613 lines
20 KiB
Markdown
613 lines
20 KiB
Markdown
---
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phase: 03-processing-skills
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plan: 01
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type: execute
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wave: 1
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depends_on: []
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files_modified:
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- kb_pipeline/processors/__init__.py
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- kb_pipeline/processors/asr.py
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- kb_pipeline/schemas.py
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- tests/test_processors/__init__.py
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- tests/test_processors/test_asr.py
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autonomous: true
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requirements: [] # 平台支持已在Phase 2覆盖，ASR是内部处理能力
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user_setup:
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- service: whisper.cpp
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why: "本地语音识别引擎"
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env_vars: []
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dashboard_config:
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- task: "安装 whisper.cpp 和下载模型"
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location: "git clone https://github.com/ggml-org/whisper.cpp.git ~/whisper.cpp && cd ~/whisper.cpp && make && ./models/download-ggml-model.sh base"
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- service: pyannote.audio (可选)
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why: "说话者分离功能"
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env_vars:
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- name: HF_TOKEN
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source: "HuggingFace Settings -> Access Tokens"
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dashboard_config:
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- task: "接受 pyannote 模型用户协议"
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location: "https://huggingface.co/pyannote/speaker-diarization-3.1"
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must_haves:
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truths:
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- "B站视频可以自动下载官方字幕"
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- "无官方字幕时自动调用 Whisper.cpp 进行语音识别"
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- "HF_TOKEN 配置时自动启用说话者分离"
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- "输出包含 txt/srt/json 三种格式"
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- "ASR 完成后状态记录到 workflow_state.json"
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- "临时音频文件处理后自动清理"
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artifacts:
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- path: "kb_pipeline/processors/__init__.py"
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provides: "处理器模块初始化"
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exports: ["ASRProcessor"]
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- path: "kb_pipeline/processors/asr.py"
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provides: "ASR 处理器实现"
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contains: "class ASRProcessor"
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min_lines: 150
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- path: "kb_pipeline/schemas.py"
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provides: "ASR 输入输出 Schema"
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contains: "class ASRInput"
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contains: "class ASROutput"
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key_links:
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- from: "kb_pipeline/processors/asr.py"
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to: "kb_pipeline/schemas.py"
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via: "from ..schemas import ASRInput, ASROutput"
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pattern: "ASRInput|ASROutput"
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- from: "kb_pipeline/processors/asr.py"
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to: "kb_pipeline/state.py"
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via: "WorkflowStateManager"
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pattern: "state_manager\\.mark_step"
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- from: "kb_pipeline/processors/asr.py"
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to: "kb-bilibili-content/bilibili_content.py"
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via: "download_subtitle 逻辑复用"
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pattern: "download_subtitle|has_subtitle"
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- from: "kb_pipeline/processors/asr.py"
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to: "kb-whisper/whisper.py"
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via: "Whisper.cpp 调用逻辑复用"
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pattern: "whisper-cli|convert_to_wav"
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---
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<objective>
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创建 ASRProcessor 处理器，统一封装 B站官方字幕下载和 Whisper.cpp 语音识别，遵循 Phase 1/2 建立的管道模式。
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Purpose: 为视频内容提供统一的文字提取入口，优先使用高质量官方字幕，无字幕时自动降级到本地语音识别。
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Output: ASRProcessor 类，支持官方字幕下载、Whisper.cpp 调用、说话者分离、状态记录。
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</objective>
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<execution_context>
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@/home/starmen1265/.claude/get-shit-done/workflows/execute-plan.md
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@/home/starmen1265/.claude/get-shit-done/templates/summary.md
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</execution_context>
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<context>
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@.planning/PROJECT.md
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@.planning/ROADMAP.md
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@.planning/STATE.md
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@.planning/phases/03-processing-skills/03-CONTEXT.md
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@.planning/phases/03-processing-skills/03-RESEARCH.md
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## 现有代码模式
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### Phase 1 Schema 体系 (kb_pipeline/schemas.py)
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```python
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class StepInput(BaseModel):
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content_id: str
