# MIT OpenCourseWare Lecture Transcript Scraper (`scrapersdelight/mit-ocw-transcript-scraper`) Actor Extract MIT OpenCourseWare video-lecture transcripts โ€” no login, no ASR. Give it a course (crawls every lecture) or specific lecture URLs and get the full transcript text, timestamped segments, and SRT/VTT, plus course and lecture titles. Free, Creative-Commons educational content. - **URL**: https://apify.com/scrapersdelight/mit-ocw-transcript-scraper.md - **Developed by:** [Scrapers Delight](https://apify.com/scrapersdelight) (community) - **Categories:** Videos, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 1,000 per record returneds 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 ## ๐ŸŽ“ MIT OpenCourseWare Lecture Transcript Scraper **Pull MIT OpenCourseWare video-lecture transcripts โ€” no login, no AI transcription. MIT OCW publishes a transcript for every lecture, and this actor reads it: full text, timestamped segments, and SRT/VTT, plus course and lecture titles. Give it a course (it crawls every lecture) or specific lecture URLs.** It reads OCW's own captions, so there's no speech-to-text compute โ€” fast and cheap. (MIT OCW is free, Creative-Commons educational content.) --- ### What does it do? For each lecture (from a course crawl or direct URLs) it returns: - ๐Ÿ“ **Full transcript** (plain text) โ€” always included - โฒ๏ธ **Timestamped segments** โ€” `{start, end, text}` - ๐ŸŽฌ **SRT / VTT subtitles** - ๐Ÿท๏ธ **Course title + lecture title** No ASR, no API key โ€” it reads the published `.vtt` caption track. --- ### What data does it extract? For every lecture: `url`, `course_title`, `lecture_title`, `transcript`, `segments[]`, `srt`, `vtt`, `segment_count`, `is_new` (monitor), `scraped_at`. --- ### Who is it for? - ๐ŸŽ“ **Learners & educators** turning lectures into searchable notes and study guides. - ๐Ÿค– **AI / RAG builders** โ€” rigorous, structured lecture content is excellent training/retrieval data. - ๐ŸŒ **Localization / accessibility** workflows. --- ### How to use it (step by step) 1. Click **Try for free**. 2. Paste a **course URL** (`https://ocw.mit.edu/courses/{slug}/`) โ€” or specific **lecture URLs**. 3. *(Optional)* add `srt`/`vtt`/`segments` formats. 4. Click **Start**, open the **Dataset** tab to view/export. 5. *(Optional)* set **monitorMode** + a **Schedule** to capture lectures as courses update. #### Quick start ```json { "courseUrls": ["https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/"], "transcriptFormats": ["txt", "srt"] } ```` *** ### Input | Field | What it does | |-------|--------------| | `courseUrls` | OCW course URLs (crawls each course's lectures) | | `lectureUrls` | specific lecture resource URLs | | `transcriptFormats` | `txt` ยท `segments` ยท `srt` ยท `vtt` | | `maxLectures` | hard cap per run (0 = all) | | `monitorMode`, `alertOnNewLecture` | recurring watcher + alerts | | `webhookUrl`, `slackWebhookUrl`, `emailRecipients` | alert channels | | `proxyConfiguration`, `requestConcurrency` | proxy + parallelism | *** ### Output Each lecture is one dataset record (fields above). Export to **JSON, CSV, Excel, HTML, or RSS**, or fetch via the **Apify API**. *** ### How much does it cost? Pay-per-event โ€” and with **no transcription compute**, it's cheap: | Event | What it covers | Suggested price | |-------|----------------|-----------------| | `lot-scraped` | each lecture returned | ~$0.003 / lecture | | `lot-detail-enriched` | each transcript fetched | ~$0.003 / lecture | | `monitor-run-completed` | each scheduled watch run | ~$0.05 / run | | `new-lot-detected` | each new lecture | ~$0.02 / lecture | | `alert-delivered` | each Slack/email/webhook push | ~$0.005 / alert | *(Final per-event prices are set on the actor's pricing page.)* *** ### Is it legal to scrape OCW transcripts? MIT OpenCourseWare is published **free to the public under a Creative Commons BY-NC-SA license**. This actor reads those public transcripts. You must **comply with the CC BY-NC-SA terms โ€” attribute MIT OCW, non-commercial use, share-alike** โ€” and review OCW's site terms. You are responsible for your use. *** ### FAQ **Does it crawl a whole course?** Yes โ€” give a course URL and it finds + transcribes every video lecture. **Is there a Whisper/ASR step?** No โ€” it reads OCW's `.vtt` captions, so it's fast and cheap. **Can I get subtitles?** Yes โ€” add `srt` and/or `vtt` to `transcriptFormats`. **How do I export?