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.

How does Markdown improve embedding quality on audio content?

Embeddings encode everything in the input. A flat transcript chunk that contains half of one speaker's sentence and half of another's encodes incoherent content — the resulting vector points at "transcript noise". Speaker-grouped chunks encode one coherent thought from one person, and the vector points at what was actually said.

What chunk size works best for transcript embeddings?

Use the speaker turn as the primary boundary, then sub-split anything over 600-1000 tokens with 50-100 overlap. Short interjections stay as their own small chunks (which is fine — they often are the answer to specific lookup questions); long monologues get split without losing speaker attribution.

Should I store timestamps as queryable metadata?

Yes, always. Store timestamp as both a string in metadata (for display in synthesis) and as seconds-since-start (for range queries). Pinecone and Qdrant both support numeric range filters; "find every chunk between 00:30:00 and 00:45:00 of meeting X" becomes a one-line filter.

Pinecone vs Chroma vs Weaviate vs Qdrant — which for audio?

All four handle the workload. Pinecone for managed simplicity. Chroma for local development. Weaviate when you want hybrid retrieval (transcripts have lots of exact phrases worth matching lexically). Qdrant when speaker- and time-range filtering dominate. Pick on ops preferences — the input quality matters more than the engine.

How do I keep the vector DB fresh as new recordings come in?

Build a per-recording ingestion script: convert audio to Markdown via the web tool (or any OSS transcription pipeline you run locally), chunk and embed, upsert with the recording filename as a metadata key. To refresh, delete by that key and re-insert. Most production audio archives append-only, so deletes are rare.

Audio to Markdown for Vector DBs — Speaker-Aware Embeddings

What "semantic search over audio" actually requires

Three things in order. (1) A transcript that preserves who said what — flat text loses too much for production retrieval. (2) Chunking that respects conversation structure — character-count chunking on transcripts produces incoherent embeddings. (3) Metadata that survives ingestion — speaker name, timestamp, source recording — so retrieval can filter and synthesis can attribute.

Markdown with speaker headings provides all three by construction. The conversion gives you structured text. Header-aware chunking gives you coherent units. Heading metadata survives any ingestion pipeline. Pinecone, Chroma, Weaviate, and Qdrant all handle the resulting vectors equally well.

Recommended schema

Per-chunk metadata to store: speaker (string, indexed), timestamp (HH:MM:SS, indexed), source_file (the original audio filename), source_date (when the recording was made), topic (optional, from ### subheadings). Indexing speaker and timestamp lets you scope retrieval to specific people or time ranges; indexing source_date lets you query "what did anyone say about X in Q1".

Vector DB choice

For audio corpora specifically: Pinecone if you want managed and don't want to think about ops; Chroma for local development and small archives; Weaviate when hybrid retrieval (keyword + vector) matters because exact phrase matches happen often in transcripts; Qdrant when filter-heavy queries (per-speaker, per-time-range) dominate your access patterns. Pair with PDF and web sources via PDF for Vector DBs and URL for Vector DBs for unified retrieval.

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 Vector Databases — Searchable Audio Content

What "semantic search over audio" actually requires

Recommended schema

Vector DB choice

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