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platform: Literal["bilibili", "zhihu"]
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class StepOutput(BaseModel):
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success: bool
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error_message: Optional[str] = None
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completed_at: datetime = Field(default_factory=datetime.now)
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class WorkflowState(BaseModel):
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def mark_step_completed(self, step_name: str, output_summary: Optional[str] = None) -> None
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def mark_step_failed(self, step_name: str, error_message: str) -> None
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```
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### Phase 2 平台适配器模式 (kb_pipeline/platforms/bilibili.py)
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```python
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class BilibiliAdapter(PlatformAdapter):
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async def fetch_metadata(self, content_id: str, content_type: str) -> Dict[str, Any]
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async def fetch_content(self, content_id: str, content_type: str) -> str
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```
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### 现有 ASR 工具 (kb-whisper/whisper.py)
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- `convert_to_wav()`: 音频转换逻辑
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- `run_whisper()`: Whisper.cpp 调用
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- `create_workflow_json()`: 标准输出格式
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### 现有字幕下载 (kb-bilibili-content/bilibili_content.py)
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- `download_subtitle()`: B站官方字幕下载和 SRT 转换
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- 返回格式：`{success, has_subtitle, filepath, message}`
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</context>
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<interfaces>
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<!-- Key types and contracts the executor needs -->
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From kb_pipeline/schemas.py:
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```python
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class StepInput(BaseModel):
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content_id: str = Field(..., min_length=1)
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platform: Literal["bilibili", "zhihu"] = Field(...)
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class StepOutput(BaseModel):
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success: bool = Field(...)
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error_message: Optional[str] = Field(None)
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completed_at: datetime = Field(default_factory=datetime.now)
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class WorkflowState(BaseModel):
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def mark_step_completed(self, step_name: str, output_summary: Optional[str] = None) -> None
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def mark_step_failed(self, step_name: str, error_message: str) -> None
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```
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From kb_pipeline/state.py:
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```python
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class WorkflowStateManager:
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STATE_FILENAME = "workflow_state.json"
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def load(self) -> Optional[WorkflowState]
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def save(self, state: WorkflowState) -> None
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def get_or_create(self, content_id: str, platform: str, source_url: str) -> WorkflowState
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```
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From kb_pipeline/config.py:
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```python
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class KBSettings(BaseSettings):
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hf_token: SecretStr = Field(default=SecretStr(""))
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# 其他配置...
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```
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From kb_pipeline/retry.py:
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```python
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retry_on_network_error = retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=2, max=10),
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retry=retry_if_exception_type(NetworkError),
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)
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```
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</interfaces>
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<tasks>
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<task type="auto" tdd="true">
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<name>Task 1: Define ASR Schemas</name>
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<files>kb_pipeline/schemas.py, tests/test_processors/test_asr.py</files>
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<behavior>
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- Test 1: ASRInput 继承 StepInput，包含 video_path 和 content_type 字段
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- Test 2: ASROutput 继承 StepOutput，包含 tool/has_subtitle/output_files/segments/text/duration_seconds 字段
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- Test 3: ASROutput 的 tool 字段只能是 "subtitle" | "whisper" | "whisper-diarization"
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</behavior>
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<action>
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在 kb_pipeline/schemas.py 中添加 ASR 专用 Schema：
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1. ASRInput 类（继承 StepInput）：
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- video_path: str - 视频文件路径
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- content_type: str = "video" - 内容类型
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2. ASROutput 类（继承 StepOutput）：
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- tool: Literal["subtitle", "whisper", "whisper-diarization"] - 使用的工具
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- has_subtitle: bool - 是否有官方字幕
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- output_files: Dict[str, str] - 输出文件路径 {txt, srt, json}
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- segments: List[Dict] - 带时间戳的分段列表
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- text: str - 完整文本
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- duration_seconds: float - 音频时长
