🌟 YouTube Video Transcript Scraper > Accurate, timestamped transcripts for full-length YouTube videos — chapters, subtitles, and multi-language support. --- ## 📖 Overview This Actor extracts clean, timestamped transcripts from full-length YouTube videos (standard watch URLs, youtu.be, and /watch?v= formats). It's designed for longer content: works with multiple caption tracks, handles chapters, and produces export-ready captions (SRT/VTT) alongside structured JSON suitable for analytics and LLM pipelines. --- ## 💡 Why Full-Video Focus? * Full videos often contain chapters, multiple speakers, and longer dialogues — transcripts must preserve timing and structure. * Supports official captions when available and high-quality ASR fallbacks when not. * Produces SRT/VTT, plain text, and structured JSON for downstream processing. --- ## 🔧 Key Features * ✅ Multi-format URL normalization (/watch?v=, youtu.be). * ✅ Prefer official caption tracks; fallback to ASR extraction when captions are missing. * ✅ Preserve chapters and video metadata (title, duration, thumbnails). * ✅ Export as JSON, plain text, SRT, and VTT. * ✅ Optional speaker diarization and language detection. * ✅ Configurable chunking for very long videos and resume/retry support. * ✅ Proxy-compatible and production-ready for large-scale jobs. --- ## ⚡ Quick Start — Console 1. Open the Actor on Apify Console. 2. Paste one or more YouTube video URLs into the input (watch links or youtu.be links accepted). 3. Click Run — results appear in the Dataset and Files (SRT/VTT) tabs. --- ## ⚙️ Quick Start — CLI & Python ### CLI bash apify call neuro-scraper/youtube-transcript-fetcher --input ./videos_input.json ### Python (apify-client) python from apify_client import ApifyClient client = ApifyClient('<APIFY_TOKEN>') run = client.actor('neuro-scraper/youtube-transcript-fetcher').call( run_input={ "startUrls": [{"url": "https://www.youtube.com/watch?v=EXAMPLE"}], "workers": 3, "exportFormats": ["json","srt","vtt"] } ) for item in client.dataset(run['defaultDatasetId']).list_items()['items']: print(item['Transcript']['plain_text'][:400]) --- ## 📝 Inputs (Video-focused) | Name | Type | Required | Default | Example | Notes | | -------------------- | ------- | -------- | ---------- | ------------------------------------------------------ | -------------------------------------- | | startUrls | array | Yes | [] | [{"url":"https://www.youtube.com/watch?v=abcd1234"}] | List of YouTube video URLs | | workers | integer | Optional | 5 | 10 | Max concurrent fetches | | exportFormats | array | Optional | ["json"] | ["json","srt","vtt"] | Output formats to generate | | speakerDiarization | boolean | Optional | false | true | Enable speaker detection (best-effort) | | language | string | Optional | null | "en" | Force output language (ISO code) | | proxyConfiguration | object | Optional | {} | {"useApifyProxy": true} | Proxy settings | Example input (Console JSON): json { "startUrls": [ {"url": "https://www.youtube.com/watch?v=abcd1234"}, {"url": "https://youtu.be/abcd1234"} ], "workers": 5, "exportFormats": ["json","srt","vtt"], "speakerDiarization": true, "proxyConfiguration": {"useApifyProxy": true} } --- ## 📄 Outputs Each Dataset item contains rich metadata and multiple transcript representations. Example: json { "inputUrl": "https://www.youtube.com/watch?v=abcd1234", "fetchedAt": "2025-11-04T10:00:00Z", "success": true, "video": { "title": "Example Video", "duration": 3720, "chapters": [ {"title": "Intro", "start": 0}, {"title": "Main topic", "start": 60} ] }, "Transcript": { "plain_text": "Full transcript text...", "with_timestamps": [ {"text": "Hello and welcome to the show.", "start": 0.2, "end": 4.5}, {"text": "Today we'll talk about...", "start": 5.0, "end": 9.3} ], "speaker_segments": [ {"speaker": "Speaker 1", "start": 0.2, "end": 4.5, "text": "Hello and welcome to the show."} ] }, "files": { "srt": "runs/<runId>/files/abcd1234.srt", "vtt": "runs/<runId>/files/abcd1234.vtt" } } Notes: Files (SRT/VTT) are attached to the run and accessible from the Files tab for easy download. --- ## 🔑 Environment Variables * APIFY_TOKEN — required for authentication. * HTTP_PROXY, HTTPS_PROXY — optional custom proxies. * APIFY_PROXY_PASSWORD — use with Apify Proxy. Store credentials securely as secrets — never in plaintext. --- ## ▶️ How to Run (short checklist) 1. Open Apify Console → Actors → YouTube Transcript Fetcher. 2. Provide video URLs (watch or youtu.be), set desired export formats, and toggle options. 3. Run and inspect Dataset and Files tabs for JSON/SRT/VTT outputs. --- ## 🛠 Logs & Troubleshooting * No transcript available — video may lack captions and audio quality may be too poor for ASR. * Partial transcripts — long videos may be chunked; check run logs for retry or chunk status. * Timeouts / failures — lower workers or increase timeouts; enable proxy if region-restricted. Monitor real-time logs in the Console Run Log panel for detailed error messages. --- ## ⏱ Scheduling & Webhooks * Schedule daily or weekly runs for channel-level ingestion. * Use Webhooks to push transcript files or Dataset updates to downstream systems (storage, search index, or ML pipelines). --- ## 🔟 Changelog * 1.0.0 — 2025-11-04: Initial release — full-video support. --- ## 📝 Notes & TODO * TODO: Add example of chapter-aware summarization pipeline. * TODO: Improve speaker diarization accuracy with optional external ASR. --- ## ✅ Final note This README is designed for researchers, media teams, and engineers who need robust, exportable transcripts from full-length YouTube videos — suitable for analytics, captioning, and training data generation.