMOSS 的第三方模型，不是 FunASR 模型；“无需外部 VAD”不表示模型内部没有分块或分段。当前 vLLM 路径固定 nightly commit，升级前必须重跑真实多人长音频。",
|
||||
"status_label": "社区验证",
|
||||
"operations": ["固定模型 revision、vLLM nightly commit、CUDA/Torch 与 trust_remote_code 审计结果", "用真实会议验证说话人一致性、重叠语音、长静音、时间戳和最大生成长度", "vLLM 的 json 返回原始 [Sxx] 标记文本；需要 verbose_json 结构化 speaker segments 时使用 SGLang Omni 并单独验证"],
|
||||
"security": ["把服务绑定内网，由网关完成认证、TLS、限流及音频大小/时长限制", "固定并审计 trust_remote_code 对应的模型 revision，不执行浮动 main 代码", "隔离模型缓存、上传临时目录和生成日志，按数据保留策略清理原始音频"],
|
||||
"troubleshooting": ["启动前确认 CUDA 12 使用 cu129 nightly 源，CUDA 13 使用上游文档列出的 cu130 源", "长音频被截断时提高 max_completion_tokens，并同时监控显存、延迟和输出完整性", "vLLM json 响应没有 segments 字段是当前接口边界；从 text 解析 [start][Sxx]text[end]，或改用 SGLang Omni verbose_json"]
|
||||
},
|
||||
"en": {
|
||||
"name": "MOSS unified transcription and diarization",
|
||||
"summary": "Use the third-party Apache-2.0 model published by OpenMOSS to produce long-form transcripts, timestamps, and speaker labels in one pass from the FunASR deployment center.",
|
||||
"fit": ["Multi-speaker meetings, interviews, podcasts, and calls", "One model output for text, timestamps, and speaker identifiers", "NVIDIA GPU deployments that need an OpenAI-compatible audio transcription endpoint"],
|
||||
"not_fit": ["Realtime streaming captions or low-latency endpoint detection", "CPU, desktop-edge, or native Windows deployments", "Projects that require FunASR-owned model weights or the FunASR AutoModel API"],
|
||||
"selection_reason": "The model jointly emits transcription and speaker labels so users do not need to assemble an external VAD, ASR, and diarization pipeline; both vLLM and SGLang Omni expose /v1/audio/transcriptions.",
|
||||
"primary_limitation": "This is an OpenMOSS third-party model, not a FunASR model; no external VAD requirement does not imply an absence of internal segmentation. The vLLM path pins a nightly commit and must be revalidated on real long multi-speaker audio before upgrades.",
|
||||
"status_label": "Community verified",
|
||||
"operations": ["Pin the model revision, vLLM nightly commit, CUDA/Torch stack, and trust_remote_code audit", "Validate speaker consistency, overlap, long silence, timestamps, and generation limits on real meetings", "vLLM json returns raw [Sxx]-tagged text; use and separately validate SGLang Omni verbose_json when structured speaker segments are required"],
|
||||
"security": ["Bind workers to a private network and put authentication, TLS, rate limits, and audio size/duration limits at the gateway", "Pin and audit the trust_remote_code model revision instead of executing a floating main revision", "Isolate model caches, upload directories, and generation logs; remove source audio according to retention policy"],
|
||||
"troubleshooting": ["Use the cu129 nightly source for CUDA 12 and the upstream cu130 source for CUDA 13", "If long audio is truncated, raise max_completion_tokens while monitoring memory, latency, and output completeness", "A vLLM json response without segments is the current API boundary; parse [start][Sxx]text[end] from text or use SGLang Omni verbose_json"]
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "vllm",
|
||||
"routes": {"zh": "/deploy/vllm.html", "en": "/en/deploy/vllm.html"},
|
||||
|
||||
@@ -15,6 +15,7 @@ from registry import deployment_pairs, load_registry, validate_registry # noqa:
|
||||
|
||||
REGISTRY = SITE_ROOT / 'data' / 'deployments.json'
|
||||
EXPECTED_IDS = {
|
||||
'moss-transcribe-diarize',
|
||||
'vllm',
|
||||
'sensevoice-tensorrt',
|
||||
'llama-cpp',
|
||||
@@ -95,11 +96,70 @@ def test_vllm_contract_tracks_native_funasr_release_and_h100_validation(valid_re
|
||||
assert 'official funasr split-engine' in limitation
|
||||
|
||||
|
||||
def test_moss_transcribe_diarize_contract_tracks_third_party_upstream(valid_registry):