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- speakers: Optional[List[str]] - 说话者列表（仅说话者分离时）
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3. 添加必要的 import（List, Dict, Literal, Optional）
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确保 Schema 符合 Phase 1 的 StepInput/StepOutput 继承体系。
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</action>
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<verify>
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<automated>pytest tests/test_processors/test_asr.py::test_asr_input_schema -x -v</automated>
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<automated>pytest tests/test_processors/test_asr.py::test_asr_output_schema -x -v</automated>
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</verify>
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<done>
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- ASRInput 类定义完整，继承 StepInput
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- ASROutput 类定义完整，继承 StepOutput
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- 两个测试通过
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</done>
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</task>
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<task type="auto" tdd="true">
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<name>Task 2: Create ASRProcessor with subtitle download</name>
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<files>kb_pipeline/processors/__init__.py, kb_pipeline/processors/asr.py, tests/test_processors/test_asr.py</files>
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<behavior>
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- Test 1: ASRProcessor 初始化成功
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- Test 2: _try_official_subtitle 调用成功时返回 ASROutput (tool="subtitle")
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- Test 3: _try_official_subtitle 无字幕时返回 None
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- Test 4: process() 方法优先尝试官方字幕
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</behavior>
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<action>
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创建 kb_pipeline/processors/ 目录和 ASRProcessor 类：
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1. 创建 kb_pipeline/processors/__init__.py：
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```python
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from .asr import ASRProcessor
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__all__ = ["ASRProcessor"]
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```
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2. 创建 kb_pipeline/processors/asr.py：
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ASRProcessor 类结构：
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```python
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class ASRProcessor:
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STEP_NAME = "asr"
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def __init__(self, state_manager: Optional[WorkflowStateManager] = None):
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self.settings = get_settings()
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self.state_manager = state_manager
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async def process(self, input_data: ASRInput) -> ASROutput:
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"""主入口：先尝试官方字幕，无则调用 Whisper"""
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# 1. 如果是 B站视频，尝试官方字幕
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# 2. 无官方字幕则调用 Whisper
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# 3. 记录状态
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async def _try_official_subtitle(self, input_data: ASRInput) -> Optional[ASROutput]:
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"""尝试下载 B站官方字幕"""
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# 复用 kb-bilibili-content/bilibili_content.py 的 download_subtitle 逻辑
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# 返回 None 表示无字幕（不是错误）
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```
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3. _try_official_subtitle 实现：
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- 使用 aiohttp 调用 B站 API 获取字幕列表
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- API: https://api.bilibili.com/x/player/pagelist?bvid={bvid}
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- API: https://api.bilibili.com/x/player/v2?bvid={bvid}&cid={cid}
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- 下载字幕 JSON 并转换为 SRT 格式
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- 同时生成 txt 和 json 输出
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- 参考 kb-bilibili-content/bilibili_content.py 的 download_subtitle 方法
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4. 关键点：
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- 无字幕返回 None（不是抛异常），让流程继续到 Whisper
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- 有字幕时返回 ASROutput，tool="subtitle"
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- 使用 @retry_on_network_error 装饰器处理网络错误
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不要实现 Whisper 调用部分（Task 3）。
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</action>
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<verify>
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<automated>pytest tests/test_processors/test_asr.py::test_asr_processor_init -x -v</automated>
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<automated>pytest tests/test_processors/test_asr.py::test_official_subtitle_found -x -v</automated>
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<automated>pytest tests/test_processors/test_asr.py::test_official_subtitle_not_found -x -v</automated>
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</verify>
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<done>
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- ASRProcessor 类创建完成
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- _try_official_subtitle 方法实现
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- 三个测试通过
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</done>
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</task>
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<task type="auto" tdd="true">
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<name>Task 3: Implement Whisper.cpp integration</name>
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<files>kb_pipeline/processors/asr.py, tests/test_processors/test_asr.py</files>
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<behavior>
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- Test 1: _run_whisper 调用成功返回 ASROutput (tool="whisper")
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- Test 2: 临时音频文件处理后自动清理
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- Test 3: Whisper 路径不存在时抛出 RuntimeError
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</behavior>
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<action>
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在 ASRProcessor 中添加 Whisper.cpp 调用逻辑：
