diff --git a/docs/deployment_matrix.md b/docs/deployment_matrix.md index 1eb2c24fb..0f70fb7b1 100644 --- a/docs/deployment_matrix.md +++ b/docs/deployment_matrix.md @@ -15,7 +15,7 @@ Use this page to choose the shortest deployment path for a product, demo, benchm | 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. | | 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. | | 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. | -| 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 integrated with FunASR `AutoModel`; choose local HF (`backend="hf"`) or vLLM (`backend="vllm"`), or serve it independently through native SGLang Omni. SGLang Omni is not an `AutoModel` backend. The model remains published and maintained by OpenMOSS. | +| 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 integrated with FunASR `AutoModel`; choose local HF (`backend="hf"`), vLLM (`backend="vllm"`), or SGLang Omni (`backend="sglang"`). The model remains published and maintained by OpenMOSS. | | 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. | | Subtitle generator | SRT/VTT from long audio or video | [Subtitle example](../examples/subtitle/) | Use verbose segments and speaker labels when readability matters. | | 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. | diff --git a/docs/deployment_matrix_ja.md b/docs/deployment_matrix_ja.md index 7cd83378f..5d29f6a03 100644 --- a/docs/deployment_matrix_ja.md +++ b/docs/deployment_matrix_ja.md @@ -13,7 +13,7 @@ | 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。 | | 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。 | | 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。 | -| 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 FunASR `AutoModel` に統合済みです。local HF（`backend="hf"`）または vLLM（`backend="vllm"`）を選択でき、native SGLang Omni から独立して serve することもできます。SGLang Omni は `AutoModel` backend ではなく、model の公開・保守主体は OpenMOSS のままです。 | +| 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 FunASR `AutoModel` に統合済みです。local HF（`backend="hf"`）、vLLM（`backend="vllm"`）、または SGLang Omni（`backend="sglang"`）を選択できます。model の公開・保守主体は OpenMOSS のままです。 | | 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。 | | 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。 | | 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。 | diff --git a/docs/deployment_matrix_ko.md b/docs/deployment_matrix_ko.md index 038afe8c9..3b4e2c306 100644 --- a/docs/deployment_matrix_ko.md +++ b/docs/deployment_matrix_ko.md @@ -13,7 +13,7 @@ | 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. | | 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. | | vLLM acceleration | Fun-ASR-Nano의 LLM-based ASR throughput 향상 | [vLLM guide](./vllm_guide.md) | LLM decoder throughput용입니다. non-autoregressive Paraformer에는 적용되지 않습니다. | -| MOSS-Transcribe-Diarize | 긴 다중 화자 transcription, timestamp, speaker label | [Third-party MOSS guide](./moss_transcribe_diarize.md) | OpenMOSS의 Apache-2.0 model이며 FunASR `AutoModel`에 통합되어 있습니다. local HF(`backend="hf"`) 또는 vLLM(`backend="vllm"`)을 선택하거나 native SGLang Omni에서 독립적으로 serve할 수 있습니다. SGLang Omni는 `AutoModel` backend가 아니며, model의 공개 및 유지 관리는 계속 OpenMOSS가 담당합니다. | +| MOSS-Transcribe-Diarize | 긴 다중 화자 transcription, timestamp, speaker label | [Third-party MOSS guide](./moss_transcribe_diarize.md) | OpenMOSS의 Apache-2.0 model이며 FunASR `AutoModel`에 통합되어 있습니다. local HF(`backend="hf"`), vLLM(`backend="vllm"`) 또는 SGLang Omni(`backend="sglang"`)를 선택할 수 있습니다. model의 공개 및 유지 관리는 계속 OpenMOSS가 담당합니다. | | MCP server | Claude/Cursor/desktop agent speech tool | [MCP example](../examples/mcp_server/) | ASR 결과를 local tool로 Agent에 전달할 때 유용합니다. | | Subtitle generator | 긴 audio/video에서 SRT/VTT 생성 | [Subtitle example](../examples/subtitle/) | readability가 중요하면 verbose segment와 speaker label을 사용합니다. | | Batch ASR script | Archive, meeting, dataset, 반복 offline run | [Batch example](../examples/batch_asr_improved.py) | production에서는 queue, manifest, retry log를 추가하세요. | diff --git a/docs/deployment_matrix_zh.md b/docs/deployment_matrix_zh.md index ed01089a8..a9970f0e5 100644 --- a/docs/deployment_matrix_zh.md +++ b/docs/deployment_matrix_zh.md @@ -15,7 +15,7 @@ | 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。 | | 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。 | | 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。 | -| 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 Apache-2.0 模型，已接入 FunASR `AutoModel`；可选本地 HF（`backend="hf"`）或 vLLM（`backend="vllm"`），也可通过原生 SGLang Omni 独立服务。SGLang Omni 不是 `AutoModel` backend。模型仍由 OpenMOSS 发布和维护。 | +| 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 Apache-2.0 模型，已接入 FunASR `AutoModel`；可选本地 HF（`backend="hf"`）、vLLM（`backend="vllm"`）或 SGLang Omni（`backend="sglang"`）。模型仍由 OpenMOSS 发布和维护。 | | 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。 | | 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。 | | 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。 | diff --git a/docs/moss_transcribe_diarize.md b/docs/moss_transcribe_diarize.md index 04d43e017..a7e348642 100644 --- a/docs/moss_transcribe_diarize.md +++ b/docs/moss_transcribe_diarize.md @@ -6,8 +6,8 @@ This guide connects the third-party [OpenMOSS/MOSS-Transcribe-Diarize](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize) model to the FunASR deployment ecosystem. The model is published by OpenMOSS under Apache-2.0; it is not a FunASR model. FunASR provides an adapter for its -public Transformers and vLLM interfaces while retaining the OpenMOSS model -name, license, and upstream revision. +public Transformers, vLLM, and SGLang Omni interfaces while retaining the +OpenMOSS model name, license, and upstream revision. MOSS-Transcribe-Diarize jointly generates transcription, timestamps, and speaker labels such as `[S01]`. An application therefore does not need to @@ -259,6 +259,16 @@ claims as measurements of your host. Follow the pinned upstream SGLang Omni installation guide for CUDA 13, then download the immutable model snapshot and serve the local directory: +```bash +git clone https://github.com/sgl-project/sglang-omni.git +git -C sglang-omni checkout 3f819f9cdae3d4eeec22f73306c9067a1ec2542e +``` + +This source pin includes the transcription API's `max_new_tokens` forwarding. +The original #914 merge predates that request field, so it is not sufficient +for the long-audio command below even though its published H100 benchmark +remains useful upstream evidence. + ```bash hf download OpenMOSS-Team/MOSS-Transcribe-Diarize \ --revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 \ @@ -287,6 +297,31 @@ current `verbose_json` contract, the speaker identifier is retained as the Parse and validate that prefix before wiring the response into subtitles, meeting notes, or analytics. +The FunASR adapter performs that validation and maps the official SGLang +segments into the same `sentence_info` contract as the HF and vLLM paths: + +```python +from funasr import AutoModel + +model = AutoModel( + model="OpenMOSS-Team/MOSS-Transcribe-Diarize", + backend="sglang", + sglang_base_url="http://127.0.0.1:8898/v1", + sglang_model="OpenMOSS-Team/MOSS-Transcribe-Diarize", + max_new_tokens=65536, + disable_update=True, +) +result = model.generate(input="audio.wav", max_new_tokens=65536)[0] +for segment in result["sentence_info"]: + print(segment["start"], segment["end"], segment["spk"], segment["text"]) +``` + +Do not pass `vad_model` or `spk_model`: MOSS performs segmentation and +anonymous speaker attribution jointly, and external splitting can destroy +speaker consistency across long turns. The adapter preserves the upstream +tagged transcript in `raw_text`, strips only the validated `[Sxx]` prefix from +each normalized segment, and fails closed if SGLang omits that prefix. + The native runtime was merged in SGLang Omni [#914](https://github.com/sgl-project/sglang-omni/pull/914). Its single-H100 Seed-TTS EN benchmark completed 1088/1088 clips with no request failures. WER diff --git a/docs/moss_transcribe_diarize_zh.md b/docs/moss_transcribe_diarize_zh.md index 19c9a02bc..6f5803800 100644 --- a/docs/moss_transcribe_diarize_zh.md +++ b/docs/moss_transcribe_diarize_zh.md @@ -5,8 +5,8 @@ 本文把第三方 [OpenMOSS/MOSS-Transcribe-Diarize](https://github.com/OpenMOSS/MOSS-Transcribe-Diarize) 模型接入 FunASR 部署生态。模型由 OpenMOSS 以 Apache-2.0 发布，不是 FunASR -自有模型。FunASR 只为其公开的 Transformers 与 vLLM 接口提供适配器，并保留 -OpenMOSS 模型名称、许可证和上游 revision。 +自有模型。FunASR 为其公开的 Transformers、vLLM 与 SGLang Omni 接口提供适配器， +并保留 OpenMOSS 模型名称、许可证和上游 revision。 MOSS-Transcribe-Diarize 会联合生成转写、时间戳和 `[S01]` 等说话人标签，应用侧 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 @@ -231,6 +231,15 @@ LocalAI `master@a7cc5873ef5b7c909fc9ff7d349d51738ba9bb05` 已包含 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 +git clone https://github.com/sgl-project/sglang-omni.git +git -C sglang-omni checkout 3f819f9cdae3d4eeec22f73306c9067a1ec2542e +``` + +该源码 pin 已把 `max_new_tokens` 传入 transcription 生成请求。最初的 #914 +merge 早于这个请求字段，因此虽然它的 H100 benchmark 仍可作为上游证据，却不能 +支撑下方长音频命令。 + ```bash hf download OpenMOSS-Team/MOSS-Transcribe-Diarize \ --revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 \ @@ -257,6 +266,30 @@ curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions \ `verbose_json` 合同把说话人编号保留为 `segments[].text` 的 `[Sxx]` 前缀， 并没有单独的 `speaker` 字段。接入字幕、会议纪要或分析系统前，应解析并校验此前缀。 +FunASR 适配器会完成该校验，并把 SGLang 官方 segments 映射为与 HF、vLLM +一致的 `sentence_info` 合同： + +```python +from funasr import AutoModel + +model = AutoModel( + model="OpenMOSS-Team/MOSS-Transcribe-Diarize", + backend="sglang", + sglang_base_url="http://127.0.0.1:8898/v1", + sglang_model="OpenMOSS-Team/MOSS-Transcribe-Diarize", + max_new_tokens=65536, + disable_update=True, +) +result = model.generate(input="audio.wav", max_new_tokens=65536)[0] +for segment in result["sentence_info"]: + print(segment["start"], segment["end"], segment["spk"], segment["text"]) +``` + +不要传入 `vad_model` 或 `spk_model`：MOSS 在一次生成中联合完成分段和匿名说话人 +归属，外部分段会破坏长轮次中的说话人一致性。