hearsay 0.2.0

crawl4ai for video & audio — turn any YouTube video, podcast episode, or local recording into clean, timestamped, LLM-ready markdown

hearsay

crawl4ai for video & audio. One command turns any YouTube video, podcast episode, or local recording into clean, timestamped, chunked, LLM-ready markdown — for RAG pipelines and AI agents.

Why

Getting a transcript into your RAG pipeline usually means gluing together yt-dlp, Whisper, and a pile of timestamp-wrangling scripts — and you still end up with one line per caption fragment or an undifferentiated wall of text. hearsay does the whole thing in one command and gives you back markdown a human and a model can read: readable paragraphs, real timestamps, chapter headings, and an optional JSON sidecar with a stable schema.

Install

uv tool install hearsay          # recommended
# or
pipx install hearsay
# MCP server support:
uv tool install "hearsay[mcp]"
# Apple Silicon: add the fast Parakeet engine (~3x faster than CPU Whisper):
uv tool install "hearsay[parakeet]"

System requirement: ffmpeg on your PATH.

Transcription engines

hearsay transcribes with the fastest engine your machine has. The default --model auto picks:

• Parakeet (NVIDIA Parakeet-TDT on Apple's MLX) on Apple Silicon when the parakeet extra is installed — about 3× faster than whisper-small on CPU (~24× vs ~7× realtime on an M1 Pro), with comparable accuracy. parakeet is multilingual (25 European languages); parakeet-en is English-only.
• Whisper (faster-whisper, CPU int8) everywhere else, or when you pass an explicit size (tiny…large-v3).

If the Parakeet extra isn't installed, auto falls back to whisper-small automatically, so hearsay works the same everywhere — it's just faster on a Mac.

From source (for development)

git clone https://github.com/mudassar531/hearsay
cd hearsay
uv sync && uv run hearsay --help    # or: uv tool install .

30-second quickstart

# YouTube → markdown via captions (fast — no download)
hearsay "https://www.youtube.com/watch?v=VIDEO_ID"

# Local audio/video → markdown (fast Parakeet on Apple Silicon, else CPU Whisper)
hearsay talk.mp3

# Force local transcription on a YouTube URL, pick an engine, also emit JSON
hearsay "https://youtu.be/VIDEO_ID" --transcribe --model parakeet --json

# Music/song? Add --no-vad so the lyrics aren't filtered out as "non-speech"
hearsay "https://youtu.be/SONG_ID" --no-vad

# A podcast feed or YouTube playlist: list, then ingest a selection
hearsay "https://example.com/feed.xml"
hearsay "https://example.com/feed.xml" --all --limit 3 --output-dir ./out

No captions on a video? hearsay falls back to local transcription automatically.

What you get

---
title: "You Would Be a Terrible Leader"
source: "https://www.youtube.com/watch?v=rStL7niR7gs"
channel: "CGP Grey"
duration: "00:18:13"
ingested: "2026-06-13T10:00:00Z"
method: "captions"
language: "en"
---

# You Would Be a Terrible Leader

## [00:00:00 – 00:05:21]

**[00:00:00]** Do you want to rule? Do you see the problems in your country and
know how to fix them? If only you had the power to do so. Well. You've come to
the right place. But, before we begin this lesson in political power, ask
yourself, why don't rulers see as clearly as you...

Pass --json for a sidecar matching the Transcript schema: metadata plus chunks[], each with start_s, end_s, section, and text — ready to embed.

How it compares

	hearsay	DIY yt-dlp + Whisper	markitdown / docling
Input	video & audio	video & audio (you wire it)	documents (pdf/docx/pptx)
One command	✅	❌ multi-step plumbing	✅ (for docs)
Captions-first (no download)	✅	✗ usually re-transcribes	n/a
Timestamps + paragraph grouping	✅ readable	✗ raw segments	n/a
Chapters → sections	✅	✗ manual	n/a
Podcasts · playlists · batch	✅	✗ manual	✗
JSON sidecar for RAG	✅ stable schema	✗ manual	varies
MCP server for agents	✅	✗	varies

hearsay does media; document tools like markitdown and docling do documents. Use both.

