# YouTube Transcript Scraper - JSON, SRT, VTT, RAG (`jamhimself/youtube-transcript-extractor`) Actor Extract YouTube transcripts & subtitles as JSON, text, SRT, VTT, or RAG chunks - bulk, 100+ languages, timestamps & deep links. Pay per video, no subscription. - **URL**: https://apify.com/jamhimself/youtube-transcript-extractor.md - **Developed by:** [Jaime Martinez](https://apify.com/jamhimself) (community) - **Categories:** Developer tools, Videos, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $7.50 / 1,000 transcripts This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## YouTube Transcript API for developers and AI pipelines YouTube Transcript API for developers and AI pipelines — extract transcripts and subtitles as JSON, text, SRT, VTT, or RAG chunks with timestamps, in bulk, across 100+ languages, with no API key. YouTube blocks naive scrapers with PoToken and datacenter-IP bans, so free libraries like `youtube-transcript-api` and `yt-dlp` break in production — this actor's hosted residential-proxy + uptime layer succeeds where they fail, and you only pay for transcripts actually delivered. Built for developers and AI builders who need YouTube captions at scale: feed an LLM, build a RAG pipeline, summarize videos, translate, search, or repurpose content. --- ### Why this actor - ✅ **Actually works in 2026.** YouTube now blocks naive transcript scrapers (empty responses behind their bot-check). This actor solves YouTube's current proof-of-origin requirement, so it keeps returning real captions where older tools return nothing. Health-monitored and fixed fast when YouTube changes. - ⚡ **Bulk & fast.** Pass one URL or thousands; concurrent extraction with automatic retries. - 🎯 **Every format you need.** Plain text, timestamped segments, **SRT**, **VTT**, and **RAG chunks** with deep links — in a single run. - 🌍 **100+ languages**, manual and auto-generated captions, with language preference and fallback. - 💸 **Pay per video.** No monthly fee. You're only charged for transcripts we actually deliver. ### Use cases - **RAG / LLM ingestion** — turn long videos into clean, chunked, timestamped context for vector databases (LangChain, LlamaIndex). - **Summaries & notes** — feed transcripts to Claude/GPT for summaries, chapters, show notes. - **Subtitles** — export ready-to-use `.srt` / `.vtt` files. - **Search & analytics** — index spoken content across a channel. - **Translation & repurposing** — transcribe once, translate anywhere. ### Input | Field | Type | Description | |-------|------|-------------| | `videoUrls` | array | **Required.** YouTube video URLs (`watch`, `youtu.be`, `shorts`, `embed`, `live`) or raw 11-char video IDs. | | `languages` | array | Preferred language codes in order, e.g. `["en","es"]`. Falls back to English, then first available. Default `["en"]`. | | `preferManual` | boolean | Prefer human-uploaded captions over auto-generated. Default `true`. | | `includeSegments` | boolean | Include timestamped `{start, duration, text}` segments. Default `true`. | | `includePlainText` | boolean | Include the whole transcript as one clean string. Default `true`. | | `includeSrt` | boolean | Include a SubRip `.srt` string. Default `false`. | | `includeVtt` | boolean | Include a WebVTT `.vtt` string. Default `false`. | | `ragChunking` | boolean | Emit overlapping chunks with timestamps + deep links for embeddings. Default `false`. | | `chunkMaxChars` / `chunkOverlapChars` | integer | Chunk sizing for RAG. Defaults 1500 / 200. | | `concurrency` | integer | Videos processed in parallel (1–10). Default 5. | #### Example input ```json { "videoUrls": [ "https://www.youtube.com/watch?v=jNQXAC9IVRw", "https://youtu.be/8S0FDjFBj8o" ], "languages": ["en"], "includeSrt": true, "ragChunking": true } ```` ### Output One dataset item per video: ```json { "ok": true, "videoId": "jNQXAC9IVRw", "url": "https://www.youtube.com/watch?v=jNQXAC9IVRw", "title": "Me at the zoo", "channelName": "jawed", "durationSeconds": 19, "viewCount": 358000000, "language": "en", "captionKind": "manual", "availableLanguages": [{ "languageCode": "en", "kind": "manual", "name": "English" }], "segmentCount": 6, "text": "All right, so here we are, in front of the elephants...", "segments": [{ "start": 1.2, "duration": 2.16, "text": "All right, so here we are, in front of the elephants" }], "srt": "1\n00:00:01,200 --> 00:00:03,360\nAll right, so here we are...", "chunks": [{ "index": 0, "text": "...", "startSeconds": 1.2, "endSeconds": 18.5, "deepLink": "https://www.youtube.com/watch?v=jNQXAC9IVRw&t=1s" }] } ``` Successful transcripts are written to the **default dataset**. Videos with no captions, or that are private/unavailable/invalid, are written to a separate **`skipped`** dataset (each with the reason) — and are **never charged**. You only pay for transcripts actually delivered. ### Pricing You're billed **only per successfully delivered transcript**. Videos with no captions, or that are private/unavailable, are skipped for free — no subscription, no minimums. See the **Pricing** tab for the current per-transcript rate. ### FAQ **Does it work for auto-generated captions?** Yes — manual and ASR (auto) captions, in 100+ languages. **Playlists and channels?** Pass individual video URLs for now. Playlist/channel expansion is coming. **Is this legal?** It accesses publicly available caption data. You are responsible for complying with YouTube's Terms of Service and applicable copyright law in your use of the output. **Why do some videos return no transcript?