适配器把上游带标签原文保存在 +`raw_text`，只从标准化 segment 中移除已经校验的 `[Sxx]` 前缀；如果 SGLang +没有返回此前缀，则明确失败，不会伪造说话人身份。 + 原生 runtime 已通过 SGLang Omni [#914](https://github.com/sgl-project/sglang-omni/pull/914) 合并。其单张 H100 Seed-TTS EN benchmark 完成 1088/1088 条请求且无请求失败。WER 是在移除 diff --git a/funasr/models/moss_transcribe_diarize/model.py b/funasr/models/moss_transcribe_diarize/model.py index 64d476071..9ef8f24f1 100644 --- a/funasr/models/moss_transcribe_diarize/model.py +++ b/funasr/models/moss_transcribe_diarize/model.py @@ -29,6 +29,7 @@ _NUMBER = r"(?:\d+(?:\.\d+)?|\.\d+)" _SEGMENT_START = re.compile(r"\[(%s)\]\[(S\d+)\]" % _NUMBER) _TIMESTAMP = re.compile(r"\[(%s)\]" % _NUMBER) +_SGLANG_SPEAKER_PREFIX = re.compile(r"^\s*\[(S\d{2,})\]\s*") def _parse_transcript(transcript): @@ -188,6 +189,87 @@ def _result_from_diarized_response(key, response): } +def _result_from_sglang_response(key, response): + """Normalize SGLang Omni's MOSS ``verbose_json`` response.""" + raw_text = response.get("text") if isinstance(response, dict) else None + segments = response.get("segments") if isinstance(response, dict) else None + if not isinstance(raw_text, str) or not isinstance(segments, list): + raise RuntimeError( + "SGLang verbose_json response requires text and segments fields" + ) + source_segments = _parse_transcript(raw_text) + + sentence_info = [] + timestamps = [] + texts = [] + previous_start = None + for index, segment in enumerate(segments): + if not isinstance(segment, dict): + raise RuntimeError("SGLang segment %d must be an object" % index) + start = segment.get("start") + end = segment.get("end") + tagged_text = segment.get("text") + numeric = ( + isinstance(start, (int, float)) + and not isinstance(start, bool) + and isinstance(end, (int, float)) + and not isinstance(end, bool) + ) + if ( + not numeric + or not math.isfinite(start) + or not math.isfinite(end) + or start < 0 + or end < start + or not isinstance(tagged_text, str) + ): + raise RuntimeError("SGLang segment %d has an invalid contract" % index) + if previous_start is not None and start < previous_start: + raise RuntimeError("SGLang segment %d has non-monotonic start time" % index) + previous_start = start + speaker_match = _SGLANG_SPEAKER_PREFIX.match(tagged_text) + if speaker_match is None: + raise RuntimeError( + "SGLang segment %d is missing the required speaker prefix" % index + ) + text = tagged_text[speaker_match.end() :].strip() + if not text: + raise RuntimeError("SGLang segment %d has an invalid contract" % index) + if index >= len(source_segments): + raise RuntimeError( + "SGLang segment %d is not backed by raw transcript markers" % index + ) + _, _, source_speaker, source_text = source_segments[index] + if source_speaker != speaker_match.group(1) or source_text != text: + raise RuntimeError( + "SGLang segment %d is not backed by raw transcript markers" % index + ) + timestamp = [int(round(start * 1000)), int(round(end * 1000))] + timestamps.append(timestamp) + texts.append(text) + sentence_info.append( + { + "start": timestamp[0], + "end": timestamp[1], + "text": text, + "sentence": text, + "spk": speaker_match.group(1), + "timestamp": [timestamp], + } + ) + if len(source_segments) != len(segments): + raise RuntimeError( + "SGLang segments are not backed by raw transcript speaker markers" + ) + return { + "key": key, + "text": _join_texts(texts), + "raw_text": raw_text, + "timestamp": timestamps, + "sentence_info": sentence_info, + } + + @tables.register("model_classes", "MOSS-Transcribe-Diarize") @tables.register("model_classes", DEFAULT_MODEL) class MossTranscribeDiarize(nn.Module): @@ -203,8 +285,8 @@ def __init__(self, **kwargs): ) self.backend = kwargs.get("backend", "hf").lower() - if self.backend not in {"hf", "vllm"}: - raise ValueError("backend must be 'hf' or 'vllm'") + if self.backend not in {"hf", "vllm", "sglang"}: + raise ValueError("backend must be 'hf', 'vllm', or 'sglang'") self.model_path = ( kwargs.get("model_path") or kwargs.get("model") or DEFAULT_MODEL ) @@ -225,12 +307,19 @@ def __init__(self, **kwargs): if self.vllm_response_format not in {"json", "diarized_json"}: raise ValueError("vllm_response_format must be 'json' or 'diarized_json'") self.http_session = kwargs.get("http_session") + self.sglang_base_url = kwargs.get("sglang_base_url") + self.sglang_model = kwargs.get("sglang_model", DEFAULT_MODEL) + self.sglang_api_key = kwargs.get("sglang_api_key", "EMPTY") + self.sglang_timeout = float(kwargs.get("sglang_timeout", 600.0)) self.hf_model = None self.processor = None if self.backend == "vllm": if not self.vllm_base_url: raise ValueError("vllm_base_url is required when backend='vllm'") + elif self.backend == "sglang": + if not self.sglang_base_url: + raise ValueError("sglang_base_url is