Web UI

Prefer a browser? hearsay web starts a tiny local web UI — paste a YouTube URL or drop in an audio/video file, pick the model, and get a live markdown preview with copy and download. It's a single self-contained page with no extra dependencies (built on the standard library).

hearsay web                      # → http://localhost:8756
hearsay web --port 9000          # custom port

(Single video URLs and file uploads; for playlists and podcast feeds use the CLI.)

Give your agent ears

hearsay ships an MCP server so AI agents can ingest media themselves. It exposes two tools — ingest_url(url, transcribe?, lang?) and ingest_file(path) — that each return clean, timestamped markdown.

uv tool install "hearsay[mcp]"
hearsay mcp                      # stdio MCP server (Ctrl-C to stop)

Claude Code:

claude mcp add hearsay -- hearsay mcp

or add to .mcp.json (project) / ~/.claude.json (user):

{
  "mcpServers": {
    "hearsay": {
      "type": "stdio",
      "command": "hearsay",
      "args": ["mcp"]
    }
  }
}

Claude Desktop — add to claude_desktop_config.json (Settings → Developer → Edit Config; macOS: ~/Library/Application Support/Claude/, Windows: %APPDATA%\Claude\):

{
  "mcpServers": {
    "hearsay": {
      "type": "stdio",
      "command": "hearsay",
      "args": ["mcp"],
      "env": {
        "HEARSAY_MODEL": "auto"
      }
    }
  }
}

If hearsay is not on the host's PATH, use the absolute path (which hearsay), or "command": "python", "args": ["-m", "hearsay", "mcp"].

Server configuration (env vars, since MCP tool signatures are fixed):

Variable	Default	Effect
HEARSAY_MODEL	auto	auto, parakeet, parakeet-en, or a Whisper size (tiny…large-v3)
HEARSAY_LANG	(unset)	Default language: English captions, else transcription auto-detect
HEARSAY_VAD	1	Voice-activity filter (Whisper); set 0 for music/songs
HEARSAY_PARAKEET_MODEL	(unset)	Override the Parakeet MLX repo id (advanced)

Speech vs. music: hearsay is tuned for spoken audio (podcasts, talks, interviews, meetings), where transcription is accurate. For music, pass --no-vad so the vocals aren't discarded — but expect a rough, approximate lyric transcript, since Whisper is a speech model, not a lyrics transcriber.

CLI reference

hearsay <SOURCE> [options]      SOURCE = YouTube video/playlist URL, podcast RSS, or local file

  -o, --output PATH    Output file for a single source (default ./<id>.md)
  --output-dir PATH    Output directory for batch (playlist/feed) ingestion (default ./hearsay-out)
  --lang CODE          Language: captions default to English; transcription auto-detects
  --transcribe         Force local transcription even when captions exist
  --model MODEL        auto (default) | parakeet | parakeet-en | tiny | base | small | medium | large-v3
  --no-vad             Disable voice-activity filtering (Whisper; use for music/songs)
  --json               Also write a .json sidecar (Transcript schema)
  --latest             Batch: ingest only the most recent item
  --episode N          Batch: ingest only item N (1-indexed)
  --all [--limit N]    Batch: ingest all items (optionally capped)

hearsay web            Run the local web UI (--host, --port)
hearsay mcp            Run the MCP stdio server
hearsay --version      Show the version

Requirements

• Python 3.11+
• ffmpeg on your PATH. hearsay decodes most audio/video directly (faster-whisper bundles its own decoder), but ffmpeg is the safe baseline and is used for some yt-dlp format merges.

OS	Install ffmpeg
macOS (Homebrew)	brew install ffmpeg
Debian / Ubuntu	sudo apt install ffmpeg
Fedora	sudo dnf install ffmpeg
Arch	sudo pacman -S ffmpeg
Windows (winget)	winget install Gyan.FFmpeg
Windows (Chocolatey)	choco install ffmpeg

The first transcription downloads the chosen model once (Whisper: tens of MB to ~1.5 GB; Parakeet v3: ~2.5 GB), then caches it for offline use.

Apple Silicon speed: the parakeet extra (uv tool install "hearsay[parakeet]") runs NVIDIA Parakeet on MLX, transcribing ~3× faster than CPU Whisper (~24× realtime on an M1 Pro). It requires macOS on arm64; on other platforms hearsay uses CPU Whisper automatically.

Contributing

See CONTRIBUTING.md and the good first issues. hearsay does one thing well — media → great markdown — and aims to keep doing exactly that.

License

MIT