** JSON, CSV, Excel, HTML, or RSS from the Dataset tab, or via the Apify API. *** ### Feedback Want PDF-notes extraction or per-department crawling? Open an issue on the actor. # Actor input Schema ## `courseUrls` (type: `array`): MIT OCW course URLs (https://ocw.mit.edu/courses/{slug}/) โ€” the actor finds and transcribes every video lecture in the course. ## `lectureUrls` (type: `array`): Specific lecture resource URLs to transcribe instead of (or in addition to) whole courses. ## `transcriptFormats` (type: `array`): Which transcript representations to include. Plain text is always included. ## `maxLectures` (type: `integer`): Hard cap per run (0 = all). ## `monitorMode` (type: `boolean`): Recurring watcher: diff against the prior run's seen lectures (per course) and output/alert ONLY new lectures. ## `alertOnNewLecture` (type: `boolean`): In monitor mode, alert for each new lecture. ## `webhookUrl` (type: `string`): POST endpoint for new-lecture alerts. ## `slackWebhookUrl` (type: `string`): Slack incoming-webhook URL. ## `emailRecipients` (type: `array`): Emails for the digest (via apify/send-mail). ## `proxyConfiguration` (type: `object`): Proxy settings. Datacenter rotation is plenty. ## `requestConcurrency` (type: `integer`): Max parallel lecture fetches. ## `diagnose` (type: `boolean`): Dev only. Logs the first resolved lecture + its transcript size, then exits. ## Actor input object example ```json { "courseUrls": [ "https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/" ], "transcriptFormats": [ "txt" ], "maxLectures": 0, "monitorMode": false, "alertOnNewLecture": true, "proxyConfiguration": { "useApifyProxy": true }, "requestConcurrency": 4, "diagnose": false } ``` # Actor output Schema ## `transcripts` (type: `string`): The dataset of lecture transcripts (one item per lecture). # 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 = { "courseUrls": [ "https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapersdelight/mit-ocw-transcript-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "courseUrls": ["https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/"] } # Run the Actor and wait for it to finish run = client.actor("scrapersdelight/mit-ocw-transcript-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "courseUrls": [ "https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/" ] }' | apify call scrapersdelight/mit-ocw-transcript-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapersdelight/mit-ocw-transcript-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MIT OpenCourseWare Lecture Transcript Scraper", "description": "Extract MIT OpenCourseWare video-lecture transcripts โ€” no login, no ASR. Give it a course (crawls every lecture) or specific lecture URLs and get the full transcript text, timestamped segments, and SRT/VTT, plus course and lecture titles. Free, Creative-Commons educational content.", "version": "0.1", "x-build-id": "7Uu6lhWy2rwQlnLte" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapersdelight~mit-ocw-transcript-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapersdelight-mit-ocw-transcript-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapersdelight~mit-ocw-transcript-scraper/runs": { "post": { "operationId": "runs-sync-scrapersdelight-mit-ocw-transcript-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapersdelight~mit-ocw-transcript-scraper/run-sync": { "post": { "operationId": "run-sync-scrapersdelight-mit-ocw-transcript-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "courseUrls": { "title": "Course URLs", "type": "array", "description": "MIT OCW course URLs (https://ocw.mit.edu/courses/{slug}/) โ€” the actor finds and transcribes every video lecture in the course.", "items": { "type": "string" } }, "lectureUrls": { "title": "Lecture URLs (optional)", "type": "array", "description": "Specific lecture resource URLs to transcribe instead of (or in addition to) whole courses.", "items": { "type": "string" } }, "transcriptFormats": { "title": "Transcript formats", "type": "array", "description": "Which transcript representations to include. Plain text is always included.", "items": { "type": "string", "enum": [ "txt", "segments", "srt", "vtt" ], "enumTitles": [ "Plain text", "Timestamped segments (array)", "SRT subtitles", "VTT subtitles" ] }, "default": [ "txt" ] }, "maxLectures": { "title": "Max lectures per run", "minimum": 0, "type": "integer", "description": "Hard cap per run (0 = all).", "default": 0 }, "monitorMode": { "title": "Monitor mode (new-lecture watcher)", "type": "boolean", "description": "Recurring watcher: diff against the prior run's seen lectures (per course) and output/alert ONLY new lectures.", "default": false }, "alertOnNewLecture": { "title": "Alert on new lectures", "type": "boolean", "description": "In monitor mode, alert for each new lecture.", "default": true }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "POST endpoint for new-lecture alerts." }, "slackWebhookUrl": { "title": "Slack webhook URL", "type": "string", "description": "Slack incoming-webhook URL." }, "emailRecipients": { "title": "Email recipients", "type": "array", "description": "Emails for the digest (via apify/send-mail).", "items": { "type": "string" } }, "proxyConfiguration": { "title": "Proxy", "type": "object", "description": "Proxy settings. Datacenter rotation is plenty.", "default": { "useApifyProxy": true } }, "requestConcurrency": { "title": "Request concurrency", "minimum": 1, "maximum": 8, "type": "integer", "description": "Max parallel lecture fetches.", "default": 4 }, "diagnose": { "title": "Diagnostic mode (dev)", "type": "boolean", "description": "Dev only. Logs the first resolved lecture + its transcript size, then exits.", "default": false } } }, "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 } } } } } } } } } } ```