required when backend='sglang'") else: self._load_hf_backend(kwargs) @@ -316,6 +405,9 @@ def inference(self, data_in, data_lengths=None, key=None, **kwargs): results.append( _result_from_transcript(keys[index], response["text"]) ) + elif self.backend == "sglang": + response = self._transcribe_sglang(audio, **kwargs) + results.append(_result_from_sglang_response(keys[index], response)) else: transcript = self._transcribe_hf(audio, **kwargs) results.append(_result_from_transcript(keys[index], transcript)) @@ -384,8 +476,9 @@ def _transcribe_hf(self, audio, **kwargs): generated, skip_special_tokens=True ).strip() - def _transcriptions_url(self): - base = self.vllm_base_url.rstrip("/") + @staticmethod + def _transcriptions_url(base_url): + base = base_url.rstrip("/") if base.endswith("/v1/audio/transcriptions"): return base if not base.endswith("/v1"): @@ -415,7 +508,7 @@ def _transcribe_vllm(self, audio, **kwargs): if self.vllm_api_key and self.vllm_api_key != "EMPTY": headers["Authorization"] = "Bearer " + self.vllm_api_key response = session.post( - self._transcriptions_url(), + self._transcriptions_url(self.vllm_base_url), data=data, files={"file": (filename, payload, content_type)}, headers=headers, @@ -433,6 +526,36 @@ def _transcribe_vllm(self, audio, **kwargs): ) return result + def _transcribe_sglang(self, audio, **kwargs): + import requests + + session = self.http_session or requests.Session() + filename, payload, content_type = self._multipart_audio(audio) + data = { + "model": self.sglang_model, + "response_format": "verbose_json", + "temperature": str(kwargs.get("temperature", 0)), + "max_new_tokens": str(kwargs.get("max_new_tokens", self.max_new_tokens)), + } + prompt = kwargs.get("prompt") + if prompt: + data["prompt"] = prompt + headers = {} + if self.sglang_api_key and self.sglang_api_key != "EMPTY": + headers["Authorization"] = "Bearer " + self.sglang_api_key + response = session.post( + self._transcriptions_url(self.sglang_base_url), + data=data, + files={"file": (filename, payload, content_type)}, + headers=headers, + timeout=self.sglang_timeout, + ) + response.raise_for_status() + result = response.json() + if not isinstance(result, dict): + raise RuntimeError("SGLang transcription response must be a JSON object") + return result + @staticmethod def _multipart_audio(audio): if isinstance(audio, np.ndarray): diff --git a/tests/test_moss_transcribe_diarize_docs.py b/tests/test_moss_transcribe_diarize_docs.py index f74da8196..234740899 100644 --- a/tests/test_moss_transcribe_diarize_docs.py +++ b/tests/test_moss_transcribe_diarize_docs.py @@ -26,6 +26,7 @@ def test_moss_guides_pin_upstream_and_separate_serving_contracts(guide: Path) -> "OpenMOSS-Team/MOSS-Transcribe-Diarize", "e8681d68e7042738ffca8ac8212bc8fcb1131ab8", "6e448d0ea9bf3d88d898b65449ca6dc2aec170ac", + "3f819f9cdae3d4eeec22f73306c9067a1ec2542e", "bf0d52faa2a51e7a01c6856a7a8a2d1307fd0ff711415d34168a67ffac0fa47b", "vllm[audio]", "vLLM 0.27.1", @@ -38,6 +39,9 @@ def test_moss_guides_pin_upstream_and_separate_serving_contracts(guide: Path) -> "response_format=diarized_json", "response_format=verbose_json", 'vllm_response_format="diarized_json"', + 'backend="sglang"', + 'sglang_base_url="http://127.0.0.1:8898/v1"', + "max_new_tokens=65536", "[S01]", "Apache-2.0", "sentence_info", @@ -66,6 +70,7 @@ def test_all_deployment_matrices_link_moss_guide(matrix: Path) -> None: assert "moss_transcribe_diarize.md" in moss_row assert '`backend="hf"`' in moss_row assert '`backend="vllm"`' in moss_row + assert '`backend="sglang"`' in moss_row for stale_claim in ( "not a FunASR-owned model or `AutoModel` backend", "不是 FunASR 自有模型或 `AutoModel` 后端", diff --git a/tests/test_moss_transcribe_diarize_model.py b/tests/test_moss_transcribe_diarize_model.py index a73452837..6e4db9a67 100644 --- a/tests/test_moss_transcribe_diarize_model.py +++ b/tests/test_moss_transcribe_diarize_model.py @@ -264,5 +264,171 @@ def test_rejects_malformed_vllm_diarized_segment(self): model.inference([np.zeros(1600, dtype=np.float32)], key=["meeting"]) +class MossSglangBackendTest(unittest.TestCase): + def test_auto_model_sglang_path_bypasses_weight_download(self): + from funasr.auto.auto_model import AutoModel + + with patch( + "funasr.auto.auto_model.download_model", + side_effect=AssertionError( + "SGLang client mode must not download model weights" + ), + ): + auto_model = AutoModel( + model="OpenMOSS-Team/MOSS-Transcribe-Diarize", + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + device="cpu", + disable_update=True, + ) + + self.assertIsInstance(auto_model.model, MossTranscribeDiarize) + + def test_normalizes_official_sglang_verbose_json_response(self): + session = MagicMock() + response = MagicMock() + response.json.return_value = { + "text": "[0.25][S01]hello[1.5][2.0][S02]again[3.5]", + "segments": [ + {"start": 0.25, "end": 1.5, "text": "[S01]hello"}, + {"start": 2.0, "end": 