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1. _run_whisper 方法：
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```python
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async def _run_whisper(self, input_data: ASRInput) -> ASROutput:
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"""执行 Whisper.cpp 语音识别"""
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# 1. 检查 HF_TOKEN 决定是否启用说话者分离
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# 2. 转换音频格式
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# 3. 调用 whisper-cli
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# 4. 清理临时文件
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```
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2. 复用 kb-whisper/whisper.py 的核心逻辑：
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- get_whisper_cpp_path(): 获取 whisper.cpp 路径
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- convert_to_wav(): 音频转换（ffmpeg 16kHz WAV）
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- run_whisper(): 调用 whisper-cli 命令
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3. 命令构建：
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```python
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cmd = [
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os.path.join(whisper_path, 'whisper-cli'),
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'-m', os.path.join(whisper_path, 'models', 'ggml-base.bin'),
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'-f', audio_path,
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'-of', output_file,
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'-l', 'zh',
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'--output-txt', '--output-srt', '--output-json',
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]
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```
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4. 临时文件清理：
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- 处理完成后删除 *_audio.wav 文件
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- 使用 try/except 静默处理删除失败
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5. HF_TOKEN 检查：
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```python
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hf_token = self.settings.hf_token.get_secret_value()
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if hf_token:
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return await self._run_whisper_with_diarization(input_data)
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else:
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return await self._run_whisper_basic(input_data)
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```
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6. 错误处理：
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- whisper.cpp 路径不存在 → RuntimeError
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- 模型不存在 → FileNotFoundError
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- whisper-cli 执行失败 → RuntimeError
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先实现 _run_whisper_basic，_run_whisper_with_diarization 在 Task 4 实现。
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</action>
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<verify>
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<automated>pytest tests/test_processors/test_asr.py::test_whisper_basic -x -v</automated>
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<automated>pytest tests/test_processors/test_asr.py::test_temp_file_cleanup -x -v</automated>
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<automated>pytest tests/test_processors/test_asr.py::test_whisper_path_not_found -x -v</automated>
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</verify>
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<done>
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- _run_whisper 方法实现完成
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- _run_whisper_basic 实现完成
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- 临时文件清理逻辑正确
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- 三个测试通过
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</done>
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</task>
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<task type="auto" tdd="true">
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<name>Task 4: Implement speaker diarization</name>
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<files>kb_pipeline/processors/asr.py, tests/test_processors/test_asr.py</files>
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<behavior>
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- Test 1: HF_TOKEN 未配置时使用普通 Whisper
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- Test 2: HF_TOKEN 配置时自动启用说话者分离
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- Test 3: 说话者分离结果包含 speaker 字段
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</behavior>
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<action>
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在 ASRProcessor 中添加说话者分离支持：
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1. _run_whisper_with_diarization 方法：
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```python
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async def _run_whisper_with_diarization(self, input_data: ASRInput) -> ASROutput:
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"""执行带说话者分离的 Whisper"""
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# 1. 先运行普通 Whisper
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# 2. 运行 pyannote.audio 说话者分离
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# 3. 合并结果
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```
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2. 复用 kb-whisper-diarization/whisper_diarization.py 的逻辑：
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- run_diarization(): pyannote.audio 调用
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- assign_speaker(): 为段落分配说话者
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- merge_results(): 合并 Whisper 和说话者分离结果
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3. pyannote.audio 调用：
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```python
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from huggingface_hub import login
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from pyannote.audio import Pipeline
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login(token=hf_token)
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1")
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diarization = pipeline(audio_path)
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```
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4. 合并逻辑：
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- 遍历 Whisper 的每个 segment
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- 根据 start/end 时间匹配说话者
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- 在 segment 中添加 speaker 字段
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5. 输出格式：
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- tool: "whisper-diarization"
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- speakers: ["SPEAKER_00", "SPEAKER_01", ...]
