How do I convert a PDF to Markdown for free?

Upload your PDF to mdisbetter.com, click Convert, and get clean structured Markdown in seconds. No signup, no installation — it works directly in your browser.

Why is Markdown better than PDF for AI?

Markdown reduces token usage by up to 95% compared to PDF when feeding documents to AI models like ChatGPT or Claude. PDF contains layout metadata, fonts, and binary data that waste tokens. Markdown preserves only the content structure that AI actually needs.

What file types can MDisBetter convert to Markdown?

MDisBetter converts PDF, Word (.docx), plain text, YouTube videos (transcript extraction), audio files (MP3, WAV, M4A, OGG, FLAC, WEBM), and any web page URL to clean Markdown.

Is MDisBetter free to use?

Yes, MDisBetter is completely free. You get 10 conversions per day with no signup required. All tools work directly in your browser.

How do I extract a YouTube transcript as Markdown?

Paste the YouTube video URL into the YouTube to Markdown tool on mdisbetter.com and click Convert. The tool extracts the transcript and structures it as clean, formatted Markdown with headings and timestamps.

MarkdownNodeParser vs SentenceSplitter for video transcripts?

MarkdownNodeParser respects the chapter/speaker structure the converter emits, so each node is one coherent section with attribution metadata. SentenceSplitter cuts purely on sentence boundaries and loses chapter and speaker structure entirely. For video Markdown, always prefer MarkdownNodeParser.

Can I index hundreds of conference talks at once?

Yes — drop all converted talk .md files in one directory, load with SimpleDirectoryReader, parse with MarkdownNodeParser, index. Add per-talk metadata at load time (conference, year, speaker, track, abstract) and retrieval can filter and faceted-search across the whole catalogue.

How does auto-merging-retriever help with video data?

It lets you index at fine granularity (one node per chapter, or even per paragraph within long chapters) while retrieving at coarser granularity when context matters. A query matches a specific quote; retrieval merges up to the full chapter or speaker turn automatically. Better synthesis without losing precise matching.

Does each node carry the timestamp from the original video?

Yes — the timestamp from the heading (e.g. [00:24:15] ) is preserved as part of the chapter metadata. You can filter retrieval by time range, sort results chronologically within a video, or include timestamps in synthesis so answers reference verifiable moments in the recording.

How do I update the index when I re-convert a video with corrected speaker names?

LlamaIndex's ingestion pipeline supports document IDs based on file path — re-converting and reloading a transcript with the same filename triggers an update path that deletes old nodes and inserts new ones, without manual cleanup. Useful for talks where you hand-correct chapter titles or speaker labels after initial conversion.

Video to Markdown for LlamaIndex — Hierarchical Video Indexing

Why MarkdownNodeParser changes the math on video

Flat caption parsing on video loses everything that makes a long talk usable: where chapters begin and end, who is speaking on multi-host content, and what time range a given idea spans. MarkdownNodeParser reads the structure the converter emits — ## chapter or speaker headings, ### subtopic headings — and builds a node tree that mirrors the video's real shape.

Retrieval over that tree gets two superpowers immediately. Auto-merging-retriever can climb from a specific quote to the chapter's full content to the video's top-level structure. Hierarchical summary indexes can summarise per-chapter, per-speaker, or per-time-window without re-chunking.

The workflow

Convert each video on Video to Markdown (YouTube URL or uploaded file), save the .md files into an ingestion directory, load with SimpleDirectoryReader (filtered to .md), parse with MarkdownNodeParser. The same pattern works for podcast back-catalogues, conference archives, and course corpora.

Tool	Cost	Unit
Text to MD, EPUB to MD, MD to PDF, MD Cleaner, Merger, Chunker, Token Counter, Context Builder	Free	—
Word to MD	0.5 credit	per page
Excel to MD	0.5 credit	per conversion
Single URL Scrape	0.5 credit	per call
Site Crawl	1 credit	per page
Translate	1 credit	per 10 000 chars (min 1, free re-translation on cache hit)
Prompt Optimizer	1 credit	per call
System Prompt Generator	1 credit	per call
Audio to MD	2 credits	per minute
Video to MD	2 credits	per minute
YouTube to MD	2 credits	per minute
Image OCR	4 credits	per image (0 on cache hit)
PDF to MD	4 credits	per page
PPTX to MD	4 credits	per slide

Video to Markdown for LlamaIndex — Chapter and Speaker Nodes

Why MarkdownNodeParser changes the math on video

The workflow

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