3.5, "text": "[S02] again"}, + ], + } + session.post.return_value = response + + model = MossTranscribeDiarize( + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + sglang_model="OpenMOSS-Team/MOSS-Transcribe-Diarize", + sglang_api_key="secret", + http_session=session, + ) + results, _ = model.inference( + [np.zeros(1600, dtype=np.float32)], + key=["meeting"], + prompt="transcribe and diarize", + max_new_tokens=8192, + ) + + request = session.post.call_args + self.assertEqual( + request.args[0], + "http://sglang.test:8000/v1/audio/transcriptions", + ) + self.assertEqual( + request.kwargs["data"], + { + "model": "OpenMOSS-Team/MOSS-Transcribe-Diarize", + "response_format": "verbose_json", + "temperature": "0", + "prompt": "transcribe and diarize", + "max_new_tokens": "8192", + }, + ) + self.assertEqual(request.kwargs["headers"], {"Authorization": "Bearer secret"}) + self.assertEqual(results[0]["text"], "hello again") + self.assertEqual( + results[0]["raw_text"], + "[0.25][S01]hello[1.5][2.0][S02]again[3.5]", + ) + self.assertEqual(results[0]["timestamp"], [[250, 1500], [2000, 3500]]) + self.assertEqual( + [(item["spk"], item["text"]) for item in results[0]["sentence_info"]], + [("S01", "hello"), ("S02", "again")], + ) + + def test_rejects_sglang_segment_without_speaker_prefix(self): + session = MagicMock() + response = MagicMock() + response.json.return_value = { + "text": "hello", + "segments": [{"start": 0.0, "end": 1.0, "text": "hello"}], + } + session.post.return_value = response + model = MossTranscribeDiarize( + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + http_session=session, + ) + + with self.assertRaisesRegex(RuntimeError, "segment 0.*speaker prefix"): + model.inference([np.zeros(1600, dtype=np.float32)], key=["meeting"]) + + def test_rejects_sglang_fallback_speaker_not_backed_by_raw_transcript(self): + session = MagicMock() + response = MagicMock() + response.json.return_value = { + "text": "hello", + "segments": [{"start": 0.0, "end": 1.0, "text": "[S01]hello"}], + } + session.post.return_value = response + model = MossTranscribeDiarize( + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + http_session=session, + ) + + with self.assertRaisesRegex(RuntimeError, "not backed by raw transcript"): + model.inference([np.zeros(1600, dtype=np.float32)], key=["meeting"]) + + def test_rejects_one_digit_sglang_speaker_label(self): + session = MagicMock() + response = MagicMock() + response.json.return_value = { + "text": "[0][S1]hello[1]", + "segments": [{"start": 0.0, "end": 1.0, "text": "[S1]hello"}], + } + session.post.return_value = response + model = MossTranscribeDiarize( + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + http_session=session, + ) + + with self.assertRaisesRegex(RuntimeError, "segment 0.*speaker prefix"): + model.inference([np.zeros(1600, dtype=np.float32)], key=["meeting"]) + + def test_rejects_sglang_segments_with_decreasing_start_times(self): + session = MagicMock() + response = MagicMock() + response.json.return_value = { + "text": "[2][S01]late[3][1][S02]early[1.5]", + "segments": [ + {"start": 2.0, "end": 3.0, "text": "[S01]late"}, + {"start": 1.0, "end": 1.5, "text": "[S02]early"}, + ], + } + session.post.return_value = response + model = MossTranscribeDiarize( + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + http_session=session, + ) + + with self.assertRaisesRegex(RuntimeError, "segment 1.*non-monotonic"): + model.inference([np.zeros(1600, dtype=np.float32)], key=["meeting"]) + + def test_accepts_sglang_timestamp_clamp_when_speaker_and_text_match_raw(self): + session = MagicMock() + response = MagicMock() + response.json.return_value = { + "text": "[0][S01]hello[99]", + "segments": [{"start": 0.0, "end": 10.0, "text": "[S01]hello"}], + } + session.post.return_value = response + model = MossTranscribeDiarize( + backend="sglang", + sglang_base_url="http://sglang.test:8000/v1", + http_session=session, + ) + + results, _ = model.inference( + [np.zeros(1600, dtype=np.float32)], key=["meeting"] + ) + + self.assertEqual(results[0]["timestamp"], [[0, 10000]]) + self.assertEqual(results[0]["sentence_info"][0]["spk"], "S01") + + if __name__ == "__main__": unittest.main() diff --git a/web-pages/product-site/data/deployments.json b/web-pages/product-site/data/deployments.json index 8be836a57..873aa24e7 100644 --- a/web-pages/product-site/data/deployments.json +++ b/web-pages/product-site/data/deployments.json @@ -13,7 +13,7 @@ "models": ["OpenMOSS-Team/MOSS-Transcribe-Diarize (third-party Apache-2.0 model)"], "operating_systems": ["Linux"], "interfaces": ["OpenAI-compatible HTTP", "vLLM", "SGLang Omni", "Transformers", "LocalAI / moss-transcribe.cpp"], - "tested": {"funasr": "AutoModel diarized_json adapter; third-party model@e8681d68", "runtime": "vLLM 0.27.1 / Torch 2.13.0+cu129 / H100 80GB", "verified": "2026-08-30"}, + "tested": {"funasr": "AutoModel