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- 每个 segment 包含 speaker 字段
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6. 依赖处理：
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- 使用 try/except 导入 pyannote.audio
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- 导入失败时回退到普通 Whisper
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</action>
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<verify>
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<automated>pytest tests/test_processors/test_asr.py::test_no_hf_token_uses_basic_whisper -x -v</automated>
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<automated>pytest tests/test_processors/test_asr.py::test_diarization_with_hf_token -x -v</automated>
|
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<automated>pytest tests/test_processors/test_asr.py::test_diarization_result_has_speaker -x -v</automated>
|
||
</verify>
|
||
<done>
|
||
- _run_whisper_with_diarization 方法实现
|
||
- HF_TOKEN 自动检测逻辑正确
|
||
- 说话者分离结果格式正确
|
||
- 三个测试通过
|
||
</done>
|
||
</task>
|
||
|
||
<task type="auto" tdd="true">
|
||
<name>Task 5: Add state recording</name>
|
||
<files>kb_pipeline/processors/asr.py, tests/test_processors/test_asr.py</files>
|
||
<behavior>
|
||
- Test 1: ASR 成功时状态记录为 completed
|
||
- Test 2: ASR 失败时状态记录为 failed
|
||
- Test 3: output_summary 包含 tool 和 segments 数量
|
||
</behavior>
|
||
<action>
|
||
在 ASRProcessor.process() 方法中添加状态记录：
|
||
|
||
1. 成功时记录：
|
||
```python
|
||
if self.state_manager:
|
||
state = self.state_manager.load()
|
||
if state:
|
||
state.mark_step_completed(
|
||
self.STEP_NAME,
|
||
output_summary=f"tool={result.tool}, segments={len(result.segments)}"
|
||
)
|
||
self.state_manager.save(state)
|
||
```
|
||
|
||
2. 失败时记录：
|
||
```python
|
||
except Exception as e:
|
||
if self.state_manager:
|
||
state = self.state_manager.load()
|
||
if state:
|
||
state.mark_step_failed(self.STEP_NAME, str(e))
|
||
self.state_manager.save(state)
|
||
raise
|
||
```
|
||
|
||
3. 状态记录时机：
|
||
- 只在所有重试都失败后记录失败（不是每次重试）
|
||
- 成功后立即记录
|
||
|
||
4. output_summary 格式：
|
||
- "tool=subtitle, segments=120"
|
||
- "tool=whisper, segments=85"
|
||
- "tool=whisper-diarization, segments=85, speakers=2"
|
||
|
||
5. 确保 state_manager 是可选的（None 时不记录状态）
|
||
</action>
|
||
<verify>
|
||
<automated>pytest tests/test_processors/test_asr.py::test_state_recording_success -x -v</automated>
|
||
<automated>pytest tests/test_processors/test_asr.py::test_state_recording_failure -x -v</automated>
|
||
<automated>pytest tests/test_processors/test_asr.py::test_state_no_manager -x -v</automated>
|
||
</verify>
|
||
<done>
|
||
- 状态记录逻辑正确
|
||
- output_summary 格式正确
|
||
- state_manager 可选处理正确
|
||
- 三个测试通过
|
||
</done>
|
||
</task>
|
||
|
||
<task type="auto">
|
||
<name>Task 6: Create test infrastructure</name>
|
||
<files>tests/test_processors/__init__.py, tests/test_processors/conftest.py, tests/test_processors/test_asr.py</files>
|
||
<action>
|
||
创建测试基础设施：
|
||
|
||
1. 创建 tests/test_processors/__init__.py（空文件）
|
||
|
||
2. 创建 tests/test_processors/conftest.py：
|
||
```python
|
||
import pytest
|
||
from pathlib import Path
|
||
from unittest.mock import AsyncMock, MagicMock, patch
|
||
|
||
@pytest.fixture
|
||
def mock_whisper_path(tmp_path):
|
||
"""Mock whisper.cpp 路径"""
|
||
whisper_dir = tmp_path / "whisper.cpp"
|
||
whisper_dir.mkdir()
|
||
(whisper_dir / "whisper-cli").write_text("")
|
||
(whisper_dir / "models").mkdir()
|
||
(whisper_dir / "models" / "ggml-base.bin").write_text("")
|
||
return str(whisper_dir)
|
||
|
||
@pytest.fixture
|
||
def mock_bilibili_api():
|
||
"""Mock B站 API 响应"""
|
||
with patch("aiohttp.ClientSession.get") as mock_get:
|
||
yield mock_get
|
||
|
||
@pytest.fixture
|
||
def sample_video(tmp_path):
|
||
"""创建测试视频文件"""
|
||
video_path = tmp_path / "test_video.mp4"
|
||
video_path.write_text("fake video content")
|
||
return str(video_path)
|
||
|
||
@pytest.fixture
|
||
def sample_subtitle_response():
|
||
"""B站字幕 API 响应示例"""
|
||
return {
|
||
"data": {
|
||
"subtitle": {
|
||
"list": [{
|
||
"lan": "ai-zh",
|
||
"lan_doc": "中文（自动生成）",
|
||
"subtitle_url": "//example.com/subtitle.json"
|
||
}]
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
3. 创建 tests/test_processors/test_asr.py 骨架：
|
||
- 包含所有测试用例的占位符
|
||
- 每个测试使用 pytest.raises 或 assert 验证
|
||
|
||
4. 测试文件结构：
|
||
```python
|
||
# Schema tests
|
||
def test_asr_input_schema(): ...
