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.

MarkdownHeaderTextSplitter vs SentenceSplitter for transcripts?

MarkdownHeaderTextSplitter respects speaker boundaries when the source uses ## for speakers, so each chunk is one speaker turn. SentenceSplitter cuts on prose sentence boundaries and loses turn information entirely. For audio Markdown, always prefer the header-aware splitter.

Can I attach speaker metadata to every chunk?

Yes — that's automatic with MarkdownHeaderTextSplitter. The heading text ( Sarah Chen [00:14:22] ) becomes a metadata field on each chunk derived from that section. Your retrieval can filter by speaker, your synthesis can cite by name and timestamp.

How is this different from LangChain's SRT loader?

SRT loaders give you flat text plus per-cue timestamps — useful for video subtitle workflows, awkward for conversational analysis. Markdown gives you speaker-grouped turns with timestamps as part of the heading, which is what conversation analysis actually needs.

Does this scale to a whole archive of meetings?

Yes — point TextLoader at a directory of .md files, run each through the same splitter, embed, upsert. Add per-file metadata (meeting date, project, participants list) at load time, and your retrieval can filter across the whole archive by any of those dimensions.

What's the right chunk size for transcript content?

Speaker turns are naturally varied — a one-line interjection vs a five-minute monologue. Let MarkdownHeaderTextSplitter do the primary split (one chunk per turn), then sub-split anything over 1000 tokens with a 120-token overlap. Short turns stay intact; long turns get split without losing speaker attribution.

Audio to Markdown for LangChain — Speaker-Aware Splitting

Why MarkdownHeaderTextSplitter is perfect for transcripts

The whole point of MarkdownHeaderTextSplitter is to chunk on document structure rather than character count. For prose documents the relevant structure is ## sections; for transcripts the relevant structure is ## speaker headings. Either way, the splitter respects boundaries the document's author intended, and the heading text becomes per-chunk metadata for free.

The result on a 60-minute meeting transcript: ~80-150 documents, each containing one speaker's turn, each tagged with that speaker's name. Retrieval can now filter by speaker. Synthesis prompts can quote with attribution. The same pipeline works for podcasts, interviews, panel discussions — anything multi-speaker.

The workflow

Convert audio on Audio to Markdown, save the .md file, point TextLoader at it, run through MarkdownHeaderTextSplitter, embed, upsert. Pair with PDF transcripts and web docs (PDF for LangChain, URL for LangChain) for a multi-source pipeline that handles every common input format.

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

Audio to Markdown for LangChain — Transcript as Structured Document

Why MarkdownHeaderTextSplitter is perfect for transcripts

The workflow

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