vLLM + SGLang adapters; third-party model@e8681d68", "runtime": "vLLM 0.27.1 / Torch 2.13.0+cu129 / H100 80GB", "verified": "2026-08-30"}, "commands": { "install": [ "uv venv --python 3.12 .venv-moss && curl -fL https://github.com/vllm-project/vllm/releases/download/v0.27.1/vllm-0.27.1%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl -o vllm-0.27.1+cu129-cp38-abi3-manylinux_2_28_x86_64.whl && echo 'bf0d52faa2a51e7a01c6856a7a8a2d1307fd0ff711415d34168a67ffac0fa47b vllm-0.27.1+cu129-cp38-abi3-manylinux_2_28_x86_64.whl' | sha256sum -c - && uv pip install --python .venv-moss/bin/python --torch-backend=auto \"vllm[audio] @ file://$PWD/vllm-0.27.1+cu129-cp38-abi3-manylinux_2_28_x86_64.whl\"", @@ -61,14 +61,14 @@ }, { "id": "sglang-omni", - "tested": "SGLang Omni merge 8458f76a / single H100 upstream benchmark", + "tested": "SGLang Omni 3f819f9c / FunASR adapter contract-tested / #914 H100 upstream benchmark", "translations": { - "zh": {"name": "SGLang Omni 原生服务", "summary": "使用已合并的原生 MOSS pipeline 和 OpenAI-compatible verbose_json 接口；它是独立上游 runtime，不是 FunASR AutoModel backend。"}, - "en": {"name": "Native SGLang Omni serving", "summary": "Use the merged native MOSS pipeline and OpenAI-compatible verbose_json endpoint. This is an independent upstream runtime, not a FunASR AutoModel backend."} + "zh": {"name": "SGLang Omni + FunASR AutoModel", "summary": "使用已合并的原生 MOSS pipeline 和 OpenAI-compatible verbose_json 接口，并通过 FunASR AutoModel 校验 [Sxx] 前缀、统一为 sentence_info。"}, + "en": {"name": "SGLang Omni + FunASR AutoModel", "summary": "Use the merged native MOSS pipeline and OpenAI-compatible verbose_json endpoint, then validate [Sxx] prefixes and normalize them into sentence_info through FunASR AutoModel."} }, "commands": { "install": [ - "git clone https://github.com/sgl-project/sglang-omni.git && cd sglang-omni && git checkout 8458f76ab25f5ba9152b05929b40e07618aff2ce && uv venv .venv -p 3.12 && uv pip install --python .venv/bin/python -v -e .", + "git clone https://github.com/sgl-project/sglang-omni.git && cd sglang-omni && git checkout 3f819f9cdae3d4eeec22f73306c9067a1ec2542e && uv venv .venv -p 3.12 && uv pip install --python .venv/bin/python -v -e .", "HF_HUB_ENABLE_HF_TRANSFER=1 hf download OpenMOSS-Team/MOSS-Transcribe-Diarize --revision e8681d68e7042738ffca8ac8212bc8fcb1131ab8 --local-dir .models/moss-transcribe-diarize", "curl -fsSL https://raw.githubusercontent.com/modelscope/FunASR/8d65a38a8f4f3b5301be72905096219dde443f73/runtime/llama.cpp/tests/sample.wav -o moss-sample.wav && echo 'ea03e1f473ad1618a03da3327a545369cb8f6f06cb0f4115535e5a866167d47e moss-sample.wav' | sha256sum -c -" ], @@ -81,7 +81,8 @@ ], "smoke": [ "curl -fsS http://127.0.0.1:8898/v1/audio/transcriptions -F file=@moss-sample.wav -F model=OpenMOSS-Team/MOSS-Transcribe-Diarize -F response_format=verbose_json | tee /tmp/moss-sglang-transcription.json", - "python - <<'PY'\nimport json\nimport re\n\nwith open('/tmp/moss-sglang-transcription.json', encoding='utf-8') as stream:\n payload = json.load(stream)\nsegments = payload.get('segments', [])\nassert payload.get('text', '').strip() and segments, payload\nassert all(item.get('start') <= item.get('end') and re.match(r'^\\[S\\d{2,}\\]', item.get('text', '')) for item in segments), payload\nprint(payload['text'])\nprint([(item['start'], item['end'], item['text'][:5]) for item in segments])\nPY" + "python - <<'PY'\nimport json\nimport re\n\nwith open('/tmp/moss-sglang-transcription.json', encoding='utf-8') as stream:\n payload = json.load(stream)\nsegments = payload.get('segments', [])\nassert payload.get('text', '').strip() and segments, payload\nassert all(item.get('start') <= item.get('end') and re.match(r'^\\[S\\d{2,}\\]', item.get('text', '')) for item in segments), payload\nprint(payload['text'])\nprint([(item['start'], item['end'], item['text'][:5]) for item in segments])\nPY", + "python - <<'PY'\nfrom funasr import AutoModel\n\nmodel = AutoModel(model='OpenMOSS-Team/MOSS-Transcribe-Diarize', backend='sglang', sglang_base_url='http://127.0.0.1:8898/v1', sglang_model='OpenMOSS-Team/MOSS-Transcribe-Diarize', max_new_tokens=65536, disable_update=True)\nresult = model.generate('moss-sample.wav', max_new_tokens=65536)[0]\nassert result['raw_text'] and result['sentence_info'], result\nprint(result['text'])\nprint(result['sentence_info'])\nPY" ] } } @@ -134,24 +135,24 @@ "summary": "通过 FunASR AutoModel 或 OpenAI 兼容服务接入 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 vLLM 或 SGLang Omni 的 OpenAI 兼容音频转写接口", "通过 LocalAI / moss-transcribe.cpp 在 CPU 或桌面边缘 GPU 上运行第三方 GGUF 实现"], "not_fit": ["实时流式字幕或低延迟端点检测", "要求 FunASR AutoModel 与 LocalAI C++ 路径输出完全相同结果的部署", "未经目标硬件复测的原生 Windows 部署", "要求 FunASR 自有模型权重的项目"], - "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；FunASR AutoModel 为 Transformers 与 vLLM 提供统一的结构化结果，SGLang Omni 提供独立的原生上游服务，LocalAI 则提供独立的第三方 C++/GGUF 边缘路径。", - "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。