Story 1.3: Transcript Extraction Service #2

New Issue

demo · 2025-08-25T04:44:38Z

demo commented

2025-08-25 04:44:38 +00:00

User Story

As a user
I want the system to automatically extract transcripts from YouTube videos
so that I have the text content needed for AI summarization

Acceptance Criteria

System extracts video transcripts using multiple fallback methods (YouTube Transcript API → auto-captions → audio transcription)
Transcripts are cached to avoid repeated API calls for the same video
Multiple language support with preference for English when available
Failed transcript extraction returns informative error messages with suggested solutions
System handles videos with no available transcripts gracefully
Transcript extraction is non-blocking and provides progress feedback

Tasks

Task 1: Primary Transcript Extraction
- Create TranscriptService class in backend/services/transcript_service.py
- Implement YouTube Transcript API integration with retry logic
- Add transcript caching with video ID-based keys
- Implement multi-language transcript detection and prioritization
Task 2: Fallback Transcript Methods
- Integrate auto-generated captions extraction as secondary method
- Implement audio transcription fallback using OpenAI Whisper API
- Create fallback chain orchestration with error handling
- Add logging for fallback method usage and success rates
Task 3: Transcript Processing Pipeline
- Create transcript cleaning and formatting utilities
- Implement timestamp preservation for chapter creation
- Add text chunking for large transcripts (token limit management)
- Create progress tracking for multi-step extraction process
Task 4: API Integration
- Create /api/transcripts/{video_id} GET endpoint
- Implement background transcript extraction with job status tracking
- Add WebSocket support for real-time progress updates
- Create comprehensive error response system with recovery suggestions
Task 5: Cache Management
- Implement Redis-based transcript caching with 24-hour TTL
- Add cache warming for popular videos
- Create cache invalidation strategy for updated transcripts
- Add cache analytics and hit rate monitoring
Task 6: Integration Testing
- Test transcript extraction across different video types and lengths
- Verify fallback chain handles edge cases (private videos, no captions, etc.)
- Test caching behavior and cache invalidation
- Validate error handling and user-facing error messages

Development Notes

Generated from BMad story: 1.3
Status: Draft
Created: 2025-08-25T00:44:37.285222

## User Story **As a** user **I want** the system to automatically extract transcripts from YouTube videos **so that** I have the text content needed for AI summarization ## Acceptance Criteria 1. System extracts video transcripts using multiple fallback methods (YouTube Transcript API → auto-captions → audio transcription) 2. Transcripts are cached to avoid repeated API calls for the same video 3. Multiple language support with preference for English when available 4. Failed transcript extraction returns informative error messages with suggested solutions 5. System handles videos with no available transcripts gracefully 6. Transcript extraction is non-blocking and provides progress feedback ## Tasks - [ ] **Task 1: Primary Transcript Extraction** - [ ] Create `TranscriptService` class in `backend/services/transcript_service.py` - [ ] Implement YouTube Transcript API integration with retry logic - [ ] Add transcript caching with video ID-based keys - [ ] Implement multi-language transcript detection and prioritization - [ ] **Task 2: Fallback Transcript Methods** - [ ] Integrate auto-generated captions extraction as secondary method - [ ] Implement audio transcription fallback using OpenAI Whisper API - [ ] Create fallback chain orchestration with error handling - [ ] Add logging for fallback method usage and success rates - [ ] **Task 3: Transcript Processing Pipeline** - [ ] Create transcript cleaning and formatting utilities - [ ] Implement timestamp preservation for chapter creation - [ ] Add text chunking for large transcripts (token limit management) - [ ] Create progress tracking for multi-step extraction process - [ ] **Task 4: API Integration** - [ ] Create `/api/transcripts/{video_id}` GET endpoint - [ ] Implement background transcript extraction with job status tracking - [ ] Add WebSocket support for real-time progress updates - [ ] Create comprehensive error response system with recovery suggestions - [ ] **Task 5: Cache Management** - [ ] Implement Redis-based transcript caching with 24-hour TTL - [ ] Add cache warming for popular videos - [ ] Create cache invalidation strategy for updated transcripts - [ ] Add cache analytics and hit rate monitoring - [ ] **Task 6: Integration Testing** - [ ] Test transcript extraction across different video types and lengths - [ ] Verify fallback chain handles edge cases (private videos, no captions, etc.) - [ ] Test caching behavior and cache invalidation - [ ] Validate error handling and user-facing error messages ## Development Notes --- *Generated from BMad story: 1.3* *Status: Draft* *Created: 2025-08-25T00:44:37.285222*

demo referenced this issue from a commit

2025-08-25 04:55:21 +00:00

fix: Resolve issue #2

demo referenced a pull request that will close this issue

2025-08-25 04:56:57 +00:00

Fix: Story 1.3 - Transcript Extraction Service #5

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