diff --git a/notebooks/luke-scribe-colab.ipynb b/notebooks/luke-scribe-colab.ipynb index 530e034..d0f026c 100644 --- a/notebooks/luke-scribe-colab.ipynb +++ b/notebooks/luke-scribe-colab.ipynb @@ -171,7 +171,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": "# 13) 벤치마크 — 샘플 manifest 사용 (선택, 시간 소요)\n# 간단 manifest 생성\nimport yaml\nmanifest = {\n 'name': 'colab-quick',\n 'dataset_version': '1.0',\n 'language': 'ko',\n 'targets': {'entity_preservation': 0.95, 'cer': 0.15},\n 'clips': [\n {'name': 'ko-en-tech', 'audio_path': 'samples/colab-ko-en.mp3',\n 'duration_sec': 10.5, 'entities': ['vLLM', 'Kubernetes', 'GPU']},\n ],\n}\nyaml.safe_dump(manifest, open('/content/manifest.yaml', 'w'))\n\n# 실행 (기본: turbo만 → 빠름)\n!luke-scribe bench /content/manifest.yaml --models large-v3-turbo --device auto --repeats 2 --output /content/bench-report.json 2>&1 | tail -20\n" + "source": "# 13) 벤치마크 — 샘플 manifest 사용 (선택, 시간 소요)\n# entities는 {canonical, surface, start_char, end_char} dict여야 한다\n# (문자열이면 entity_retention이 .get() 호출에 실패해 clip이 실패 처리됨)\nimport yaml\n\nREF_TEXT = '오늘은 vLLM 서버를 Kubernetes 클러스터에 배포하는 방법을 설명합니다. GPU 가속 추론으로 대기 시간을 줄일 수 있습니다.'\nwith open('/content/reference.txt', 'w', encoding='utf-8') as f:\n f.write(REF_TEXT)\n\nentities = []\nfor name in ('vLLM', 'Kubernetes', 'GPU'):\n idx = REF_TEXT.index(name)\n entities.append({\n 'canonical': name, 'surface': name,\n 'start_char': idx, 'end_char': idx + len(name),\n })\n\nmanifest = {\n 'name': 'colab-quick',\n 'dataset_version': '1.0',\n 'language': 'ko',\n 'targets': {'entity_preservation': 0.95, 'cer': 0.15},\n 'clips': [\n {'id': 'ko-en-tech', 'audio_path': 'samples/colab-ko-en.mp3',\n 'reference_path': '/content/reference.txt',\n 'duration_sec': 10.5, 'entities': entities},\n ],\n}\nyaml.safe_dump(manifest, open('/content/manifest.yaml', 'w'))\n\n# 실행 (기본: turbo만 → 빠름)\n!luke-scribe bench /content/manifest.yaml --models large-v3-turbo --device auto --repeats 2 --output /content/bench-report.json 2>&1 | tail -20\n" }, { "cell_type": "markdown", diff --git a/scripts/build_colab_notebook.py b/scripts/build_colab_notebook.py index 1ceff61..a453b6b 100644 --- a/scripts/build_colab_notebook.py +++ b/scripts/build_colab_notebook.py @@ -332,16 +332,31 @@ def cells() -> list[dict]: ), code( "# 13) 벤치마크 — 샘플 manifest 사용 (선택, 시간 소요)\n" - "# 간단 manifest 생성\n" + "# entities는 {canonical, surface, start_char, end_char} dict여야 한다\n" + "# (문자열이면 entity_retention이 .get() 호출에 실패해 clip이 실패 처리됨)\n" "import yaml\n" + "\n" + "REF_TEXT = '오늘은 vLLM 서버를 Kubernetes 클러스터에 배포하는 방법을 설명합니다. GPU 가속 추론으로 대기 시간을 줄일 수 있습니다.'\n" + "with open('/content/reference.txt', 'w', encoding='utf-8') as f:\n" + " f.write(REF_TEXT)\n" + "\n" + "entities = []\n" + "for name in ('vLLM', 'Kubernetes', 'GPU'):\n" + " idx = REF_TEXT.index(name)\n" + " entities.append({\n" + " 'canonical': name, 'surface': name,\n" + " 'start_char': idx, 'end_char': idx + len(name),\n" + " })\n" + "\n" "manifest = {\n" " 'name': 'colab-quick',\n" " 'dataset_version': '1.0',\n" " 'language': 'ko',\n" " 'targets': {'entity_preservation': 0.95, 'cer': 0.15},\n" " 'clips': [\n" - " {'name': 'ko-en-tech', 'audio_path': 'samples/colab-ko-en.mp3',\n" - " 'duration_sec': 10.5, 'entities': ['vLLM', 'Kubernetes', 'GPU']},\n" + " {'id': 'ko-en-tech', 'audio_path': 'samples/colab-ko-en.mp3',\n" + " 'reference_path': '/content/reference.txt',\n" + " 'duration_sec': 10.5, 'entities': entities},\n" " ],\n" "}\n" "yaml.safe_dump(manifest, open('/content/manifest.yaml', 'w'))\n"