vLLM 路径固定 0.27.1，SGLang Omni 路径固定 merge 8458f76a；升级前必须重跑真实多人长音频。SGLang Omni 不是 FunASR AutoModel backend，其 verbose_json 把说话人编号保留在 segments[].text 的 [Sxx] 前缀。LocalAI / moss-transcribe.cpp 也是独立第三方重写。", + "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；FunASR AutoModel 为 Transformers、vLLM 与 SGLang Omni 提供统一的结构化结果，LocalAI 则提供独立的第三方 C++/GGUF 边缘路径。", + "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。vLLM 路径固定 0.27.1，SGLang Omni 路径固定 3f819f9c；升级前必须重跑真实多人长音频。SGLang Omni 的 verbose_json 把说话人编号保留在 segments[].text 的 [Sxx] 前缀，FunASR adapter 会校验该分段能够回溯到 raw_text 后再解析。LocalAI / moss-transcribe.cpp 也是独立第三方重写。", "status_label": "社区验证", - "operations": ["固定模型 revision、FunASR adapter merge、vLLM 0.27.1、SGLang Omni merge 8458f76a、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", "SGLang Omni 的 1088 条单说话人英文 benchmark 不覆盖 diarization 或 timestamp 准确率，生产前必须单独验证", "LocalAI 路径固定第三方 C++ backend 与 GGUF revision，并分别验证 CPU/GPU backend、量化精度和 OpenAI 接口响应", "FunASR AutoModel 可用 vLLM diarized_json 直接统一 text、timestamp 和带 spk 的 sentence_info；json 兼容路径仍在 raw_text 保留原始 [Sxx] 标记文本"], + "operations": ["固定模型 revision、FunASR adapter merge、vLLM 0.27.1、SGLang Omni 3f819f9c、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", "SGLang Omni #914 的 1088 条单说话人英文 benchmark 不覆盖 diarization 或 timestamp 准确率，生产前必须单独验证", "LocalAI 路径固定第三方 C++ backend 与 GGUF revision，并分别验证 CPU/GPU backend、量化精度和 OpenAI 接口响应", "FunASR AutoModel 可把 vLLM diarized_json 与 SGLang Omni verbose_json 统一为 text、timestamp 和带 spk 的 sentence_info；SGLang 分段必须能回溯到 raw_text，避免接受上游合成的 S01 fallback"], "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": ["CUDA 12 使用已校验 SHA256 的 vLLM 0.27.1 cu129 wheel；SGLang Omni 当前固定 CUDA 13 安装契约和 merge 8458f76a", "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 diarized_json 提供独立 speaker 字段；SGLang Omni verbose_json 当前把说话人编号放在 segments[].text 的 [Sxx] 前缀"] + "troubleshooting": ["CUDA 12 使用已校验 SHA256 的 vLLM 0.27.1 cu129 wheel；SGLang Omni 当前固定 CUDA 13 安装契约和 3f819f9c", "长音频被截断时，vLLM 提高 max_completion_tokens，SGLang Omni 提高 max_new_tokens，并同时监控显存、延迟和输出完整性", "vLLM diarized_json 提供独立 speaker 字段；SGLang Omni verbose_json 当前把说话人编号放在 segments[].text 的 [Sxx] 前缀"] }, "en": { "name": "MOSS unified transcription and diarization", "summary": "Use FunASR AutoModel or an OpenAI-compatible service with the third-party Apache-2.0 model published by OpenMOSS to produce long-form transcripts, timestamps, and speaker labels in one pass.", "fit": ["Multi-speaker meetings, interviews, podcasts, and calls", "One model output for text, timestamps, and speaker identifiers", "NVIDIA GPU deployments that need a vLLM or SGLang Omni OpenAI-compatible audio transcription endpoint", "The third-party LocalAI / moss-transcribe.cpp GGUF path on CPU or desktop-edge GPUs"], "not_fit": ["Realtime streaming captions or low-latency endpoint detection", "Deployments that require identical output from FunASR AutoModel and the LocalAI C++ path", "Native Windows deployments without validation on the target hardware", "Projects that require FunASR-owned model weights"], - "selection_reason": "The model jointly emits transcription and speaker labels so users do not need to assemble an external VAD, ASR, and diarization pipeline; FunASR AutoModel provides one structured result across Transformers and vLLM, SGLang Omni provides an independent native upstream service, and LocalAI exposes a separate third-party C++/GGUF edge path.", - "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 0.27.1 and the SGLang Omni path pins merge 8458f76a; revalidate real long multi-speaker audio before upgrades. SGLang Omni is not a FunASR AutoModel backend, and its verbose_json keeps the speaker identifier in the [Sxx] prefix of segments[].text. LocalAI / moss-transcribe.cpp is another independent third-party reimplementation.", + "selection_reason": "The model jointly emits transcription and speaker labels so users do not need to assemble an external VAD, ASR, and diarization pipeline; FunASR AutoModel provides one structured result across Transformers, vLLM, and SGLang Omni, while LocalAI exposes a separate third-party C++/GGUF edge path.", + "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 0.27.1 and the SGLang Omni path pins 3f819f9c; revalidate real long multi-speaker audio before upgrades. SGLang Omni verbose_json keeps the speaker identifier in the [Sxx] prefix of segments[].text, which the FunASR adapter parses only after validating that the segment is backed by raw_text. LocalAI / moss-transcribe.cpp is another independent third-party reimplementation.", "status_label": "Community verified", - "operations": ["Pin the model revision, FunASR adapter merge, vLLM 0.27.1, SGLang Omni merge 8458f76a, CUDA/Torch stack, and trust_remote_code audit", "Validate speaker consistency, overlap, long silence, timestamps, and generation limits on real meetings", "The 1088-clip