** The video genuinely has captions disabled, is private/age-restricted/region-locked, or is a live stream without a caption track. *** Questions or a format you need? Open an issue on the actor — it's actively maintained. # Actor input Schema ## `videoUrls` (type: `array`): YouTube video URLs or IDs — extract the transcript / subtitles / captions of each as JSON, SRT, VTT, or RAG chunks. Accepts watch, youtu.be, shorts, embed, and live URLs or raw 11-character video IDs. One transcript per video. ## `languages` (type: `array`): Language codes in order of preference (e.g. \["en", "es"]). Falls back to English, then the first available track. ## `preferManual` (type: `boolean`): When both exist, pick human-uploaded captions over YouTube's auto-generated (ASR) ones. ## `includeSegments` (type: `boolean`): Include the full array of {start, duration, text} segments. ## `includePlainText` (type: `boolean`): Include the whole transcript as one clean text string. ## `includeSrt` (type: `boolean`): Include the transcript as a SubRip (.srt) subtitle string. ## `includeVtt` (type: `boolean`): Include the transcript as a WebVTT (.vtt) subtitle string. ## `ragChunking` (type: `boolean`): Split the transcript into overlapping chunks with start/end timestamps and a clickable deep link per chunk — ready for embedding pipelines (LangChain, LlamaIndex, vector DBs). ## `chunkMaxChars` (type: `integer`): Maximum characters per RAG chunk. ## `chunkOverlapChars` (type: `integer`): Approximate character overlap between consecutive chunks. ## `concurrency` (type: `integer`): How many videos to process in parallel (1-10). ## `proxyCountryCode` (type: `string`): Optional two-letter country code for proxies (affects which captions YouTube serves for region-locked content). ## Actor input object example ```json { "videoUrls": [ "https://www.youtube.com/watch?v=jNQXAC9IVRw" ], "languages": [ "en" ], "preferManual": true, "includeSegments": true, "includePlainText": true, "includeSrt": false, "includeVtt": false, "ragChunking": false, "chunkMaxChars": 1500, "chunkOverlapChars": 200, "concurrency": 5 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "videoUrls": [ "https://www.youtube.com/watch?v=jNQXAC9IVRw" ] }; // Run the Actor and wait for it to finish const run = await client.actor("jamhimself/youtube-transcript-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "videoUrls": ["https://www.youtube.com/watch?v=jNQXAC9IVRw"] } # Run the Actor and wait for it to finish run = client.actor("jamhimself/youtube-transcript-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "videoUrls": [ "https://www.youtube.com/watch?v=jNQXAC9IVRw" ] }' | apify call jamhimself/youtube-transcript-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jamhimself/youtube-transcript-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "YouTube Transcript Scraper - JSON, SRT, VTT, RAG", "description": "Extract YouTube transcripts & subtitles as JSON, text, SRT, VTT, or RAG chunks - bulk, 100+ languages, timestamps & deep links. Pay per video, no subscription.", "version": "1.0", "x-build-id": "O0bMgemLiERGTT8l7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jamhimself~youtube-transcript-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jamhimself-youtube-transcript-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jamhimself~youtube-transcript-extractor/runs": { "post": { "operationId": "runs-sync-jamhimself-youtube-transcript-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jamhimself~youtube-transcript-extractor/run-sync": { "post": { "operationId": "run-sync-jamhimself-youtube-transcript-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "videoUrls" ], "properties": { "videoUrls": { "title": "Video URLs or IDs", "type": "array", "description": "YouTube video URLs or IDs — extract the transcript / subtitles / captions of each as JSON, SRT, VTT, or RAG chunks. Accepts watch, youtu.be, shorts, embed, and live URLs or raw 11-character video IDs. One transcript per video.", "items": { "type": "string" } }, "languages": { "title": "Preferred languages", "type": "array", "description": "Language codes in order of preference (e.g. [\"en\", \"es\"]). Falls back to English, then the first available track.", "default": [ "en" ], "items": { "type": "string" } }, "preferManual": { "title": "Prefer manual captions over auto-generated", "type": "boolean", "description": "When both exist, pick human-uploaded captions over YouTube's auto-generated (ASR) ones.", "default": true }, "includeSegments": { "title": "Include timestamped segments", "type": "boolean", "description": "Include the full array of {start, duration, text} segments.", "default": true }, "includePlainText": { "title": "Include plain text", "type": "boolean", "description": "Include the whole transcript as one clean text string.", "default": true }, "includeSrt": { "title": "Include SRT", "type": "boolean", "description": "Include the transcript as a SubRip (.srt) subtitle string.", "default": false }, "includeVtt": { "title": "Include VTT", "type": "boolean", "description": "Include the transcript as a WebVTT (.vtt) subtitle string.", "default": false }, "ragChunking": { "title": "RAG-ready chunks", "type": "boolean", "description": "Split the transcript into overlapping chunks with start/end timestamps and a clickable deep link per chunk — ready for embedding pipelines (LangChain, LlamaIndex, vector DBs).", "default": false }, "chunkMaxChars": { "title": "Chunk size (characters)", "minimum": 200, "maximum": 20000, "type": "integer", "description": "Maximum characters per RAG chunk.", "default": 1500 }, "chunkOverlapChars": { "title": "Chunk overlap (characters)", "minimum": 0, "maximum": 5000, "type": "integer", "description": "Approximate character overlap between consecutive chunks.", "default": 200 }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many videos to process in parallel (1-10).", "default": 5 }, "proxyCountryCode": { "title": "Proxy country", "type": "string", "description": "Optional two-letter country code for proxies (affects which captions YouTube serves for region-locked content)." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```