|
||
def test_asr_output_schema(): ...
|
||
|
||
# Processor tests
|
||
def test_asr_processor_init(): ...
|
||
def test_official_subtitle_found(): ...
|
||
def test_official_subtitle_not_found(): ...
|
||
def test_whisper_basic(): ...
|
||
def test_temp_file_cleanup(): ...
|
||
def test_whisper_path_not_found(): ...
|
||
def test_no_hf_token_uses_basic_whisper(): ...
|
||
def test_diarization_with_hf_token(): ...
|
||
def test_diarization_result_has_speaker(): ...
|
||
def test_state_recording_success(): ...
|
||
def test_state_recording_failure(): ...
|
||
def test_state_no_manager(): ...
|
||
```
|
||
|
||
使用 unittest.mock 来模拟外部依赖（aiohttp、subprocess、whisper.cpp）。
|
||
</action>
|
||
<verify>
|
||
<automated>pytest tests/test_processors/test_asr.py --collect-only | grep "test_" | wc -l</automated>
|
||
</verify>
|
||
<done>
|
||
- tests/test_processors/ 目录创建完成
|
||
- conftest.py 包含所有 fixtures
|
||
- test_asr.py 包含所有测试用例骨架
|
||
- 测试数量 >= 13 个
|
||
</done>
|
||
</task>
|
||
|
||
</tasks>
|
||
|
||
<verification>
|
||
## Phase Verification
|
||
|
||
完成所有任务后，验证以下条件：
|
||
|
||
1. **Schema 验证**
|
||
```bash
|
||
pytest tests/test_processors/test_asr.py::test_asr_input_schema tests/test_processors/test_asr.py::test_asr_output_schema -v
|
||
```
|
||
|
||
2. **官方字幕下载**
|
||
```bash
|
||
pytest tests/test_processors/test_asr.py::test_official_subtitle_found tests/test_processors/test_asr.py::test_official_subtitle_not_found -v
|
||
```
|
||
|
||
3. **Whisper 集成**
|
||
```bash
|
||
pytest tests/test_processors/test_asr.py::test_whisper_basic tests/test_processors/test_asr.py::test_temp_file_cleanup -v
|
||
```
|
||
|
||
4. **说话者分离**
|
||
```bash
|
||
pytest tests/test_processors/test_asr.py::test_diarization_with_hf_token tests/test_processors/test_asr.py::test_diarization_result_has_speaker -v
|
||
```
|
||
|
||
5. **状态记录**
|
||
```bash
|
||
pytest tests/test_processors/test_asr.py::test_state_recording_success tests/test_processors/test_asr.py::test_state_recording_failure -v
|
||
```
|
||
|
||
6. **完整测试套件**
|
||
```bash
|
||
pytest tests/test_processors/test_asr.py -v
|
||
```
|
||
</verification>
|
||
|
||
<success_criteria>
|
||
1. ASRProcessor 类可被实例化和调用
|
||
2. B站视频可以自动下载官方字幕（优先策略）
|
||
3. 无官方字幕时自动调用 Whisper.cpp 进行语音识别
|
||
4. HF_TOKEN 配置时自动启用说话者分离
|
||
5. 输出包含 txt/srt/json 三种格式
|
||
6. ASR 完成后状态正确记录到 workflow_state.json
|
||
7. 临时音频文件处理后自动清理
|
||
8. 所有测试通过（>= 13 个测试）
|
||
</success_criteria>
|
||
|
||
<output>
|
||
After completion, create `.planning/phases/03-processing-skills/03-01-SUMMARY.md`
|
||
</output>
|