SGLang Omni single-speaker English benchmark does not cover diarization or timestamp accuracy; validate both independently before production", "For LocalAI, pin the third-party C++ backend and GGUF revision, then validate CPU/GPU backends, quantization accuracy, and the OpenAI response independently", "FunASR AutoModel can map vLLM diarized_json directly into text, timestamp, and sentence_info with spk; the json compatibility path still preserves raw [Sxx]-tagged text in raw_text"], + "operations": ["Pin the model revision, FunASR adapter merge, vLLM 0.27.1, SGLang Omni 3f819f9c, CUDA/Torch stack, and trust_remote_code audit", "Validate speaker consistency, overlap, long silence, timestamps, and generation limits on real meetings", "The 1088-clip SGLang Omni #914 single-speaker English benchmark does not cover diarization or timestamp accuracy; validate both independently before production", "For LocalAI, pin the third-party C++ backend and GGUF revision, then validate CPU/GPU backends, quantization accuracy, and the OpenAI response independently", "FunASR AutoModel maps both vLLM diarized_json and SGLang Omni verbose_json into text, timestamp, and sentence_info with spk; SGLang segments must be backed by raw_text so an upstream synthesized S01 fallback is not accepted"], "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": ["For CUDA 12, use the SHA256-verified vLLM 0.27.1 cu129 wheel; the SGLang Omni path currently pins its CUDA 13 installation contract and merge 8458f76a", "If long audio is truncated, raise max_completion_tokens while monitoring memory, latency, and output completeness", "vLLM diarized_json returns a separate speaker field; SGLang Omni verbose_json currently keeps the speaker identifier in the [Sxx] prefix of segments[].text"] + "troubleshooting": ["For CUDA 12, use the SHA256-verified vLLM 0.27.1 cu129 wheel; the SGLang Omni path currently pins its CUDA 13 installation contract and 3f819f9c", "If long audio is truncated, raise max_completion_tokens for vLLM or max_new_tokens for SGLang Omni while monitoring memory, latency, and output completeness", "vLLM diarized_json returns a separate speaker field; SGLang Omni verbose_json currently keeps the speaker identifier in the [Sxx] prefix of segments[].text"] } } }, diff --git a/web-pages/product-site/tests/test_registry.py b/web-pages/product-site/tests/test_registry.py index bc0716ebe..007af2f38 100644 --- a/web-pages/product-site/tests/test_registry.py +++ b/web-pages/product-site/tests/test_registry.py @@ -107,7 +107,7 @@ def test_moss_transcribe_diarize_contract_tracks_third_party_upstream(valid_regi 'OpenMOSS-Team/MOSS-Transcribe-Diarize (third-party Apache-2.0 model)' ] assert entry['tested'] == { - 'funasr': 'AutoModel diarized_json adapter; third-party model@e8681d68', + 'funasr': 'AutoModel vLLM + SGLang adapters; third-party model@e8681d68', 'runtime': 'vLLM 0.27.1 / Torch 2.13.0+cu129 / H100 80GB', 'verified': '2026-08-30', } @@ -120,7 +120,7 @@ def test_moss_transcribe_diarize_contract_tracks_third_party_upstream(valid_regi 'vLLM 0.27.1 / Torch 2.13.0+cu129 / H100 80GB; FunASR adapter verified' ) assert runtime_paths['sglang-omni']['tested'] == ( - 'SGLang Omni merge 8458f76a / single H100 upstream benchmark' + 'SGLang Omni 3f819f9c / FunASR adapter contract-tested / #914 H100 upstream benchmark' ) sglang_commands = '\n'.join( @@ -128,12 +128,15 @@ def test_moss_transcribe_diarize_contract_tracks_third_party_upstream(valid_regi for group in ('install', 'launch', 'health', 'smoke') for command in runtime_paths['sglang-omni']['commands'][group] ) - assert 'git checkout 8458f76ab25f5ba9152b05929b40e07618aff2ce' in sglang_commands + assert 'git checkout 3f819f9cdae3d4eeec22f73306c9067a1ec2542e' in sglang_commands assert 'sgl-omni serve' in sglang_commands assert '--model-path .models/moss-transcribe-diarize' in sglang_commands assert 'response_format=verbose_json' in sglang_commands assert "payload.get('segments'" in sglang_commands - assert 'backend=\'sglang\'' not in sglang_commands + assert "from funasr import AutoModel" in sglang_commands + assert "backend='sglang'" in sglang_commands + assert "sglang_base_url='http://127.0.0.1:8898/v1'" in sglang_commands + assert 'max_new_tokens=65536' in sglang_commands install = '\n'.join(entry['commands']['install']) assert 'vllm[audio]' in install @@ -188,6 +191,8 @@ def test_moss_transcribe_diarize_contract_tracks_third_party_upstream(valid_regi assert 'diarized_json' in english['operations'][-1] assert 'diarized_json' in english['troubleshooting'][-1] assert 'internal segmentation' in english['primary_limitation'] + assert 'not a FunASR AutoModel backend' not in english['primary_limitation'] + assert 'SGLang Omni' in english['operations'][-1] assert 'FunASR model' in english['primary_limitation'] assert any('LocalAI' in item and 'GGUF' in item for item in english['fit']) assert not any(