|
||||
entry = next(
|
||||
item for item in valid_registry['deployments']
|
||||
if item['id'] == 'moss-transcribe-diarize'
|
||||
)
|
||||
|
||||
assert entry['maturity'] == 'community-verified'
|
||||
assert entry['models'] == [
|
||||
'OpenMOSS-Team/MOSS-Transcribe-Diarize (third-party Apache-2.0 model)'
|
||||
]
|
||||
assert entry['tested'] == {
|
||||
'funasr': 'ecosystem contract; third-party model@e8681d68',
|
||||
'runtime': 'vLLM 0.23.1rc1.dev949+g68b4a1d58 / Torch 2.11.0+cu129 / H100 80GB',
|
||||
'verified': '2026-08-29',
|
||||
}
|
||||
|
||||
install = '\n'.join(entry['commands']['install'])
|
||||
assert 'vllm[audio]' in install
|
||||
assert 'wheels.vllm.ai/68b4a1d582818e67adc903bf1b8fc5a5447da2fa/cu129' in install
|
||||
assert 'OpenMOSS-Team/MOSS-Transcribe-Diarize' in install
|
||||
assert 'e8681d68e7042738ffca8ac8212bc8fcb1131ab8' in install
|
||||
|
||||
launch = '\n'.join(entry['commands']['launch'])
|
||||
assert 'vllm serve OpenMOSS-Team/MOSS-Transcribe-Diarize' in launch
|
||||
assert '--trust-remote-code' in launch
|
||||
smoke = '\n'.join(entry['commands']['smoke'])
|
||||
assert '/v1/audio/transcriptions' in smoke
|
||||
assert 'response_format=json' in smoke
|
||||
assert '[S01]' in smoke
|
||||
|
||||
evidence_urls = {item['url'] for item in entry['evidence']}
|
||||
assert 'https://github.com/OpenMOSS/MOSS-Transcribe-Diarize' in evidence_urls
|
||||
assert (
|
||||
'https://github.com/OpenMOSS/MOSS-Transcribe-Diarize/commit/'
|
||||
'cb765f2b0fe6f7a298aa2002e2281ae693d1f3c3'
|
||||
) in evidence_urls
|
||||
assert (
|
||||
'https://huggingface.co/OpenMOSS-Team/MOSS-Transcribe-Diarize/tree/'
|
||||
'e8681d68e7042738ffca8ac8212bc8fcb1131ab8'
|
||||
) in evidence_urls
|
||||
|
||||
english = entry['translations']['en']
|
||||
assert 'OpenMOSS' in english['summary']
|
||||
assert 'third-party' in english['summary'].lower()
|
||||
assert 'external VAD' in english['selection_reason']
|
||||
assert 'verbose_json' in english['operations'][-1]
|
||||
assert 'SGLang' in english['operations'][-1]
|
||||
assert 'internal segmentation' in english['primary_limitation']
|
||||
assert 'FunASR model' in english['primary_limitation']
|
||||
assert any(
|
||||
benchmark['hardware'] == 'NVIDIA H100 80GB HBM3'
|
||||
and '6.000 s' in benchmark['audio']
|
||||
and '19.147 s' in benchmark['audio']
|
||||
and 'S01 -> S02 -> S01' in benchmark['result']
|
||||
and 'functional API contract' in benchmark['qualification']
|
||||
for benchmark in entry['benchmarks']
|
||||
)
|
||||
|
||||
|
||||
def test_audio_cpp_contract_tracks_mainline_nano_and_sensevoice(valid_registry):
|
||||
entry = next(item for item in valid_registry['deployments'] if item['id'] == 'audio-cpp')
|
||||
llama_cpp = next(item for item in valid_registry['deployments'] if item['id'] == 'llama-cpp')
|
||||
|
||||
assert valid_registry['verified'] == '2026-08-13'
|
||||
assert valid_registry['verified'] == '2026-08-29'
|
||||
assert entry['maturity'] == 'community-verified'
|
||||
assert entry['selector_rank'] > llama_cpp['selector_rank']
|
||||
assert entry['tested'] == {
|
||||
@@ -265,7 +325,7 @@ def test_sensevoice_native_server_contract_tracks_merged_runtime(valid_registry)
|
||||
llama_cpp = next(item for item in valid_registry['deployments'] if item['id'] == 'llama-cpp')
|
||||
audio_cpp = next(item for item in valid_registry['deployments'] if item['id'] == 'audio-cpp')
|
||||
|
||||
assert valid_registry['verified'] == '2026-08-13'
|
||||
assert valid_registry['verified'] == '2026-08-29'
|
||||
assert entry['maturity'] == 'production-verified'
|
||||
assert llama_cpp['selector_rank'] < entry['selector_rank'] < audio_cpp['selector_rank']
|
||||
assert entry['tested'] == {
|
||||
|
||||
Reference in New Issue
Block a user